Dataset Information

Prediction of disease-associated mutations in the transmembrane regions of proteins with known 3D structure.

ABSTRACT: Being able to assess the phenotypic effects of mutations is a much required capability in precision medicine. However, most of the currently available structure-based methods actually predict stability changes caused by mutations rather than their pathogenic potential. There are also no dedicated methods to predict damaging mutations specifically in transmembrane proteins. In this study we developed and applied a machine-learning approach to discriminate between disease-associated and benign point mutations in the transmembrane regions of proteins with known 3D structure. The method, called BorodaTM (BOosted RegressiOn trees for Disease-Associated mutations in TransMembrane proteins), was trained on sequence-, structure-, and energy-derived descriptors. When compared with the state-of-the-art methods, BorodaTM is superior in classifying point mutations in transmembrane regions. Using BorodaTM we have conducted a large-scale mutation analysis in the transmembrane regions of human proteins with known 3D structures. For each protein we generated structural models for all point mutations by replacing each residue to 19 possible residue types. We classified ~1.8 millions point mutations as benign or diseased-associated and made all predictions available as a Web-server at https://www.iitm.ac.in/bioinfo/MutHTP/boroda.php.

SUBMITTER: Popov P

PROVIDER: S-EPMC6620012 | biostudies-literature | 2019

REPOSITORIES: biostudies-literature

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Prediction of disease-associated mutations in the transmembrane regions of proteins with known 3D structure.

Popov Petr P Bizin Ilya I Gromiha Michael M A Kulandaisamy K Frishman Dmitrij D

PloS one 20190710 7

Being able to assess the phenotypic effects of mutations is a much required capability in precision medicine. However, most of the currently available structure-based methods actually predict stability changes caused by mutations rather than their pathogenic potential. There are also no dedicated methods to predict damaging mutations specifically in transmembrane proteins. In this study we developed and applied a machine-learning approach to discriminate between disease-associated and benign poi ...[more]

PMID: 31291347

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Project description:BACKGROUND: Transmembrane ?-barrel proteins are a special class of transmembrane proteins which play several key roles in human body and diseases. Due to experimental difficulties, the number of transmembrane ?-barrel proteins with known structures is very small. Over the years, a number of learning-based methods have been introduced for recognition and structure prediction of transmembrane ?-barrel proteins. Most of these methods emphasize on homology search rather than any biological or chemical basis. RESULTS: We present a novel graph-theoretic model for classification and structure prediction of transmembrane ?-barrel proteins. This model folds proteins based on energy minimization rather than a homology search, avoiding any assumption on availability of training dataset. The ab initio model presented in this paper is the first method to allow for permutations in the structure of transmembrane proteins and provides more structural information than any known algorithm. The model is also able to recognize ?-barrels by assessing the pseudo free energy. We assess the structure prediction on 41 proteins gathered from existing databases on experimentally validated transmembrane ?-barrel proteins. We show that our approach is quite accurate with over 90% F-score on strands and over 74% F-score on residues. The results are comparable to other algorithms suggesting that our pseudo-energy model is close to the actual physical model. We test our classification approach and show that it is able to reject ?-helical bundles with 100% accuracy and ?-barrel lipocalins with 97% accuracy. CONCLUSIONS: We show that it is possible to design models for classification and structure prediction for transmembrane ?-barrel proteins which do not depend essentially on training sets but on combinatorial properties of the structures to be proved. These models are fairly accurate, robust and can be run very efficiently on PC-like computers. Such models are useful for the genome screening.

Dataset Information

Prediction of disease-associated mutations in the transmembrane regions of proteins with known 3D structure.

Publications

Prediction of disease-associated mutations in the transmembrane regions of proteins with known 3D structure.

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