Evidence map›Paper›PMID 37935419›Full record

ArticleBioinformatics (Oxford, England)2023

PROSTATA: a framework for protein stability assessment using transformers.

Dmitriy Umerenkov, Fedor Nikolaev, Tatiana I Shashkova, Pavel V Strashnov, Maria Sindeeva, Andrey Shevtsov, Nikita V Ivanisenko, Olga L Kardymon

Abstract read
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Article in Bioinformatics (Oxford, England), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers.

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0cells of the map it votes in
22citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

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3 · Its place in the literature

Who cites it

22 citing papers in PubMed.

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  13. ESM-scan-A tool to guide amino acid substitutions.Protein science : a publication of the Protein Society · 2024
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  20. Transfer learning to leverage larger datasets for improved prediction of protein stability changes.Proceedings of the National Academy of Sciences of the United States of America · 2024
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4 · The record

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5 · Who and what money

Authors and funding

8 authors.

Dmitriy UmerenkovSber AI Lab, Moscow 105064, Russia.
Fedor NikolaevBioinformatics Group, AIRI, Moscow 121170, Russia.
Tatiana I ShashkovaBioinformatics Group, AIRI, Moscow 121170, Russia.
Pavel V StrashnovBioinformatics Group, AIRI, Moscow 121170, Russia.
Maria SindeevaBioinformatics Group, AIRI, Moscow 121170, Russia.
Andrey ShevtsovBioinformatics Group, AIRI, Moscow 121170, Russia.
Nikita V IvanisenkoBioinformatics Group, AIRI, Moscow 121170, Russia.ORCID 0000-0002-0333-8117
Olga L KardymonBioinformatics Group, AIRI, Moscow 121170, Russia.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

motivationAccurate 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.

resultsIn 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.

Indexed as

Machine LearningNeural Networks, ComputerPoint MutationProtein Stability

Identifiers

PMID37935419
PMCPMC10651431

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.