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Found 6 papers, 1 papers with code
Substrate Prediction for RiPP Biosynthetic Enzymes via Masked Language Modeling and Transfer Learning
Similarly, masked language modeling of LazDEF substrate preferences produced embeddings that improved the performance of classification models of both LazBF and LazDEF substrates.
Leveraging Machine Learning Models for Peptide-Protein Interaction Prediction
Peptides play a pivotal role in a wide range of biological activities through participating in up to 40% protein-protein interactions in cellular processes.
Adaptive Sampling Methods for Molecular Dynamics in the Era of Machine Learning
Molecular Dynamics (MD) simulations are fundamental computational tools for the study of proteins and their free energy landscapes.
Billion-years old proteins show the importance of N-lobe orientation in Imatinib-kinase selectivity
The molecular origins of proteins' functions are a combinatorial search problem in the proteins' sequence space, which requires enormous resources to solve.
Thirty years of molecular dynamics simulations on posttranslational modifications of proteins
Posttranslational modifications (PTMs) are an integral component to how cells respond to perturbation.
REinforcement learning based Adaptive samPling: REAPing Rewards by Exploring Protein Conformational Landscapes
One of the key limitations of Molecular Dynamics simulations is the computational intractability of sampling protein conformational landscapes associated with either large system size or long timescales.
Biomolecules Biological Physics Chemical Physics
