ru
ru
Source
Bioinformatics
DATE OF PUBLICATION
11/03/2023
Authors
Nikita Ivanisenko Nikolay Shvetsov Dmitriy Umerenkov Olga Kardymon Tatiana Shashkova Pavel Strashnov Fedor Nikolaev
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PROSTATA: a framework for protein stability assessment using transformers

Abstract

Motivation

Accurate prediction of change in protein stability due to point mutations is an attractive goal that remains unachieved. Despite the high interest in this area, little consideration has been given to the transformer architecture, which is dominant in many fields of machine learning.
Results

In this work, we introduce PROSTATA, a predictive model built in a knowledge-transfer fashion on a new curated dataset. PROSTATA demonstrates advantage over existing solutions based on neural networks. We show that the large improvement margin is due to both the architecture of the model and the quality of the new training dataset. This work opens up opportunities to develop new lightweight and accurate models for protein stability assessment.

Availability and implementation

PROSTATA is available at https://github.com/AIRI-Institute/PROSTATA and https://prostata.airi.net.

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