BIO 3356 MOLECULAR MODELING NAME: CITY TECH, CUNY DATE: Lab06 – Literature summary on Homology Modeling (50 pts) The purpose of this assignment is to summarize and evaluate existing "literature" (or published material) in order to establish current knowledge on the subject of homology modeling of proteins. You can use the paper that I posted or use some other from the literature but do not forget to cite them. What format should you use for the review? A literature review of formal academic writing includes: Introduction Body Conclusion Introduction • It defines or identifies the general topic, provides an appropriate context for describing the available methods found in the literature. Body • It summarizes individual studies or articles with as much or as little detail as each merits according to its comparative importance in the literature, remembering that space (length) denotes significance. • You should not keep the title Body in your report but instead create different titles relative to the content. Conclusion • It summarizes major contributions of significant studies and articles to the body of knowledge under review, maintaining the focus established in the introduction. References • Identify at least two sources that could be useful for your summary. They may be news articles, non-review journal articles, websites, software, or any other material from the Internet. Write down the reference citation in a proper APA format. To guide you, you will find 2 articles about homology modeling, and a very recent research (from 2016) on the modeling of Zika virus proteins. Your summary should answer the following questions in the body: 1. Define Comparative/Homology Modeling. 2. Enumerate the steps in Comparative/Homology Modeling? 3. What tools/software are needed for each steps. BIO 3356 MOLECULAR MODELING CITY TECH, CUNY 4. Draw or provide a flow chart of the steps involved in homology protein structure modeling. (you can use an image found online (cite the source) or draw it yourself.) 5. Which are the most commonly used homology-modeling tools/software/websites? 6. What does template structure mean? 7. Can we trust the models obtained blindly? 8. What are the possible applications of Comparative/Homology Modeling? 9. Discuss one example of a homology modeling study. BONUS ACTIVITY: MODELLER with GUI INTERFACE (10 pts) For this bonus activity, your task is to search for a homology modeling software that uses Modeller within a GUI graphical interface allowing a friendlier use of the python scripts. Once you find one, describe briefly what it does, and use the target sequence below to build models. Provide a snapshot and explanation of your steps. Target Sequence: MVHLTPEEKSAVTALWGKVNVDEVGGEALGRLLVVYPWTQRFFESFGDL STPDAVMGNPKVKAHGKKVLGAFSDGLAHLDNLKGTFATLSELHCDKLH VDPENFR NIH Public Access Author Manuscript Methods Mol Biol. Author manuscript; available in PMC 2014 July 23. NIH-PA Author Manuscript Published in final edited form as: Methods Mol Biol. 2010 ; 673: 73–94. doi:10.1007/978-1-60761-842-3_6. Template-Based Protein Structure Modeling Andras Fiser Abstract NIH-PA Author Manuscript Functional characterization of a protein is often facilitated by its 3D structure. However, the fraction of experimentally known 3D models is currently less than 1% due to the inherently timeconsuming and complicated nature of structure determination techniques. Computational approaches are employed to bridge the gap between the number of known sequences and that of 3D models. Template-based protein structure modeling techniques rely on the study of principles that dictate the 3D structure of natural proteins from the theory of evolution viewpoint. Strategies for template-based structure modeling will be discussed with a focus on comparative modeling, by reviewing techniques available for all the major steps involved in the comparative modeling pipeline. Keywords Homology modeling; Comparative protein structure modeling; Template-based modeling; Loop modeling; Side chain modeling; Sequence-to-structure alignment 1. Introduction NIH-PA Author Manuscript The class of methods referred to as template-based modeling includes both the threading techniques that return a full 3D description for the target and comparative modeling (1). This class of protein structure modeling relies on detectable similarity spanning most of the modeled sequence and at least one known structure. Comparative modeling refers to those template-based modeling cases where not only the fold is determined from a possible set of available templates, but a full atom model is also built (2). In practice, it means that if the structure of at least one protein in the family has been determined by experimentation, the other members of the family can be modeled based on their alignment to the known structure. It is possible because a small change in the protein sequence usually results in a small change in its 3D structure (3). It is also facilitated by the fact that 3D structure of proteins from the same family is more conserved than their amino-acid sequences (4). Therefore, if similarity between two proteins is detectable at the sequence level, then structural similarity can usually be assumed. The increasing applicability of template-based modeling is owing to the observation that the number of different folds that proteins adopt is rather limited and because worldwide Structural Genomics projects are aggressively mapping out the universe of possible folds (5–7). Template-based approaches to structure prediction have their advantages and limitations. Comparative protein structure modeling usually provides high-quality models that are comparable with low-resolution X-ray crystallography or medium-resolution NMR solution structures. However, the applicability of these approaches is limited to those sequences that Fiser Page 2 NIH-PA Author Manuscript can be confidently mapped to known structures. Currently, the probability of finding related proteins of known structure for a sequence picked randomly from a genome ranges approximately from 30 to 80%, depending on the genome. Approximately 70% of all known sequences have at least one domain that is detectably related to at least one protein of known structure (8). This fraction is more than an order of magnitude larger than the number of experimentally determined protein structures deposited in the Protein Data Bank (PDB) (9). As we will see, in practice, template-based modeling always includes information that is independent from the template, in the form of various force restraints from general statistical observations or molecular mechanical force fields. As a consequence of improving force fields and search algorithms, the most successful approaches often explore more and more template-independent conformational space (10, 11). 2. Methods NIH-PA Author Manuscript All current comparative modeling methods consist of five sequential steps: (1) to search for proteins with known 3D structures that are related to the target sequence, (2) to pick those structures that will be used as templates, (3) to align their sequences with the target sequence, (4) to build the model for the target sequence given its alignment with the template structures, and (5) to evaluate the model, using a variety of criteria. There are several computer programs and web servers that automate the comparative modeling process (Table 1). While the web servers are convenient and useful (10, 12–14), the best results are still obtained by nonautomated, expert use of the various modeling tools (15). Complex decisions for selecting the structurally and biologically most relevant templates, optimally combining multiple template information, refining alignments in nontrivial cases, selecting segments for loop modeling, including cofactors and ligands in the model, or specifying external restraints require an expert knowledge that is difficult to fully automate (16), although more and more efforts on automation point to this direction (17, 18). 