Evolutionary-scale prediction of atomic-level protein structure with a language model

2023/03/16 by Zeming Lin, Halil Akin, Roshan Rao +12 · 486 citations
Biochemistry, Genetics and Molecular Biology · #Genomics and Phylogenetic Studies #Machine Learning in Bioinformatics #RNA and protein synthesis mechanisms

paper · doi:10.1126/science.ade2574

openalex publication_date 2023/03/16 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/30

Abstract

Recent advances in machine learning have leveraged evolutionary information in multiple sequence alignments to predict protein structure. We demonstrate direct inference of full atomic-level protein structure from primary sequence using a large language model. As language models of protein sequences are scaled up to 15 billion parameters, an atomic-resolution picture of protein structure emerges in the learned representations. This results in an order-of-magnitude acceleration of high-resolution structure prediction, which enables large-scale structural characterization of metagenomic proteins. We apply this capability to construct the ESM Metagenomic Atlas by predicting structures for >617 million metagenomic protein sequences, including >225 million that are predicted with high confidence, which gives a view into the vast breadth and diversity of natural proteins.

Citations

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• Accurate prediction of protein structures and interactions using a three-track neural network
• КОМПЬЮТЕРНАЯ ТОМОГРАФИЯ – ОСНОВА ОПТИМИЗАЦИИ МИНИТОРАКОТОМНОГО ДОСТУПА
• Learning inverse folding from millions of predicted structures
• ColabFold: making protein folding accessible to all
• Robust deep learning–based protein sequence design using ProteinMPNN

