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Protein Language Model

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Use of protein language models in protein property prediction

코드

관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.

https://github.com/ChakradharG/PeptideBERT

공식 코드

PeptideBERT: A Language Model based on Transformers for Peptide Property Prediction

https://github.com/KoubaPetr/Flexpert

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Learning to engineer protein flexibility

https://github.com/LinearDesignSoftware/LinearDesign

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Algorithm for Optimized mRNA Design Improves Stability and Immunogenicity

https://github.com/PaddlePaddle/PaddleHelix

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HelixFold-Single: MSA-free Protein Structure Prediction by Using Protein Language Model as an Alternative

https://github.com/aayush-shah14/peptidegpt

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Peptide-GPT: Generative Design of Peptides using Generative Pre-trained Transformers and Bio-informatic Supervision

https://github.com/agemagician/Ankh

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Ankh: Optimized Protein Language Model Unlocks General-Purpose Modelling

https://github.com/alisaadatv/esm2-missense-impact-analysis

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Fine-tuning the ESM2 protein language model to understand the functional impact of missense variants

https://github.com/amazon-science/LC-PLM

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Long-context Protein Language Modeling Using Bidirectional Mamba with Shared Projection Layers

https://github.com/amirgroup-codes/interactionrecovery

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On Recovering Higher-order Interactions from Protein Language Models

https://github.com/aryopg/vaxformer

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Vaxformer: Antigenicity-controlled Transformer for Vaccine Design Against SARS-CoV-2

https://github.com/beam-labs/crossbind

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CrossBind: Collaborative Cross-Modal Identification of Protein Nucleic-Acid-Binding Residues

https://github.com/biochunan/abrank-walle-affinity

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AbRank: A Benchmark Dataset and Metric-Learning Framework for Antibody-Antigen Affinity Ranking

https://github.com/bitbol-lab/diffpalm

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Pairing interacting protein sequences using masked language modeling

https://github.com/bitbol-lab/iterative_masking

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Generative power of a protein language model trained on multiple sequence alignments

https://github.com/bozhenhhu/a-review-of-plms-and-methods-for-protein-structure-prediction

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Protein Language Models and Structure Prediction: Connection and Progression

https://github.com/brineylab/balm-paper

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Improving antibody language models with native pairing

https://github.com/bytedance/dplm

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Diffusion Language Models Are Versatile Protein Learners

https://github.com/chandar-lab/amplify

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Structure-Aligned Protein Language Model

https://github.com/deepgraphlearning/esm-s

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Structure-Informed Protein Language Model

https://github.com/deepgraphlearning/s3f

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Multi-Scale Representation Learning for Protein Fitness Prediction

https://github.com/dimenwarper/llm-for-clinical-variants

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Protein language model rescue mutations highlight variant effects and structure in clinically relevant genes

https://github.com/ggchen1997/steps_bioinformatics

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Structure-aware Protein Self-supervised Learning

https://github.com/ginnm/proteinpretraining

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PETA: Evaluating the Impact of Protein Transfer Learning with Sub-word Tokenization on Downstream Applications

https://github.com/hliulab/tcrlm

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tcrLM: a lightweight protein language model for predicting T cell receptor and epitope binding specificity

https://github.com/keiserlab/face-plm

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Leveraging Protein Language Model Embeddings for Catalytic Turnover Prediction of Adenylate Kinase Orthologs in a Low-Data Regime

https://github.com/kewei2023/ampcliff-generation

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AMPCliff: quantitative definition and benchmarking of activity cliffs in antimicrobial peptides

https://github.com/kingstdio/ECRECer

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ECRECer: Enzyme Commission Number Recommendation and Benchmarking based on Multiagent Dual-core Learning

https://github.com/lkny123/agn

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Decoupled Sequence and Structure Generation for Realistic Antibody Design

https://github.com/mahesh-ak/cognatetransformer

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Cognate Transformer for Automated Phonological Reconstruction and Cognate Reflex Prediction

https://github.com/ml4bio/uspnet

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Unbiased organism-agnostic and highly sensitive signal peptide predictor with deep protein language model

https://github.com/oxpig/antifold

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AntiFold: Improved antibody structure-based design using inverse folding

https://github.com/pemami4911/ppde

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Plug & Play Directed Evolution of Proteins with Gradient-based Discrete MCMC

https://github.com/pengsl-lab/esm-nbr

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ESM-NBR: fast and accurate nucleic acid-binding residue prediction via protein language model feature representation and multi-task learning

https://github.com/tonytu16/protigeno

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ProtiGeno: a prokaryotic short gene finder using protein language models

https://github.com/tyang816/protrem

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Retrieval-Enhanced Mutation Mastery: Augmenting Zero-Shot Prediction of Protein Language Model

https://github.com/wwzll123/esm-nbr

공식 코드

ESM-NBR: fast and accurate nucleic acid-binding residue prediction via protein language model feature representation and multi-task learning

출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.