2.1. Searching for Structures Related to the Target Sequence NIH-PA Author Manuscript Comparative modeling usually starts by searching the PDB (9) for known protein structures using the target sequence as the query. This search is generally done by comparing the target sequence with the sequence of each of the structures in the database. There are two main classes of protein comparison methods that are useful in fold identification. The first class compares the sequences of the target with each of the database templates by using pairwise sequence–sequence comparisons (such as FASTA and BLAST (19)) (20–22) and fold assignments (23). To improve the sensitivity of the sequence-based searches, evolutionary information can be incorporated in the form of multiple sequence alignment (24–28). These approaches begin by finding all sequences in a sequence database that are clearly related to the target and easily aligned with it (29, 30). The multiple alignment of these sequences is the target sequence profile, which implicitly carries additional information about the location and pattern of evolutionarily conserved positions of the protein. The most well-known program in this class is PSI-BLAST (27), which implements a heuristic search algorithm for short motifs. A further step to increase the Methods Mol Biol. Author manuscript; available in PMC 2014 July 23. Fiser Page 3 NIH-PA Author Manuscript sensitivity of this approach is to precalculate sequence profiles for all the known structures and then use pairwise dynamic programming algorithm to compare the two profiles. This has been implemented, among other programs, in COACH (31) and FFAS03 (32, 33). The construction of profile-based Hidden Markov Models (HMM) is another sensitive way to locate universally conserved motifs among sequences (34). A substantial improvement in HMM approaches was achieved by incorporating information about predicted secondary structural elements (35, 36). Another development in this group of methods is the phylogenetic tree-driven HMM, which selects a different subset of sequences for profile HMM analysis at each node in the evolutionary tree (37). Locating sequence intermediates that are homologous to both sequences may also enhance the template searches (22, 38). These more sensitive fold identification techniques are especially useful for finding significant structural relationships when sequence identity between the target and the template drops below 25%. More accurate sequence profiles and structural alignments can be constructed with consistency-based approaches such as T-Coffee (39), PROMAL (and PROMAL3D for structures) (40, 41), and ProbCons (42). NIH-PA Author Manuscript The second class of methods relies on pairwise comparison of a protein sequence and a protein structure; the target sequence is matched against a library of 3D profiles or threaded through a library of 3D folds. These methods are also called fold assignment, threading, or 3D template matching (32, 43–47). These methods are especially useful when sequence profiles are not possible to construct because there are not enough known sequences that are clearly related to the target or potential templates. Template search methods “outperform” the needs of comparative modeling in the sense that they are able to locate sequences that are so remotely related as to render construction of a reliable comparative model impossible. The reason for this is that sequence relationships are often established on short conserved segments, while a successful comparative modeling exercise requires an overall correct alignment for the entire modeled part of the protein. 2.2. Selecting Templates Once a list of potential templates is obtained using searching methods, it is necessary to select one or more templates that are appropriate for the particular modeling problem. Several factors need to be taken into account when selecting a template. NIH-PA Author Manuscript 2.2.1. Considerations in Template Selection—The simplest template selection rule is to select the structure with the highest sequence similarity to the modeled sequence. The construction of a multiple alignment and a phylogenetic tree (48) can help in selecting the template from the subfamily that is closest to the target sequence. The similarity between the “environment” of the template and the environment in which the target needs to be modeled should also be considered. The term “environment” is used here in a broad sense, including everything that is not the protein itself (e.g., solvent, pH, ligands, quaternary interactions). If possible, a template bound to the same or similar ligands as the modeled sequence should generally be used. The quality of the experimentally determined structure is another important factor in template selection. Resolution and R-factor of a crystal structure and the number of restraints per residue for an NMR structure are indicative of their accuracy. The Methods Mol Biol. Author manuscript; available in PMC 2014 July 23. Fiser Page 4 NIH-PA Author Manuscript criteria for selecting templates also depend on the purpose of a comparative model. For example, if a protein–ligand model is to be constructed, the choice of the template that contains a similar ligand is probably more important than the resolution of the template. 2.2.2. Advantage of Using Multiple Templates—It is not necessary to select only one template. In fact, the optimal use of several templates increases the model accuracy (13, 17, 49, 50); however, not all modeling programs are designed to accept more than one template. The benefit of combining multiple template structures can be twofold. First, multiple template structures may be aligned with different domains of the target, with little overlap between them, in which case, the modeling procedure can construct a homology-based model of the whole target sequence. Second, the template structures may be aligned with the same part of the target and build the model on the locally best template. NIH-PA Author Manuscript An elaborate way to select suitable templates is to generate and evaluate models for each candidate template structure and/or their combinations. The optimized all-atom models can then be evaluated by an energy or scoring function, such as the Z-score of PROSA (46) or VERIFY3D (51). These scoring methods are often sufficiently accurate to allow selection of the most accurate of the generated models (52). This trial-and-error approach can be viewed as limited threading (i.e., the target sequence is threaded through similar template structures). However, these approaches are good only at selecting various templates on a global level. A recently developed method M4T (Multiple Mapping Method with Multiple Templates) selects and combines multiple template structures through an iterative clustering approach that takes into account the “unique” contribution of each template, their sequence similarity among themselves and to the target sequence, and their experimental resolution (13, 17). The resulting models systematically outperformed models that were based on the single best template. NIH-PA Author Manuscript Another important observation from the same study was that below 40% sequence identity, models built using multiple templates are more accurate than those built using a single template only, and this trend is accentuated as one moves into more remote target–template pair cases. Meanwhile, the advantage of using multiple templates gradually disappears above 40% target–template sequence identity cases. This suggests that in this range, the average differences between the template and target structures are smaller than the average differences among alternative template structures that are all highly similar to the target (17). 2.3. Sequence-to-Structure Alignment To build a model, all comparative modeling programs depend on a list of assumed structural equivalences between the target and template residues. This list is defined by the alignment of the target and template sequences. Many template search methods will produce such an alignment, and these sometimes can directly be used as the input for modeling. Often, however, especially in the difficult cases, this initial alignment is not the optimal target– template alignment. This is because search methods may be tuned for detection of remote relationships, which is often realized on a local motif and not on a full-length, optimal Methods Mol Biol. Author manuscript; available in PMC 2014 July 23. Fiser Page 5 NIH-PA Author Manuscript alignment. Therefore, once the templates are selected, an alignment method should be used to align them with the target sequence. When the target–template sequence identity is lower than 40%, the alignment accuracy becomes the most important factor affecting the quality of the resulting model. A misalignment by only one residue position will result in an error of approximately 4 Å in the model. 