Computational Peptide Science

Machine Learning Prediction of Antimicrobial Peptides.- Tools for Characterizing Proteins: Circular Variance, Mutual Proximity, Chameleon Sequences and Subsequence Propensities.- Exploring the Peptide Potential Of Genomes.- Computational Identification and Design of Complementary beta-strand Sequences.- Dynamics of Amyloid Formation from Simplified Representation to Atomistic Simulations.- Predicting Membrane-Active Peptide Dynamics in Fluidic Lipid Membranes.- Coarse-grain simulations of membrane-adsorbed helical peptides.- Peptide dynamics and metadynamics: leveraging enhanced sampling molecular dynamics to robustly model long-timescale transitions.- Metadynamics Simulations to Study the Structural Ensembles and Binding Processes of Intrinsically Disordered Proteins.- Computational and Experimental Protocols to Study Cyclo-Dihistidine Self- and Co-Assembly: Minimalistic Bio-assemblies with Enhanced Fluorescence and Drug Encapsulation Properties.- Computational Tools and Strategies to Develop Peptide-Based Inhibitors of Protein-Protein Interactions.- Rapid Rational Design of Cyclic Peptides Mimicking Protein-Protein Interfaces.- Structural prediction of peptide-MHC binding modes.- Molecular Simulation of Stapled Peptides.- Free Energy-Based Computational Methods for the Study of Protein-Peptide Binding Equilibria.- Computational Evolution Protocol for Peptide Design.-Computational design of miniprotein binders.- Computational Design of LD Motif-Peptides with Improved Recognition of the Focal Adhesion Kinase FAT Domain.- Knowledge-based unfolded state model for protein design.