Global and local model quality estimation at CASP8 using the scoring functions QMEAN and QMEANclust

Chemoenzymatic Synthesis of a New Germacrene Derivative Named Germacrene F

The new farnesyl pyrophosphate (FPP) derivative with a shifted olefinic double bond from C6-C7 to C7-C8 is accepted and converted by the sesquiterpene cyclases protoilludene synthase (Omp7) as well as viridiflorene synthase (Tps32). In both cases, a so far unknown germacrene derivative was found to be formed, which we name "germacrene F". Both cases are examples in which a modification around the central olefinic double bond in FPP leads to a change in the mode of initial cyclization (from 1→11 to 1→10). For Omp7 a rationale for this behaviour was found by carrying out molecular docking studies. Temperature-dependent NMR experiments, accompanied by NOE studies, show that germacrene F adopts a preferred mirror-symmetric conformation with both methyl groups oriented in the same directions in the cyclodecane ring.

Heterologous expression and characterization of ToxA1 haplotype from India and its interaction with Tsn1 for spot blotch susceptibility in spring wheat

BACKGROUND

ToxA, a necrotrophic effector protein, is present in the genome of fungal species like Parastagnospora nodorum, Pyrenophora tritici-repentis and Bipolaris sorokiniana. Tsn1 is the sensitivity gene in the host whose presence indicates more susceptibility to ToxA carrying pathogen, and ToxA-Tsn1 interaction follows an inverse gene-for-gene relationship.

METHODS AND RESULTS

The present study involved cloning and expressing the ToxA1 haplotype from B. sorokiniana. It was found that the amplicon exhibited an expected product size of 471 bp. Sequence analysis of the ToxA1 nucleotide sequence revealed the highest identity, 99.79%, with P. tritici-repentis. The protein expression analysis showed peak expression at 16.5 kDa. Phylogenetic analysis of the ToxA1 sequence from all the Bipolaris isolates formed an independent clade along with P. tritici-repentis and diverged from P. nodorum. ToxA-Tsn1 interaction was studied in 18 wheat genotypes (11 Tsn1 and 7 tsn1) at both seedling and adult stages, validating the inverse gene-for-gene relationship, as the toxin activity was highest in the K68 genotype (Tsn1) and lowest in WAMI280 (tsn1).

CONCLUSION

The study indicates that the haplotype ToxA1 is prevailing in the Indian population of B. sorokiniana. It would be desirable for wheat breeders to select genotypes with tsn1 locus for making wheat resistant to spot blotch.

Characterization of CcTFL1 Governing Plant Architecture in Pigeon pea (Cajanus cajan (L.) Millsp.)

Growth habits are among the essential adaptive traits acted upon by evolution during plant speciation. They have brought remarkable changes in the morphology and physiology of plants. Inflorescence architecture varies greatly between wild relatives and cultivars of pigeon pea. The present study isolated the CcTFL1 (Terminal Flowering Locus 1) locus using six varieties showing determinate (DT) and indeterminate (IDT) growth habits. Multiple alignments of CcTFL1 sequences revealed the presence of InDel, which describes a 10 bp deletion in DT varieties. At the same time, IDT varieties showed no deletion. InDel altered the translation start point in DT varieties, resulting in the shortening of exon 1. This InDel was validated in ten varieties of cultivated species and three wild relatives differing in growth habits. The predicted protein structure showed the absence of 27 amino acids in DT varieties, which was reflected in mutant CcTFL1 by the absence of two α-helices, a connecting loop, and shortened β-sheet. By subsequent motif analysis, it was found that the wild-type protein had a phosphorylation site for protein kinase C, but the mutant protein did not. In silico analysis revealed that the InDel-driven deletion of amino acids spans, containing a phosphorylation site for kinase protein, may have resulted in the non-functionality of the CcTFL1 protein, rendering the determinate growth habit. This characterization of the CcTFL1 locus could be used to modulate growth habits through genome editing.

Contact-Assisted Threading in Low-Homology Protein Modeling

The ability to successfully predict the three-dimensional structure of a protein from its amino acid sequence has made considerable progress in the recent past. The progress is propelled by the improved accuracy of deep learning-based inter-residue contact map predictors coupled with the rising growth of protein sequence databases. Contact map encodes interatomic interaction information that can be exploited for highly accurate prediction of protein structures via contact map threading even for the query proteins that are not amenable to direct homology modeling. As such, contact-assisted threading has garnered considerable research effort. In this chapter, we provide an overview of existing contact-assisted threading methods while highlighting the recent advances and discussing some of the current limitations and future prospects in the application of contact-assisted threading for improving the accuracy of low-homology protein modeling.

