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Our group's tools, gadgets and sorceries for molecular modeling, protein engineering, virtual screening and machine learning
Runs a genetic algorithm for protein design given a reference structure. The population of initial sequences can also be provided, otherwise will use random. The objective function can be chosen, but the default one optimizes ΔGfold. https://github.com/izzetbiophysicist/prot_eng_GA
Carries out consensus design given a multifasta and a reference structure. Can also perform hybrid consensus, where the best residues at non-consensus regions are searched using Rosetta design https://github.com/izzetbiophysicist/consensus_design
Automatic data processing for Machine Learning pipelines for virtual screening (peptides and small molecules). Given a set o FASTA sequences or Smiles and a response variable, can automatically process data for training https://github.com/izzetbiophysicist/mol_data_prep
Generates a convex hull around a set of data points. Useful for domain applicability analysis in Machine Learning https://github.com/izzetbiophysicist/Data_convex_hull
Fetches segments of PDB structures matching a given PFAM domain https://github.com/izzetbiophysicist/DomainFetcher
Stochastic cyclic optimization of binding free energy of peptide ligands through iterative mutagenesis. Has special parameters for helical peptides, can also be used for other protein binders https://github.com/jsartori12/HeliBO
Calculates the Murcko scaffolds for a given list of compounds and returns the scaffolds along with their frequencies https://github.com/izzetbiophysicist/Murcko_scaffold_frequencies
Convert an AA sequence into physcicochemial descriptor (zscales or VHSE) https://github.com/riveros94/descriptor_converter
An assortment of handy protein design functions https://github.com/izzetbiophysicist/protein_design_functions
Some useful Gromacs stuff for Molecuar dynamics https://github.com/izzetbiophysicist/useful_gromacs_inputs