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Keyword: protein function prediction ×Clear all

5 claims from 3 papers

1. Biochemistry, Genetics and Molecular Biology › Protein Structure and Dynamics

   Unified rational protein engineering with sequence-based deep representation learning

   Alley, Khimulya, Biswas, AlQuraishi and Church · Nature Methods · 2019

   The authors trained deep learning on unlabelled amino-acid sequences to make a protein representation, UniRep, and report it predicts stability and function competitively and improves efficiency in a protein engineering task.

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   1. UncheckedSimple models built on UniRep, a learned summary of protein sequences, are said to work across many tasks and on unseen regions of sequence space.“We show that the simplest models built on top of this unified representation (UniRep) are broadly applicable and generalize to unseen regions of sequence space.”
   2. UncheckedA model trained on unlabelled protein sequences predicts protein stability and mutant function about as well as leading existing methods.“Our data-driven approach predicts the stability of natural and de novo designed proteins, and the quantitative function of molecularly diverse mutants, competitively with the state-of-the-art methods.”
   3. UncheckedThe paper states that UniRep, a learned summary of protein sequences, cut the effort needed in one protein engineering task by about a hundredfold.“UniRep further enables two orders of magnitude efficiency improvement in a protein engineering task.”

2. Biochemistry, Genetics and Molecular Biology › Protein Structure and Dynamics

   Scalable emulation of protein equilibrium ensembles with generative deep learning

   Lewis, Hempel, Jiménez-Luna et al. · Science · 2025

   The paper introduces BioEmu, a generative deep learning system that emulates protein equilibrium ensembles, producing thousands of independent structures per hour on one GPU.

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   1. UncheckedBioEmu, a deep learning model, is reported to capture several kinds of protein motion and to predict relative free energies to within 1 kcal/mol of reference data.“It captures diverse functional motions—including cryptic pocket formation, local unfolding, and domain rearrangements—and predicts relative free energies with 1 kilocalorie per mole accuracy compared with millisecond-scale MD and experimental data.”

3. Biochemistry, Genetics and Molecular Biology › Machine Learning in Bioinformatics

   SaProt: Protein Language Modeling with Structure-aware Vocabulary

   Su, Han, Zhou, Shan, Zhou and Yuan · bioRxiv (Cold Spring Harbor Laboratory) · 2023

   The authors build SaProt, a protein language model that combines residue tokens with structure tokens from Foldseek, trained on about 40 million protein sequences and structures.

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   1. UncheckedThe SaProt protein model is reported to beat well-established baseline models across 10 downstream tasks, which the authors take as showing broad applicability.“Through extensive evaluation, our SaProt model surpasses well-established and renowned baselines across 10 significant downstream tasks, demonstrating its exceptional capacity and broad applicability.”