ArticlePLoS computational biology2023
Computational design of novel Cas9 PAM-interacting domains using evolution-based modelling and structural quality assessment.
Article in PLoS computational biology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed, 13 citations in OpenAlex.
- Designing molecular RNA switches with Restricted Boltzmann machines.Nature communications · 2025Article
- Precision gene editing: The power of CRISPR-Cas in modern genetics.Molecular therapy. Nucleic acids · 2025Review
- PAM-interacting domain turn-helix 51 motifs can improve Cas9-SpRY activity.Nucleic acids research · 2025Article
- Article
- Sam-Sam Association Between EphA2 and SASH1: In Silico Studies of Cancer-Linked Mutations.Molecules (Basel, Switzerland) · 2025Article
- Exploring a Potential Optimization Route for Peptide Ligands of the Sam Domain from the Lipid Phosphatase Ship2.International journal of molecular sciences · 2024Article
- Machine learning for functional protein design.Nature biotechnology · 2024Review
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Authors and funding
6 authors at 1 institution in 1 country.
Funding
Abstract
We present here an approach to protein design that combines (i) scarce functional information such as experimental data (ii) evolutionary information learned from a natural sequence variants and (iii) physics-grounded modeling. Using a Restricted Boltzmann Machine (RBM), we learn a sequence model of a protein family. We use semi-supervision to leverage available functional information during the RBM training. We then propose a strategy to explore the protein representation space that can be informed by external models such as an empirical force-field method (FoldX). Our approach is applied to a domain of the Cas9 protein responsible for recognition of a short DNA motif. We experimentally assess the functionality of 71 variants generated to explore a range of RBM and FoldX energies. Sequences with as many as 50 differences (20% of the protein domain) to the wild-type retained functionality. Overall, 21/71 sequences designed with our method were functional. Interestingly, 6/71 sequences showed an improved activity in comparison with the original wild-type protein sequence. These results demonstrate the interest in further exploring the synergies between machine-learning of protein sequence representations and physics grounded modeling strategies informed by structural information.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.