2.3.1. Taking Advantage of Structural Information in Alignments—Alignments in comparative modeling represent a unique class because on one side of the alignment there is always a 3D structure, the template. Therefore, alignments can be improved by including structural information from the template. For example, gaps should be avoided in secondary structure elements, in buried regions, or between two residues that are far in space. Some alignment methods take such criteria into account (47, 53, 54). NIH-PA Author Manuscript When multiple template structures are available, a good strategy is to superpose them with each other first, to obtain a multiple structure-based alignment highlighting structurally conserved residues (55–57). In the next step, the target sequence is aligned with this multiple structure-based alignment. The benefits of using multiple structures and multiple sequences are that they provide evolutionary and structural information about the templates, as well as evolutionary information about the target sequence, and they often produce a better alignment for modeling than the pairwise sequence alignment methods (22, 58). NIH-PA Author Manuscript Multiple Mapping Method (MMM) directly relies on information from the 3D structure (14, 59). MMM minimizes alignment errors by selecting and optimally splicing differently aligned fragments from a set of alternative input alignments. This selection is guided by a scoring function that determines the preference of each alternatively aligned fragment of the target sequence in the structural environment of the template. The scoring function has four terms, which are used to assess the compatibility of alternative variable segments in the protein environment:(a) environment specific substitution matrices from FUGUE (47), (b) residue substitution matrix, Blosum (60), (c) A 3D–1D substitution matrix, H3P2, that scores the matches of predicted secondary structure of the target sequence to the observed secondary structures and accessibility types of the template residues (61), and (d) a statistically derived residue–residue contact energy term (62). MMM essentially performs a limited and inverse threading of short fragments: in this exercise the actual question is not the identification of a right fold, but identification of the correct alignment mapping, among many alternatives, for sequence segments that are threaded on the same fold. These local mappings are evaluated in the context of the rest of the model, where alignments provide a consistent solution and framework for the evaluation. 2.4. Model Building When discussing the model building step within comparative protein structure modeling, it is useful to distinguish two parts: template-dependent and template-independent modeling. This distinction is necessary because certain parts of the target must be built without the aid of any template. These parts correspond to gaps in the template sequence within the target– template alignment. Modeling of these regions is commonly referred to as loop modeling problem. It is evident that these loops are responsible for the most characteristic differences Methods Mol Biol. Author manuscript; available in PMC 2014 July 23. Fiser Page 6 NIH-PA Author Manuscript between the template and target, and therefore are chiefly responsible for structural and consequently functional differences. In contrast to these loops, the rest of the target, and in particular the conserved core of the fold of the target, is built using information from the template structure. 2.4.1. Template-Dependent Modeling 2.4.1.1. Modeling by Assembly of Rigid Bodies: A comparative model can be assembled from a framework of small number of rigid bodies obtained from the aligned template protein structures (63–65). The approach is based on the natural dissection of the protein structure into conserved core regions, variable loops that connect them, and side chains that decorate the backbone (66). A widely used program in this class is COMPOSER (67). The accuracy of a model can be somewhat increased when more than one template structure is used to construct the framework (68). NIH-PA Author Manuscript 2.4.1.2. Modeling by Segment Matching or Coordinate Reconstruction: Comparative models can be constructed by using a subset of atomic positions from template structures as “guiding” positions, such as the Cα atoms, and by identifying and assembling short, allatom segments that fit these guiding positions. The all-atom segments that fit the guiding positions can be obtained either by scanning all the known protein structures (69, 70) or by a conformational search restrained by an energy function (71, 72) or by a general method for modeling by segment matching (SEGMOD) (73). Even some side-chain modeling methods (74) and the class of loop construction methods based on finding suitable fragments in the database of known structures (75) can be seen as segment matching or coordinate reconstruction methods. NIH-PA Author Manuscript 2.4.1.3. Modeling by Satisfaction of Spatial Restraints: The methods in this class begin by generating many constraints or restraints on the structure of the target sequence, using its alignment to related protein structures as a guide in a procedure that is conceptually similar to that used in determination of protein structures from NMR-derived restraints. The restraints are generally obtained by assuming that the corresponding distances between aligned residues in the template and the target structures are similar. These homologyderived restraints are usually supplemented by stereochemical restraints on bond lengths, bond angles, dihedral angles, and nonbonded atom–atom contacts that are obtained from a molecular mechanics force field (76). The model is then derived by minimizing the violations of all the restraints. Comparative modeling by satisfaction of spatial restraints is implemented in the computer program MODELLER (16, 77), currently the most popular comparative protein modeling program. In MODELLER, the various spatial relationships of distances, angles are expressed as conditional probability density functions (pdfs) and can be used directly as spatial restraints. For example, probabilities for different values of the main chain dihedral angles are calculated from the type of residue considered, from the main chain conformation of an equivalent template residue, and from sequence similarity between the two proteins. An important feature of the method is that the forms of spatial restraints were obtained empirically, from a database of protein structure alignments, without any user imposed subjective assumption. Finally, the model is obtained by optimizing the objective function in Cartesian space by the use of the variable target function method (78), Methods Mol Biol. Author manuscript; available in PMC 2014 July 23. Fiser Page 7 employing methods of conjugate gradients and molecular dynamics with simulated annealing (79). NIH-PA Author Manuscript A similar comprehensive package is NEST that can build a homology model based on single sequence–template alignment or from multiple templates. It can also consider different structures for different parts of the target (55). NIH-PA Author Manuscript 2.4.1.4. Combining Alignments, Combining Structures: It is frequently difficult to select the best templates or calculate a good alignment. One way of improving a comparative model in such cases is to proceed with an iteration of template selection, alignment, and model building, guided by model assessment, until no improvement in the model is detected (80, 81). Some of these approaches are automated (55, 82). In one example, this task was achieved by a genetic algorithm protocol that starts with a set of initial alignments and then iterates through realignment, model building, and model assessment to optimize a model assessment score. Comparative models corresponding to various evolving alignments are built and assessed by a variety of criteria, partly depending on an atomic statistical potential. In another approach, a genetic algorithm was applied to automatically combine templates and alignments. A relatively simple structure-dependent scoring function was used to evaluate the sampled combinations (18). Other attempts to optimize target–template alignments include the Robetta server, where alignments are generated by dynamic programming using a scoring function that combines information on many protein features, including a novel measure of how obligate a sequence region is to the protein fold. By systematically varying the weights on the different features that contribute to the alignment score, very large ensembles of diverse alignments are generated. A variety of approaches to select the best models from the ensemble, including consensus of the alignments, a hydrophobic burial measure, low- and high-resolution energy functions, and combinations of these evaluation methods were explored (83). Those metaserver approaches that do not simply score and rank alternative models obtained from a variety of methods but further combine them could also be perceived as approaches that explore the alignment and conformational space for a given target sequence (84). NIH-PA Author Manuscript Another alternative for combined servers is provided by M4T. The M4T program automatically identifies the best templates and explores and optimally splices alternative alignments according to its internal scoring function that focuses on the features of the structural environment of each template (17). 2.4.1.5. Metaservers: Metaserver approaches have been developed to take advantage of the variety of other existing programs. Metaservers collect models from alternative methods and either use them for inputs to make new models or look for consensus solutions within them. For instance, FAMS-ACE (85) takes inputs from other servers as starting points for refinement and remodeling after which Verify3D (51) is used to select the most accurate solution. Other consensus approaches include PCONS, a neural network approach that identifies a consensus model by combining information on reliability scores and structural Methods Mol Biol. Author manuscript; available in PMC 2014 July 23. Fiser Page 8 similarity of models obtained from other techniques (86). 