Home > Methods > Binding free energy estimation

Binding free energy estimation

 
X-Score: http://sw16.im.med.umich.edu/software/xtool/ is basically a “scoring function”, which computes the binding affinities of the given ligand molecules to their target protein. It can be applied to structure-based drug design studies in combination with molecular docking or de novo structure generation programs. X-Score is developed by Dr. Renxiao Wang in Dr. Shaomeng Wang’s group at the Department of Internal Medicine, University of Michigan Medical School. The first paper that reported X-Score was published on Journal of Computer-Aided Molecular Design, 16: 11–26, 2002. Note that X-Score was formerly known as X-CScore for a short while. To learn more about the X-Score program please read the X-Score on-line manual. X-Score is released to the public for free. The latest release is X-Score v1.2. You can download the program by clicking the link below. You will go through a license agreement and fill in some necessary registration information. Once we have received your signed license agreement, we will send you instructions of how to log on our server and download the X-Score package. The X-Score v1.2 package includes the program (executable and source codes), user manual, examples, references and the protein-ligand complex data set originally used for developing X-Score. Click here to get the X-Score v1.2 package now!

 

eHiTS 2009 Binding Affinity Prediction: eHiTS has a novel scoring function that takes advantage of temperature factor information provided in PDB files to give a more complete picture of interactions. All atoms in a PDB file have a temperature factor (B) associated with them. This temperature factor indicates the how much the atom varies from the mean position. Some atom positions are very precisely defined while others vary greatly, this has a very strong influence on the weight that should be assigned to the position. The novel approach in eHiTS uses the probability of the atom position during the statistic collection to create a statistically derived empirical scoring function. The eHiTS scoring function provides a scoring function that is smooth, and accurately represents a wide variety of problems at hand. One of the most recent studies with eHiTS Score 2009 was done using the PDBBind-2008 dataset. Please see a picture below for correlation of eHiTS Score to the experimental binding affinity. http://www.simbiosys.ca/ehits/ehits_score.html

 

DrugScore-Online (DSX): http://pc1664.pharmazie.uni-marburg.de/drugscore/ DSXONLINE is a web-based user interface for the knowledge-based scoring function DSX. DSXONLINE enables you to score (putative) protein-ligand complexes of your interest, to browse and download the scoring results, and to visualize the per-atom score contributions (see section Visualization). DSX: DSX pair potentials are derived in analogy the the DrugScore formalism developed by Gohlke et al. However, another set of atom types is used and contact types are clustered to circumvent problems with the reference state. Torsion potentials and solvent accessible surface ratio potentials are derived using the same formalism. For more details see the upcoming publication which is currently in preparation. For more consistences, DSX always assigns its own atom types and hydrogens are not regarded. If you have ligand poses from GOLD docking where water molecules were included it is possible to consider the corresponding ON-marked waters in the solutions file. Please note that there are even more options (like considering solutions from a docking with flexible receptor residues) available in the DSX standalone version, which will be freely available after publication. Visualization of the per-atom score contributions: The visualization of the per-atom score contributions is an intuitive way to learn about differences between putative ligand geometries, the effects of scaffold modifications or about the importance of certain binding regions.

 

BAPPL serve: http://www.scfbio-iitd.res.in/software/drugdesign/bappl.jsp Binding Affinity Prediction of Protein-Ligand (BAPPL) server computes the binding free energy of a non-metallo protein-ligand complex using an all atom energy based empirical scoring function BAPPL server provides two methods as options: Method 1 : Input should be an energy minimized protein-ligand complex with hydrogens added, protonation states, partial atomic charges and van der Waals parameters (R* and ε) assigned for each atom. The server directly computes the binding affinity of the complex using the assigned parameters. For format specifications on the input, please refer to the README file. Method 2 : Input should be an energy minimized protein-ligand complex with hydrogens added and protonation states assigned. The net charge on the ligand should be specified. The server derives the partial atomic charges of the ligand using the AM1-BCC procedure and GAFF force field for van der Waals parameters. Cornell et al. force field is used to assign partial atomic charges and van der Waals parameters for the proteins. For format specifications on the input, please refer to the README file.

