Binding affinity detection software tools | Drug discovery data analysis
Accurate prediction of binding free energy is of particular importance to computational biology and structure-based drug design. Among those methods for binding affinity predictions, the end-point approaches, such as MM/PBSA and LIE, have been widely used because they can achieve a good balance between prediction accuracy and computational cost.
Gives access to many free software tools for sequence analysis. EMBOSS aims to serve the molecular biology community. It permits the creation and the release of software in an open source spirit. This tool is useful for sequence analysis into a seamless whole. It is free of charge and is available in open source.
Provides numerous programs that work together to setup, perform, and analyze molecular dynamics (MD) simulations. AMBER is a biomolecular simulation package which also contains software designed to parameterize more complex molecules and fragments not currently present in the force field libraries. The suite can be used to carry out complete molecular dynamics simulations, with either explicit water or generalized Born solvent models.
Implements the molecular mechanics Poisson-Boltzmann surface area (MM-PBSA) approach using subroutines written in-house or sourced from the GROMACS and APBS packages. g_mmpbsa was developed as part of the Open Source Drug Discovery (OSDD) consortium. Its aim is to integrate high-throughput molecular dynamics (MD) simulations with binding energy calculations. The tool provides options to select alternative atomic radii and different nonpolar solvation models including models based on the solvent accessible surface area (SASA), solvent accessible volume (SAV), and a model which contains both repulsive (SASA-SAV) and attractive components (described using a Weeks-Chandler-Andersen like integral method).
Allows to build model automatically to combine applications in order to form computational pipelines. autocorrelator permits to determine the optimum parameters for docking, superposition, and molecular mechanics applications to correlate computational scores with binding affinity. It can run multiple rounds of sets of parameter values trying to evolve to the optimal set of parameters.
Predicts the impact of single-point mutations on protein stability and protein–protein and protein–nucleic acid affinity. mCSM is an approach, which relies on graph-based signatures, for studying the impact of missense mutations in proteins. The software perceives residue environment density and depth implicitly, without relying on direct calculations or thresholds. It was applied to predict stability changes of mutations occurring in p53, demonstrating its applicability in a challenging disease scenario.
Evaluates the change in binding affinity between proteins (or protein chains) caused by single-site mutations in their sequence. The predictions are based on the structure of the protein-protein complex. BeAtMuSiC gives the possibility to perform either manually specified mutations, or a systematic scan of all possible mutations in a protein chain (or group of chains), or at the protein-protein interface.
Optimizes candidate ligands for predicted binding affinity and other druglike properties. AutoGrow attempts to introduce some chemical intuition into the automated optimization process. It uses the rules of click chemistry to guide optimization, greatly enhancing synthesizability. The tool is able to generate druglike molecules with high predicted binding affinities. It helps ensure chemical synthesizability.