Designing and evaluating the MULTICOM protein local and global model quality prediction methods in the CASP10 experiment

Cao, Renzhi; Wang, Zheng; Cheng, Jianlin

doi:10.1186/1472-6807-14-13

Cited by 50 publications

(44 citation statements)

References 36 publications

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“…The best model in terms of fraction of predicted native contacts ( f nat = 0.6577, acceptable in overall CAPRI evaluation) was built using protein model 2 of the YASARA server and chains 1 and 7 from the octameric biounit of 1jq5 structure as the docking template. The best model in terms of the backbone interface RMSD ( i ‐RMSD = 1.98 Å, medium quality) utilized model 5 of the MULTICOM‐CONSTRUCT server as the monomer structure, and chains A and B from the asymmetric unit of 3iv7 as the docking template. Both template structures are oxidoreductases from Corynebacterium glutamicum bacteria with high structural (TM‐score 0.84), but low sequence similarity (18% sequence identity per TM‐align), used to generate four other near‐native models.…”

Section: Resultsmentioning

confidence: 99%

Modeling CAPRI targets 110‐120 by template‐based and free docking using contact potential and combined scoring function

et al. 2017

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The paper presents analysis of our template-based and free docking predictions in the joint CASP12/CAPRI37 round. A new scoring function for template-based docking was developed, benchmarked on the Dockground resource, and applied to the targets. The results showed that the function successfully discriminates the incorrect docking predictions. In correctly predicted targets, the scoring function was complemented by other considerations, such as consistency of the oligomeric states among templates, similarity of the biological functions, biological interface relevance, etc. The scoring function still does not distinguish well biological from crystal packing interfaces, and needs further development for the docking of bundles of α-helices. In the case of the trimeric targets, sequence-based methods did not find common templates, despite similarity of the structures, suggesting complementary use of structure- and sequence-based alignments in comparative docking. The results showed that if a good docking template is found, an accurate model of the interface can be built even from largely inaccurate models of individual subunits. Free docking however is very sensitive to the quality of the individual models. However, our newly developed contact potential detected approximate locations of the binding sites.

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Section: Resultsmentioning

confidence: 99%

Modeling CAPRI targets 110‐120 by template‐based and free docking using contact potential and combined scoring function

et al. 2017

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“…Therefore, we looked for methods and servers that could predict holo sites well (Supporting Information section 2 Tables S4 and S7). Predictions from servers Multicom-construct 29 and IntFOLD4 30 performed well for all six holo sites. These methods likely employed algorithms that benefited from ligand-binding template information and for modeling.…”

Section: Differential Performance On Holo and Apo Sitesmentioning

confidence: 94%

Biological and functional relevance of CASP predictions

et al. 2017

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Our goal is to answer the question: compared with experimental structures, how useful are predicted models for functional annotation? We assessed the functional utility of predicted models by comparing the performances of a suite of methods for functional characterization on the predictions and the experimental structures. We identified 28 sites in 25 protein targets to perform functional assessment. These 28 sites included nine sites with known ligand binding (holo‐sites), nine sites that are expected or suggested by experimental authors for small molecule binding (apo‐sites), and Ten sites containing important motifs, loops, or key residues with important disease‐associated mutations. We evaluated the utility of the predictions by comparing their microenvironments to the experimental structures. Overall structural quality correlates with functional utility. However, the best‐ranked predictions (global) may not have the best functional quality (local). Our assessment provides an ability to discriminate between predictions with high structural quality. When assessing ligand‐binding sites, most prediction methods have higher performance on apo‐sites than holo‐sites. Some servers show consistently high performance for certain types of functional sites. Finally, many functional sites are associated with protein‐protein interaction. We also analyzed biologically relevant features from the protein assemblies of two targets where the active site spanned the protein‐protein interface. For the assembly targets, we find that the features in the models are mainly determined by the choice of template.

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“…The incomplete server models or highly similar models (eg, GDT‐TS > 0.95) from the same server group were filtered out. The side chains of the remaining models were repacked by SCWRL in order to have the consistent side chain packing before they were evaluated, which was shown to improve the performance of model QA . If the target was identified as multiple‐domain protein, the server models were divided into individual domain models.…”

Section: Methodsmentioning

confidence: 99%

“…The side chains of the remaining models were repacked by SCWRL 46 in order to have the consistent side chain packing before they were evaluated, which was shown to improve the performance F I G U R E 1 The pipeline of MULTICOM server and human prediction systems of model QA. 47 If the target was identified as multiple-domain protein, the server models were divided into individual domain models.…”

Section: An Overview Of the Multicom Systemmentioning

confidence: 99%

Protein tertiary structure modeling driven by deep learning and contact distance prediction in CASP13

et al. 2019

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Predicting residue-residue distance relationships (e.g. contacts) has become the key direction to advance protein structure prediction since 2014 CASP11 experiment, while deep learning has revolutionized the technology for contact and distance distribution prediction since its debut in 2012 CASP10 experiment. During 2018 CASP13 experiment, we enhanced our MULTICOM protein structure prediction system with three major components: contact distance prediction based on deep convolutional neural networks, distance-driven template-free (ab initio) modeling, and protein model ranking empowered by deep learning and contact prediction. Our experiment demonstrates that contact distance prediction and deep learning methods are the key reasons that MULTICOM was ranked 3rd out of all 98 predictors in both template-free and template-based structure modeling in CASP13. Deep convolutional neural network can utilize global information in pairwise residue-residue features such as co-evolution scores to substantially improve contact distance prediction, which played a decisive role in correctly folding some free modeling and hard template-based modeling targets. Deep learning also successfully integrated 1D structural features, 2D contact information, and 3D structural quality scores to improve protein model quality assessment, where the contact prediction was demonstrated to consistently enhance ranking of protein models for the first time. The success of MULTICOM system clearly shows that protein contact distance prediction and model selection driven by deep learning holds the key of solving protein structure prediction problem. However, there are still challenges in accurately predicting protein contact distance when there are few homologous sequences, folding proteins from noisy contact distances, and ranking models of hard targets.

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Designing and evaluating the MULTICOM protein local and global model quality prediction methods in the CASP10 experiment

Cited by 50 publications

References 36 publications

Modeling CAPRI targets 110‐120 by template‐based and free docking using contact potential and combined scoring function

Modeling CAPRI targets 110‐120 by template‐based and free docking using contact potential and combined scoring function

Biological and functional relevance of CASP predictions

Protein tertiary structure modeling driven by deep learning and contact distance prediction in CASP13

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