Evolutionary Profiles for Protein Fitness Prediction

• URL: http://arxiv.org/abs/2510.07286v1
• Date: Wed, 08 Oct 2025 17:46:02 GMT
• Title: Evolutionary Profiles for Protein Fitness Prediction
• Authors: Jigang Fan, Xiaoran Jiao, Shengdong Lin, Zhanming Liang, Weian Mao, Chenchen Jing, Hao Chen, Chunhua Shen,
• Abstract summary: EvoIF fuses sequence-structure representations with evolutionary signals to yield calibrated probabilities for log-odds scoring.<n>On ProteinGym (217 mutational assays; >2.5M mutants), EvoIF and its MSA-enabled variant achieve state-of-the-art or competitive performance while using only 0.15% of the training depths.
• Score: 45.945064429964084
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Predicting the fitness impact of mutations is central to protein engineering but constrained by limited assays relative to the size of sequence space. Protein language models (pLMs) trained with masked language modeling (MLM) exhibit strong zero-shot fitness prediction; we provide a unifying view by interpreting natural evolution as implicit reward maximization and MLM as inverse reinforcement learning (IRL), in which extant sequences act as expert demonstrations and pLM log-odds serve as fitness estimates. Building on this perspective, we introduce EvoIF, a lightweight model that integrates two complementary sources of evolutionary signal: (i) within-family profiles from retrieved homologs and (ii) cross-family structural-evolutionary constraints distilled from inverse folding logits. EvoIF fuses sequence-structure representations with these profiles via a compact transition block, yielding calibrated probabilities for log-odds scoring. On ProteinGym (217 mutational assays; >2.5M mutants), EvoIF and its MSA-enabled variant achieve state-of-the-art or competitive performance while using only 0.15% of the training data and fewer parameters than recent large models. Ablations confirm that within-family and cross-family profiles are complementary, improving robustness across function types, MSA depths, taxa, and mutation depths. The codes will be made publicly available at https://github.com/aim-uofa/EvoIF.

Related papers

• Self Distillation Fine-Tuning of Protein Language Models Improves Versatility in Protein Design [61.2846583160056]
  Supervised fine-tuning (SFT) is a standard approach for adapting large language models to specialized domains.<n>This is in part because high-quality annotated data are far more difficult to obtain for proteins than for natural language.<n>We present a simple and general recipe for fast SFT of PLMs, designed to improve the fidelity, reliability, and novelty of generated protein sequences.
  arXiv  Detail & Related papers  (2025-12-10T05:34:47Z)
• Understanding protein function with a multimodal retrieval-augmented foundation model [4.281723404774888]
  PoET-2 is a retrieval-augmented protein foundation model that incorporates in-context learning of family-specific evolutionary constraints.<n>PoET-2 achieves state-of-the-art performance on zero-shot variant effect prediction.
  arXiv  Detail & Related papers  (2025-08-05T15:11:25Z)
• Lightweight MSA Design Advances Protein Folding From Evolutionary Embeddings [51.731441632457226]
  Multiple sequence alignments (MSAs) underperform on low-homology and orphan proteins.<n>We introduce PLAME, a lightweight MSA design framework that generates MSAs that better support downstream folding.<n>On AlphaFold2 low-homology/orphan benchmarks, PLAME delivers state-of-the-art improvements in structure accuracy.
  arXiv  Detail & Related papers  (2025-06-17T04:11:30Z)
• JanusDDG: A Thermodynamics-Compliant Model for Sequence-Based Protein Stability via Two-Fronts Multi-Head Attention [0.0]
  understanding how residue variations affect protein stability is crucial for designing functional proteins.<n>Recent advances in protein language models (PLMs) have revolutionized computational protein analysis.<n>We introduce JanusDDG, a deep learning framework that leverages PLM-derived embeddings and a bidirectional cross-attention transformer architecture.
  arXiv  Detail & Related papers  (2025-04-04T09:02:32Z)
• Retrieval-Enhanced Mutation Mastery: Augmenting Zero-Shot Prediction of Protein Language Model [3.4494754789770186]
  Deep learning methods for protein modeling have demonstrated superior results at lower costs compared to traditional approaches. In mutation effect prediction, the key to pre-training deep learning models lies in accurately interpreting the complex relationships among protein sequence, structure, and function. This study introduces a retrieval-enhanced protein language model for comprehensive analysis of native properties from sequence and local structural interactions.
  arXiv  Detail & Related papers  (2024-10-28T15:28:51Z)
• MSAGPT: Neural Prompting Protein Structure Prediction via MSA Generative Pre-Training [48.398329286769304]
  Multiple Sequence Alignment (MSA) plays a pivotal role in unveiling the evolutionary trajectories of protein families. MSAGPT is a novel approach to prompt protein structure predictions via MSA generative pretraining in the low MSA regime.
  arXiv  Detail & Related papers  (2024-06-08T04:23:57Z)
• Diffusion Language Models Are Versatile Protein Learners [75.98083311705182]
  This paper introduces diffusion protein language model (DPLM), a versatile protein language model that demonstrates strong generative and predictive capabilities for protein sequences. We first pre-train scalable DPLMs from evolutionary-scale protein sequences within a generative self-supervised discrete diffusion probabilistic framework. After pre-training, DPLM exhibits the ability to generate structurally plausible, novel, and diverse protein sequences for unconditional generation.
  arXiv  Detail & Related papers  (2024-02-28T18:57:56Z)
• Efficiently Predicting Mutational Effect on Homologous Proteins by Evolution Encoding [7.067145619709089]
  EvolMPNN is an efficient model to learn evolution-aware protein embeddings. Our model shows up to 6.4% better than state-of-the-art methods and attains 36X inference speedup.
  arXiv  Detail & Related papers  (2024-02-20T23:06:21Z)
• Efficiently Predicting Protein Stability Changes Upon Single-point Mutation with Large Language Models [51.57843608615827]
  The ability to precisely predict protein thermostability is pivotal for various subfields and applications in biochemistry. We introduce an ESM-assisted efficient approach that integrates protein sequence and structural features to predict the thermostability changes in protein upon single-point mutations.
  arXiv  Detail & Related papers  (2023-12-07T03:25:49Z)
• Unsupervisedly Prompting AlphaFold2 for Few-Shot Learning of Accurate Folding Landscape and Protein Structure Prediction [28.630603355510324]
  We present EvoGen, a meta generative model, to remedy the underperformance of AlphaFold2 for poor MSA targets. By prompting the model with calibrated or virtually generated homologue sequences, EvoGen helps AlphaFold2 fold accurately in low-data regime.
  arXiv  Detail & Related papers  (2022-08-20T10:23:17Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.