3D-JURY operates along the same idea; its selection is mainly based on the consensus of model structure similarity (87). NIH-PA Author Manuscript 2.4.2. Template Independent Modeling: Modeling Loops, Insertions—In comparative modeling, target sequences often have inserted residues relative to the template structures or have regions that are structurally different from the corresponding regions in the templates. Therefore, no structural information about these inserted segments can be extracted from the template structures. These regions frequently correspond to surface loops. Loops often play an important role in defining the functional specificity of a given protein framework, forming the functional, ligand-binding active sites. The accuracy of loop modeling is a major factor determining the usefulness of comparative models in applications such as ligand docking or functional annotation. Loops are generally too short to provide sufficient information about their local fold, and the environment of each loop is uniquely defined by the solvent and the protein that cradles it. In a few rare cases, it was shown that even identical decapeptides in different proteins do not always have the same conformation (88, 89). NIH-PA Author Manuscript There are two main classes of loop modeling methods: (1) the database search approaches and (2) the conformational search approaches (90–92). There are also methods that combine these two approaches (93–95). NIH-PA Author Manuscript 2.4.2.1. Fragment-Based Approach to Loop Modeling: Earlier, it was predicted that it is unlikely that structure databanks will ever reach a point when fragment-based approaches become efficient to model loops (96), which resulted in a boost in the development of conformational search approaches from around 2000. However, many details of the fold universe have been explored during the last decade due to the large number of new folds solved experimentally, which had a profound effect on the extent of known structural fragments. Recent analyses showed that loop fragments are not only well represented in current structure databanks, but shorter segments are also possibly completely explored already (97). It was reported that sequence segments up to 10–12 residues had a related (i.e. at least 50% identical) segment in PDB with a known conformation, and despite the six-fold increase in the sequence databank size and the doubling of PDB since 2002, there was not a single unique loop conformation or sequence segment entered in the PDB ever since. Consequently, more recent efforts have been taken to classify loop conformations into more general categories, thus extending the applicability of the database search approach for more cases (98, 99). A recent work described the advantage of using HMM sequence profiles in classifying and predicting loops (100). An another recently published loop prediction approach first predicts conformation for a query loop sequence and then structurally aligns the predicted structural fragments to a set of nonredundant loop structural templates. These sequence–template loop alignments are then quantitatively evaluated with an artificial neural network model trained on a set of predictions with known outcomes (101). ArchPred (98, 102), currently perhaps the most accurate database loop modeling approach, exploits a hierarchical and multidimensional database that has been set up to classify about 300,000 loop fragments and loop flanking secondary structures. Besides the length of the loops and types of bracing secondary structures, the database is organized along four Methods Mol Biol. Author manuscript; available in PMC 2014 July 23. Fiser Page 9 NIH-PA Author Manuscript internal coordinates, a distance and three types of angles characterizing the geometry of stem regions (103). Candidate fragments are selected from this library by matching the length, the types of bracing secondary structures of the query and by satisfying the geometrical restraints of the stems and subsequently inserted in the query protein framework where their fit is assessed by the root mean squared deviation (RMSD) of stem regions and by the number of rigid body clashes with the environment. In the final step, remaining candidate loops are ranked by a Z-score that combines information on sequence similarity and fit of predicted and observed ϕ/ψ main chain dihedral angle propensities. Confidence Zscore cutoffs are determined for each loop length. A web server implements the method. Predicted segments are returned, or optionally, these can be completed with side-chain reconstruction and subsequently annealed in the environment of the query protein by conjugate gradient minimization. In summary, the recent reports about the more favorable coverage of loop conformations in the PDB suggest that database approaches are now rather limited by their ability to recognize suitable fragments, and not by the lack of these segments (i.e., sampling), as thought earlier. NIH-PA Author Manuscript 2.4.2.2. Ab Initio Modeling of Loops: To overcome the limitations of the database search methods, conformational search methods were developed. There are many such methods, exploiting different protein representations, objective function terms, and optimization or enumeration algorithms. The search strategies include the minimum perturbation method (104), molecular dynamics simulations (92), genetic algorithms (105), Monte Carlo and simulated annealing (106, 107), multiple-copy simultaneous search (108), self-consistent field optimization (109), and an enumeration based on the graph theory (110). Loop prediction by optimization is applicable to both simultaneous modeling of several loops and those loops interacting with ligands, neither of which is straightforward for the database search approaches, where fragments are collected from unrelated structures with different environments. NIH-PA Author Manuscript The MODLOOP module in MODELLER implements the optimization-based approach (111, 112). Loop optimization in MODLOOP relies on conjugate gradients and molecular dynamics with simulated annealing. The pseudoenergy function is a sum of many terms, including some terms from the CHARMM-22 molecular mechanics force field (76) and spatial restraints based on distributions of distances (113, 114) and dihedral angles in known protein structures. The performance of the approach later was further improved by using CHARMM molecular mechanic force field with Generalized Born (GB) solvation potential to rank final conformations (115). Incorporation of solvation terms in the scoring function was a central theme in several other subsequent studies (95, 116–118). Improved loop prediction accuracy resulted from the incorporation of an entropy like term to the scoring function, the “colony energy,” derived from geometrical comparisons and clustering of sampled loop conformations (119, 120). The continuous improvement of scoring functions delivers improved loop modeling methods. Two recent loop modeling procedures have been introduced that are utilizing the effective statistical pair potential that is encoded in DFIRE (121–123). Another method is developed to predict very long loops using the Rosetta approach, essentially performing a mini folding exercise for the loop segments (124). In the Methods Mol Biol. Author manuscript; available in PMC 2014 July 23. Fiser Page 10 NIH-PA Author Manuscript Prime program, large numbers of loops are generated by using a dihedral angle-based building procedure followed by iterative cycles of clustering, side-chain optimization, and complete energy minimization of selected loop structures using a full-atom molecular mechanic force field (OPLS) with implicit solvation model (125). 