Method for binding site identification by molecular dynamics simulation (silcs: site identification by ligand competitive saturation)

Abstract

The invention describes an explicit solvent all-atom molecular dynamics methodology (SILCS: Site Identification by Ligand Competitive Saturation) that uses small aliphatic and aromatic molecules plus water molecules to map the affinity pattern of a large molecule for hydrophobic groups, aromatic groups, hydrogen bond donors, and hydrogen bond acceptors. By simultaneously incorporating ligands representative of all these functionalities, the method is an in silico free energy-based competition assay that generates three-dimensional probability maps of fragment binding (FragMaps) indicating favorable fragment:large molecule interactions. The FragMaps may be used to qualitatively inform the design of small-molecule ligands or as scoring grids for high-throughput in silico docking that incorporates both an atomic-level description of solvation and the large molecule's flexibility.

Description

STATEMENT REGARDING FEDERALLY SPONSORED RESEARCH[0001]This invention was made with government support under Grant Numbers GM 51501, CA107331, CA120215 (ADM), and F32CA1197712 (OG) awarded by the National Institutes of Health. The government has certain rights in the invention.BACKGROUND OF THE INVENTION[0002]1. Field of the Invention[0003]The present invention is directed to a method of computational chemistry for identifying binding sites by molecular dynamics simulations using ligand competitive saturation. In particular, the method overcomes the problem of small nonpolar molecule aggregation to allow competitive saturation in an aqueous solution at physiological conditions. More particularly, the method, when used in a two-tier approach, may determine which one of several multiple fragment molecules has a highest probability of improving binding to a surface region of a large molecule that is proximate to a bound ligand, in order to produce an optimized lead compound for drug dis...

AI Technical Summary

Benefits of technology

[0037]Yet another embodiment of the invention provides for a method of computational chemistry for identifying binding sites, by molecular dynamics (MD) simulations using ligand competitive saturation, for drug design optimization. The method includes: inputting a 3-dimensional (3D) structure of a complex of a large molecule and a bound ligand, in which the large molecule comprises one of DNA, RNA, a carbohydrate and a glycolipid; preparing multiple solutions for ligand competitive saturation, each of the multiple solutions corresponding to a volumetric grid in which either a benzene, a propane, or a water molecule is randomly placed on a grid point, such that, a spacing of the grid points and the random placing of the benzene, propane, or water molecules corresponds to a concentration sufficient for competitive saturation of the benzene and propane molecules; overlaying the 3D structure of the large molecule and the bound ligand with each of the multiple solutions and removing all benzene, propane and water molecules that overlay the large molecule and the bound ligand in the volumetric grid; introducing a repulsive interaction energy between the benzene and the propane molecules to prevent non-polar aggregation; minimizing energies and equilibrating each of the multiple solutions with the large molecule and the bound ligand by MD simulations to produce multiple equilibrated systems; propagating a first tier of MD simulations for each of the multiple equilibrated systems; binning atoms from each of the propagated MD simulations into voxels, in which benzene and propane carbon atoms are binned as aromatic and aliphatic carbons, respectively, and in which water oxygen and hydrogen atoms are binned as hydrogen bond acceptors and hydrogen bond donors, respectively; using bin counts for each of the aromatic and aliphatic carbons, and the hydrogen bond acceptors and hydrogen bond donors, based on their free energies of binding, from the first tier of propagated MD simulations to identify surface regions of the large molecule having a high probability to bind one of an aromatic carbon atom, an aliphatic carbon atom, a hydrogen bond acceptor oxygen atom, and a hydrogen bond donor hydrogen atom; identifying a surface region of the large molecule that is proximate to the bound ligand and has a high probability of binding to at least two of an aromatic carbon atom, an aliphatic carbon atom, a hydrogen bond acceptor oxygen atom, and a hydrogen bond donor hydrogen atom; selecting multiple fragment molecules, containing the at least two of an aromatic carbon atom, an aliphatic carbon atom, a hydrogen bond acceptor oxygen atom, and a hydrogen bond donor hydrogen atom, for a second tier of MD simulations; preparing multiple solutions for ligand competitive saturation of the selected multiple fragment molecules; introducing an intermolecular repulsive interaction energy between nonpolar regions of the selected multiple fragment molecules to prevent non-polar aggregation; minimizing energies and equilibrating each of the multiple solutions with the large molecule and the bound ligand by MD simulations to produce multiple equilibrated systems; propagating the second tier of MD simulations for each of the multiple equilibrated systems; and analyzing the second tier of MD simulations to determine which one of the selected multiple fragment molecules has a highest probability of improving binding to the surface region of the large molecule that is proximate to the bound ligand.

Problems solved by technology

This limit means that even the best fragments, having an LE value of 0.4-0.5 kcal*mol−1 per heavy atom, still have weak affinities for their target regions, making their screening by traditional assays difficult.

However, despite the utility of NMR spectroscopy and x-ray crystallography to detect fragment binding, there are significant time, labor, and materials costs associated with these two biophysical fragment-based drug discovery approaches.

As a result, these computational approaches are limited in their ability to accurately account for the exemplary protein molecule's conformational heterogeneity and solvation effects, contributions that are essential to compute free energies of binding of small molecules to the target region of the exemplary protein molecule (Guvench O, et al.

Nonetheless, approximations used in computational approaches to date still limit the accuracy of fragment placement and fragment scoring, which relates to affinity of the fragment for the binding site, and, ultimately, the determination of the most suitable fragment for a selected binding-site region of the protein molecule.

However, such MD simulated free-energy calculations are computationally expensive, limiting MD simulations from being used directly for high-throughput in silico screening.

Images