Hydrophobic hydration plays a key role in a vast variety of biological processes, ranging from the formation of cells to protein folding and ligand binding. Hydrophobicity scales simplify the complex process of hydration by assigning a value describing the averaged hydrophobic character to each amino acid. Previously published scales were not able to calculate the enthalpic and entropic contributions to the hydrophobicity directly. We present a new method, based on Molecular Dynamics simulations and Grid Inhomogeneous Solvation Theory, that calculates hydrophobicity from enthalpic and entropic contributions. Instead of deriving these quantities from the temperature dependence of the free energy of hydration or as residual of the free energy and the enthalpy, we directly obtain these values from the phase space occupied by water molecules. Additionally, our method is able to identify regions with specific enthalpic and entropic properties, allowing to identify so-called “unhappy water” molecules, which are characterized by weak enthalpic interactions and unfavorable entropic constraints.

Conformational dynamics are central for understanding biomolecular structure and function, since biological macromolecules are inherently flexible at room temperature and in solution. Computational methods are nowadays capable of providing valuable information on the conformational ensembles of biomolecules. However, analysis tools and intuitive metrics that capture dynamic information from in silico generated structural ensembles are limited. In standard work-flows, flexibility in a conformational ensemble is represented through residue-wise root-mean-square fluctuations or B-factors following a global alignment. Consequently, these approaches relying on global alignments discard valuable information on local dynamics. Results inherently depend on global flexibility, residue size, and connectivity. In this study we present a novel approach for capturing positional fluctuations based on multiple local alignments instead of one single global alignment. The method captures local dynamics within a structural ensemble independent of residue type by splitting individual local and global degrees of freedom of protein backbone and side-chains. Dependence on residue type and size in the side-chains is removed via normalization with the B-factors of the isolated residue. As a test case, we demonstrate its application to a molecular dynamics simulation of bovine pancreatic trypsin inhibitor (BPTI) on the millisecond time scale. This allows for illustrating different time scales of backbone and side-chain flexibility. Additionally, we demonstrate the effects of ligand binding on side-chain flexibility of three serine proteases. We expect our new methodology for quantifying local flexibility to be helpful in unraveling local changes in biomolecular dynamics.

Biomolecular recognition is crucial in cellular signal transduction. Signaling is mediated through molecular interactions at protein-protein interfaces. Still, specificity and promiscuity of protein-protein interfaces cannot be explained using simplistic static binding models. Our study rationalizes specificity of the prototypic protein-protein interface between thrombin and its peptide substrates relying solely on binding site dynamics derived from molecular dynamics simulations. We find conformational selection and thus dynamic contributions to be a key player in biomolecular recognition. Arising entropic contributions complement chemical intuition primarily reflecting enthalpic interaction patterns. The paradigm “dynamics govern specificity” might provide direct guidance for the identification of specific anchor points in biomolecular recognition processes and structure-based drug design.

The anomalous binding modes of five highly similar fragments of TIE2 inhibitors, showing three distinct binding poses, are investigated. We report a quantitative rationalization for the changes in binding pose based on molecular dynamics simulations. We investigated five fragments in complex with the transforming growth factor β receptor type 1 kinase domain. Analyses of these simulations using Grid Inhomogeneous Solvation Theory (GIST), pKA calculations, and a tool to investigate enthalpic differences upon binding unraveled the various thermodynamic contributions to the different binding modes. While one binding mode flip can be rationalized by steric repulsion, the second binding pose flip revealed a different protonation state for one of the ligands, leading to different enthalpic and entropic contributions to the binding free energy. One binding pose is stabilized by the displacement of entropically unfavored water molecules (binding pose determined by solvation entropy), ligands in the other binding pose are stabilized by strong enthalpic interactions, overcompensating the unfavorable water entropy in this pose (binding pose determined by enthalpic interactions). This analysis elucidates unprecedented details determining the flipping of the binding modes, which can elegantly explain the experimental findings for this system.

Serine proteases of the Chymotrypsin family are structurally very similar but have very different substrate preferences. This study investigates a set of 9 different proteases of this family comprising proteases that prefer substrates containing positively charged amino acids, negatively charged amino acids, and uncharged amino acids with varying degree of specificity. Here, we show that differences in electrostatic substrate preferences can be predicted reliably by electrostatic molecular interaction fields employing customized GRID probes. Thus, we are able to directly link protease structures to their electrostatic substrate preferences. Additionally, we present a new metric that measures similarities in substrate preferences focusing only on electrostatics. It efficiently compares these electrostatic substrate preferences between different proteases. This new metric can be interpreted as the electrostatic part of our previously developed substrate similarity metric. Consequently, we suggest, that substrate recognition in terms of electrostatics and shape complementarity are rather orthogonal aspects of substrate recognition. This is in line with a 2‐step mechanism of protein‐protein recognition suggested in the literature.