Domain definition and target classification for CASP6

Easy Not Easy: Comparative Modeling with High-Sequence Identity Templates

Homology modeling is the most common technique to build structural models of a target protein based on the structure of proteins with high-sequence identity and available high-resolution structures. This technique is based on the idea that protein structure shows fewer changes than sequence through evolution. While in this scenario single mutations would minimally perturb the structure, experimental evidence shows otherwise: proteins with high conformational diversity impose a limit of the paradigm of comparative modeling as the same protein sequence can adopt dissimilar three-dimensional structures. These cases present challenges for modeling; at first glance, they may seem to be easy cases, but they have a complexity that is not evident at the sequence level. In this chapter, we address the following questions: Why should we care about conformational diversity? How to consider conformational diversity when doing template-based modeling in a practical way?

Addressing the Role of Conformational Diversity in Protein Structure Prediction

Computational modeling of tertiary structures has become of standard use to study proteins that lack experimental characterization. Unfortunately, 3D structure prediction methods and model quality assessment programs often overlook that an ensemble of conformers in equilibrium populates the native state of proteins. In this work we collected sets of publicly available protein models and the corresponding target structures experimentally solved and studied how they describe the conformational diversity of the protein. For each protein, we assessed the quality of the models against known conformers by several standard measures and identified those models ranked best. We found that model rankings are defined by both the selected target conformer and the similarity measure used. 70% of the proteins in our datasets show that different models are structurally closest to different conformers of the same protein target. We observed that model building protocols such as template-based or ab initio approaches describe in similar ways the conformational diversity of the protein, although for template-based methods this description may depend on the sequence similarity between target and template sequences. Taken together, our results support the idea that protein structure modeling could help to identify members of the native ensemble, highlight the importance of considering conformational diversity in protein 3D quality evaluations and endorse the study of the variability of the native structure for a meaningful biological analysis.

Bhageerath-H: a homology/ab initio hybrid server for predicting tertiary structures of monomeric soluble proteins

BACKGROUND

The advent of human genome sequencing project has led to a spurt in the number of protein sequences in the databanks. Success of structure based drug discovery severely hinges on the availability of structures. Despite significant progresses in the area of experimental protein structure determination, the sequence-structure gap is continually widening. Data driven homology based computational methods have proved successful in predicting tertiary structures for sequences sharing medium to high sequence similarities. With dwindling similarities of query sequences, advanced homology/ ab initio hybrid approaches are being explored to solve structure prediction problem. Here we describe Bhageerath-H, a homology/ ab initio hybrid software/server for predicting protein tertiary structures with advancing drug design attempts as one of the goals.

RESULTS

Bhageerath-H web-server was validated on 75 CASP10 targets which showed TM-scores ≥ 0.5 in 91% of the cases and Cα RMSDs ≤ 5 Å from the native in 58% of the targets, which is well above the CASP10 water mark. Comparison with some leading servers demonstrated the uniqueness of the hybrid methodology in effectively sampling conformational space, scoring best decoys and refining low resolution models to high and medium resolution.

CONCLUSION

Bhageerath-H methodology is web enabled for the scientific community as a freely accessible web server. The methodology is fielded in the on-going CASP11 experiment.

DomHR: accurately identifying domain boundaries in proteins using a hinge region strategy

Motivation

The precise prediction of protein domains, which are the structural, functional and evolutionary units of proteins, has been a research focus in recent years. Although many methods have been presented for predicting protein domains and boundaries, the accuracy of predictions could be improved.

Results

In this study we present a novel approach, DomHR, which is an accurate predictor of protein domain boundaries based on a creative hinge region strategy. A hinge region was defined as a segment of amino acids that covers part of a domain region and a boundary region. We developed a strategy to construct profiles of domain-hinge-boundary (DHB) features generated by sequence-domain/hinge/boundary alignment against a database of known domain structures. The DHB features had three elements: normalized domain, hinge, and boundary probabilities. The DHB features were used as input to identify domain boundaries in a sequence. DomHR used a nonredundant dataset as the training set, the DHB and predicted shape string as features, and a conditional random field as the classification algorithm. In predicted hinge regions, a residue was determined to be a domain or a boundary according to a decision threshold. After decision thresholds were optimized, DomHR was evaluated by cross-validation, large-scale prediction, independent test and CASP (Critical Assessment of Techniques for Protein Structure Prediction) tests. All results confirmed that DomHR outperformed other well-established, publicly available domain boundary predictors for prediction accuracy.

