On the Accuracy of One and Two Particle Solvation Entropies

Evaluating solvation entropies directly and combining with direct energy calculations is one way of calculating free energies of solvation and is used by Inhomogeneous Fluid Solvation Theory (IFST). The configurational entropy of a fluid is a function of the interatomic correlations and can thus be expressed in terms of correlation functions. The entropies in this work are directly calculated from a truncated series of integrals over these correlation functions. Many studies truncate all terms higher than solvent- solute correlation. This study includes an additional solvent-solvent correlation term and assesses the associated free energy when IFST is applied to a fixed Lennard-Jones particle solvated in neon. The strength of the central potential is varied to imitate larger solutes. Average free energy estimates with both levels of IFST theory are able to reproduce the estimate made using Free energy Perturbation (FEP) to within 0.16 kcal/mol. We find that the signal from the solvent-solvent correlations is very weak. Our conclusion is that for monatomic fluids simulated by pairwise classical potentials the correction term is relatively small in magnitude. This study shows it is possible to reproduce the free energy from a path based method like FEP, by only considering the endpoints of the path. This method can be directly applied to more complex solutes which break the spherical symmetry of this study.Benedict W. J. Irwin acknowledges nancial support from the EPSRC Centre for Doc- toral Training in Computational Methods for Materials Science under grant EP/L015552/1. This work was performed using the Darwin Supercomputer of the University of Cambridge High Performance Computing Service (http://www.hpc.cam.ac.uk/), provided by Dell Inc. using Strategic Research Infrastructure Funding from the Higher Education Funding Coun- cil for England and funding from the Science and Technology Facilities Council. Work in David J. Huggins lab was supported by the Medical Research Council (MRC) under grant ML/L007266/1

Imputation of Assay Bioactivity Data Using Deep Learning.

We describe a novel deep learning neural network method and its application to impute assay pIC50 values. Unlike conventional machine learning approaches, this method is trained on sparse bioactivity data as input, typical of that found in public and commercial databases, enabling it to learn directly from correlations between activities measured in different assays. In two case studies on public domain data sets we show that the neural network method outperforms traditional quantitative structure-activity relationship (QSAR) models and other leading approaches. Furthermore, by focusing on only the most confident predictions the accuracy is increased to R2 > 0.9 using our method, as compared to R2 = 0.44 when reporting all predictions

Prediction of GABARAP interaction with the GABA type A receptor.

We have performed docking simulations on GABARAP interacting with the GABA type A receptor using SwarmDock. We have also used a novel method to study hydration sites on the surface of these two proteins; this method identifies regions around proteins where desolvation is relatively easy, and these are possible locations where proteins can bind each other. There is a high degree of consistency between the predictions of these two methods. Moreover, we have also identified binding sites on GABARAP for other proteins, and listed possible binding sites for as yet unknown proteins on both GABARAP and the GABA type A receptor intracellular domain

Research data supporting On the Accuracy of One and Two Particle Solvation Entropies

This is the data associated with the publication "On the Accuracy of One and Two Particle Solvation Entropies" by B.W.J Irwin and D.J. Huggins. It contains: 1) The input files for the NAMD code used to run the simulations mentioned in the work. 2) The data files used to make plots shown in the paper. 3) A spreadsheet used to keep track of calculations made for the paper. 4) C codes for entropy calculations 5) Perl codes for running MD simulations in NAM

Functional movements of the GABA type A receptor.

We have performed a parallel tempering crankshaft motion Monte Carlo simulation on a model of the GABA type A receptor with the aim of exploring a wide variety of local conformational space. We develop a novel method to analyse the protein movements in terms of a correlation tensor and use this to explore the gating process, that is, how agonist binding could cause ion channel opening. We find that simulated binding impulses to varying clusters of GABA binding site residues produce channel opening, and that equivalent impulses to single GABA sites produce partial opening

Functional movements of the GABA type A receptor.

We have performed a parallel tempering crankshaft motion Monte Carlo simulation on a model of the GABA type A receptor with the aim of exploring a wide variety of local conformational space. We develop a novel method to analyse the protein movements in terms of a correlation tensor and use this to explore the gating process, that is, how agonist binding could cause ion channel opening. We find that simulated binding impulses to varying clusters of GABA binding site residues produce channel opening, and that equivalent impulses to single GABA sites produce partial opening