 

PreDDICTA: http://www.scfbio-iitd.res.in/software/drugdesign/preddicta.jsp employs an all-atom energy based function for computing the binding affinity of a DNA oligomer with a non-covalently bound drug. The function has been validated against experimental binding free energies, ΔGo bind and change in melting temperature of the DNA oligomer upon drug binding, ΔTm, for 50 DNA Drug complexes. Click here to access the DNA-drug complex dataset. DNA is an important anticancer/antibiotic target and PreDDICTA can be employed to aid and expedite rational drug design attempts for DNA.Click here to know more about DNA Drug interaction How to use PreDDICTA: 1. Tool 1 incorporates the PreDDICTA energy function which calculates the electrostatics, van der Waals, rotational and translational entropy and hydration free energy change for the DNA-drug complex. These are summed to yield the total calculated binding energy which is converted to the binding free energy and ΔTm based on the relations reported in. Input for this tool is a PDB file for any DNA-minor groove binder complex, conforming to the standard PDB format, as described in Input format 2. Tool 2 simply converts any number input as ΔTm to the corresponding expected binding free energy, using the relation between these two quantities reported in. 3. Tool 3 converts any number input as binding free energy to the corresponding expected ΔTm value, using the relation between these two quantities reported in.

 

PharmaGist: http://bioinfo3d.cs.tau.ac.il/pharma/about.html Predicting molecular interactions is a major goal in rational drug design. Pharmacophore, which is the spatial arrangement of features that is essential for a molecule to interact with a specific target receptor, is important for achieving this goal. PharmaGist is a freely available web server for pharmacophore detection. The employed method is ligand based. It does not require the structure of the target receptor. Instead, the input is a set of structures of drug-like molecules that are known to bind to the receptor. We compute candidate pharmacophores by multiple flexible alignments of the input ligands. The main innovation of this approach is that the flexibility of the input ligands is handled explicitly and in deterministic manner within the alignment process. The method is highly efficient, where a typical run with up to 32 drug-like molecules takes seconds to a few minutes on a stardard PC. Another important characteristic of the method is the capability of detecting pharmacophores shared by different subsets of input molecules. This capability is a key advantage when the ligands belong to different binding modes or when the input contains outliers. The download version includes virtual screening capability. The performance of PharmaGist for virtual screening was successfully evaluated on a commonly used data set of G-Protein Coupled Receptor alpha1A. Additionally, a large-scale evaluation using the DUD (directory of useful decoys) data set was performed. DUD contains 2950 active ligands for 40 different receptors, with 36 decoy compounds for each active ligand. PharmaGist enrichment rates are comparable with other state-of-the-art tools for virtual screening.

 
IC50-to-Ki converter: http://botdb.abcc.ncifcrf.gov/toxin/kiConverter.jsp The IC50-to-Ki converter computes Ki values from experimentally determined IC50 values for inhibitors of enzymes that obey classic Michaelis-Menten kinetics and of protein-ligand interactions. A new web-server tool estimates Ki values from experimentally determined IC50 values for inhibitors of enzymes and of binding reactions between macromolecules (e.g. proteins, polynucleic acids) and ligands. This converter was developed to enable end users to help gauge the quality of the underlying assumptions used in these calculations which depend on the type of mechanism of inhibitor action and the concentrations of the interacting molecular species. Additional calculations are performed for nonclassical, tightly bound inhibitors of enzyme-substrate or of macromolecule-ligand systems in which free, rather than total concentrations of the reacting species are required. Required user-defined input values include the total enzyme (or another target molecule) and substrate (or ligand) concentrations, the Km of the enzyme-substrate (or the Kd of the target-ligand) reaction, and the IC50 value. Assumptions and caveats for these calculations are discussed along with examples taken from the literature. The host database for this converter contains kinetic constants and other data for inhibitors of the proteolytic clostridial neurotoxins (http://botdb.abcc.ncifcrf.gov/toxin/kiConverter.jsp).

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