Tuning Local Hydration Enables a Deeper Understanding of Protein-Ligand Binding: The PP1-Src Kinase Case

Study of Protein Hydration Water with the V4S Structural Index: Focus on Binding Site Description

V4S, a new structural indicator for water specially designed to be suitable for hydration and nanoconfined contexts, has been recently introduced and preliminarily applied for water in contact with self-assembled monolayers and graphene-like systems. This index enabled an accurate detection of defective high local density water molecules (called HDA-like given their structural resemblance with the high-density amorphous ice, HDA). In the present work, we shall apply this new metric to characterize protein hydration water with particular interest in protein binding sites. As a first result, we shall find that protein hydration water has a higher concentration of HDA-like molecular arrangements compared to the bulk. Significantly, we shall show that the concentration of HDA-like molecules sharply decreases beyond the first hydration layer. Finally, we shall also reveal a highly nonuniform spatial distribution of the V4S values for the first hydration shell on the protein surface, where the higher hydrophobicity inherent to the ligand binding site will be evident from an enrichment in HDA-like molecules as compared to the population exhibited by the global protein surface.

Solvation free energy in governing equations for DNA hybridization, protein-ligand binding, and protein folding

This work examines the thermodynamics of model biomolecular interactions using a governing equation that accounts for the participation of bulk water in the equilibria. In the first example, the binding affinities of two DNA duplexes, one of nine and one of 10 base pairs in length, are measured and characterized by isothermal titration calorimetry (ITC) as a function of concentration. The results indicate that the change in solvation free energy that accompanies duplex formation (ΔGS) is large and unfavorable. The duplex with the larger number of G:C pairings yields the largest change in solvation free energy, ΔGS = +460 kcal·mol-1per base pair at 25 °C. A van't Hoff analysis of the data is complicated by the varying degree of intramolecular base stacking within each DNA strand as a function of temperature. A modeling study reveals how the solvation free energy alters the output of a typical ITC experiment and leads to a good, though misleading, fit to the classical equilibrium equation. The same thermodynamic framework is applied to a model protein-ligand interaction, the binding of ribonuclease A with the nucleotide inhibitor 3'-UMP, and to a conformational equilibrium, the change in tertiary structure of α-lactalbumin in molar guanidinium chloride solutions. The ribonuclease study yields a value of ΔGS = +160 kcal·mol-1, whereas the folding equilibrium yields ΔGS ≈ 0, an apparent characteristic of hydrophobic interactions. These examples provide insight on the role of solvation energy in binding equilibria and suggest a pivot in the fundamental application of thermodynamics to solution chemistry.

Increased throughput in methods for simulating protein ligand binding and unbinding

By incorporating full flexibility and enabling the quantification of crucial parameters such as binding free energies and residence times, methods for investigating protein-ligand binding and unbinding via molecular dynamics provide details on the involved mechanisms at the molecular level. While these advancements hold promise for impacting drug discovery, a notable drawback persists: their relatively time-consuming nature limits throughput. Herein, we survey recent implementations which, employing a blend of enhanced sampling techniques, a clever choice of collective variables, and often machine learning, strive to enhance the efficiency of new and previously reported methods without compromising accuracy. Particularly noteworthy is the validation of these methods that was often performed on systems mirroring real-world drug discovery scenarios.

Heterogeneous and Allosteric Role of Surface Hydration for Protein-Ligand Binding

