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Processing multimode binding situations in simulation-based prediction of ligand-macromolecule affinities.

ABSTRACT: The linear response (LR) approximation and similar approaches belong to practical methods for estimation of ligand-receptor binding affinities. The approaches correlate experimental binding affinities with the changes upon binding of the ligand electrostatic and van der Waals energies and of solvation characteristics. These attributes are expressed as ensemble averages that are obtained by conformational sampling of the protein-ligand complex and of the free ligand by molecular dynamics or Monte Carlo simulations. We observed that outliers in the LR correlations occasionally exhibit major conformational changes of the complex during sampling. We treated the situation as a multimode binding case, for which the observed association constant is the sum of the partial association constants of individual states/modes. The resulting nonlinear expression for the binding affinities contains all the LR variables for individual modes that are scaled by the same two to four adjustable parameters as in the one-mode LR equation. The multimode method was applied to inhibitors of a matrix metalloproteinase, where this treatment improved the explained variance in experimental activity from 75% for the unimode case to about 85%. The predictive ability scaled accordingly, as verified by extensive cross-validations.

SUBMITTER: Khandelwal A

PROVIDER: S-EPMC2896054 | biostudies-literature | 2005 Jul

REPOSITORIES: biostudies-literature

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Processing multimode binding situations in simulation-based prediction of ligand-macromolecule affinities.

Khandelwal Akash A Lukacova Viera V Kroll Daniel M DM Raha Soumyendu S Comez Dogan D Balaz Stefan S

The journal of physical chemistry. A 20050701 29

The linear response (LR) approximation and similar approaches belong to practical methods for estimation of ligand-receptor binding affinities. The approaches correlate experimental binding affinities with the changes upon binding of the ligand electrostatic and van der Waals energies and of solvation characteristics. These attributes are expressed as ensemble averages that are obtained by conformational sampling of the protein-ligand complex and of the free ligand by molecular dynamics or Monte ...[more]

PMID: 16833982

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Project description:A computational docking strategy using multiple conformations of the target protein is discussed and evaluated. A series of low molecular weight, competitive, nonpeptide protein tyrosine phosphatase inhibitors are considered for which the x-ray crystallographic structures in complex with protein tyrosine phosphatase 1B (PTP1B) are known. To obtain a quantitative measure of the impact of conformational changes induced by the inhibitors, these were docked to the active site region of various structures of PTP1B using the docking program FlexX. Firstly, the inhibitors were docked to a PTP1B crystal structure cocrystallized with a hexapeptide. The estimated binding energies for various docking modes as well as the RMS differences between the docked compounds and the crystallographic structure were calculated. In this scenario the estimated binding energies were not predictive inasmuch as docking modes with low estimated binding energies corresponded to relatively large RMS differences when aligned with the corresponding crystal structure. Secondly, the inhibitors were docked to their parent protein structures in which they were cocrystallized. In this case, there was a good correlation between low predicted binding energy and a correct docking mode. Thirdly, to improve the predictability of the docking procedure in the general case, where only a single target protein structure is known, we evaluate an approach which takes possible protein side-chain conformational changes into account. Here, side chains exposed to the active site were considered in their allowed rotamer conformations and protein models containing all possible combinations of side-chain rotamers were generated. To evaluate which of these modeled active sites is the most likely binding site conformation for a certain inhibitor, the inhibitors were docked against all active site models. The receptor rotamer model corresponding to the lowest estimated binding energy is taken as the top candidate. Using this protocol, correct inhibitor binding modes could successfully be discriminated from proposed incorrect binding modes. Moreover, the ranking of the estimated ligand binding energies was in good agreement with experimentally observed binding affinities.

Project description:Virtual compound screening using molecular docking is widely used in the discovery of new lead compounds for drug design. However, the docking scores are not sufficiently precise to represent the protein-ligand binding affinity. Here, we developed an efficient computational method for calculating protein-ligand binding affinity, which is based on molecular mechanics generalized Born/surface area (MM-GBSA) calculations and Jarzynski identity. Jarzynski identity is an exact relation between free energy differences and the work done through non-equilibrium process, and MM-GBSA is a semimacroscopic approach to calculate the potential energy. To calculate the work distribution when a ligand is pulled out of its binding site, multiple protein-ligand conformations are randomly generated as an alternative to performing an explicit single-molecule pulling simulation. We assessed the new method, multiple random conformation/MM-GBSA (MRC-MMGBSA), by evaluating ligand-binding affinities (scores) for four target proteins, and comparing these scores with experimental data. The calculated scores were qualitatively in good agreement with the experimental binding affinities, and the optimal docking structure could be determined by ranking the scores of the multiple docking poses obtained by the molecular docking process. Furthermore, the scores showed a strong linear response to experimental binding free energies, so that the free energy difference of the ligand binding (??G) could be calculated by linear scaling of the scores. The error of calculated ??G was within ? ± 1.5 kcal.mol(-1) of the experimental values. Particularly, in the case of flexible target proteins, the MRC-MMGBSA scores were more effective in ranking ligands than those generated by the MM-GBSA method using a single protein-ligand conformation. The results suggest that, owing to its lower computational costs and greater accuracy, the MRC-MMGBSA offers efficient means to rank the ligands, in the post-docking process, according to their binding affinities, and to compare these directly with the experimental values.

Dataset Information

Processing multimode binding situations in simulation-based prediction of ligand-macromolecule affinities.

Publications

Processing multimode binding situations in simulation-based prediction of ligand-macromolecule affinities.

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