Nikolay Shvetsov
Dmitriy Umerenkov
Olga Kardymon
Tatiana Shashkova
Pavel Strashnov
Fedor Nikolaev
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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