Availability

The DomHR is available at http://cal.tongji.edu.cn/domain/.

Assignment of protein sequences to existing domain and family classification systems: Pfam and the PDB

MOTIVATION

Automating the assignment of existing domain and protein family classifications to new sets of sequences is an important task. Current methods often miss assignments because remote relationships fail to achieve statistical significance. Some assignments are not as long as the actual domain definitions because local alignment methods often cut alignments short. Long insertions in query sequences often erroneously result in two copies of the domain assigned to the query. Divergent repeat sequences in proteins are often missed.

RESULTS

We have developed a multilevel procedure to produce nearly complete assignments of protein families of an existing classification system to a large set of sequences. We apply this to the task of assigning Pfam domains to sequences and structures in the Protein Data Bank (PDB). We found that HHsearch alignments frequently scored more remotely related Pfams in Pfam clans higher than closely related Pfams, thus, leading to erroneous assignment at the Pfam family level. A greedy algorithm allowing for partial overlaps was, thus, applied first to sequence/HMM alignments, then HMM-HMM alignments and then structure alignments, taking care to join partial alignments split by large insertions into single-domain assignments. Additional assignment of repeat Pfams with weaker E-values was allowed after stronger assignments of the repeat HMM. Our database of assignments, presented in a database called PDBfam, contains Pfams for 99.4% of chains >50 residues.

AVAILABILITY

The Pfam assignment data in PDBfam are available at http://dunbrack2.fccc.edu/ProtCid/PDBfam, which can be searched by PDB codes and Pfam identifiers. They will be updated regularly.

A quality metric for homology modeling: the H-factor

BACKGROUND

The analysis of protein structures provides fundamental insight into most biochemical functions and consequently into the cause and possible treatment of diseases. As the structures of most known proteins cannot be solved experimentally for technical or sometimes simply for time constraints, in silico protein structure prediction is expected to step in and generate a more complete picture of the protein structure universe. Molecular modeling of protein structures is a fast growing field and tremendous works have been done since the publication of the very first model. The growth of modeling techniques and more specifically of those that rely on the existing experimental knowledge of protein structures is intimately linked to the developments of high resolution, experimental techniques such as NMR, X-ray crystallography and electron microscopy. This strong connection between experimental and in silico methods is however not devoid of criticisms and concerns among modelers as well as among experimentalists.

RESULTS

In this paper, we focus on homology-modeling and more specifically, we review how it is perceived by the structural biology community and what can be done to impress on the experimentalists that it can be a valuable resource to them. We review the common practices and provide a set of guidelines for building better models. For that purpose, we introduce the H-factor, a new indicator for assessing the quality of homology models, mimicking the R-factor in X-ray crystallography. The methods for computing the H-factor is fully described and validated on a series of test cases.

CONCLUSIONS

We have developed a web service for computing the H-factor for models of a protein structure. This service is freely accessible at http://koehllab.genomecenter.ucdavis.edu/toolkit/h-factor.

Protein domain assignment from the recurrence of locally similar structures

Domains are basic units of protein structure and essential for exploring protein fold space and structure evolution. With the structural genomics initiative, the number of protein structures in the Protein Databank (PDB) is increasing dramatically and domain assignments need to be done automatically. Most existing structural domain assignment programs define domains using the compactness of the domains and/or the number and strength of intra-domain versus inter-domain contacts. Here we present a different approach based on the recurrence of locally similar structural pieces (LSSPs) found by one-against-all structure comparisons with a dataset of 6373 protein chains from the PDB. Residues of the query protein are clustered using LSSPs via three different procedures to define domains. This approach gives results that are comparable to several existing programs that use geometrical and other structural information explicitly. Remarkably, most of the proteins that contribute the LSSPs defining a domain do not themselves contain the domain of interest. This study shows that domains can be defined by a collection of relatively small locally similar structural pieces containing, on average, four secondary structure elements. In addition, it indicates that domains are indeed made of recurrent small structural pieces that are used to build protein structures of many different folds as suggested by recent studies.