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• Sequence-Only Prediction of Binding Affinity Changes: A Robust and Interpretable Model for Antibody Engineering
• PandoGen: Generating complete instances of future SARS-CoV-2 sequences using Deep Learning
• pLM4Alg: Protein Language Model-Based Predictors for Allergenic Proteins and Peptides
• E2EATP: Fast and High-Accuracy Protein–ATP Binding Residue Prediction via Protein Language Model Embedding
• Evolution of a plant growth-regulatory protein interaction specificity
• Beyond Biosecurity by Taxonomic Lists: Lessons, Challenges, and Opportunities
• Plug-and-Play Guidance for Discrete Diffusion Models via Gradient-Informed Logit Correction
• Protein FID: Improved Evaluation of Protein Structure Generative Models
• Bridging Large Language Models and Single-Cell Transcriptomics in Dissecting Selective Motor Neuron Vulnerability
• Relative Overfitting and Accept-Reject Framework
• Multi-Objective-Guided Discrete Flow Matching for Controllable Biological Sequence Design
• NbBench: Benchmarking Language Models for Comprehensive Nanobody Tasks
• MolClaw: An Autonomous Agent with Hierarchical Skills for Drug Molecule Evaluation, Screening, and Optimization
• Scoring-Assisted Generative Exploration for Proteins (SAGE-Prot): A Framework for Multi-Objective Protein Optimization via Iterative Sequence Generation and Evaluation
• TurboESM: Ultra-Efficient 3-Bit KV Cache Quantization for Protein Language Models with Orthogonal Rotation and QJL Correction
• Rethinking Benchmarks and Models for Enzyme Specificity Prediction
• Persistent local Laplacian prediction of protein-ligand binding affinities
• AlphaFunctor: Bridging The Gap Between Protein Function Annotation and Property Prediction
• G2P Explorer: A Native iOS Framework for Residue-Level Genomics to Proteomics Visualization and Structural Variant Interpretation
• Structure-Regularized Interpretable TCR-Epitope Prediction
• Towards coevolution-aware ancestral sequence reconstruction
• scpFormer: A Foundation Model for Unified Representation and Integration of the Single-Cell Proteomics
• Large Language Models as AI Agents for Digital Atoms and Molecules: Catalyzing a New Era in Computational Biophysics
• Protein-Based Fish Species Identification: Dataset, Models, and Insights from Native Bangladeshi Fish
• Reverse Distillation: Consistently Scaling Protein Language Model Representations
• SeekRBP: Leveraging Sequence-Structure Integration with Reinforcement Learning for Receptor-Binding Protein Identification
• Distribution-Conditioned Transport
• ForceGen: End-to-end de novo protein generation based on nonlinear mechanical unfolding responses using a protein language diffusion model
• The resistance awakens: Diversity at the DNA, RNA, and protein levels informs engineering of plant immune receptors from Arabidopsis to crops
• AlloGen: Conformation-Selective Binder Generation with Differential State Scoring
• CryoNet.Refine: A One-step Diffusion Model for Rapid Refinement of Structural Models with Cryo-EM Density Map Restraints
• Conditionally Site-Independent Neural Evolution of Antibody Sequences
• Toward Interpretable and Generalizable AI in Regulatory Genomics
• Parameter-free representations outperform single-cell foundation models on downstream benchmarks
• Cryo-SWAN: the Multi-Scale Wavelet-decomposition-inspired Autoencoder Network for molecular density representation of molecular volumes
• Piecewise integrability of the discrete Hasimoto map for analytic prediction and design of helical peptides
• Demystifying Multimodal Biomolecular Co-design With Intrinsic Geodesic Coupling
• On the Relationship Between Activation Outliers and Feature Death in Sparse Autoencoders
• AMix-2: Establishing Protein as a Native Modality in Large Language Models
• Hermes: Large DEL Datasets Train Generalizable Protein-Ligand Binding Prediction Models
• C3P: Contrastive promoter-protein pretraining yields representations capturing bacterial gene regulation
• Atom-level Protein Representation Learning Improves Protein Structure Prediction
• Ligand-Conditioned Discrete Diffusion for Protein Sequence-Structure Co-Design
• Frequency-Space Mechanics: A Sequence and Coordinate-Free Representation for Protein Function Prediction
• Protein Circuit Tracing via Cross-layer Transcoders
• Yeti: A compact protein structure tokenizer for reconstruction and multi-modal generation
• TD3B: Transition-Directed Discrete Diffusion for Allosteric Binder Generation
• SCOPE: Siamese Contrastive Operon Pair Embeddings for Functional Sequence Representation and Classification
• TCRTransBench: A Comprehensive Benchmark for Bidirectional TCR-Peptide Sequence Generation
• Retrieval and competition: how a protein foundation model starts a protein
• ProtDBench: A Unified Benchmark of Protein Binder Design and Evaluation
• CRC-Screen: Certified DNA-Synthesis Hazard Screening Under Taxonomic Shift
• Benchmarking virtual cell models for in-the-wild perturbation response
• Accurate, Interdisciplinary and Transparent Structure-property Understanding with Deep Native Structural Reasoning
• PROTOCOL: Late Interaction Retrieval for Protein Homolog Search
• Reinforcement-guided generative protein language models enable de novo design of highly diverse AAV capsids
• CAPSUL: A Comprehensive Human Protein Benchmark for Subcellular Localization
• Polyformer: a generative framework for thermodynamic modeling of polymeric molecules
• AntigenLM: Structure-Aware DNA Language Modeling for Influenza
• Limitations of Sequence-Based Protein Representations for Parkinson's Disease Classification: A Leakage-Free Benchmark
• Epistemic Blinding: An Inference-Time Protocol for Auditing Prior Contamination in LLM-Assisted Analysis
• ViraHinter: a dual-modal artificial intelligence framework for predicting virus-host interactions
• RAG-GNN: Integrating Retrieved Knowledge with Graph Neural Networks for Precision Medicine
• Classification of SARS-CoV-2 Variants through The Epistatical Circos Plots with Convolutional Neural Networks
• Minimal-Action Discrete Schrödinger Bridge Matching for Peptide Sequence Design
• EnzyPGM: Pocket-conditioned Generative Model for Substrate-specific Enzyme Design
• Flow-based Extremal Mathematical Structure Discovery
• AI Developments for T and B Cell Receptor Modeling and Therapeutic Design
• PhageMind: Generalized Strain-level Phage Host Range Prediction via Meta-learning
• SolarGPT-QA: A Domain-Adaptive Large Language Model for Educational Question Answering in Space Weather and Heliophysics
• DiverseDiT++: Quantifying, Analyzing, and Promoting Representation Diversity in Diffusion Transformers
• Fold-switching proteins push the boundaries of conformational ensemble prediction
• SeedProteo: Accurate De Novo All-Atom Design of Protein Binders
• CAML: Commutative algebra machine learning -- a case study on protein-ligand binding affinity prediction
• In-Context Learning can distort the relationship between sequence likelihoods and biological fitness
• A Blind Spot in Alignment: Quantifying Biosecurity Risks in Large Language Models
• A Folding-Docking-Affinity framework for protein-ligand binding affinity prediction
• Bidirectional Hierarchical Protein Multi-Modal Representation Learning
• Synergistic Benefits of Joint Molecule Generation and Property Prediction
• Prot42: a Novel Family of Protein Language Models for Target-aware Protein Binder Generation
• Heterogeneous networks in drug-target interaction prediction
• From sequence to protein structure and conformational dynamics with AI/ML
• An All-Atom Generative Model for Designing Protein Complexes
• EpiBench: Can LLMs Understand Epitopes for Antibody Drug Discovery?
• ProtFlow: Fast Protein Sequence Design via Flow Matching on Compressed Protein Language Model Embeddings
• Elucidating the Design Space of Multimodal Protein Language Models
• α-Flow: A Unified Framework for Continuous-State Discrete Flow Matching Models
• Customizing Spider Silk: Generative Models with Mechanical Property Conditioning for Protein Engineering
• ViralQC: A Tool for Assessing Completeness and Contamination of Predicted Viral Contigs
• EquiCPI: SE(3)-Equivariant Geometric Deep Learning for Structure-Aware Prediction of Compound-Protein Interactions
• Leveraging State Space Models in Long Range Genomics
• SCMPPI: Supervised Contrastive Multimodal Framework for Predicting Protein-Protein Interactions

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