Machine learning for damaging mutations prediction

Scientists from Russia and India have proposed a novel machine-learning-based method for predicting damaging mutations in the protein atomic structure. The new method targets human membrane proteins and will help to develop personalized medicine approaches. The results of their research were published in Plos One journal.

The new-generation sequencing technology has ushered in a new era in medicine, making it easier to identify a sequence of nucleotides in the DNA or a sequence of amino acids in the proteins of a specific individual and use this information for both diagnosis and treatment. Minute alterations in these sequences, mutations can be indicative of a minor disorder and, sometimes, a grave disease.

Scientists from Skoltech, the Technical University of Munich, St. Petersburg Polytechnic University and the Indian Institute of Technology Madras (Chennai, India) developed a machine-learning-based method that allows analyzing the atomic structures of proteins and predicting the pathogenicity of mutations. The method is adapted for transmembrane proteins that account for 25-30% of all the proteins in a cell and often serve as targets for drugs.

“In this study, we used a combination of 1D information on the amino acid sequences of proteins and 3D information on the protein’s atomic structures to create an effective machine-learning-based model that helps identify disease-associated amino acid substitutions in membrane proteins,” says the first author of the study and Assistant Professor at Skoltech, Petr Popov.

Contact information:
Skoltech Communications
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