Target domain definition and classification in CASP8

In order to be successful CASP experiments require experimentally determined protein structures. These structures form the basis of the experiment. Structural genomics groups have provided the vast majority of these structures in recent editions of CASP. Before the structure prediction assessment can begin these target structures must be divided into structural domains for assessment purposes and each assessment unit must be assigned to one or more tertiary structure prediction categories. In CASP8 target domain boundaries were based on visual inspection of targets and their experimental data, and on superpositions of the target structures with related template structures. As in CASP7 target domains were broadly classified into two different categories: "template-based modeling" and "free modeling." Assessment categories were determined by structural similarity between the target domain and the nearest structural templates in the PDB and by whether or not related structural templates were used to build the models. The vast majority of the 164 assessment units in CASP8 were classified as template-based modeling. Just 10 target domains were defined as free modeling. In addition three targets were assessed in both the free modeling and template based categories and a subset of 50 template-based models was evaluated as part of the "high accuracy" subset. The targets submitted for CASP8 confirmed a trend that has been apparent since CASP5: targets submitted to the CASP experiments are becoming easier to predict.

OPUS-Dom: applying the folding-based method VECFOLD to determine protein domain boundaries

In this article, we present a de novo method for predicting protein domain boundaries, called OPUS-Dom. The core of the method is a novel coarse-grained folding method, VECFOLD, which constructs low-resolution structural models from a target sequence by folding a chain of vectors representing the predicted secondary-structure elements. OPUS-Dom generates a large ensemble of folded structure decoys by VECFOLD and labels the domain boundaries of each decoy by a domain parsing algorithm. Consensus domain boundaries are then derived from the statistical distribution of the putative boundaries and three empirical sequence-based domain profiles. OPUS-Dom generally outperformed several state-of-the-art domain prediction algorithms over various benchmark protein sets. Even though each VECFOLD-generated structure contains large errors, collectively these structures provide a more robust delineation of domain boundaries. The success of OPUS-Dom suggests that the arrangement of protein domains is more a consequence of limited coordination patterns per domain arising from tertiary packing of secondary-structure segments, rather than sequence-specific constraints.

MetaMQAP: a meta-server for the quality assessment of protein models

Background

Computational models of protein structure are usually inaccurate and exhibit significant deviations from the true structure. The utility of models depends on the degree of these deviations. A number of predictive methods have been developed to discriminate between the globally incorrect and approximately correct models. However, only a few methods predict correctness of different parts of computational models. Several Model Quality Assessment Programs (MQAPs) have been developed to detect local inaccuracies in unrefined crystallographic models, but it is not known if they are useful for computational models, which usually exhibit different and much more severe errors.

Results

The ability to identify local errors in models was tested for eight MQAPs: VERIFY3D, PROSA, BALA, ANOLEA, PROVE, TUNE, REFINER, PROQRES on 8251 models from the CASP-5 and CASP-6 experiments, by calculating the Spearman's rank correlation coefficients between per-residue scores of these methods and local deviations between C-alpha atoms in the models vs. experimental structures. As a reference, we calculated the value of correlation between the local deviations and trivial features that can be calculated for each residue directly from the models, i.e. solvent accessibility, depth in the structure, and the number of local and non-local neighbours. We found that absolute correlations of scores returned by the MQAPs and local deviations were poor for all methods. In addition, scores of PROQRES and several other MQAPs strongly correlate with 'trivial' features. Therefore, we developed MetaMQAP, a meta-predictor based on a multivariate regression model, which uses scores of the above-mentioned methods, but in which trivial parameters are controlled. MetaMQAP predicts the absolute deviation (in Ångströms) of individual C-alpha atoms between the model and the unknown true structure as well as global deviations (expressed as root mean square deviation and GDT_TS scores). Local model accuracy predicted by MetaMQAP shows an impressive correlation coefficient of 0.7 with true deviations from native structures, a significant improvement over all constituent primary MQAP scores. The global MetaMQAP score is correlated with model GDT_TS on the level of 0.89.

