A Data Science Approach to Understanding Water Networks Around Biomolecules: The Case of Tri-Alanine in Liquid Water

Beyond Local Structures in Critical Supercooled Water through Unsupervised Learning

The presence of a second critical point in water has been a topic of intense investigation for the last few decades. The molecular origins underlying this phenomenon are typically rationalized in terms of the competition between local high-density (HD) and low-density (LD) structures. Their identification often requires designing parameters that are subject to human intervention. Herein, we use unsupervised learning to discover structures in atomistic simulations of water close to the liquid-liquid critical point (LLCP). Encoding the information on the environment using local descriptors, we do not find evidence for two distinct thermodynamic structures. In contrast, when we deploy nonlocal descriptors that probe instead heterogeneities on the nanometer length scale, this leads to the emergence of LD and HD domains rationalizing the microscopic origins of the density fluctuations close to criticality.

Zirconium Coordination Chemistry and Its Role in Optimizing Hydroxymate Chelation: Insights from Molecular Dynamics

In the past decade, there has been a growth in using Zirconium-89 (89Zr) as a radionuclide in nuclear medicine for cancer diagnostic imaging and drug discovery processes. Although one of the most popular chelators for 89Zr, desferrioxamine (DFO) is typically presented as a hexadentate ligand, our work suggests a different scenario. The coordination structure of the Zr4+-DFO complex has primarily been informed by DFT-based calculations, which typically ignore temperature and therefore entropic and dynamic solvent effects. In this work, free energy calculations using molecular dynamics simulations, where the conformational fluctuations of both the ligand and the solvent are explicitly included, are used to compare the binding of Zr4+ cations with two different chelators, DFO and 4HMS, the latter of which is an octadentate ligand that has been recently proposed as a better chelator due to the presence of four hydroxymate groups. We find that thermally induced disorder leads to an open hexadentate chelate structure of the Zr4+-DFO complex, leaving the Zr4+ metal exposed to the solvent. A stable coordination of Zr4+ with 4HMS, however, is formed by involving both hydroxamate groups and water molecules in a more closely packed structure.

The collective burst mechanism of angular jumps in liquid water

Understanding the microscopic origins of collective reorientational motions in aqueous systems requires techniques that allow us to reach beyond our chemical imagination. Herein, we elucidate a mechanism using a protocol that automatically detects abrupt motions in reorientational dynamics, showing that large angular jumps in liquid water involve highly cooperative orchestrated motions. Our automatized detection of angular fluctuations, unravels a heterogeneity in the type of angular jumps occurring concertedly in the system. We show that large orientational motions require a highly collective dynamical process involving correlated motion of many water molecules in the hydrogen-bond network that form spatially connected clusters going beyond the local angular jump mechanism. This phenomenon is rooted in the collective fluctuations of the network topology which results in the creation of defects in waves on the THz timescale. The mechanism we propose involves a cascade of hydrogen-bond fluctuations underlying angular jumps and provides new insights into the current localized picture of angular jumps, and its wide use in the interpretations of numerous spectroscopies as well in reorientational dynamics of water near biological and inorganic systems. The role of finite size effects, as well as of the chosen water model, on the collective reorientation is also elucidated.

DADApy: Distance-based analysis of data-manifolds in Python

DADApy is a Python software package for analyzing and characterizing high-dimensional data manifolds. It provides methods for estimating the intrinsic dimension and the probability density, for performing density-based clustering, and for comparing different distance metrics. We review the main functionalities of the package and exemplify its usage in a synthetic dataset and in a real-world application. DADApy is freely available under the open-source Apache 2.0 license.