Quality Estimates for 3D Protein Models

Protein structure modeling is one of the most advanced and complex processes in computational biology. One of the major problems for the protein structure prediction field has been how to estimate the accuracy of the predicted 3D models, on both a local and global level, in the absence of known structures. We must be able to accurately measure the confidence that we have in the quality predicted 3D models of proteins for them to become widely adopted by the general bioscience community. To address this major issue, it was necessary to develop new model quality assessment (MQA) methods and integrate them into our pipelines for building 3D protein models. Our MQA method, called ModFOLD, has been ranked as one of the most accurate MQA tools in independent blind evaluations. This chapter discusses model quality assessment in the protein modeling field, demonstrating both its strengths and limitations. We also present some of the best methods according to independent benchmarking data, which has been gathered in recent years.

QDeep: distance-based protein model quality estimation by residue-level ensemble error classifications using stacked deep residual neural networks

MOTIVATION

Protein model quality estimation, in many ways, informs protein structure prediction. Despite their tight coupling, existing model quality estimation methods do not leverage inter-residue distance information or the latest technological breakthrough in deep learning that has recently revolutionized protein structure prediction.

RESULTS

We present a new distance-based single-model quality estimation method called QDeep by harnessing the power of stacked deep residual neural networks (ResNets). Our method first employs stacked deep ResNets to perform residue-level ensemble error classifications at multiple predefined error thresholds, and then combines the predictions from the individual error classifiers for estimating the quality of a protein structural model. Experimental results show that our method consistently outperforms existing state-of-the-art methods including ProQ2, ProQ3, ProQ3D, ProQ4, 3DCNN, MESHI, and VoroMQA in multiple independent test datasets across a wide-range of accuracy measures; and that predicted distance information significantly contributes to the improved performance of QDeep.

AVAILABILITY AND IMPLEMENTATION

https://github.com/Bhattacharya-Lab/QDeep.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Allelic characterization and protein structure analysis reveals the involvement of splice site mutation for growth habit differences in Lablab purpureus (L.) Sweet

BACKGROUND

Interrelationship between growth habit and flowering played a key role in the domestication history of pulses; however, the actual genes responsible for these traits have not been identified in Indian bean. Determinate growth habit is desirable due to its early flowering, photo-insensitivity, synchronous pod maturity, ease in manual harvesting and short crop duration. The present study aimed to identify, characterize and validate the gene responsible for growth habit by using a candidate gene approach coupled with sequencing, multiple sequence alignment, protein structure prediction and binding pocket analysis.

RESULTS

Terminal flowering locus was amplified from GPKH 120 (indeterminate) and GNIB-21 (determinate) using the primers designed from PvTFL1y locus of common bean. Gene prediction revealed that the length of the third and fourth exons differed between the two alleles. Allelic sequence comparison indicated a transition from guanine to adenine at the end of the third exon in GNIB 21. This splice site single-nucleotide polymorphism (SNP) was validated in germplasm lines by sequencing. Protein structure analysis indicated involvement of two binding pockets for interaction of terminal flowering locus (TFL) protein with other proteins.

CONCLUSION

The splice site SNP present at the end of the third exon of TFL locus is responsible for the transformation of shoot apical meristem into a reproductive fate in the determinate genotype GNIB 21. The splice site SNP leads to absence of 14 amino acids in mutant TFL protein of GNIB 21, rendering the protein non-functional. This deletion disturbed previously reported anion-binding pocket and secondary binding pocket due to displacement of small β-sheet away from an external loop. This finding may enable the modulation of growth habit in Indian bean and other pulse crops through genome editing.

Structural bases that underline Trypanosoma cruzi calreticulin proinfective, antiangiogenic and antitumor properties

Microbes have developed mechanisms to resist the host immune defenses and some elicit antitumor immune responses. About 6 million people are infected with Trypanosoma cruzi, the protozoan agent of Chagas' disease, the sixth neglected tropical disease worldwide. Eighty years ago, G. Roskin and N. Klyuyeva proposed that T. cruzi infection mediates an anti-cancer activity. This observation has been reproduced by several other laboratories, but no molecular basis has been proposed. We have shown that the highly pleiotropic chaperone calreticulin (TcCalr, formerly known as TcCRT), translocates from the parasite ER to the exterior, where it mediates infection. Similar to its human counterpart HuCALR (formerly known as HuCRT), TcCalr inhibits C1 in its capacity to initiate the classical pathway of complement activation. We have also proposed that TcCalr inhibits angiogenesis and it is a likely mediator of antitumor effects. We have generated several in silico structural TcCalr models to delimit a peptide (VC-TcCalr) at the TcCalr N-domain. Chemically synthesized VC-TcCalr did bind to C1q and was anti-angiogenic in Gallus gallus chorioallantoic membrane assays. These properties were associated with structural features, as determined in silico. VC-TcCalr, a strong dipole, interacts with charged proteins such as collagen-like tails and scavenger receptors. Comparatively, HuCALR has less polarity and spatial stability, probably due to at least substitutions of Gln for Gly, Arg for Lys, Arg for Asp and Ser for Arg that hinder protein-protein interactions. These differences can explain, at least in part, how TcCalr inhibits the complement activation pathway and has higher efficiency as an antiangiogenic and antitumor agent than HuCALR.