Conclusion

Finally, we compared our method with the MQAPs that scored best in the 7th edition of CASP, using CASP7 server models (not included in the MetaMQAP training set) as the test data. In our benchmark, MetaMQAP is outperformed only by PCONS6 and method QA_556 – methods that require comparison of multiple alternative models and score each of them depending on its similarity to other models. MetaMQAP is however the best among methods capable of evaluating just single models.

We implemented the MetaMQAP as a web server available for free use by all academic users at the URL

Structure prediction of domain insertion proteins from structures of individual domains

Multidomain proteins continue to be a major challenge in protein structure prediction. Here we present a Monte Carlo (MC) algorithm, implemented within Rosetta, to predict the structure of proteins in which one domain is inserted into another. Three MC moves combine rigid-body and loop movements to search the constrained conformation by structure disruption and subsequent repair of chain breaks. Local searches find that the algorithm samples and recovers near-native structures consistently. Further global searches produced top-ranked structures within 5 A in 31 of 50 cases in low-resolution mode, and refinement of top-ranked low-resolution structures produced models within 2 A in 21 of 50 cases. Rigid-body orientations were often correctly recovered despite errors in linker conformation. The algorithm is broadly applicable to de novo structure prediction of both naturally occurring and engineered domain insertion proteins.

Assessment of CASP7 predictions for template-based modeling targets

This manuscript presents the assessment of the template-based modeling category of the seventh Critical Assessment of Techniques for Protein Structure Prediction (CASP7). The accuracy of predicted protein models for 108 target domains was assessed based on a detailed comparison between the experimental and predicted structures. The assessment was performed using numerical measures for backbone and structural alignment accuracy, and by scoring correctly modeled hydrogen bond interactions in the predictions. Based on these criteria, our statistical analysis identified a number of groups whose predictions were on average significantly more accurate. Furthermore, the predictions for six target proteins were evaluated for the accuracy of their modeled cofactor binding sites. We also assessed the ability of predictors to improve over the best available single template structure, which showed that the best groups produced models closer to the target structure than the best single template for a significant number of targets. In addition, we assessed the accuracy of the error estimates (local confidence values) assigned to predictions on a per residue basis. Finally, we discuss some general conclusions about the state of the art of template-based modeling methods and their usefulness for practical applications.

Domain definition and target classification for CASP7

Experimentally determined protein structures formed the basis of the CASP7 prediction assessments. These target structures were assigned to one or more tertiary structure prediction categories and where necessary were divided into structural domains. Boundaries for these domains were based on visual inspection of the targets and superpositions of the target with template structures. Target domains were classified into three different categories for assessment: "high accuracy modeling," "template-based modeling," and "free modeling." Assessment categories were determined by structural similarity between the target domain and the nearest structural templates in the PDB and by the accuracy of the models submitted by the predictors or by whether or not template information was used to generate the predictions. In CASP7 108 of the 123 target domains were evaluated in the template-based modeling category and the remaining 15 target domains were classified as free modeling. A total of 28 target domains from the template-based modeling category were also assessed in the high accuracy category and four overlapped with the free modeling category.

ProCKSI: a decision support system for Protein (structure) Comparison, Knowledge, Similarity and Information

Background

We introduce the decision support system for Protein (Structure) Comparison, Knowledge, Similarity and Information (ProCKSI). ProCKSI integrates various protein similarity measures through an easy to use interface that allows the comparison of multiple proteins simultaneously. It employs the Universal Similarity Metric (USM), the Maximum Contact Map Overlap (MaxCMO) of protein structures and other external methods such as the DaliLite and the TM-align methods, the Combinatorial Extension (CE) of the optimal path, and the FAST Align and Search Tool (FAST). Additionally, ProCKSI allows the user to upload a user-defined similarity matrix supplementing the methods mentioned, and computes a similarity consensus in order to provide a rich, integrated, multicriteria view of large datasets of protein structures.