2.5. Model Evaluation After a model is built, it is important to check it for possible errors (see Note 1). The quality of a model can be approximately predicted from the sequence similarity between the target and the template and by performing internal and external evaluations. Sequence identity above 30% is a relatively good predictor of the expected accuracy of a model. If the target–template sequence identity falls below 30%, the sequence identity becomes significantly less reliable as a measure of the expected accuracy of a single model (see Note 2). It is in such cases that model evaluation methods are most informative. NIH-PA Author Manuscript “Internal” evaluation of self-consistency checks whether or not a model satisfies the restraints used to calculate it, including restraints that originate from the template structure or obtained from statistical observations. Assessment of the stereochemistry of a model (e.g., bonds, bond angles, dihedral angles, and nonbonded atom–atom distances) with programs such as PROCHECK (126) and WHATCHECK (127) is an example of internal evaluation. Although errors in stereochemistry are rare and less informative than errors detected by methods for external evaluation, a cluster of stereochemical errors may indicate that the corresponding region also contains other larger errors (e.g., alignment errors). NIH-PA Author Manuscript “External” evaluation relies on information that was not used in the calculation of the model and as a minimum test whether or not a correct template was used. A wrong template can be detected relatively easily with the currently available scoring functions. A more challenging task for the scoring functions is the prediction of unreliable regions in the model. One way to approach this problem is to calculate a “pseudoenergy” profile of a model, such as that produced by PROSA (128) or Verify3D (51). The profile reports the energy for each position in the model. Peaks in the profile frequently correspond to errors in the model. Other recent approaches usually combine a variety of inputs to assess the models, either wholly (129) or locally (130). In benchmarks, the best quality assessor techniques use a simple consensus approach, where reliability of a model is assessed by the agreement among alternative models that are sometimes obtained from a variety of methods (131, 132). 3. Accuracy of Modeling Methods and Typical Errors in Template Based Models 3.1. Accuracy of Methods An informative way to test protein structure modeling methods, including comparative modeling, is provided by the biannual meetings on Critical Assessment of Techniques for Protein Structure Prediction (CASP) (133). Protein modelers are challenged to model sequences with unknown 3D structure and to submit their models to the organizers before the meeting. At the same time, the 3D structures of the prediction targets are being Methods Mol Biol. Author manuscript; available in PMC 2014 July 23. Fiser Page 11 NIH-PA Author Manuscript NIH-PA Author Manuscript determined by X-ray crystallography or NMR methods. They only become available after the models are calculated and submitted. Thus, a bona fide evaluation of protein structure modeling methods is possible, although in these exercises it is not trivial to separate the contributions from programs and human expert knowledge. Alternatively a large-scale, continuous, and automated prediction benchmarking experiment is implemented in the program EVA – EValuation of Automatic protein structure prediction (134). Every week EVA submits prereleased PDB sequences to participating modeling servers, collects the results, and provides detailed statistics on secondary structure prediction, fold recognition, comparative modeling, and prediction on 3D contacts. The LiveBench program has implemented its evaluations in a similar spirit (135). After many years of operations, these benchmark platforms are not kept up to date lately, although their service would be essential to keep the user community well informed about latest developments and the bestperforming techniques available. A rigorous statistical evaluation (136) of a blind prediction experiment illustrated that the accuracies of the various model-building methods, using segment matching, rigid body assembly, satisfaction of spatial restraints, or any combinations of these are relatively similar when used optimally (137, 138). This also reflects on the fact that such major factors as template selection and alignment accuracy have a large impact on the overall model accuracy, and that the core of protein structures is highly conserved. 3.2. Errors in Comparative Models NIH-PA Author Manuscript The overall accuracy of comparative models spans a wide range. At the low end of the spectrum are the low resolution models whose only essentially correct feature is their fold. At the high end of the spectrum are the models with an accuracy comparable to mediumresolution crystallographic structures (139). Even low-resolution models are often useful to address biological questions because function can many times be predicted from only coarse structural features of a model. The errors in comparative models can be divided into five categories: (1) Errors in side-chain packing, (2) Distortions or shifts of a region that is aligned correctly with the template structures, (3) Distortions or shifts of a region that does not have an equivalent segment in any of the template structures, (4) Distortions or shifts of a region that is aligned incorrectly with the template structures, and (5) A misfolded structure resulting from using an incorrect template. Approximately 90% of the main-chain atoms are likely to be modeled with an RMS error of about 1 Å when the overall sequence identity is above 40% (140). When sequence identity is between 30 and 40%, the structural differences become larger, and the gaps in the alignment are more frequent and longer; misalignments and insertions in the target sequence become the major problems. As a result, the main-chain RMS error rises to about 1.5 Å for about 80% of residues. When sequence identity drops below 30%, the main problem becomes the identification of related templates and their alignment with the sequence to be modeled. In general, it can be expected that about 20% of residues will be misaligned and consequently incorrectly modeled with an error larger than 3 Å, at this level of sequence similarity. To put the errors in comparative models into perspective, we list the differences among structures of the same protein that have been determined experimentally. A 1 Å accuracy of main-chain atom positions corresponds to X-ray structures defined at a low resolution of about 2.5 Å and with an Rfactor of about 25% (141), as well as to medium-resolution NMR structures determined Methods Mol Biol. Author manuscript; available in PMC 2014 July 23. Fiser Page 12 NIH-PA Author Manuscript from ten interproton distance restraints per residue. Similarly, differences between the highly refined X-ray and NMR structures of the same protein also tend to be about 1 Å (142). Changes in the environment (e.g., oligomeric state, crystal packing, solvent, ligands) can also have a significant effect on the structure (143). The performance of comparative modeling may sometimes appear overstated because what is usually discussed in the literature are the mean values of backbone deviations. 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Author manuscript; available in PMC 2014 July 23. Fiser Page 19 Table 1 NIH-PA Author Manuscript Names and www addresses of some online tools useful for various aspects of comparative modeling Template search and alignments NIH-PA Author Manuscript BLAST/PSI-BLAST http://www.ncbi.nlm.nih.gov/BLAST/ FastA/SSEARCH http://www.ebi.ac.uk/fasta33 FASS03 http://www.ffas.ljcrf.edu/ffas-cgi/cgi/ffas.pl PSIPRED http://www.bioinf.cs.ucl.ac.uk/psipred/ 123D http://www.123d.ncifcrf.gov UCLA-DOE http://www.doe-mbi.ucla.edu/Services/FOLD/ PHYRE/3D-PSSM http://www.sbg.bio.ic.ac.uk/~3dpssm FUGUE http://www.cryst.bioc.cam.ac.uk/~fugue LOOPP http://www.cbsuapps.tc.cornell.edu/ MUSTER http://www.zhang.bioinformatics.ku.edu/MUSTER/ SAM-T06 http://www.soe.ucsc.edu/research/compbio/SAM_T06/T06-query.html Prospect http://www.compbio.ornl.gov/structure/prospect Smith–Waterman http://www.jaligner.sourceforge.net/ ClustalW http://www.ebi.ac.uk/clustalw/ MUSCLE http://www.drive5.com/lobster/ T-COFFEE http://www.tcoffee.vital-it.ch/ PROMALS http://www.prodata.swmed.edu/promals/promals.php PROBCONS http://www.probcons.stanford.edu Homology modeling, loop and side-chain modeling NIH-PA Author Manuscript MMM http://www.fiserlab.org/servers/MMM M4T http://www.fiserlab.org/servers/M4T MODELLER http://www.salilab.org/modeller/modeller.html MODWEB http://www.modbase.compbio.ucsf.edu/ModWeb20-html/modweb.html I-TASSER http://www.zhang.bioinformatics.ku.edu/I-TASSER/ HHPRED http://www.toolkit.tuebingen.mpg.de/hhpred 3D-JIGSAW http://www.bmm.icnet.uk/servers/3djigsaw/ CPH-MODELS http://www.cbs.dtu.dk/services/CPHmodels/ COMPOSER http://www.cryst.bioc.cam.ac.uk SWISSMODEL http://swissmodel.expasy.org/workspace/ FAMS http://www.pharm.kitasato-u.ac.jp/fams/ WHATIF http://www.cmbi.kun.nl/whatif/ PUDGE http://www.wiki.c2b2.columbia.edu/honiglab_public/index.php/Software 3D-JURY http://www.meta.bioinfo.pl RAPPER http://www.mordred.bioc.cam.ac.uk/~rapper ESYPRED3D http://www.fundp.ac.be/sciences/biologie/urbm/bioinfo/esypred/ CONSENSUS http://www.structure.bu.edu/cgi-bin/consensus/consensus.cgi PCONS http://www.pcons.net Methods Mol Biol. Author manuscript; available in PMC 2014 July 23. Fiser Page 20 NIH-PA Author Manuscript SCWRL http://www.dunbrack.fccc.edu/SCWRL3.php WLOOP http://www.bioserv.rpbs.jussieu.fr/cgi-bin/WLoop ARCHPRED http://www.fiserlab.org/servers/archpred MODLOOP http://www.salilab.org/modloop Model evaluation PROCHECK http://www.biochem.ucl.ac.uk/~roman/procheck/procheck.html WHATCHECK http://www.swift.cmbi.ru.nl/gv/whatcheck/ Prosa-web http://www.prosa.services.came.sbg.ac.at/prosa.php