ModFOLD6: an accurate web server for the global and local quality estimation of 3D protein models

Methods that reliably estimate the likely similarity between the predicted and native structures of proteins have become essential for driving the acceptance and adoption of three-dimensional protein models by life scientists. ModFOLD6 is the latest version of our leading resource for Estimates of Model Accuracy (EMA), which uses a pioneering hybrid quasi-single model approach. The ModFOLD6 server integrates scores from three pure-single model methods and three quasi-single model methods using a neural network to estimate local quality scores. Additionally, the server provides three options for producing global score estimates, depending on the requirements of the user: (i) ModFOLD6_rank, which is optimized for ranking/selection, (ii) ModFOLD6_cor, which is optimized for correlations of predicted and observed scores and (iii) ModFOLD6 global for balanced performance. The ModFOLD6 methods rank among the top few for EMA, according to independent blind testing by the CASP12 assessors. The ModFOLD6 server is also continuously automatically evaluated as part of the CAMEO project, where significant performance gains have been observed compared to our previous server and other publicly available servers. The ModFOLD6 server is freely available at: http://www.reading.ac.uk/bioinf/ModFOLD/.

Identification of a Novel Elastin-Degrading Enzyme from the Fish Pathogen Flavobacterium psychrophilum

Elastin is an important proteinaceous component of vertebrate connective tissues (e.g., blood vessels, lung, and skin), to which it confers elasticity. Elastases have been identified in a number of pathogenic bacteria. They are thought to be required for tissue penetration and dissemination, acting as “spreading factors.” Flavobacterium psychrophilum is a devastating bacterial pathogen of salmonid fish (salmon and trout) that is responsible for severe economic losses worldwide. This pathogen displays strong proteolytic activities. Using a variety of techniques, including genome comparisons, we identified a gene encoding a novel elastase in F. psychrophilum. The encoded protein is predicted to be a cell-surface-exposed lipoprotein with no homology to previously reported elastases. In addition, this elastase likely belongs to a new family of proteases that seems to be present only in some members of this important group of bacteria.

Hydrolytic extracellular enzymes degrading host tissues potentially play a role in bacterial pathogenesis. Flavobacterium psychrophilum is an important bacterial pathogen of salmonid fish reared in freshwater throughout the world. Diversity among isolates has been described at the phenotypic, serological, and genomic levels, but the links between these various traits remain poorly understood. Using a genome-wide association study, we identified a gene encoding a novel elastinolytic enzyme in F. psychrophilum. To formally demonstrate enzymatic activity, this gene (FP0506 from strain JIP 02/86) was expressed in the elastinolysis-deficient strain OSU THCO2-90, resulting in proficient elastin-degrading cells. The encoded protein is predicted to be a cell-surface-exposed lipoprotein with no homology to previously reported elastases. FP0506 might belong to the zincin tribe and gluzincin clan of metalloproteases, and this new elastase-encoding gene seems to be present only in some members of the family Flavobacteriaceae.

IMPORTANCE Elastin is an important proteinaceous component of vertebrate connective tissues (e.g., blood vessels, lung, and skin), to which it confers elasticity. Elastases have been identified in a number of pathogenic bacteria. They are thought to be required for tissue penetration and dissemination, acting as “spreading factors.” Flavobacterium psychrophilum is a devastating bacterial pathogen of salmonid fish (salmon and trout) that is responsible for severe economic losses worldwide. This pathogen displays strong proteolytic activities. Using a variety of techniques, including genome comparisons, we identified a gene encoding a novel elastase in F. psychrophilum. The encoded protein is predicted to be a cell-surface-exposed lipoprotein with no homology to previously reported elastases. In addition, this elastase likely belongs to a new family of proteases that seems to be present only in some members of this important group of bacteria.

hCALCRL mutation causes autosomal recessive nonimmune hydrops fetalis with lymphatic dysplasia

Using genetic, pharmacological and animal model approaches, we elucidate a novel human mutation in a G protein coupled receptor that impairs receptor oligomerization and trafficking leading to fatal, non-immune hydrops fetalis associated with arrested lymphatic development.

We report the first case of nonimmune hydrops fetalis (NIHF) associated with a recessive, in-frame deletion of V205 in the G protein–coupled receptor, Calcitonin Receptor-Like Receptor (hCALCRL). Homozygosity results in fetal demise from hydrops fetalis, while heterozygosity in females is associated with spontaneous miscarriage and subfertility. Using molecular dynamic modeling and in vitro biochemical assays, we show that the hCLR(V205del) mutant results in misfolding of the first extracellular loop, reducing association with its requisite receptor chaperone, receptor activity modifying protein (RAMP), translocation to the plasma membrane and signaling. Using three independent genetic mouse models we establish that the adrenomedullin–CLR–RAMP2 axis is both necessary and sufficient for driving lymphatic vascular proliferation. Genetic ablation of either lymphatic endothelial Calcrl or nonendothelial Ramp2 leads to severe NIHF with embryonic demise and placental pathologies, similar to that observed in humans. Our results highlight a novel candidate gene for human congenital NIHF and provide structure–function insights of this signaling axis for human physiology.

