Protein Language Model
46 papers with code • 1 benchmarks • 5 datasets
Use of protein language models in protein property prediction
Libraries
Use these libraries to find Protein Language Model models and implementations
DAVIS-DTA
Most implemented papers
Algorithm for Optimized mRNA Design Improves Stability and Immunogenicity
Messenger RNA (mRNA) vaccines are being used for COVID-19, but still suffer from the critical issue of mRNA instability and degradation, which is a major obstacle in the storage, distribution, and efficacy of the vaccine.
ESM-NBR: fast and accurate nucleic acid-binding residue prediction via protein language model feature representation and multi-task learning
Meanwhile, the ESM-NBR obtains the MCC values for DNA-binding residues prediction of 0. 427 and 0. 391 on two independent test sets, which are 18. 61 and 10. 45% higher than those of the second-best methods, respectively.
ESM All-Atom: Multi-scale Protein Language Model for Unified Molecular Modeling
In this paper, we propose ESM-AA (ESM All-Atom), a novel approach that enables atom-scale and residue-scale unified molecular modeling.
Retrieval-Enhanced Mutation Mastery: Augmenting Zero-Shot Prediction of Protein Language Model
Enzyme engineering enables the modification of wild-type proteins to meet industrial and research demands by enhancing catalytic activity, stability, binding affinities, and other properties.
MSA Transformer
The-AI-Summer/self-attention-cv
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Unsupervised protein language models trained across millions of diverse sequences learn structure and function of proteins.
ECRECer: Enzyme Commission Number Recommendation and Benchmarking based on Multiagent Dual-core Learning
Take UniPort protein "A0A0U5GJ41" as an example (1. 14.-.-), ECRECer annotated it with "1. 14. 11. 38", which supported by further protein structure analysis based on AlphaFold2.
Structure-aware Protein Self-supervised Learning
ggchen1997/steps_bioinformatics
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Furthermore, we propose to leverage the available protein language model pretrained on protein sequences to enhance the self-supervised learning.
Generative power of a protein language model trained on multiple sequence alignments
bitbol-lab/iterative_masking
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Moreover, for small protein families, our generation method based on MSA Transformer outperforms Potts models.
DistilProtBert: A distilled protein language model used to distinguish between real proteins and their randomly shuffled counterparts
yarongef/DistilProtBert
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bioRxiv 2022
Here, we adapted this concept to the problem of protein sequence analysis, by developing DistilProtBert, a distilled version of the successful ProtBert model.
HelixFold-Single: MSA-free Protein Structure Prediction by Using Protein Language Model as an Alternative
Our proposed method, HelixFold-Single, first pre-trains a large-scale protein language model (PLM) with thousands of millions of primary sequences utilizing the self-supervised learning paradigm, which will be used as an alternative to MSAs for learning the co-evolution information.