VERIFY3D http://www.nihserver.mbi.ucla.edu/Verify_3D ANOLEA http://www.protein.bio.puc.cl/cardex/servers/anolea/ AQUA http://www.urchin.bmrb.wisc.edu/~jurgen/Aqua/server/ PROQ http://www.sbc.su.se/~bjornw/ProQ/ProQ.cgi NIH-PA Author Manuscript NIH-PA Author Manuscript Methods Mol Biol. Author manuscript; available in PMC 2014 July 23. Comparative Protein Structure Modeling and its Applications to Drug Discovery Matthew Jacobson1 and Andrej Sali1,2 1 Department of Pharmaceutical Chemistry, California Institute for Quantitative Biomedical Research, Mission Bay Genentech Hall, 600 16th Street, University of California, San Francisco, CA 94143-2240, USA 2 Department of Biopharmaceutial Sciences, California Institute for Quantitative Biomedical Research, Mission Bay Genentech Hall, 600 16th Street, University of California, San Francisco, CA 94143-2240, USA Contents 1. Introduction 2. Fold assignment and sequence-structure alignment 3. Comparative model building 4. Loop modeling 5. Sidechain modeling 6. Comparative modeling by MODELLER 7. Physics-based approaches to comparative model construction and refinement 8. Accuracy of comparative models 9. Modeling on a genomic scale 10. Applications of comparative modeling to drug discovery 10.1. Comparative models vs experimental structures in virtual screening 10.2. Use of comparative models to obtain novel drug leads 10.3. Comparative models of kinases in virtual screening 10.4. GPCR comparative models for drug development 10.5. Other uses of comparative models in drug development 10.6. Future directions 11. Conclusions References 259 261 261 262 263 264 264 266 266 267 267 268 269 270 271 272 273 273 1. INTRODUCTION Homology or comparative protein structure modeling constructs a three-dimensional model of a given protein sequence based on its similarity to one or more known structures. In this perspective, we begin by describing the comparative modeling technique and the accuracy of the models. We then discuss the significant role that comparative prediction plays in drug discovery. We focus on virtual ligand screening against comparative models and illustrate the state-of-the-art by a number of specific examples. The genome sequencing efforts are providing us with complete genetic blueprints for hundreds of organisms, including humans. We are now faced with describing, ANNUAL REPORTS IN MEDICINAL CHEMISTRY, VOLUME 39 ISSN: 0065-7743 DOI 10.1016/S0065-7743(04)39020-2 q 2004 Elsevier Inc. All rights reserved 260 M. Jacobson and A. Sali controlling, and modifying the functions of proteins encoded by these genomes. This task is generally facilitated by protein three-dimensional structures [1], which are best determined by experimental methods such as X-ray crystallography and nuclear magnetic resonance (NMR) spectroscopy. Despite significant advances in these techniques, many protein sequences are not easily accessible to structure determination by experiment. Over the last two years, the number of sequences in the comprehensive public sequence databases, such as SwissProt/TrEMBL [2] and GenPept [3], increased by a factor of 2.3 from 522,959 to 1,215,803 on 26 April 2004. In contrast, despite structural genomics, the number of experimentally determined structures deposited in the Protein Data Bank (PDB) increased by only a factor of 1.4 over the same period, from 16,612 to 23,793 [4]. Thus, the gap between the numbers of known sequences and structures continues to grow. Protein structure prediction methods attempt to bridge this gap [5]. The first class of protein structure prediction methods, including threading and comparative modeling, rely on detectable similarity spanning most of the modeled sequence and at least one known structure [6]. The second class of methods, de novo or ab initio methods, predict the structure from sequence alone, without relying on similarity at the fold level between the modeled sequence and any of the known structures [7]. Despite progress in ab initio protein structure prediction [7,8], comparative modeling remains the most reliable method to predict the 3D structure of a protein with an accuracy that can be comparable to a low-resolution, experimentally determined structure [6]. Comparative modeling is carried out in four sequential steps: finding known structures (templates) related to the sequence to be modeled (target), aligning the target sequence with the templates, building the model, and assessing the model. Therefore, comparative modeling is only applicable when the target sequence is detectably related to a known protein structure. Using automated comparative modeling, the fraction of sequences with comparative models for at least one domain remained at , 57% over the last two years [9]. A number of servers for automated comparative modeling are available (http:// salilab.org/bioinformatics_resources.shtml). Automation makes comparative modeling accessible to both experts and the non-specialists alike. Many of the servers are tested at the bi-annual CAFASP meetings and continually by the LiveBench and EVA [10 –14] web servers for assessment of automated structure prediction methods. However, in spite of automation, manual intervention is generally still needed to maximize the accuracy of the models. Comparative modeling will benefit from structural genomics [15]. Structural genomics aims to structurally characterize most protein sequences by an efficient combination of experiment and prediction [16,17]. This aim will be achieved by careful selection of target proteins and their structure determination by X-ray crystallography or NMR spectroscopy. There are a variety of target selection schemes, ranging from focusing on only novel folds to selecting all proteins in a model genome [1]. A model-centric view requires that targets be selected such that most of the remaining sequences can be modeled with useful accuracy by comparative modeling. Even with structural genomics, the structure of most proteins will be modeled, not determined by experiment. As discussed below, the accuracy of comparative models, and correspondingly the variety of their applications, decreases sharply below the 30% sequence identity cutoff, mainly as a result of a rapid increase in alignment errors. Thus, structural genomics aims Comparative Protein Structure Modeling and its Applications to Drug Discovery 261 to determine protein structures such that most of the remaining sequences are related to at least one known structure at higher than 30% sequence identity [1,15,17]. It was recently estimated that this cutoff requires a minimum of 16,000 structures to cover 90% of all protein domain families, including those of membrane proteins [17]. These 16,000 structures will allow the modeling of a very much larger number of proteins. For example, the New York Structural Genomics Research Consortium measured the impact of its structures by documenting the number and quality of the corresponding models for detectably related proteins in the non-redundant sequence database. For each new structure, on average , 100 protein sequences without any prior structural characterization could be modeled at least at the fold level [9]. This large leverage of structure determination by protein structure modeling illustrates and justifies the premise of structural genomics. We begin by reviewing the methods needed for each of the four steps of comparative modeling. While we only briefly touch on fold assignment, sequence-structure alignment, and model assessment, we elaborate on model building; we emphasize the modeling of loops and sidechains, because of their importance in ligand docking and rational drug discovery. We continue by describing MODBASE, a comprehensive database of comparative models. Next, we describe the role of comparative modeling in drug discovery, focusing on ligand docking against comparative models. We compare successes of docking against models and x-ray structures, and illustrate the computational docking against models with a number of examples, including kinases and G-protein coupled receptors. 2. FOLD ASSIGNMENT AND SEQUENCE-STRUCTURE ALIGNMENT The templates for modeling may be found by pairwise sequence alignment methods, such as BLAST and FASTA, profile-sequence alignment methods, such as PSI-BLAST, profile – profile alignment methods, such as SALIGN, Hidden Markov Models, such as SAM-T02, and sequence-structure threading methods that can sometimes reveal more distant relationships than purely sequence-based methods [18 –23]. Threading methods assign the fold by threading the sequence through each of the structures in a library of all known folds; each sequence-structure alignment is assessed by the energy of a corresponding coarse model, not by sequence similarity as in sequence comparison methods. Recently, the accuracy of aligning a sequence to a remotely related protein structure has been improved by a genetic algorithm protocol that iterates through alignment, model building, and model assessment [24]. 