Increased efficiency of Campylobacter jejuni N-oligosaccharyltransferase PglB by structure-guided engineering

Conjugate vaccines belong to the most efficient preventive measures against life-threatening bacterial infections. Functional expression of N-oligosaccharyltransferase (N-OST) PglB of Campylobacter jejuni in Escherichia coli enables a simplified production of glycoconjugate vaccines in prokaryotic cells. Polysaccharide antigens of pathogenic bacteria can be covalently coupled to immunogenic acceptor proteins bearing engineered glycosylation sites. Transfer efficiency of PglB_Cj is low for certain heterologous polysaccharide substrates. In this study, we increased glycosylation rates for Salmonella enterica sv. Typhimurium LT2 O antigen (which lacks N-acetyl sugars) and Staphylococcus aureus CP5 polysaccharides by structure-guided engineering of PglB. A three-dimensional homology model of membrane-associated PglB_Cj, docked to the natural C. jejuni N-glycan attached to the acceptor peptide, was used to identify potential sugar-interacting residues as targets for mutagenesis. Saturation mutagenesis of an active site residue yielded the enhancing mutation N311V, which facilitated fivefold to 11-fold increased in vivo glycosylation rates as determined by glycoprotein-specific ELISA. Further rounds of in vitro evolution led to a triple mutant S80R-Q287P-N311V enabling a yield improvement of S. enterica LT2 glycoconjugates by a factor of 16. Our results demonstrate that bacterial N-OST can be tailored to specific polysaccharide substrates by structure-guided protein engineering.

The SIB Swiss Institute of Bioinformatics' resources: focus on curated databases

The SIB Swiss Institute of Bioinformatics (www.isb-sib.ch) provides world-class bioinformatics databases, software tools, services and training to the international life science community in academia and industry. These solutions allow life scientists to turn the exponentially growing amount of data into knowledge. Here, we provide an overview of SIB's resources and competence areas, with a strong focus on curated databases and SIB's most popular and widely used resources. In particular, SIB's Bioinformatics resource portal ExPASy features over 150 resources, including UniProtKB/Swiss-Prot, ENZYME, PROSITE, neXtProt, STRING, UniCarbKB, SugarBindDB, SwissRegulon, EPD, arrayMap, Bgee, SWISS-MODEL Repository, OMA, OrthoDB and other databases, which are briefly described in this article.

Random forest-based protein model quality assessment (RFMQA) using structural features and potential energy terms

Recently, predicting proteins three-dimensional (3D) structure from its sequence information has made a significant progress due to the advances in computational techniques and the growth of experimental structures. However, selecting good models from a structural model pool is an important and challenging task in protein structure prediction. In this study, we present the first application of random forest based model quality assessment (RFMQA) to rank protein models using its structural features and knowledge-based potential energy terms. The method predicts a relative score of a model by using its secondary structure, solvent accessibility and knowledge-based potential energy terms. We trained and tested the RFMQA method on CASP8 and CASP9 targets using 5-fold cross-validation. The correlation coefficient between the TM-score of the model selected by RFMQA (TMRF) and the best server model (TMbest) is 0.945. We benchmarked our method on recent CASP10 targets by using CASP8 and 9 server models as a training set. The correlation coefficient and average difference between TMRF and TMbest over 95 CASP10 targets are 0.984 and 0.0385, respectively. The test results show that our method works better in selecting top models when compared with other top performing methods. RFMQA is available for download from http://lee.kias.re.kr/RFMQA/RFMQA_eval.tar.gz.

DL-PRO: A Novel Deep Learning Method for Protein Model Quality Assessment

Computational protein structure prediction is very important for many applications in bioinformatics. In the process of predicting protein structures, it is essential to accurately assess the quality of generated models. Although many single-model quality assessment (QA) methods have been developed, their accuracy is not high enough for most real applications. In this paper, a new approach based on C-α atoms distance matrix and machine learning methods is proposed for single-model QA and the identification of native-like models. Different from existing energy/scoring functions and consensus approaches, this new approach is purely geometry based. Furthermore, a novel algorithm based on deep learning techniques, called DL-Pro, is proposed. For a protein model, DL-Pro uses its distance matrix that contains pairwise distances between two residues' C-α atoms in the model, which sometimes is also called contact map, as an orientation-independent representation. From training examples of distance matrices corresponding to good and bad models, DL-Pro learns a stacked autoencoder network as a classifier. In experiments on selected targets from the Critical Assessment of Structure Prediction (CASP) competition, DL-Pro obtained promising results, outperforming state-of-the-art energy/scoring functions, including OPUS-CA, DOPE, DFIRE, and RW.

