Computational design of novel Cas9 PAM-interacting domains using evolution-based modelling and structural quality assessment.

Computational design of novel Cas9 PAM-interacting domains using evolution-based modelling and structural quality assessment.

Blog Article

We present here an approach to protein design that combines (i) scarce functional information such as experimental data (ii) evolutionary information D-Sub/Serial 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 Bike Units - Dual Sport 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.