Atomistic-level understanding of surface hydration mediating protein-protein interactions and ligand binding has been a challenge due to the dynamic nature of water molecules near the surface. We develop a computational method to evaluate the solvation free energy based on the density map of the first hydration shell constructed from all-atom molecular dynamics simulation and use it to examine the binding of two intrinsically disordered ligands to their target protein domain. One ligand is from the human protein, and the other is from the 1918 Spanish flu virus. We find that the viral ligand incurs a 6.9 kcal/mol lower desolvation penalty upon binding to the target, which is consistent with its stronger binding affinity. The difference arises from the spatially fragmented and nonuniform water density profiles of the first hydration shell. In particular, residues that are distal from the ligand-binding site contribute to a varying extent to the desolvation penalty, among which the "entropy hotspot" residues contribute significantly. Thus, ligand binding alters hydration on remote sites in addition to affecting the binding interface. The nonlocal effect disappears when the conformational motion of the protein is suppressed. The present results elucidate the interplay between protein conformational dynamics and surface hydration. Our approach of measuring the solvation free energy based on the water density of the first hydration shell is tolerant of the conformational fluctuation of protein, and we expect it to be applicable to investigating a broad range of biomolecular interfaces.

Boosting the Full Potential of PyMOL with Structural Biology Plugins

Over the past few decades, the number of available structural bioinformatics pipelines, libraries, plugins, web resources and software has increased exponentially and become accessible to the broad realm of life scientists. This expansion has shaped the field as a tangled network of methods, algorithms and user interfaces. In recent years PyMOL, widely used software for biomolecules visualization and analysis, has started to play a key role in providing an open platform for the successful implementation of expert knowledge into an easy-to-use molecular graphics tool. This review outlines the plugins and features that make PyMOL an eligible environment for supporting structural bioinformatics analyses.

Practical Protocols for Efficient Sampling of Kinase-Inhibitor Binding Pathways Using Two-Dimensional Replica-Exchange Molecular Dynamics

Molecular dynamics (MD) simulations are increasingly used to study various biological processes such as protein folding, conformational changes, and ligand binding. These processes generally involve slow dynamics that occur on the millisecond or longer timescale, which are difficult to simulate by conventional atomistic MD. Recently, we applied a two-dimensional (2D) replica-exchange MD (REMD) method, which combines the generalized replica exchange with solute tempering (gREST) with the replica-exchange umbrella sampling (REUS) in kinase-inhibitor binding simulations, and successfully observed multiple ligand binding/unbinding events. To efficiently apply the gREST/REUS method to other kinase-inhibitor systems, we establish modified, practical protocols with non-trivial simulation parameter tuning. The current gREST/REUS simulation protocols are tested for three kinase-inhibitor systems: c-Src kinase with PP1, c-Src kinase with Dasatinib, and c-Abl kinase with Imatinib. We optimized the definition of kinase-ligand distance as a collective variable (CV), the solute temperatures in gREST, and replica distributions and umbrella forces in the REUS simulations. Also, the initial structures of each replica in the 2D replica space were prepared carefully by pulling each ligand from and toward the protein binding sites for keeping stable kinase conformations. These optimizations were carried out individually in multiple short MD simulations. The current gREST/REUS simulation protocol ensures good random walks in 2D replica spaces, which are required for enhanced sampling of inhibitor dynamics around a target kinase.

Structural determinants for subnanomolar inhibition of the secreted aspartic protease Sapp1p from Candida parapsilosis

Pathogenic Candida albicans yeasts frequently cause infections in hospitals. Antifungal drugs lose effectiveness due to other Candida species and resistance. New medications are thus required. Secreted aspartic protease of C. parapsilosis (Sapp1p) is a promising target. We have thus solved the crystal structures of Sapp1p complexed to four peptidomimetic inhibitors. Three potent inhibitors (K_i: 0.1, 0.4, 6.6 nM) resembled pepstatin A (K_i: 0.3 nM), a general aspartic protease inhibitor, in terms of their interactions with Sapp1p. However, the weaker inhibitor (K_i: 14.6 nM) formed fewer nonpolar contacts with Sapp1p, similarly to the smaller HIV protease inhibitor ritonavir (K_i: 1.9 µM), which, moreover, formed fewer H-bonds. The analyses have revealed the structural determinants of the subnanomolar inhibition of C. parapsilosis aspartic protease. Because of the high similarity between Saps from different Candida species, these results can further be used for the design of potent and specific Sap inhibitor-based antimycotic drugs.