LEARNING TO ENGINEER PROTEIN FLEXIBILITY

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00159816%3A_____%2F25%3A00082541" target="_blank" >RIV/00159816:_____/25:00082541 - isvavai.cz</a>

Alternative codes found

RIV/68407700:21730/25:00384618

Result on the web

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DOI - Digital Object Identifier

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Result language

angličtina

Original language name

LEARNING TO ENGINEER PROTEIN FLEXIBILITY

Original language description

Generative machine learning models are increasingly being used to design novel proteins for therapeutic and biotechnological applications. However, the current methods mostly focus on the design of proteins with a fixed backbone structure, which leads to their limited ability to account for protein flexibility, one of the crucial properties for protein function. Learning to engineer protein flexibility is problematic because the available data are scarce, heterogeneous, and costly to obtain using computational as well as experimental methods. Our contributions to address this problem are three-fold. First, we comprehensively compare methods for quantifying protein flexibility and identify data relevant to learning. Second, we design and train flexibility predictors utilizing sequential or both sequential and structural information on the input. We overcome the data scarcity issue by leveraging a pre-trained protein language model. Third, we introduce a method for fine-tuning a protein inverse folding model to steer it toward desired flexibility in specified regions. We demonstrate that our method Flexpert-Design enables guidance of inverse folding models toward increased flexibility. This opens up new possibilities for protein flexibility engineering and the development of proteins with enhanced biological activities. (C) 2025 13th International Conference on Learning Representations, ICLR 2025. All rights reserved.

Czech name

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Czech description

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Type

D - Article in proceedings

CEP classification

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OECD FORD branch

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Project

<a href="/en/project/TN02000122" target="_blank" >TN02000122: REcombinant TEchnologies for MEDicine</a><br>

Continuities

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Publication year

2025

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Article name in the collection

13th International Conference on Learning Representations (ICLR 2025)

ISBN

979-8-3313-2085-0

ISSN

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e-ISSN

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Number of pages

23

Pages from-to

53868-53890

Publisher name

International Conference on Learning Representations

Place of publication

Appleton

Event location

Singapore

Event date

Apr 24, 2025

Type of event by nationality

WRD - Celosvětová akce

UT code for WoS article

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