Literature DB >> 12203463

Approaches to the description and prediction of the binding affinity of small-molecule ligands to macromolecular receptors.

Holger Gohlke1, Gerhard Klebe.   

Abstract

The influence of a xenobiotic compound on an organism is usually summarized by the expression biological activity. If a controlled, therapeutically relevant, and regulatory action is observed the compound has potential as a drug, otherwise its toxicity on the biological system is of interest. However, what do we understand by the biological activity? In principle, the overall effect on an organism has to be considered. However, because of the complexity of the interrelated processes involved, as a simplification primarily the "main action" on the organism is taken into consideration. On the molecular level, biological activity corresponds to the binding of a (low-molecular weight) compound to a macromolecular receptor, usually a protein. Enzymatic reactions or signal-transduction cascades are thereby influenced with respect to their function for the organism. We regard this binding as a process under equilibrium conditions; thus, binding can be described as an association or dissociation process. Accordingly, biological activity is expressed as the affinity of both partners for each other, as a thermodynamic equilibrium quantity. How well do we understand these terms and how well are they theoretically predictable today? The holy grail of rational drug design is the prediction of the biological activity of a compound. The processes involving ligand binding are extremely complicated, both ligand and protein are flexible molecules, and the energy inventory between the bound and unbound states must be considered in aqueous solution. How sophisticated and reliable are our experimental approaches to obtaining the necessary insight? The present review summarizes our current understanding of the binding affinity of a small-molecule ligand to a protein. Both theoretical and empirical approaches for predicting binding affinity, starting from the three-dimensional structure of a protein-ligand complex, will be described and compared. Experimental methods, primarily microcalorimetry, will be discussed. As a perspective, our own knowledge-based approach towards affinity prediction and experimental data on factorizing binding contributions to protein-ligand binding will be presented.

Entities:  

Mesh:

Substances:

Year:  2002        PMID: 12203463     DOI: 10.1002/1521-3773(20020802)41:15<2644::AID-ANIE2644>3.0.CO;2-O

Source DB:  PubMed          Journal:  Angew Chem Int Ed Engl        ISSN: 1433-7851            Impact factor:   15.336


  143 in total

1.  Exhaustive search and solvated interaction energy (SIE) for virtual screening and affinity prediction.

Authors:  Traian Sulea; Hervé Hogues; Enrico O Purisima
Journal:  J Comput Aided Mol Des       Date:  2011-12-25       Impact factor: 3.686

2.  Binding affinities in the SAMPL3 trypsin and host-guest blind tests estimated with the MM/PBSA and LIE methods.

Authors:  Paulius Mikulskis; Samuel Genheden; Patrik Rydberg; Lars Sandberg; Lars Olsen; Ulf Ryde
Journal:  J Comput Aided Mol Des       Date:  2011-12-25       Impact factor: 3.686

3.  Molecular simulation methods in drug discovery: a prospective outlook.

Authors:  Xavier Barril; F Javier Luque
Journal:  J Comput Aided Mol Des       Date:  2011-12-08       Impact factor: 3.686

4.  Correlation between biological activity and binding energy in systems of integrin with cyclic RGD-containing binders: a QM/MM molecular dynamics study.

Authors:  Mingli Xiang; Yuchun Lin; Gu He; Lijuan Chen; Mingli Yang; Shengyong Yang; Yirong Mo
Journal:  J Mol Model       Date:  2012-06-27       Impact factor: 1.810

5.  Docking flexible ligands in proteins with a solvent exposure- and distance-dependent dielectric function.

Authors:  Daniel P Garden; Boris S Zhorov
Journal:  J Comput Aided Mol Des       Date:  2010-01-30       Impact factor: 3.686

6.  Prediction of zanamivir efficiency over the possible 2009 influenza A (H1N1) mutants by multiple molecular dynamics simulations and free energy calculations.

Authors:  Dabo Pan; Huijun Sun; Chongliang Bai; Yulin Shen; Nengzhi Jin; Huanxiang Liu; Xiaojun Yao
Journal:  J Mol Model       Date:  2010-12-31       Impact factor: 1.810

Review 7.  Thermodynamics of protein-ligand interactions as a reference for computational analysis: how to assess accuracy, reliability and relevance of experimental data.

Authors:  Stefan G Krimmer; Gerhard Klebe
Journal:  J Comput Aided Mol Des       Date:  2015-09-16       Impact factor: 3.686

8.  Prediction of SAMPL3 host-guest binding affinities: evaluating the accuracy of generalized force-fields.

Authors:  Hari S Muddana; Michael K Gilson
Journal:  J Comput Aided Mol Des       Date:  2012-01-25       Impact factor: 3.686

Review 9.  Computations of standard binding free energies with molecular dynamics simulations.

Authors:  Yuqing Deng; Benoît Roux
Journal:  J Phys Chem B       Date:  2009-02-26       Impact factor: 2.991

10.  Energetics of displacing water molecules from protein binding sites: consequences for ligand optimization.

Authors:  Julien Michel; Julian Tirado-Rives; William L Jorgensen
Journal:  J Am Chem Soc       Date:  2009-10-28       Impact factor: 15.419

View more

北京卡尤迪生物科技股份有限公司 © 2022-2023.