Results

We present ProCKSI's architecture and workflow describing its intuitive user interface, and show its potential on three distinct test-cases. In the first case, ProCKSI is used to evaluate the results of a previous CASP competition, assessing the similarity of proposed models for given targets where the structures could have a large deviation from one another. To perform this type of comparison reliably, we introduce a new consensus method. The second study deals with the verification of a classification scheme for protein kinases, originally derived by sequence comparison by Hanks and Hunter, but here we use a consensus similarity measure based on structures. In the third experiment using the Rost and Sander dataset (RS126), we investigate how a combination of different sets of similarity measures influences the quality and performance of ProCKSI's new consensus measure. ProCKSI performs well with all three datasets, showing its potential for complex, simultaneous multi-method assessment of structural similarity in large protein datasets. Furthermore, combining different similarity measures is usually more robust than relying on one single, unique measure.

Conclusion

Based on a diverse set of similarity measures, ProCKSI computes a consensus similarity profile for the entire protein set. All results can be clustered, visualised, analysed and easily compared with each other through a simple and intuitive interface.

ProCKSI is publicly available at for academic and non-commercial use.

Evaluation of the structural quality of modeled proteins by using globularity criteria

Background

The knowledge of the three-dimensional structure of globular proteins is fundamental for a detailed investigation of their functional properties. Experimental methods are too slow for structure investigation on a large scale, while computational prediction methods offer alternatives that are continuously being improved. The international Comparative Assessment of Structure Prediction (CASP), an "a posteriori" evaluation of the quality of theoretical models when the experimental structure becomes available, demonstrates that predictions can be successful as well as unsuccessful, and this suggests the necessity for evaluations able to discard "a priori" the wrong models.

Results

We analyzed different structural properties of globular proteins for experimentally solved proteins belonging to the four different structural classes: "mainly alpha", "mainly beta", "alpha/beta" and "alpha+beta". The properties were found to be linearly correlated to protein molecular weight, but with some differences among the four classes. These results were applied to develop an evaluation test of theoretical models based on the expected globular properties of proteins. To verify the success of our test, we applied it to several protein models submitted to the sixth edition of CASP. The best theoretical models, as judged by CASP assessors, were in agreement with the expected properties, while most of the low-quality models had not passed our evaluations.

Conclusion

This study supports the need for careful checks to avoid the diffusion of incorrect structural models. Our test allows the evaluation of models in the absence of experimental reference structures, thereby preventing the diffusion of incorrect structural models and the formulation of incorrect functional hypotheses. It can be used to check the globularity of predicted models, and to supplement other methods already used to evaluate their quality.

Evaluation of domain prediction in CASP6

We present an analysis of the domain boundary prediction, a new category, in the sixth community-wide experiment on the Critical Assessment of Techniques for Protein Structure Prediction (CASP6). There were 1011 predictions submitted for 63 targets. Each prediction was compared to the set of domains defined manually by visual inspection of the experimental structure. The comparison was scored using a new domain prediction scoring scheme. As the definition of a domain is subjective, many targets were assigned alternate definitions. For such targets, each prediction was compared with all different definitions and the best score was chosen. The predictors found it difficult to accurately predict domain boundaries when the target protein contained many domains or domains made of multiple sequence segments. The CBRC-DR (P0536) and Sternberg (P0237) groups were the most successful among human experts, while Baker-Rossettadom (P0353) and Baker-Robetta-Ginzu (P0421) did well among servers.

Assessment of predictions submitted for the CASP6 comparative modeling category

Here we present a full overview of the Critical Assessment of Protein Structure Prediction (CASP6) comparative modeling category. Prediction accuracy for the 43 comparative modeling targets was assessed through detailed numerical comparisons between predicted and experimental structures. Assessments using standard measures for model backbone quality and structural alignment accuracy highlighted a small number of groups with stand out predictions and these findings were backed up by statistical comparisons. We were able to carry out evaluations of side-chain contacts predictions and side-chain rotamer accuracy, for which one group turned out to have statistically better predictions. We also assessed the prediction quality of structurally divergent regions and biologically important sites. Interestingly we were able to show that predictors were not predicting these important functional regions with any greater accuracy than the rest of the structure. In addition we investigated the ability of predictors to build models that improve on the structural template and reached some tentative conclusions from comparisons with the previous CASP experiment.