3. COMPARATIVE MODEL BUILDING Comparative protein structure prediction produces an all-atom model of a sequence, based on its alignment to one or more related protein structures. Comparative model building includes either sequential or simultaneous modeling of the core of the protein, loops, and side-chains. In the original comparative approach, a model is constructed from 262 M. Jacobson and A. Sali a few template core regions, and from loops and side-chains obtained from either aligned or unrelated structures [25 – 27]. Another family of comparative methods relies on approximate positions of conserved atoms from the templates to calculate the coordinates of other atoms [28]. A third group of methods uses either distance geometry or optimization techniques to satisfy spatial restraints obtained from the sequence-template alignment [29 –31]. Next, we review a large variety of specialized methods that focus on the modeling of loops in a fixed environment of the rest of the protein and the modeling of sidechains on a fixed backbone. 4. LOOP MODELING In comparative modeling, target sequences often have residues inserted relative to the template structures or have regions that are structurally different from the corresponding regions in the templates. Thus, no structural information about these segments can be extracted from the template structures. These regions frequently correspond to surface loops. Loops often play an important role in defining the functional specificity of a given protein, forming the active and binding sites. The accuracy of loop modeling can be a major factor determining the usefulness of comparative models in applications such as ligand docking. Loop modeling can be seen as a mini protein folding problem because the correct conformation of a given segment of a polypeptide chain has to be calculated mainly from the sequence of the segment itself. However, loops are generally too short to provide sufficient information about their local fold. Even identical decapeptides in different proteins do not always have the same conformation [32,33]. Some additional restraints are provided by the core anchor regions that span the loop and by the structure of the rest of a protein that cradles the loop. Although many loop modeling methods have been described, it is still challenging to model correctly and confidently loops longer than approximately 8 – 10 residues [34,35]. There are two main classes of loop modeling methods: (i) database search approaches that scan a database of all known protein structures to find segments fitting the anchor core regions [36,37]; (ii) conformational search approaches that rely on optimizing a scoring function [38 – 40]. There are also methods that combine these two approaches [41,42]. The database search approach to loop modeling is accurate and efficient when a database of specific loops is created to address the modeling of the same class of loops, such as b-hairpins [43], or loops on a specific fold, such as the hypervariable regions in the immunoglobulin fold [37,44]. There are attempts to classify loop conformations into more general categories, thus extending the applicability of the database search approach [45 – 47]. However, the database methods are limited because the number of possible conformations increases exponentially with the length of a loop. As a result, only loops up to 4 –7 residues long have most of their conceivable conformations present in the database of known protein structures [48,49]. This limitation is made even worse by the requirement for an overlap of at least one residue between the database fragment and the anchor core regions, which means that modeling a five residue insertion requires at least a seven residue fragment from the database [50]. Despite the rapid growth of the database of known structures, it does not seem possible to cover most Comparative Protein Structure Modeling and its Applications to Drug Discovery 263 of the conformations of a 9-residue segment in the foreseeable future. On the other hand, most of the insertions in a family of homologous proteins are shorter than 10– 12 residues [34]. To overcome the limitations of the database search methods, conformational search methods were developed [38,39]. There are many such methods, exploiting different protein representations, objective functions, and optimization or enumeration algorithms. The search algorithms include the minimum perturbation method, molecular dynamics simulations, genetic algorithms, Monte Carlo and simulated annealing, multiple copy simultaneous search, self-consistent field optimization, and enumeration based on graph theory [41,51 –59]. The accuracy of loop predictions can be further improved by clustering the sampled loop conformations and partially accounting for the entropic contribution to the free energy [60]. Another way to improve the accuracy of loop predictions is to consider the solvent effects. Improvements in implicit solvation models, such as the Generalized Born solvation model, motivated their use in loop modeling. The solvent contribution to the free energy can be added to the scoring function for optimization, or it can be used to rank the sampled loop conformations after they are generated with a scoring function that does not include the solvent terms [34,61 – 64]. 5. SIDECHAIN MODELING Two simplifications are frequently applied in the modeling of sidechain conformations. First, amino acid residue replacements often leave the backbone structure almost unchanged [65], allowing us to fix the backbone during the search for the best sidechain conformations. Second, most sidechains in high-resolution crystallographic structures can be represented by a limited number of conformers that comply with stereochemical and energetic constraints [66]. This observation motivated Ponder and Richards to develop the first library of sidechain rotamers for the 17 types of residues with dihedral angle degrees of freedom in their sidechains, based on 10 high-resolution protein structures determined by X-ray crystallography [67]. Subsequently, a number of additional libraries have been derived [68 – 72]. Rotamers on a fixed backbone are often used when all the sidechains need to be modeled on a given backbone. This approach reduces the combinatorial explosion associated with a full conformational search of all the sidechains, and is applied by some comparative modeling [27] and protein design approaches [73]. However, , 15% of the sidechains can not be represented well by these libraries [74]. In addition, it has been shown that the accuracy of sidechain modeling on a fixed backbone decreases rapidly when the backbone errors are larger than 0.5 Å [75]. Earlier methods for sidechain modeling often put less emphasis on the energy or scoring function. The function was usually greatly simplified, and consisted of the empirical rotamer preferences and simple repulsion terms for non-bonded contacts [69]. Nevertheless, these approaches have been justified by their performance. For example, a method based on a rotamer library compared favorably with that based on a molecular mechanics force field, and new methods continue to be based on the rotamer library approach [72,76,77]. The various optimization approaches include a Monte Carlo simulation, simulated annealing, a combination of Monte Carlo and simulated annealing, 264 M. Jacobson and A. Sali the dead-end elimination theorem, genetic algorithms, neural network with simulated annealing, mean field optimization, and combinatorial searches [69,78 – 86]. Several recent papers focused on the testing of more sophisticated potential functions for conformational search [86,87] and development of new scoring functions for side chain modeling [77,88], reporting higher accuracy than earlier studies. 6. COMPARATIVE MODELING BY MODELLER MODELLER is a computer program for comparative protein structure modeling [30,34]. In the simplest case, the input is an alignment of a sequence to be modeled with the template structures, the atomic coordinates of the templates, and a short script file. MODELLER then automatically calculates a model containing all non-hydrogen atoms, without any user intervention and within minutes on a Pentium processor. MODELLER implements comparative protein structure modeling by satisfaction of spatial restraints [30]. The spatial restraints include (i) homology-derived restraints on the distances and dihedral angles in the target sequence, extracted from its alignment with the template structures [30], (ii) stereochemical restraints such as bond length and bond angle preferences, obtained from the CHARMM-22 molecular mechanics force-field [89], (iii) statistical preferences for dihedral angles and non-bonded inter-atomic distances, obtained from a representative set of known protein structures [90], and (iv) optional manually curated restraints, such as those from NMR spectroscopy, rules of secondary structure packing, cross-linking experiments, fluorescence spectroscopy, image reconstruction from electron microscopy, site-directed mutagenesis, and intuition. The spatial restraints, expressed as probability density functions, are combined into an objective function that is optimized by a combination of conjugate gradients and molecular dynamics with simulated annealing. This model building procedure is similar to structure determination by NMR spectroscopy. Apart from model building, MODELLER can perform additional auxiliary tasks, including alignment of two protein sequences or their profiles, multiple alignment of protein sequences and/or structures, calculation of phylogenetic trees, and de novo modeling of loops in protein structures [34]. 