Long-Range Electrostatics in Serine Proteases: Machine Learning-Driven Reaction Sampling Yields Insights for Enzyme Design

A Zlobin, V Maslova, J Beliaeva, J Meiler… - Journal of Chemical …, 2025 - ACS Publications
Computational enzyme design is a promising technique for producing novel enzymes for
industrial and clinical needs. A key challenge that this technique faces is to consistently …

ProteusAI: An Open-Source and User-Friendly Platform for Machine Learning-Guided Protein Design and Engineering

J Funk, L Machado, SA Bradley, M Napiorkowska… - bioRxiv, 2024 - biorxiv.org
Protein design and engineering are crucial for advancements in biotechnology, medicine,
and sustainability. Machine learning (ML) models are used to design or enhance protein …

[HTML][HTML] Modeling protein-small molecule conformational ensembles with ChemNet

I Anishchenko, Y Kipnis, I Kalvet, G Zhou, R Krishna… - …, 2024 - pmc.ncbi.nlm.nih.gov
Modeling the conformational heterogeneity of protein-small molecule systems is an
outstanding challenge. We reasoned that while residue level descriptions of biomolecules …

Computational design of a thermostable de novo biocatalyst for whole cell biotransformations

W Elaily, D Stoll, M Chakatok, M Aleotti, B Grill… - bioRxiv, 2024 - biorxiv.org
Several industrially relevant catalytic strategies have emerged over the last couple of
decades, with biocatalysis gaining lots of attention in this respect. However, this type of …

[PDF][PDF] ProteinZen: combining latent and SE (3) flow matching for all-atom protein generation

AJ Li, T Kortemme - mlsb.io
De novo protein design has been greatly accelerated by the advent of generative models of
protein structure. While more coarse-grain tasks such as backbone generation are …