A Systematic Analysis of the Binding Affinity between the Pim-1 Kinase and Its Inhibitors Based on the MM/3D-RISM/KH Method

Effect of caffeine on the aggregation of amyloid-β-A 3D RISM study

Alzheimer's disease is a detrimental neurological disorder caused by the formation of amyloid fibrils due to the aggregation of amyloid-β peptide. The primary therapeutic approaches for treating Alzheimer's disease are targeted to prevent this amyloid fibril formation using potential inhibitor molecules. The discovery of such inhibitor molecules poses a formidable challenge to the design of anti-amyloid drugs. This study investigates the effect of caffeine on dimer formation of the full-length amyloid-β using a combined approach of all-atom, explicit water molecular dynamics simulations and the three-dimensional reference interaction site model theory. The change in the hydration free energy of amyloid-β dimer, with and without the inhibitor molecules, is calculated with respect to the monomeric amyloid-β, where the hydration free energy is decomposed into energetic and entropic components, respectively. Dimerization is accompanied by a positive change in the partial molar volume. Dimer formation is spontaneous, which implies a decrease in the hydration free energy. However, a reverse trend is observed for the dimer with inhibitor molecules. It is observed that the negatively charged residues primarily contribute for the formation of the amyloid-β dimer. A residue-wise decomposition reveals that hydration/dehydration of the side-chain atoms of the charged amino acid residues primarily contribute to dimerization.

3D-RISM-AI: A Machine Learning Approach to Predict Protein-Ligand Binding Affinity Using 3D-RISM

Hydration free energy (HFE) is a key factor in improving protein-ligand binding free energy (BFE) prediction accuracy. The HFE itself can be calculated using the three-dimensional reference interaction model (3D-RISM); however, the BFE predictions solely evaluated using 3D-RISM are not correlated to the experimental BFE for abundant protein-ligand pairs. In this study, to predict the BFE for multiple sets of protein-ligand pairs, we propose a machine learning approach incorporating the HFEs obtained using 3D-RISM, termed 3D-RISM-AI. In the learning process, structural metrics, intra-/intermolecular energies, and HFEs obtained via 3D-RISM of ∼4000 complexes in the PDBbind database (ver. 2018) were used. The BFEs predicted using 3D-RISM-AI were well correlated to the experimental data (Pearson's correlation coefficient of 0.80 and root-mean-square error of 1.91 kcal/mol). As important factors for the prediction, the difference in the solvent accessible surface area between the bound and unbound structures and the hydration properties of the ligands were detected during the learning process.

Molecular Recognition and Self-Organization in Life Phenomena Studied by a Statistical Mechanics of Molecular Liquids, the RISM/3D-RISM Theory

There are two molecular processes that are essential for living bodies to maintain their life: the molecular recognition, and the self-organization or self-assembly. Binding of a substrate by an enzyme is an example of the molecular recognition, while the protein folding is a good example of the self-organization process. The two processes are further governed by the other two physicochemical processes: solvation and the structural fluctuation. In the present article, the studies concerning the two molecular processes carried out by Hirata and his coworkers, based on the statistical mechanics of molecular liquids or the RISM/3D-RISM theory, are reviewed.

Characterizing hydration sites in protein-ligand complexes towards the design of novel ligands

Water is an essential part of protein binding sites and mediates interactions to ligands. Its displacement by ligand parts affects the free binding energy of resulting protein-ligand complexes. Therefore the characterization of solvation properties is important for design. Of particular interest is the propensity of localized water to be favorably displaced by a ligand. This review discusses two popular computational approaches addressing these questions, namely WaterMap based on statistical mechanics analysis of MD simulations and 3D RISM based on integral equation theory of liquids. The theoretical background and recent applications in structure-based design will be presented.

Perspective: Structural fluctuation of protein and Anfinsen's thermodynamic hypothesis

The thermodynamics hypothesis, casually referred to as "Anfinsen's dogma," is described theoretically in terms of a concept of the structural fluctuation of protein or the first moment (average structure) and the second moment (variance and covariance) of the structural distribution. The new theoretical concept views the unfolding and refolding processes of protein as a shift of the structural distribution induced by a thermodynamic perturbation, with the variance-covariance matrix varying. Based on the theoretical concept, a method to characterize the mechanism of folding (or unfolding) is proposed. The transition state, if any, between two stable states is interpreted as a gap in the distribution, which is created due to an extensive reorganization of hydrogen bonds among back-bone atoms of protein and with water molecules in the course of conformational change. Further perspective to applying the theory to the computer-aided drug design, and to the material science, is briefly discussed.

Predicting the Binding Mode of 2-Hydroxypropyl-β-cyclodextrin to Cholesterol by Means of the MD Simulation and the 3D-RISM-KH Theory

It has been found that a cyclodextrin derivative, 2-hydroxypropyl-β-cyclodextrin (HPβCD), has reasonable therapeutic effect on Niemann-Pick disease type C, which is caused by abnormal accumulation of unesterified cholesterol and glycolipids in the lysosomes and shortage of esterified cholesterol in other cellular compartments. We study the binding affinity and mode of HPβCD with cholesterol to elucidate the possible mechanism of HPβCD for removing cholesterol from the lysosomes. The dominant binding mode of HPβCD with cholesterol is found based on the molecular dynamics simulation and a statistical mechanics theory of liquids, or the three-dimensional reference interaction site model theory with Kovalenko-Hirata closure relation. We examine the two types of complexes between HPβCD and cholesterol, namely, one-to-one (1:1) and two-to-one (2:1). It is predicted that the 1:1 complex makes two or three types of stable binding mode in solution, in which the βCD ring tends to be located at the edge of the steroid skeleton. For the 2:1 complex, there are four different types of the complex conceivable, depending on the orientation between the two HPβCDs: head-to-head (HH), head-to-tail (HT), tail-to-head (TH), and tail-to-tail (TT). The HT and HH cyclodextrin dimers show higher affinity to cholesterol compared to the other dimers and to all the binding modes of 1:1 complexes. The physical reason why the HT and HH dimers have higher affinity compared to the other complexes is discussed based on the consistency with the 1:1 complex. On the one hand, in case of the HT and HH dimers, the position of each CD in the dimer along the cholesterol chain comes right on or close to one of the positions where a single CD makes a stable complex. On the other hand, one of the CD molecules is located on unstable region along the cholesterol chain, for the case of TH and TT dimers.