Protein structural model selection by combining consensus and single scoring methods

Quality assessment (QA) for predicted protein structural models is an important and challenging research problem in protein structure prediction. Consensus Global Distance Test (CGDT) methods assess each decoy (predicted structural model) based on its structural similarity to all others in a decoy set and has been proved to work well when good decoys are in a majority cluster. Scoring functions evaluate each single decoy based on its structural properties. Both methods have their merits and limitations. In this paper, we present a novel method called PWCom, which consists of two neural networks sequentially to combine CGDT and single model scoring methods such as RW, DDFire and OPUS-Ca. Specifically, for every pair of decoys, the difference of the corresponding feature vectors is input to the first neural network which enables one to predict whether the decoy-pair are significantly different in terms of their GDT scores to the native. If yes, the second neural network is used to decide which one of the two is closer to the native structure. The quality score for each decoy in the pool is based on the number of winning times during the pairwise comparisons. Test results on three benchmark datasets from different model generation methods showed that PWCom significantly improves over consensus GDT and single scoring methods. The QA server (MUFOLD-Server) applying this method in CASP 10 QA category was ranked the second place in terms of Pearson and Spearman correlation performance.

The ModFOLD4 server for the quality assessment of 3D protein models

Once you have generated a 3D model of a protein, how do you know whether it bears any resemblance to the actual structure? To determine the usefulness of 3D models of proteins, they must be assessed in terms of their quality by methods that predict their similarity to the native structure. The ModFOLD4 server is the latest version of our leading independent server for the estimation of both the global and local (per-residue) quality of 3D protein models. The server produces both machine readable and graphical output, providing users with intuitive visual reports on the quality of predicted protein tertiary structures. The ModFOLD4 server is freely available to all at: http://www.reading.ac.uk/bioinf/ModFOLD/.

Improved model quality assessment using ProQ2

Background

Employing methods to assess the quality of modeled protein structures is now standard practice in bioinformatics. In a broad sense, the techniques can be divided into methods relying on consensus prediction on the one hand, and single-model methods on the other. Consensus methods frequently perform very well when there is a clear consensus, but this is not always the case. In particular, they frequently fail in selecting the best possible model in the hard cases (lacking consensus) or in the easy cases where models are very similar. In contrast, single-model methods do not suffer from these drawbacks and could potentially be applied on any protein of interest to assess quality or as a scoring function for sampling-based refinement.

Results

Here, we present a new single-model method, ProQ2, based on ideas from its predecessor, ProQ. ProQ2 is a model quality assessment algorithm that uses support vector machines to predict local as well as global quality of protein models. Improved performance is obtained by combining previously used features with updated structural and predicted features. The most important contribution can be attributed to the use of profile weighting of the residue specific features and the use features averaged over the whole model even though the prediction is still local.

Conclusions

ProQ2 is significantly better than its predecessors at detecting high quality models, improving the sum of Z-scores for the selected first-ranked models by 20% and 32% compared to the second-best single-model method in CASP8 and CASP9, respectively. The absolute quality assessment of the models at both local and global level is also improved. The Pearson’s correlation between the correct and local predicted score is improved from 0.59 to 0.70 on CASP8 and from 0.62 to 0.68 on CASP9; for global score to the correct GDT_TS from 0.75 to 0.80 and from 0.77 to 0.80 again compared to the second-best single methods in CASP8 and CASP9, respectively. ProQ2 is available at http://proq2.wallnerlab.org.

FunFOLDQA: a quality assessment tool for protein-ligand binding site residue predictions

The estimation of prediction quality is important because without quality measures, it is difficult to determine the usefulness of a prediction. Currently, methods for ligand binding site residue predictions are assessed in the function prediction category of the biennial Critical Assessment of Techniques for Protein Structure Prediction (CASP) experiment, utilizing the Matthews Correlation Coefficient (MCC) and Binding-site Distance Test (BDT) metrics. However, the assessment of ligand binding site predictions using such metrics requires the availability of solved structures with bound ligands. Thus, we have developed a ligand binding site quality assessment tool, FunFOLDQA, which utilizes protein feature analysis to predict ligand binding site quality prior to the experimental solution of the protein structures and their ligand interactions. The FunFOLDQA feature scores were combined using: simple linear combinations, multiple linear regression and a neural network. The neural network produced significantly better results for correlations to both the MCC and BDT scores, according to Kendall’s τ, Spearman’s ρ and Pearson’s r correlation coefficients, when tested on both the CASP8 and CASP9 datasets. The neural network also produced the largest Area Under the Curve score (AUC) when Receiver Operator Characteristic (ROC) analysis was undertaken for the CASP8 dataset. Furthermore, the FunFOLDQA algorithm incorporating the neural network, is shown to add value to FunFOLD, when both methods are employed in combination. This results in a statistically significant improvement over all of the best server methods, the FunFOLD method (6.43%), and one of the top manual groups (FN293) tested on the CASP8 dataset. The FunFOLDQA method was also found to be competitive with the top server methods when tested on the CASP9 dataset. To the best of our knowledge, FunFOLDQA is the first attempt to develop a method that can be used to assess ligand binding site prediction quality, in the absence of experimental data.

