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Improving structure-based function prediction using molecular dynamics.

ABSTRACT: The number of molecules with solved three-dimensional structure but unknown function is increasing rapidly. Particularly problematic are novel folds with little detectable similarity to molecules of known function. Experimental assays can determine the functions of such molecules, but are time-consuming and expensive. Computational approaches can identify potential functional sites; however, these approaches generally rely on single static structures and do not use information about dynamics. In fact, structural dynamics can enhance function prediction: we coupled molecular dynamics simulations with structure-based function prediction algorithms that identify Ca(2+) binding sites. When applied to 11 challenging proteins, both methods showed substantial improvement in performance, revealing 22 more sites in one case and 12 more in the other, with a modest increase in apparent false positives. Thus, we show that treating molecules as dynamic entities improves the performance of structure-based function prediction methods.

SUBMITTER: Glazer DS

PROVIDER: S-EPMC2748254 | biostudies-literature | 2009 Jul

REPOSITORIES: biostudies-literature

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Improving structure-based function prediction using molecular dynamics.

Glazer Dariya S DS Radmer Randall J RJ Altman Russ B RB

Structure (London, England : 1993) 20090701 7

The number of molecules with solved three-dimensional structure but unknown function is increasing rapidly. Particularly problematic are novel folds with little detectable similarity to molecules of known function. Experimental assays can determine the functions of such molecules, but are time-consuming and expensive. Computational approaches can identify potential functional sites; however, these approaches generally rely on single static structures and do not use information about dynamics. In ...[more]

PMID: 19604472

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Project description:MotivationProtein structure prediction has been greatly improved by deep learning, but the contribution of different information is yet to be fully understood. This article studies the impacts of two kinds of information for structure prediction: template and multiple sequence alignment (MSA) embedding. Templates have been used by some methods before, such as AlphaFold2, RoseTTAFold and RaptorX. AlphaFold2 and RosetTTAFold only used templates detected by HHsearch, which may not perform very well on some targets. In addition, sequence embedding generated by pre-trained protein language models has not been fully explored for structure prediction. In this article, we study the impact of templates (including the number of templates, the template quality and how the templates are generated) on protein structure prediction accuracy, especially when the templates are detected by methods other than HHsearch. We also study the impact of sequence embedding (generated by MSATransformer and ESM-1b) on structure prediction.ResultsWe have implemented a deep learning method for protein structure prediction that may take templates and MSA embedding as extra inputs. We study the contribution of templates and MSA embedding to structure prediction accuracy. Our experimental results show that templates can improve structure prediction on 71 of 110 CASP13 (13th Critical Assessment of Structure Prediction) targets and 47 of 91 CASP14 targets, and templates are particularly useful for targets with similar templates. MSA embedding can improve structure prediction on 63 of 91 CASP14 (14th Critical Assessment of Structure Prediction) targets and 87 of 183 CAMEO targets and is particularly useful for proteins with shallow MSAs. When both templates and MSA embedding are used, our method can predict correct folds (TMscore > 0.5) for 16 of 23 CASP14 FM targets and 14 of 18 Continuous Automated Model Evaluation (CAMEO) targets, outperforming RoseTTAFold by 5% and 7%, respectively.Availability and implementationAvailable at https://github.com/xluo233/RaptorXFold.Supplementary informationSupplementary data are available at Bioinformatics online.

Project description:The G-protein coupled receptor, GPR120, has ubiquitous expression and multifaceted roles in modulating metabolic and anti-inflammatory processes. Recent implications of its role in cancer progression have presented GPR120 as an attractive oncogenic drug target. GPR120 gene knockdown in breast cancer studies revealed a role of GPR120-induced chemoresistance in epirubicin and cisplatin-induced DNA damage in tumour cells. Higher expression and activation levels of GPR120 is also reported to promote tumour angiogenesis and cell migration in colorectal cancer. Some agonists targeting GPR120 have been reported, such as TUG891 and Compound39, but to date development of small-molecule inhibitors of GPR120 is limited. Herein, following homology modelling of the receptor a pharmacophore hypothesis was derived from 300 ns all-atomic molecular dynamics (MD) simulations on apo, TUG891-bound and Compound39-bound GPR120S (short isoform) receptor models embedded in a water solvated lipid bilayer system. We performed comparative MD analysis on protein-ligand interactions between the two agonist and apo simulations on the stability of the "ionic lock" - a Class A GPCRs characteristic of receptor activation and inactivation. The detailed analysis predicted that ligand interactions with W277 and N313 are critical to conserve the "ionic-lock" conformation (R136 of Helix 3) and prevent GPR120S receptor activation. The results led to generation of a W277 and N313 focused pharmacophore hypothesis and the screening of the ZINC15 database using ZINCPharmer through the structure-based pharmacophore. 100 ns all-atomic molecular dynamics (MD) simulations were performed on 9 small molecules identified and Cpd 9, (2-hydroxy-N-{4-[(6-hydroxy-2-methylpyrimidin-4-yl) amino] phenyl} benzamide) was predicted to be a small-molecule GPR120S antagonist. The conformational results from the collective all-atomic MD analysis provided structural information for further identification and optimisation of novel druggable inhibitors of GPR120S using this rational design approach, which could have future potential for anti-cancer drug development studies.

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Improving structure-based function prediction using molecular dynamics.

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Improving structure-based function prediction using molecular dynamics.

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