Lightweight MSA Design Advances Protein Folding From Evolutionary Embeddings

• URL: http://arxiv.org/abs/2507.07032v3
• Date: Thu, 25 Sep 2025 21:22:48 GMT
• Title: Lightweight MSA Design Advances Protein Folding From Evolutionary Embeddings
• Authors: Hanqun Cao, Xinyi Zhou, Zijun Gao, Chenyu Wang, Xin Gao, Zhi Zhang, Cesar de la Fuente-Nunez, Chunbin Gu, Ge Liu, Pheng-Ann Heng,
• Abstract summary: 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.
• Score: 51.731441632457226
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Protein structure prediction often hinges on multiple sequence alignments (MSAs), which underperform on low-homology and orphan proteins. We introduce PLAME, a lightweight MSA design framework that leverages evolutionary embeddings from pretrained protein language models to generate MSAs that better support downstream folding. PLAME couples these embeddings with a conservation--diversity loss that balances agreement on conserved positions with coverage of plausible sequence variation. Beyond generation, we develop (i) an MSA selection strategy to filter high-quality candidates and (ii) a sequence-quality metric that is complementary to depth-based measures and predictive of folding gains. On AlphaFold2 low-homology/orphan benchmarks, PLAME delivers state-of-the-art improvements in structure accuracy (e.g., lDDT/TM-score), with consistent gains when paired with AlphaFold3. Ablations isolate the benefits of the selection strategy, and case studies elucidate how MSA characteristics shape AlphaFold confidence and error modes. Finally, we show PLAME functions as a lightweight adapter, enabling ESMFold to approach AlphaFold2-level accuracy while retaining ESMFold-like inference speed. PLAME thus provides a practical path to high-quality folding for proteins lacking strong evolutionary neighbors.

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)
• Evolutionary Profiles for Protein Fitness Prediction [45.945064429964084]
  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.
  arXiv  Detail & Related papers  (2025-10-08T17:46:02Z)
• AMix-1: A Pathway to Test-Time Scalable Protein Foundation Model [92.51919604882984]
  We introduce AMix-1, a powerful protein foundation model built on Flow Bayesian Networks.<n>AMix-1 is empowered by a systematic training methodology, encompassing pretraining scaling laws, emergent capability analysis, in-context learning mechanism, and test-time scaling algorithm.<n>Building on this foundation, we devise a multiple sequence alignment (MSA)-based in-context learning strategy to unify protein design into a general framework.
  arXiv  Detail & Related papers  (2025-07-11T17:02:25Z)
• DISPROTBENCH: A Disorder-Aware, Task-Rich Benchmark for Evaluating Protein Structure Prediction in Realistic Biological Contexts [76.59606029593085]
  DisProtBench is a benchmark for evaluating protein structure prediction models (PSPMs) under structural disorder and complex biological conditions.<n>DisProtBench spans three key axes: data complexity, task diversity, and Interpretability.<n>Results reveal significant variability in model robustness under disorder, with low-confidence regions linked to functional prediction failures.
  arXiv  Detail & Related papers  (2025-06-18T23:58:22Z)
• Protein Inverse Folding From Structure Feedback [78.27854221882572]
  We introduce a novel approach to fine-tune an inverse folding model using feedback from a protein folding model.<n>Our results on the CATH 4.2 test set demonstrate that DPO fine-tuning leads to a significant improvement in average TM-Score.
  arXiv  Detail & Related papers  (2025-06-03T16:02:12Z)
• NovoBench: Benchmarking Deep Learning-based De Novo Peptide Sequencing Methods in Proteomics [58.03989832372747]
  We present the first unified benchmark NovoBench for emphde novo peptide sequencing. It comprises diverse mass spectrum data, integrated models, and comprehensive evaluation metrics. Recent methods, including DeepNovo, PointNovo, Casanovo, InstaNovo, AdaNovo and $pi$-HelixNovo are integrated into our framework.
  arXiv  Detail & Related papers  (2024-06-16T08:23:21Z)
• 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)
• Fine-tuning Protein Language Models with Deep Mutational Scanning improves Variant Effect Prediction [3.2358123775807575]
  Protein Language Models (PLMs) have emerged as performant and scalable tools for predicting the functional impact and clinical significance of protein-coding variants. We present a novel fine-tuning approach to improve the performance of PLMs with experimental maps of variant effects from Deep Mutational Scanning (DMS) These findings demonstrate that DMS is a promising source of sequence diversity and supervised training data for improving the performance of PLMs for variant effect prediction.
  arXiv  Detail & Related papers  (2024-05-10T14:50:40Z)
• Beyond ESM2: Graph-Enhanced Protein Sequence Modeling with Efficient Clustering [24.415612744612773]
  Proteins are essential to life's processes, underpinning evolution and diversity. Advances in sequencing technology have revealed millions of proteins, underscoring the need for sophisticated pre-trained protein models for biological analysis and AI development. Facebook's ESM2, the most advanced protein language model to date, leverages a masked prediction task for unsupervised learning, crafting amino acid representations with notable biochemical accuracy. Yet, it lacks in delivering functional protein insights, signaling an opportunity for enhancing representation quality. This study addresses this gap by incorporating protein family classification into ESM2's training, while a contextual prediction task fine-tunes local
  arXiv  Detail & Related papers  (2024-04-24T11:09:43Z)
• AlphaFold Meets Flow Matching for Generating Protein Ensembles [11.1639408863378]
  We develop a flow-based generative modeling approach for learning and sampling the conformational landscapes of proteins. Our method provides a superior combination of precision and diversity compared to AlphaFold with MSA subsampling. Our method can diversify a static PDB structure with faster wall-clock convergence to certain equilibrium properties than replicate MD trajectories.
  arXiv  Detail & Related papers  (2024-02-07T13:44:47Z)
• 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)
• Enhancing the Protein Tertiary Structure Prediction by Multiple Sequence Alignment Generation [30.2874172276931]
  We introduce MSA-Augmenter, which generates useful, novel protein sequences not currently found in databases. Our experiments on CASP14 demonstrate that MSA-Augmenter can generate de novo sequences that retain co-evolutionary information from inferior MSAs.
  arXiv  Detail & Related papers  (2023-06-02T14:13:50Z)
• 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.