Improving a consensus approach for protein structure selection by removing redundancy

In protein tertiary structure prediction, a crucial step is to select near-native structures from a large number of predicted structural models. Over the years, extensive research has been conducted for the protein structure selection problem with most approaches focusing on developing more accurate energy or scoring functions. Despite significant advances in this area, the discerning power of current approaches is still unsatisfactory. In this paper, we propose a novel consensus-based algorithm for the selection of predicted protein structures. Given a set of predicted models, our method first removes redundant structures to derive a subset of reference models. Then, a structure is ranked based on its average pairwise similarity to the reference models. Using the CASP8 data set containing a large collection of predicted models for 122 targets, we compared our method with the best CASP8 quality assessment (QA) servers, which are all consensus based, and showed that our QA scores correlate better with the GDT-TSs than those of the CASP8 QA servers. We also compared our method with the state-of-the-art scoring functions and showed its improved performance for near-native model selection. The GDT-TSs of the top models picked by our method are on average more than 8 percent better than the ones selected by the best performing scoring function.

Evaluation of model quality predictions in CASP9

CASP has been assessing the state of the art in the a priori estimation of accuracy of protein structure prediction since 2006. The inclusion of model quality assessment category in CASP contributed to a rapid development of methods in this area. In the last experiment, 46 quality assessment groups tested their approaches to estimate the accuracy of protein models as a whole and/or on a per-residue basis. We assessed the performance of these methods predominantly on the basis of the correlation between the predicted and observed quality of the models on both global and local scales. The ability of the methods to identify the models closest to the best one, to differentiate between good and bad models, and to identify well modeled regions was also analyzed. Our evaluations demonstrate that even though global quality assessment methods seem to approach perfection point (weighted average per-target Pearson's correlation coefficients are as high as 0.97 for the best groups), there is still room for improvement. First, all top-performing methods use consensus approaches to generate quality estimates, and this strategy has its own limitations. Second, the methods that are based on the analysis of individual models lag far behind clustering techniques and need a boost in performance. The methods for estimating per-residue accuracy of models are less accurate than global quality assessment methods, with an average weighted per-model correlation coefficient in the range of 0.63-0.72 for the best 10 groups.

A multilayer evaluation approach for protein structure prediction and model quality assessment

Protein tertiary structures are essential for studying functions of proteins at molecular level. An indispensable approach for protein structure solution is computational prediction. Most protein structure prediction methods generate candidate models first and select the best candidates by model quality assessment (QA). In many cases, good models can be produced, but the QA tools fail to select the best ones from the candidate model pool. Because of incomplete understanding of protein folding, each QA method only reflects partial facets of a structure model and thus has limited discerning power with no one consistently outperforming others. In this article, we developed a set of new QA methods, including two QA methods for evaluating target/template alignments, a molecular dynamics (MD)-based QA method, and three consensus QA methods with selected references to reveal new facets of protein structures complementary to the existing methods. Moreover, the underlying relationship among different QA methods were analyzed and then integrated into a multilayer evaluation approach to guide the model generation and model selection in prediction. All methods are integrated and implemented into an innovative and improved prediction system hereafter referred to as MUFOLD. In CASP8 and CASP9, MUFOLD has demonstrated the proof of the principles in terms of both QA discerning power and structure prediction accuracy.

MUFOLD-WQA: A new selective consensus method for quality assessment in protein structure prediction

Assessing the quality of predicted models is essential in protein tertiary structure prediction. In the past critical assessment of techniques for protein structure prediction (CASP) experiments, consensus quality assessment (QA) methods have shown to be very effective, outperforming single-model methods and other competing approaches by a large margin. In the consensus QA approach, the quality score of a model is typically estimated based on pair-wise structure similarity of it to a set of reference models. In CASP8, the differences among the top QA servers were mostly in the selection of the reference models. In this article, we present a new consensus method "SelCon" based on two key ideas: (1) to adaptively select appropriate reference models based on the attributes of the whole set of predicted models and (2) to weigh different reference models differently, and in particular not to use models that are too similar or too different from the candidate model as its references. We have developed several reference selection functions in SelCon and obtained improved QA results over existing QA methods in experiments using CASP7 and CASP8 data. In the recently completed CASP9 in 2010, the new method was implemented in our MUFOLD-WQA server. Both the official CASP9 assessment and our in-house evaluation showed that MUFOLD-WQA performed very well and achieved top performances in both the global structure QA and top-model selection category in CASP9.