High-Dimensional Fluctuations in Liquid Water: Combining Chemical Intuition with Unsupervised Learning

The microscopic description of the local structure of water remains an open challenge. Here, we adopt an agnostic approach to understanding water's hydrogen bond network using data harvested from molecular dynamics simulations of an empirical water model. A battery of state-of-the-art unsupervised data-science techniques are used to characterize the free-energy landscape of water starting from encoding the water environment using local atomic descriptors, through dimensionality reduction and finally the use of advanced clustering techniques. Analysis of the free energy under ambient conditions was found to be consistent with a rough single basin and independent of the choice of the water model. We find that the fluctuations of the water network occur in a high-dimensional space, which we characterize using a combination of both atomic descriptors and chemical-intuition-based coordinates. We demonstrate that a combination of both types of variables is needed in order to adequately capture the complexity of the fluctuations in the hydrogen bond network at different length scales both at room temperature and also close to the critical point of water. Our results provide a general framework for examining fluctuations in water under different conditions.

Tautomeric Equilibrium in Condensed Phases

We present an ab initio molecular dynamics (MD) investigation of the tautomeric equilibrium for the aqueous solutions of glycine and acetone under realistic experimental conditions. Metadynamics is used to accelerate proton migration among tautomeric centers. Due to the formation of complex water-ion structures involved in the proton dynamics in the aqueous environment, standard enhanced sampling approaches may face severe limitations in providing a general description of the phenomenon. Recently, we have developed a set of collective variables (CVs) designed to study protons transfer reactions in complex condensed systems [Grifoni, E. Proc. Natl. Acad. Sci. U.S.A. 2019, 116, 4054 4057]. In this work, we applied this approach to study proton dissociation dynamics leading to tautomeric interconversion of biologically and chemically relevant prototypical systems, namely, glycine and acetone in water. Although relatively simple from a chemical point of view, the results show that even for these small systems, complex reaction pathways and nontrivial conversion dynamics are observed. The generality of our method allows obtaining these results without providing any prior information on the dissociation dynamics but only the atomic species that can exchange protons in the process. Our results agree with literature estimates and demonstrate the general applicability of this method in the study of tautomeric reactions.

A DFT study of structure and stability of pleated and rippled cross-β sheets with hydrophobic sidechains

The rippled cross-β sheet, a topography, in which mirror-image peptides are arranged with alternating chirality into a periodic two-dimensional network, is burgeoning as a new design principle for materials and biomedical applications. Experiments by the Schneider, Nilsson, and Raskatov labs have independently shown diverse racemic mixtures of aggregation-prone peptide of different sizes to favor the rippled over the pleated topography. Yet, systematic ab initio studies are lacking, and the field is yet to develop rules that would enable the design of new rippled cross-β frameworks from first principles. Here, DFT calculations were performed on a set of model systems, designed to begin understanding the impact that bulky, hydrophobic sidechains have upon the formation of pleated and rippled cross-β frameworks. It is hoped that this study will help stimulate the development of a predictive, general framework to enable rational design of rippled cross-β sheets in the future.

Water Structure and Properties at Hydrophilic and Hydrophobic Surfaces

The properties of water on both molecular and macroscopic surfaces critically influence a wide range of physical behaviors, with applications spanning from membrane science to catalysis to protein engineering. Yet, our current understanding of water interfacing molecular and material surfaces is incomplete, in part because measurement of water structure and molecular-scale properties challenges even the most advanced experimental characterization techniques and computational approaches. This review highlights progress in the ongoing development of tools working to answer fundamental questions on the principles that govern the interactions between water and surfaces. One outstanding and critical question is what universal molecular signatures capture the hydrophobicity of different surfaces in an operationally meaningful way, since traditional macroscopic hydrophobicity measures like contact angles fail to capture even basic properties of molecular or extended surfaces with any heterogeneity at the nanometer length scale. Resolving this grand challenge will require close interactions between state-of-the-art experiments, simulations, and theory, spanning research groups and using agreed-upon model systems, to synthesize an integrated knowledge of solvation water structure, dynamics, and thermodynamics.

Spontaneously Forming Dendritic Voids in Liquid Water Can Host Small Polymers

Some liquids are characterized by the presence of large voids with dendritic shapes and for this reason are dubbed transiently porous. By using a battery of data analysis tools, we demonstrate that liquid water and methane are both characterized by transient porosity. We show that the thermodynamics of porosity is distinct from that associated with cavitation á la classical nucleation theory. The shapes of dendritic voids in both liquids with very different chemistries resemble those of small polymers. We further show, using free energy calculations, that the cost of solvating small hydrophobic polymers in water is consistent with the work associated with creating dendritic voids. The entropic and enthalpic contributions associated with hosting these polymers can thus be rationalized by the thermodynamics of fluctuations in bulk water.