Assessment of CASP6 predictions for new and nearly new fold targets

This is a report of the assessment of the predictions made for the CASP6 protein structure prediction experiment conducted in 2004 in the New Fold (NF) category. There were nine protein domains that were judged to have new folds (NF) and 16 for which a similar structure was known but the sequence similarity was judged to be too low for them to be easily recognized (FR/A). We selected all NF targets and eight of the 16 FR/A targets judged to be at the borderline between NF and FR/A for evaluation in the NF category. A total of 165 prediction groups submitted over 7400 structural models for these targets. The quality of these models was evaluated using the GDT_TS scores of the structural similarity detection program LGA and by visual inspection of the top-scoring models. The best models submitted bore an overall similarity to the target structure for three or four of the nine NF targets and for all but one of the FR/A targets. High-scoring models for the NF targets were submitted by several different groups. When both the NF and FR/A targets were considered, Baker group dominated by submitting best models for seven of the 17 targets, but 14 other groups also managed to submit best models for one or more targets.

Sequence comparison and protein structure prediction

Sequence comparison is a major step in the prediction of protein structure from existing templates in the Protein Data Bank. The identification of potentially remote homologues to be used as templates for modeling target sequences of unknown structure and their accurate alignment remain challenges, despite many years of study. The most recent advances have been in combining as many sources of information as possible--including amino acid variation in the form of profiles or hidden Markov models for both the target and template families, known and predicted secondary structures of the template and target, respectively, the combination of structure alignment for distant homologues and sequence alignment for close homologues to build better profiles, and the anchoring of certain regions of the alignment based on existing biological data. Newer technologies have been applied to the problem, including the use of support vector machines to tackle the fold classification problem for a target sequence and the alignment of hidden Markov models. Finally, using the consensus of many fold recognition methods, whether based on profile-profile alignments, threading or other approaches, continues to be one of the most successful strategies for both recognition and alignment of remote homologues. Although there is still room for improvement in identification and alignment methods, additional progress may come from model building and refinement methods that can compensate for large structural changes between remotely related targets and templates, as well as for regions of misalignment.

Protein structure comparison: implications for the nature of 'fold space', and structure and function prediction

The identification of geometric relationships between protein structures offers a powerful approach to predicting the structure and function of proteins. Methods to detect such relationships range from human pattern recognition to a variety of mathematical algorithms. A number of schemes for the classification of protein structure have found widespread use and these implicitly assume the organization of protein structure space into discrete categories. Recently, an alternative view has emerged in which protein fold space is seen as continuous and multidimensional. Significant relationships have been observed between proteins that belong to what have been termed different 'folds'. There has been progress in the use of these relationships in the prediction of protein structure and function.

An analysis of the Sargasso Sea resource and the consequences for database composition

Background

The environmental sequencing of the Sargasso Sea has introduced a huge new resource of genomic information. Unlike the protein sequences held in the current searchable databases, the Sargasso Sea sequences originate from a single marine environment and have been sequenced from species that are not easily obtainable by laboratory cultivation. The resource also contains very many fragments of whole protein sequences, a side effect of the shotgun sequencing method.

These sequences form a significant addendum to the current searchable databases but also present us with some intrinsic difficulties. While it is important to know whether it is possible to assign function to these sequences with the current methods and whether they will increase our capacity to explore sequence space, it is also interesting to know how current bioinformatics techniques will deal with the new sequences in the resource.

Results

The Sargasso Sea sequences seem to introduce a bias that decreases the potential of current methods to propose structure and function for new proteins. In particular the high proportion of sequence fragments in the resource seems to result in poor quality multiple alignments.

Conclusion

These observations suggest that the new sequences should be used with care, especially if the information is to be used in large scale analyses. On a positive note, the results may just spark improvements in computational and experimental methods to take into account the fragments generated by environmental sequencing techniques.