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Long-Range Electrostatics in Serine Proteases: Machine Learning-Driven Reaction Sampling Yields Insights for Enzyme Design
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 …
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
Protein design and engineering are crucial for advancements in biotechnology, medicine,
and sustainability. Machine learning (ML) models are used to design or enhance protein …
and sustainability. Machine learning (ML) models are used to design or enhance protein …
[HTML][HTML] Modeling protein-small molecule conformational ensembles with ChemNet
Modeling the conformational heterogeneity of protein-small molecule systems is an
outstanding challenge. We reasoned that while residue level descriptions of biomolecules …
outstanding challenge. We reasoned that while residue level descriptions of biomolecules …
Computational design of a thermostable de novo biocatalyst for whole cell biotransformations
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 …
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 …
protein structure. While more coarse-grain tasks such as backbone generation are …