A sampling-based method for ranking protein structural models by integrating multiple scores and features

One of the major challenges in protein tertiary structure prediction is structure quality assessment. In many cases, protein structure prediction tools generate good structural models, but fail to select the best models from a huge number of candidates as the final output. In this study, we developed a sampling-based machine-learning method to rank protein structural models by integrating multiple scores and features. First, features such as predicted secondary structure, solvent accessibility and residue-residue contact information are integrated by two Radial Basis Function (RBF) models trained from different datasets. Then, the two RBF scores and five selected scoring functions developed by others, i.e., Opus-CA, Opus-PSP, DFIRE, RAPDF, and Cheng Score are synthesized by a sampling method. At last, another integrated RBF model ranks the structural models according to the features of sampling distribution. We tested the proposed method by using two different datasets, including the CASP server prediction models of all CASP8 targets and a set of models generated by our in-house software MUFOLD. The test result shows that our method outperforms any individual scoring function on both best model selection, and overall correlation between the predicted ranking and the actual ranking of structural quality.

A versatile method for systematic conformational searches: application to CheY

A novel molecular structure prediction method, the Z Method, is described. It provides a versatile platform for the development and use of systematic, grid-based conformational search protocols, in which statistical information (i.e., rotamers) can also be included. The Z Method generates trial structures by applying many changes of the same type to a single starting structure, thereby sampling the conformation space in an unbiased way. The method, implemented in the CHARMM program as the Z Module, is applied here to an illustrative model problem in which rigid, systematic searches are performed in a 36-dimensional conformational space that describes the relative positions of the 10 secondary structural elements of the protein CheY. A polar hydrogen representation with an implicit solvation term (EEF1) is used to evaluate successively larger fragments of the protein generated in a hierarchical build-up procedure. After a final refinement stage, and a total computational time of about two-and-a-half CPU days on AMD Opteron processors, the prediction is within 1.56 Å of the native structure. The errors in the predicted backbone dihedral angles are found to approximately cancel. Monte Carlo and simulated annealing trials on the same or smaller versions of the problem, using the same atomic model and energy terms, are shown to result in less accurate predictions. Although the problem solved here is a limited one, the findings illustrate the utility of systematic searches with atom-based models for macromolecular structure prediction and the importance of unbiased sampling in structure prediction methods.

A computational model of the LGI1 protein suggests a common binding site for ADAM proteins

Mutations of human leucine-rich glioma inactivated (LGI1) gene encoding the epitempin protein cause autosomal dominant temporal lateral epilepsy (ADTLE), a rare familial partial epileptic syndrome. The LGI1 gene seems to have a role on the transmission of neuronal messages but the exact molecular mechanism remains unclear. In contrast to other genes involved in epileptic disorders, epitempin shows no homology with known ion channel genes but contains two domains, composed of repeated structural units, known to mediate protein-protein interactions.A three dimensional in silico model of the two epitempin domains was built to predict the structure-function relationship and propose a functional model integrating previous experimental findings. Conserved and electrostatic charged regions of the model surface suggest a possible arrangement between the two domains and identifies a possible ADAM protein binding site in the β-propeller domain and another protein binding site in the leucine-rich repeat domain. The functional model indicates that epitempin could mediate the interaction between proteins localized to different synaptic sides in a static way, by forming a dimer, or in a dynamic way, by binding proteins at different times.The model was also used to predict effects of known disease-causing missense mutations. Most of the variants are predicted to alter protein folding while several other map to functional surface regions. In agreement with experimental evidence, this suggests that non-secreted LGI1 mutants could be retained within the cell by quality control mechanisms or by altering interactions required for the secretion process.

Automated protein structure modeling with SWISS-MODEL Workspace and the Protein Model Portal

Comparative protein structure modeling is a computational approach to build three-dimensional structural models for proteins using experimental structures of related protein family members as templates. Regular blind assessments of modeling accuracy have demonstrated that comparative protein structure modeling is currently the most reliable technique to model protein structures. Homology models are often sufficiently accurate to substitute for experimental structures in a wide variety of applications. Since the usefulness of a model for specific application is determined by its accuracy, model quality estimation is an essential component of protein structure prediction. Comparative protein modeling has become a routine approach in many areas of life science research since fully automated modeling systems allow also nonexperts to build reliable models. In this chapter, we describe practical approaches for automated protein structure modeling with SWISS-MODEL Workspace and the Protein Model Portal.

OpenStructure: a flexible software framework for computational structural biology

Motivation: Developers of new methods in computational structural biology are often hampered in their research by incompatible software tools and non-standardized data formats. To address this problem, we have developed OpenStructure as a modular open source platform to provide a powerful, yet flexible general working environment for structural bioinformatics. OpenStructure consists primarily of a set of libraries written in C++ with a cleanly designed application programmer interface. All functionality can be accessed directly in C++ or in a Python layer, meeting both the requirements for high efficiency and ease of use. Powerful selection queries and the notion of entity views to represent these selections greatly facilitate the development and implementation of algorithms on structural data. The modular integration of computational core methods with powerful visualization tools makes OpenStructure an ideal working and development environment. Several applications, such as the latest versions of IPLT and QMean, have been implemented based on OpenStructure—demonstrating its value for the development of next-generation structural biology algorithms.