7. PHYSICS-BASED APPROACHES TO COMPARATIVE MODEL CONSTRUCTION AND REFINEMENT In principle, an accurate and efficient method for estimating the free energy of a given protein conformation could substantially improve the accuracy of comparative models. That is, an accurate energy function combined with efficient sampling could be used to refine initially constructed comparative models and thus decrease their RMS error, assuming that the native state represents the lowest free energy state. We suggest that accurate energy-based scoring may be particularly important for accurately reproducing the fine details (e.g., specific hydrogen bonding interactions) of protein active sites, which in turn will be critical for success of virtual screening using comparative models. Two key challenges confronting this approach are (i) efficient but accurate methods for Comparative Protein Structure Modeling and its Applications to Drug Discovery 265 treating solvent (many methods have entirely ignored solvent or used low-accuracy but efficient distance-dependent dielectric representations); and (ii) the estimation of entropic contributions to free energy differences among states, which generally requires extensive sampling. As discussed above, loop and sidechain prediction algorithms rely on scoring functions to guide the sampling, aiming to identify favorable conformations and reject unfavorable ones. These scoring functions typically do not explicitly attempt to estimate the free energy of a given conformation. Rather, most scoring functions have been based on statistical analyses of native protein structures encoded in the so-called potentials-of-mean-force, heuristic functional forms, or highly simplified energetic models (e.g., only van der Waals energy terms). Electrostatic interactions and, especially, the effect of solvent are infrequently used in such algorithms, largely due to their computational expense; a large number of conformations of a protein model must typically be scored during comparative model construction, and thus the scoring function must be rapid to compute. Nonetheless, a number of groups have made efforts to use more sophisticated energetic scoring functions for comparative model construction and refinement. Recently, the convergence of increased computing power as well as accurate and efficient implicit solvent models (i.e., Poisson– Boltzmann and Generalized Born models) has bolstered these efforts. In the realm of loop modeling, there have been several groups reporting recent studies [62,91 –93]; all four of these studies employed all-atom force fields and Generalized Born solvent models for scoring. An alternative recent approach is that of Hornak and Simmerling, which uses molecular dynamics methods [94]. Xiang and Honig developed a function that attempts to mimic the entropic contribution to free energy without rigorous sampling [72]. These studies have focused on reproducing loop conformations in native protein structures. The problem of refining comparative models to improve accuracy is more challenging, and early attempts to use molecular dynamics or energy minimization in this context had mixed success, frequently increasing the RMS error of the models [95,96]. Nonetheless, physics-based energy functions, although certainly not perfect, have shown impressive abilities to distinguish native from non-native protein structures [61,97], suggesting that the model accuracy may be currently limited by incomplete sampling rather than the accuracy of the scoring function. A few recent reports about successful refinement of comparative models by restrained molecular dynamics [98,99] support this viewpoint. A complete software package for physics-based comparative model construction and refinement has been developed [146]. The energy function employed is based on the OPLS all-atom force field and Surface Generalized Born solvent model with a non-polar estimator [100 – 103]. The sampling algorithms, which all use this energy function, include side chain and loop and helix prediction; the latter capability is novel and addresses the fact that corresponding helices in homologous proteins frequently adopt differing conformations, especially at relatively low sequence identity [64,87,104]. The loop prediction algorithm is, to our knowledge, the most accurate yet reported in the literature when tested by reconstructing hundreds of loops in native structures. Of particular relevance to virtual ligand screening against comparative models (as discussed below), the comparative model construction algorithm permits inclusion of co-factors and ligands as the model is built, which can help to improve accuracy of binding site conformations. 266 M. Jacobson and A. Sali 8. ACCURACY OF COMPARATIVE MODELS The accuracy of the predicted model determines the information that can be extracted from it. Thus, estimating the accuracy of 3D protein models in the absence of the known structures is essential for interpreting them. The model can be evaluated as a whole as well as in the individual regions. There are many model evaluation programs and servers [147]. The accuracy of comparative modeling is related to the percentage sequence identity on which the model is based, correlating with the relationship between the structural and sequence similarities of two proteins [6,96,105]. High accuracy comparative models are generally based on more than 50% sequence identity to their templates. They tend to have approximately 1 Å RMS error for the main-chain atoms, which is comparable to the accuracy of a medium resolution NMR structure or a low-resolution X-ray structure. The errors are mostly mistakes in side-chain packing, small shifts or distortions of the core main-chain regions, and occasionally larger errors in loops. Medium accuracy comparative models are based on 30– 50% sequence identity. They tend to have approximately 90% of the main-chain modeled with 1.5 Å RMS error. Errors in side-chain packing, core distortion, and loop modeling errors are more frequent, and there are occasional alignment mistakes [105]. Finally, low accuracy comparative models are based on less than 30% sequence identity. Alignment errors increase rapidly below 30% sequence identity and become the most significant source of errors in comparative models. In addition, when a model is based on an almost insignificant alignment to a known structure, it may also have an entirely incorrect fold. Accuracies of the best model building methods are relatively similar when used optimally [96,106]. Other factors such as template selection and alignment accuracy usually have a larger impact on the model accuracy, especially for models based on less than 40% sequence identity to the templates. 9. MODELING ON A GENOMIC SCALE Threading and comparative modeling methods have been applied on a genomic scale [105, 107,108]. Domains in approximately one half of all 1,300,000 known protein sequences were modeled with MODPIPE [105,107] and MODELLER [30], and deposited into a comprehensive database of comparative models, MODBASE [9]. The web interface to the database allows flexible querying for fold assignments, sequence-structure alignments, models, and model assessments. An integrated sequence/structure viewer, Chimera [110], allows inspection and analysis of the query results. MODBASE is inter-linked with other applications and databases such that structures and other types of information can be easily used for functional annotation. For example, MODBASE contains binding site predictions for small ligands and a set of predicted interactions between pairs of modeled sequences from the same genome. Other resources associated with MODBASE include a comprehensive database of multiple protein structure alignments (DBALI) as well as web servers for automated comparative modeling with MODPIPE (MODWEB), modeling of loops in protein structures (MODLOOP), and predicting functional consequences of single nucleotide polymorphisms (SNPWEB) [109,111,113]. While the current number of modeled proteins may look impressive given the early stage of structural genomics, usually only one domain per protein is modeled Comparative Protein Structure Modeling and its Applications to Drug Discovery 267 (on the average, proteins have slightly mor...
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