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.
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).
A web server tailored to predict the binding affinity of a protein-small molecule complex, encompassing both protein and small-molecule complementarity in terms of shape and chemistry via graph-based structural signatures. CSM-Lig was trained and evaluated on different releases of the PDBbind databases, achieving a correlation of up to 0.86 on 10-fold cross validation and 0.80 in blind tests, performing as well as or better than other widely used methods. The web server allows users to rapidly and automatically predict binding affinities of collections of structures and assess the interactions made. We believe CSM-lig would be an invaluable tool for helping assess docking poses, the effects of multiple mutations, including insertions, deletions and alternative splicing events, in protein-small molecule affinity, unraveling important aspects that drive protein–compound recognition.
A web server for fragment-based drug discovery. ACFIS includes three computational modules, PARA GEN, CORE GEN and CAND GEN. ACFIS can generate core fragment structure from the active molecule using fragment deconstruction analysis and perform in silico screening by growing fragments to the junction of core fragment structure. An integrated energy calculation rapidly identifies which fragments fit the binding site of a protein. We constructed a simple interface to enable users to view top-ranking molecules in 2D and the binding mode in 3D for further experimental exploration. This makes the ACFIS a highly valuable tool for drug discovery.
A web service for identification of ligand & residue interactions, SNP and pathway analysis. Manoraa allows the users to input the chemical fragments and present all the unique molecular interactions to the target proteins with available three-dimensional structures in the PDB. The users can also link the ligands of interest to assess possible off-target proteins, human variants and pathway information using our all-in-one integrated tools. Taken together, we envisage that the server will facilitate and improve the study of protein–ligand interactions by allowing observation and comparison of ligand interactions with multiple proteins at the same time.
An easy-to-use pipeline tool to conduct MM/PBSA and LIE calculations. Powered by the VMD and NAMD programs, CaFE is able to handle numerous static coordinate and molecular dynamics trajectory file formats generated by different molecular simulation packages and supports various force field parameters. CaFE provides a user-friendly choice for researchers who want to perform a post-molecular dynamic energetic analysis using the end-point methods. It is a VMD plugin written in Tcl and the usage is platform-independent.
Allows prediction of affinity in protein-small ligand complexes. PRODIGY-LIG assists users to understand such interactions in protein-small ligand systems for investigating biological systems and drug design. Moreover, users have to enter different information for testing this program: (1) a 3D experimental or modelled structure of the protein-ligand complex; (2) the chain identifiers for the protein and the ligand; (3) the electrostatic energy of the complex; and (4) an email address.
Predicts binding energies of protein pairs. BADock relies on the classification of structural domains and super-secondary structures where a relevant number of such structural features were located outside the binding interface. It permits researchers to modulate and control protein-protein interactions (PPIs). This tool allows prediction of the binding affinities from the unbound tertiary structures.
Designs proteins in a computational manner. iCFN can be employed for several purpose: side chain packing, protein design for single protein, binding affinity toward multiple targets, and multistate protein design with single or multiple substates. It offers variate functionalities to specify: maximum number of conformation, maximum single nucleotide polymorphism (SNP) threshold sequence, or maximum number of discrepancy sequence.
Predicts Down syndrome cell adhesion molecule (Dscam1) self- and hetero- binding affinity for batches of Dscam1 isomers. Dscam1 Web Server helps the study of Dscam1 affinity and helps researchers navigate through the tens of millions of possible isomer combinations to isolate the strong-binding ones. It can extract the variable domain sequences or the list of exons involved. The tool includes predictions for hetero-binding ability.
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.
A program written in Python for streamlining end-state free energy calculations using ensembles derived from molecular dynamics (MD) or Monte Carlo (MC) simulations. Several implicit solvation models are available with MMPBSA.py, including the Poisson-Boltzmann Model, the Generalized Born Model, and the Reference Interaction Site Model. Vibrational frequencies may be calculated using normal mode or quasi-harmonic analysis to approximate the solute entropy. Specific interactions can also be dissected using free energy decomposition or alanine scanning. A parallel implementation significantly speeds up the calculation by dividing frames evenly across available processors. MMPBSA.py is an efficient, user-friendly program with the flexibility to accommodate the needs of users performing end-state free energy calculations.
Processes standard binding free-energy calculations. BFEE is a plug-in of the visualization software Visual Molecular Dynamics (VMD). This tool measures the standard deviation over the different binding energies to estimate errors. It automatically arranges and evaluates absolute binding free-energy calculations via the molecular dynamics engine NAMD but can be ported to other molecular dynamics engines.
Facilitates setup and execution of ligand binding free energy calculations with AMBER for multiple ligands. FEW allows setting up three types of free energy calculations that operate at different levels of rigor and computational demand: MM-PB(GB)SA, LIE, and TI calculations. The hierarchical and template-based design makes that FEW requires minimal input information but, at the same time, remains highly adaptable. Thus, the program constitutes a ‘‘gray box’’ and should be of interest to both beginners in the field of free energy calculations and expert users.
Derives the partial atomic charges of small molecules for use in protein/DNA-ligand docking and scoring. TPACM4 helps speed up the binding energy predictions. The partial charges of the tool were tested by estimating hydrogen bond dimers energies, solvation free energies and protein-ligand binding free energies. The tool overcomes the limitations of time complexity in deriving the partial atomic charges of a given molecule while retaining accuracy.
Allows users to calculate molecular surface components. HYDE is based on the estimation of hydrogen bond and dehydration energies emerging during protein–ligand binding. The functions of this tool are integrated into an optimization procedure to allow a more accurate prediction of the structure of protein–ligand complexes.
A search engine focused on retrieving candidate herb-related information with user search terms (a list of genes, a disease name, a chemical name or an herb name). HerDing integrated herb-chemical, chemical-gene and gene-disease relationships, which were obtained from other databases and text-mining of published articles, to find herb-disease relationships. Users of HerDing can search herbs related to a disease with an input of disease-related genes or a disease name. In addition, users can query a chemical name for retrieving herbs and genes related to the input chemical and herb names for extracting chemicals and genes related to the input herb.
Generates such predictions with accuracy comparable to state-of-the-art methods. mhcPreds is a framework for the development of computational studies targeting novel drug-discovery using a novel approach to protein-protein representation, interaction, and modeling. It is designed to be extensible, hackable, and flexible enough to allow researchers to both use it a tool for generating new predictions from their own sequences, as well as deep learning enthusiasts interested in novel, unexplored applications of such techniques.
Highlights the affinity and selectivity of compounds in the HMS-LINCS collection, for a gene of interest. SelectivitySelectR allows users to see the mean affinity and selectivity for all LINCS compounds with a known affinity for their target. It is composed of three steps: (1) selection of a target of interest and binding criteria, (2) selection of a region in the main plot with compounds of interest, and (3) selection of three compounds in the bottom table and visualization of their known binding affinities.
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.
Calculates ligand binding affinities in implicit and explicit solvent. YANK uses a sophisticated set of algorithms to rigorously compute biomolecular ligand binding free energies. It performs an alchemical free energy calculation in either implicit or explicit solvent, in which the interactions between a ligand are decoupled in a number of alchemical intermediates whose interactions with the environment are modified, creating an alternative thermodynamic cycle to the direct dissociation of ligand and target biomolecule.