Computer-based techniques for lead identification and optimization II: Advanced search methods

This paper focuses on advanced computational techniques for identifying and optimizing lead molecules, such as metadynamics and a novel dynamic 3D pharmacophore analysis method called Dynophores. In this paper, the first application of the funnel metadynamics of the Berberine binding to G-quadruplex DNA is depicted, disclosing hints for drug design, in particular clarifying water’s role and suggesting the design of derivatives able to replace the solvent-mediated interactions between ligand and DNA to achieve more potent and selective activity. Secondly, the novel dynamic pharmacophore approach is an extension of the classic 3D pharmacophores, with statistical and sequential information about the conformational flexibility of a molecular system derived from molecular dynamics (MD) simulations.

Examination of Trifluoroethanol Interactions with Trp-Cage in Trifluoroethanol-Water at 298 K through Molecular Dynamics Simulations and Intermolecular Nuclear Overhauser Effects

Molecular dynamics simulations of the protein model Trp-cage in 42% trifluoroethanol (TFE)-water at 298 K have been carried out with the goal of exploring peptide hydrogen-solvent fluorine nuclear spin cross-relaxation. The TFE5 model of TFE developed in a previous work was used with the TIP5P-Ew model of water. System densities and component translational diffusion coefficients predicted by the simulations were within 20% of the experimental values. Consideration of the calculated relative amounts of TFE and water surrounding the hydrogens of Trp-cage indicated that the composition of the solvent mixture beyond ∼1.5 nm from the van der Waals surface of the peptide is close to the composition of the bulk solvent, but as observed by others, TFE accumulates preferentially near the peptide surface. In the simulations, both TFE and water molecules make contacts with the peptide surface; water molecules predominate in contacts with the peptide backbone atoms and TFE molecules generally preferentially interact with side chains. Translational diffusion of solvent molecules appears to be slowed near the surface of the peptide. Depending on the location in the structure, TFE molecules form complexes with the peptide that may persist for up to ∼7 ns. Many of the peptide spin-solvent fluorine cross-relaxation parameters (Σ_HF) for which experimental values are available are reasonably well-predicted from the simulations. However, the calculated Σ_HF values were too small for some hydrogens of the 6Trp indole ring and the amino acid hydrogens near this residue in the native structure, whereas Σ_HF values for hydrogens on the side chains of 1Asn, 4Ile, and 7Leu are too large. In 42% TFE-water, persistent conformations of Trp-cage are found, which differ from the conformation found in water by the orientation of the 3Tyr ring.

Automated detection of many-particle solvation states for accurate characterizations of diffusion kinetics

Discrete-space kinetic models, i.e., Markov state models, have emerged as powerful tools for reducing the complexity of trajectories generated from molecular dynamics simulations. These models require configuration-space representations that accurately characterize the relevant dynamics. Well-established, low-dimensional order parameters for constructing this representation have led to widespread application of Markov state models to study conformational dynamics in biomolecular systems. On the contrary, applications to characterize single-molecule diffusion processes have been scarce and typically employ system-specific, higher-dimensional order parameters to characterize the local solvation state of the molecule. In this work, we propose an automated method for generating a coarse configuration-space representation, using generic features of the solvation structure-the coordination numbers about each particle. To overcome the inherent noisy behavior of these low-dimensional observables, we treat the features as indicators of an underlying, latent Markov process. The resulting hidden Markov models filter the trajectories of each feature into the most likely latent solvation state at each time step. The filtered trajectories are then used to construct a configuration-space discretization, which accurately describes the diffusion kinetics. The method is validated on a standard model for glassy liquids, where particle jumps between local cages determine the diffusion properties of the system. Not only do the resulting models provide quantitatively accurate characterizations of the diffusion constant, but they also reveal a mechanistic description of diffusive jumps, quantifying the heterogeneity of local diffusion.