Improving protein optimization with smoothed fitness landscapes

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21730%2F24%3A00380726" target="_blank" >RIV/68407700:21730/24:00380726 - isvavai.cz</a>

Result on the web

<a href="https://openreview.net/forum?id=rxlF2Zv8x0" target="_blank" >https://openreview.net/forum?id=rxlF2Zv8x0</a>

DOI - Digital Object Identifier

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Result language

angličtina

Original language name

Improving protein optimization with smoothed fitness landscapes

Original language description

The ability to engineer novel proteins with higher fitness for a desired property would be revolutionary for biotechnology and medicine. Modeling the combinatorially large space of sequences is infeasible; prior methods often constrain optimization to a small mutational radius, but this drastically limits the design space. Instead of heuristics, we propose smoothing the fitness landscape to facilitate protein optimization. First, we formulate protein fitness as a graph signal then use Tikunov regularization to smooth the fitness landscape. We find optimizing in this smoothed landscape leads to improved performance across multiple methods in the GFP and AAV benchmarks. Second, we achieve state-of-the-art results utilizing discrete energy-based models and MCMC in the smoothed landscape. Our method, called Gibbs sampling with Graph-based Smoothing (GGS), demonstrates a unique ability to achieve 2.5 fold fitness improvement (with in-silico evaluation) over its training set. GGS demonstrates potential to optimize proteins in the limited data regime. Code: https://github.com/kirjner/GGS

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/EF15_003%2F0000468" target="_blank" >EF15_003/0000468: Intelligent Machine Perception</a><br>

Continuities

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

Publication year

2024

Confidentiality

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

Article name in the collection

Proceeding The Twelfth International Conference on Learning Representations (ICLR 2024)

ISBN

9781713898658

ISSN

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e-ISSN

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Number of pages

19

Pages from-to

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Publisher name

International Conference on Learning Representations

Place of publication

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Event location

Vídeň

Event date

May 7, 2024

Type of event by nationality

WRD - Celosvětová akce

UT code for WoS article

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