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Esm
Este proyecto es una colección de modelos de lenguaje de proteínas tipo transformer preentrenados diseñados para el embedding de secuencias, predicción de estructuras, estimación de efectos de variantes y plegamiento inverso generativo. Proporciona un framework para transformar secuencias de aminoácidos en vectores numéricos de alta dimensión y predecir propiedades biológicas a través de estos embeddings.
El sistema incluye un modelo generativo para el plegamiento inverso que diseña secuencias de aminoácidos para adaptarse a una estructura de esqueleto de proteína objetivo específica. También cuenta con herramientas para el análisis de variantes de proteínas que utilizan predicción zero-shot para estimar el impacto funcional de las mutaciones en la estabilidad y actividad de las proteínas.
Las capacidades adicionales cubren la predicción de coordenadas atómicas 3D a partir de secuencias únicas, la generación de mapas de contacto de proteínas mediante mecanismos de atención y la recuperación de datos estructurales y embeddings de grandes conjuntos de datos metagenómicos. El proyecto también incluye utilidades para el diseño de secuencias de proteínas, puntuación de fitness y gestión de memoria para procesar secuencias largas en hardware limitado.
Features
- Protein Sequence Embeddings - Transforms amino acid sequences into high-dimensional numerical representations using pretrained transformer models.
- Transformer Language Models - Learns protein sequence patterns by processing amino acid strings through self-attention mechanisms to generate high-dimensional embeddings.
- Inverse Folding Sequence Sampling - Generates amino acid sequences conditioned on a target 3D structural backbone using generative sampling.
- Protein Inverse Folding Models - Designs new amino acid sequences that fit a specific target protein backbone structure using inverse folding.
- Per-Residue Representations - Generates high-dimensional numerical embeddings for individual amino acids to predict specific protein properties.
- Protein Structure Prediction - Predicts the three dimensional atomic coordinates of proteins from a single amino acid sequence using transformer models.
- Protein Variant Effect Predictors - Estimates the functional impact of mutations using zero-shot inference by comparing sequence likelihoods.
- Generative Protein Design - Implements generative models that create amino acid sequences based on learned evolutionary patterns.
- Generative Sequence Design - Uses generative models to optimize amino acid sequences based on specific structural constraints.
- Sequence Sampling - Samples candidate amino acid sequences from probability distributions conditioned on structural backbones.
- Protein Folding Coordinates - Predicts 3D atomic coordinates by mapping sequence embeddings to geometric representations through a structure-prediction transformer.
- Sequence Fitness Scoring - Calculates the conditional log-likelihood of sequences given a structure to evaluate biological fitness.
- Metagenomic Protein Data Retrieval - Provides bulk access to predicted structures, databases, and embeddings for millions of metagenomic proteins.
- Zero-Shot Mutation Likelihoods - Estimates the functional impact of mutations by comparing the model likelihood of wild-type and mutant protein sequences.
- Contact Map Predictions - Infers spatial proximity between residues by extracting interaction strengths from the internal attention heads of the transformer.
- Metagenomic Protein Analysis - Processes and retrieves structural data and embeddings for millions of proteins from large metagenomic datasets.
- Protein Contact Map Predictions - Identifies residues likely to be in physical contact by analyzing self-attention maps within a language model.
- Geometric Backbone Encoders - Transforms three-dimensional protein coordinates into numerical vectors using a geometric encoder for inverse folding tasks.
- Protein Variant Analysis - Estimates the functional impact of mutations on protein stability and activity using zero-shot prediction.
- Transformer and Language Models - Evolutionary-scale language models for structure prediction and design.
