GCMC Sampling of Occluded Protein Binding Sites
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Solution Overview
Problem
Current methods for determining the spatial distributions and thermodynamics of small molecules in aqueous and heterogeneous environments, particularly those with deep or occluded binding sites, face challenges such as low acceptance rates in Grand Canonical Monte Carlo simulations and limited accessibility to buried protein sites, restricting the study of biologically important proteins like GPCRs and nuclear receptors.
Innovation Solution
A computational method using Grand-Canonical Monte-Carlo (GCMC) Metropolis sampling with fluctuating excess chemical potential (µex) to sample the spatial distribution of solutes and water in a defined region, allowing for the identification of preferential affinities to macromolecules, including proteins, and enabling the exploration of occluded binding sites by iteratively updating µex based on concentration differences.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If Grand Canonical Monte Carlo simulations are used to determine spatial distributions and thermodynamics of small molecules, then thermodynamic properties can be obtained, but acceptance rates are low and convergence problems occur
Solution Approach 1:
The method performs preliminary actions by first identifying occluded binding sites through structural analysis before running GCMC simulations. This preliminary step allows the simulation to focus on specific regions of interest, improving convergence by avoiding random sampling of entire protein surfaces and reducing the time required to achieve meaningful thermodynamic data.
Solution Approach 2:
The invention applies local quality by restricting GCMC simulations to specific occluded binding sites rather than the entire protein surface. This localized approach concentrates computational resources on regions with high biological relevance, improving both convergence rate and the precision of thermodynamic measurements for those specific sites.
2Measurement precision
If molecular dynamics simulations are used to sample solute distributions in aqueous environments, then spatial distributions can be obtained, but diffusion time scales are long especially for deeply buried binding sites
Solution Approach 1:
The method uses GCMC simulations as an intermediary approach between standard MD and direct binding affinity calculations. GCMC provides enhanced sampling capabilities that allow solutes to reach occluded binding sites more efficiently than conventional MD, while still maintaining the ability to compute accurate spatial distributions and thermodynamic properties.
Solution Approach 2:
The invention changes simulation parameters by using GCMC ensembles with controlled solute concentrations instead of conventional MD with fixed concentrations. This parameter change allows for more efficient exploration of binding sites by controlling the chemical potential and number of solutes, thereby reducing the time required to sample spatial distributions in occluded regions.
3Productivity
If simulations focus on accessible binding sites only, then sampling efficiency is improved, but biologically important occluded sites like those in GPCRs and nuclear receptors cannot be studied
Solution Approach 1:
The method segments the protein surface into accessible and occluded binding sites, then applies specialized GCMC sampling techniques specifically to the occluded regions. This segmentation allows the methodology to maintain high sampling efficiency for accessible sites while simultaneously enabling the study of previously inaccessible occluded sites in GPCRs and nuclear receptors.
Solution Approach 2:
The invention adds a new dimension to binding site analysis by explicitly modeling and sampling occluded sites that are hidden from bulk solvent. This dimensional addition to the sampling space allows the method to access and characterize binding sites in GPCRs and nuclear receptors that were previously unreachable by conventional simulation approaches.
Data Source
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AI summary
Provided are computer implemented methods for organic solute sampling in aqueous and heterogeneous environments using oscillating chemical potentials in Grand Canonical Monte Carlo simulations. The methods involve GCMC of both the solutes and water, with the excess chemical potential (μex) of both the solute and the water oscillated to attain their target concentrations in the simulation system. In some example methods, the μex of the water and solutes over the GCMC iterations are varied to improve solute exchange probabilities and the spatial distributions of the solutes and molecular dynamics (MD) simulations may be performed in addition to GCMC to improve sampling of spatial distributions. These methods may be used to determine the hydration free energy (HFE) of the individual or multiple solutes when targeting in aqueous solutions. Also included are methods of driving solute sampling in and around macromolecules, including proteins, in aqueous environments.