churchlab / low-N-protein-engineering
Code and data to reproduce analyses in Biswas et al. (2020) "Low-N protein engineering with data-efficient deep learning".
☆58Updated 4 years ago
Alternatives and similar repositories for low-N-protein-engineering:
Users that are interested in low-N-protein-engineering are comparing it to the libraries listed below
- ☆36Updated 2 months ago
- Mutation effects predicted from sequence co-variation☆64Updated 7 years ago
- Evolutionary velocity with protein language models☆90Updated last year
- ☆49Updated 2 weeks ago
- Computational tools for extremely low-cost, massively parallel amplicon-based sequencing of every variant in protein mutant libraries.☆33Updated 2 years ago
- Rapid protein-protein interaction network creation from multiple sequence alignments with Deep Learning☆82Updated last year
- ☆37Updated 3 months ago
- Direct coupling analysis software for protein and RNA sequences☆50Updated last year
- ☆25Updated 10 months ago
- ☆67Updated 6 months ago
- A complete, open-source, end-to-end re-implementation of the Church Lab's low-N eUniRep in silico protein engineering pipeline presented …☆27Updated 4 years ago
- ☆62Updated 3 years ago
- A domain parser for Alphafold models☆34Updated last year
- RNA Multiple Sequence Alignment☆38Updated last year
- Active Learning-Assisted Directed Evolution for Protein Engineering☆46Updated 4 months ago
- Learning the language of protein-protein interactions☆62Updated 2 weeks ago
- Repository for Protein-Vec, a protein embedding mixture of experts model☆37Updated last year
- Contrastive fitness learning: Reprogramming protein language models for low-N learning of protein fitness landscape☆28Updated 7 months ago
- ☆34Updated last year
- PLM based active learning model for protein engineering☆77Updated 8 months ago
- A tool for accurate prediction of a protein's secondary structure from only it's amino acid sequence☆53Updated last week
- Protein language model trained on coding DNA☆46Updated 5 months ago
- ☆24Updated 3 years ago
- ☆39Updated 2 weeks ago
- Machine learning models for antibody sequences in PyTorch☆39Updated 3 years ago
- Prediction of binding residues for metal ions, nucleic acids, and small molecules.☆32Updated 3 months ago
- Detection of remote homology by comparison of protein language model representations☆49Updated 3 months ago
- Neural networks to fit interpretable models and quantify energies, energetic couplings, epistasis, and allostery from deep mutational sca…☆44Updated last month
- An unofficial re-implementation of AntiBERTy, an antibody-specific protein language model, in PyTorch.☆24Updated last year
- Deciphering protein evolution and fitness landscapes with latent space models☆32Updated 3 years ago