Availability: Source code licensed under the GNU lesser general public license and binaries for MacOS X, Linux and Windows are available for download at http://www.openstructure.org.

Contact:torsten.schwede@unibas.ch

Supplementary information:Supplementary data are available at Bioinformatics online.

Predicted residue-residue contacts can help the scoring of 3D models

During the 7th Critical Assessment of Protein Structure Prediction (CASP7) experiment, it was suggested that the real value of predicted residue-residue contacts might lie in the scoring of 3D model structures. Here, we have carried out a detailed reassessment of the contact predictions made during the recent CASP8 experiment to determine whether predicted contacts might aid in the selection of close-to-native structures or be a useful tool for scoring 3D structural models. We used the contacts predicted by the CASP8 residue-residue contact prediction groups to select models for each target domain submitted to the experiment. We found that the information contained in the predicted residue-residue contacts would probably have helped in the selection of 3D models in the free modeling regime and over the harder comparative modeling targets. Indeed, in many cases, the models selected using just the predicted contacts had better GDT-TS scores than all but the best 3D prediction groups. Despite the well-known low accuracy of residue-residue contact predictions, it is clear that the predictive power of contacts can be useful in 3D model prediction strategies.

Quality assessment of protein model-structures using evolutionary conservation

Motivation: Programs that evaluate the quality of a protein structural model are important both for validating the structure determination procedure and for guiding the model-building process. Such programs are based on properties of native structures that are generally not expected for faulty models. One such property, which is rarely used for automatic structure quality assessment, is the tendency for conserved residues to be located at the structural core and for variable residues to be located at the surface.

Results: We present ConQuass, a novel quality assessment program based on the consistency between the model structure and the protein's conservation pattern. We show that it can identify problematic structural models, and that the scores it assigns to the server models in CASP8 correlate with the similarity of the models to the native structure. We also show that when the conservation information is reliable, the method's performance is comparable and complementary to that of the other single-structure quality assessment methods that participated in CASP8 and that do not use additional structural information from homologs.

Availability: A perl implementation of the method, as well as the various perl and R scripts used for the analysis are available at http://bental.tau.ac.il/ConQuass/.

Contact:nirb@tauex.tau.ac.il

Supplementary information:Supplementary data are available at Bioinformatics online.

Rapid model quality assessment for protein structure predictions using the comparison of multiple models without structural alignments

MOTIVATION

The accurate prediction of the quality of 3D models is a key component of successful protein tertiary structure prediction methods. Currently, clustering- or consensus-based Model Quality Assessment Programs (MQAPs) are the most accurate methods for predicting 3D model quality; however, they are often CPU intensive as they carry out multiple structural alignments in order to compare numerous models. In this study, we describe ModFOLDclustQ--a novel MQAP that compares 3D models of proteins without the need for CPU intensive structural alignments by utilizing the Q measure for model comparisons. The ModFOLDclustQ method is benchmarked against the top established methods in terms of both accuracy and speed. In addition, the ModFOLDclustQ scores are combined with those from our older ModFOLDclust method to form a new method, ModFOLDclust2, that aims to provide increased prediction accuracy with negligible computational overhead.

RESULTS

The ModFOLDclustQ method is competitive with leading clustering-based MQAPs for the prediction of global model quality, yet it is up to 150 times faster than the previous version of the ModFOLDclust method at comparing models of small proteins (<60 residues) and over five times faster at comparing models of large proteins (>800 residues). Furthermore, a significant improvement in accuracy can be gained over the previous clustering-based MQAPs by combining the scores from ModFOLDclustQ and ModFOLDclust to form the new ModFOLDclust2 method, with little impact on the overall time taken for each prediction.

AVAILABILITY

The ModFOLDclustQ and ModFOLDclust2 methods are available to download from http://www.reading.ac.uk/bioinf/downloads/.

QMEAN server for protein model quality estimation

Model quality estimation is an essential component of protein structure prediction, since ultimately the accuracy of a model determines its usefulness for specific applications. Usually, in the course of protein structure prediction a set of alternative models is produced, from which subsequently the most accurate model has to be selected. The QMEAN server provides access to two scoring functions successfully tested at the eighth round of the community-wide blind test experiment CASP. The user can choose between the composite scoring function QMEAN, which derives a quality estimate on the basis of the geometrical analysis of single models, and the clustering-based scoring function QMEANclust which calculates a global and local quality estimate based on a weighted all-against-all comparison of the models from the ensemble provided by the user. The web server performs a ranking of the input models and highlights potentially problematic regions for each model. The QMEAN server is available at http://swissmodel.expasy.org/qmean.