Multi-indicator comparative evaluation for deep learning-based protein sequence design methods.

ABSTRACT:

Motivation

Proteins found in nature represent only a fraction of the vast space of possible proteins. Protein design presents an opportunity to explore and expand this protein landscape. Within protein design, protein sequence design plays a crucial role, and numerous successful methods have been developed. Notably, deep learning-based protein sequence design methods have experienced significant advancements in recent years. However, a comprehensive and systematic comparison and evaluation of these methods have been lacking, with indicators provided by different methods often inconsistent or lacking effectiveness.

Results

To address this gap, we have designed a diverse set of indicators that cover several important aspects, including sequence recovery, diversity, root-mean-s

SUBMITTER: Yu J 

PROVIDER: S-EPMC10868333 | biostudies-literature | 2024 Feb

REPOSITORIES: biostudies-literature