Summary: 3Dmol.js is a modern, object-oriented JavaScript library that uses the latest web technologies to provide interactive, hardware-accelerated three-dimensional representations of molecular data without the need to install browser plugins or Java. 3Dmol.js provides a full featured API for developers as well as a straightforward declarative interface that lets users easily share and embed molecular data in websites.Availability and implementation: 3Dmol.js is distributed under the permissive BSD open source license. Source code and documentation can be found at http://3Dmol.csb.pitt.eduContact: dkoes@pitt.edu

The weighted ensemble (WE) path sampling approach orchestrates an ensemble of parallel calculations with intermittent communication to enhance the sampling of rare events, such as molecular associations or conformational changes in proteins or peptides. Trajectories are replicated and pruned in a way that focuses computational effort on under-explored regions of configuration space while maintaining rigorous kinetics. To enable the simulation of rare events at any scale (e.g. atomistic, cellular), we have developed an open-source, interoperable, and highly scalable software package for the execution and analysis of WE simulations: WESTPA (The Weighted Ensemble Simulation Toolkit with Parallelization and Analysis). WESTPA scales to thousands of CPU cores and includes a suite of analysis tools that have been implemented in a massively parallel fashion. The software has been designed to interface conveniently with any dynamics engine and has already been used with a variety of molecular dynamics (e.g. GROMACS, NAMD, OpenMM, AMBER) and cell-modeling packages (e.g. BioNetGen, MCell). WESTPA has been in production use for over a year, and its utility has been demonstrated for a broad set of problems, ranging from atomically detailed host-guest associations to non-spatial chemical kinetics of cellular signaling networks. The following describes the design and features of WESTPA, including the facilities it provides for running WE simulations, storing and analyzing WE simulation data, as well as examples of input and output.

Hydrophobic interactions drive many important biomolecular selfassembly phenomena. However, characterizing hydrophobicity at the nanoscale has remained a challenge due to its nontrivial dependence on the chemistry and topography of biomolecular surfaces. Here we use molecular simulations coupled with enhanced sampling methods to systematically displace water molecules from the hydration shells of nanostructured solutes and calculate the free energetics of interfacial water density fluctuations, which quantify the extent of solute-water adhesion, and therefore solute hydrophobicity. In particular, we characterize the hydrophobicity of curved graphene sheets, self-assembled monolayers (SAMs) with chemical patterns, and mutants of the protein hydrophobin-II. We find that water density fluctuations are enhanced near concave nonpolar surfaces compared with those near flat or convex ones, suggesting that concave surfaces are more hydrophobic. We also find that patterned SAMs and protein mutants, having the same number of nonpolar and polar sites but different geometrical arrangements, can display significantly different strengths of adhesion with water. Specifically, hydroxyl groups reduce the hydrophobicity of methyl-terminated SAMs most effectively not when they are clustered together but when they are separated by one methyl group. Hydrophobin-II mutants show that a charged amino acid reduces the hydrophobicity of a large nonpolar patch when placed at its center, rather than at its edge. Our results highlight the power of water density fluctuations-based measures to characterize the hydrophobicity of nanoscale surfaces and caution against the use of additive approximations, such as the commonly used surface area models or hydropathy scales for characterizing biomolecular hydrophobicity and the associated driving forces of assembly.nanotube | curvature | chemical pattern | hydrophilicity | graphene H ydrophobic interactions drive many important biological and colloidal self-assembly processes (1-6). During such assembly, the hydration shells of the associating solutes are disrupted, replacing hydrophobic-water contacts with hydrophobic-hydrophobic ones. Characterizing how strongly water adheres to a given solute is, therefore, directly relevant to the strength of hydrophobic interactions between solutes. Macroscopically, surface-water adhesion is quantified by measuring the water droplet contact angle on a surface. However, such characterization does not translate usefully to proteins and other nanoscale solutes. Indeed, characterizing how strongly or weakly a protein surface or a specific patch on it adheres to water (i.e., its hydrophobicity) is incredibly challenging and has necessitated the use of simplifying assumptions.To this end, simple surface area (SA) models have been used to estimate the driving force for assembly, ∆G = γ∆A, where ∆A is the nonpolar SA buried upon assembly and γ is an appropriate surface tension (7-9). However, the value of γ used in popular models is nearly an order of magnitude lower t...

Interactions between proteins lie at the heart of numerous biological processes and are essential for the proper functioning of the cell. Although the importance of hydrophobic residues in driving protein interactions is universally accepted, a characterization of protein hydrophobicity, which informs its interactions, has remained elusive. The challenge lies in capturing the collective response of the protein hydration waters to the nanoscale chemical and topographical protein patterns, which determine protein hydrophobicity. To address this challenge, here, we employ specialized molecular simulations wherein water molecules are systematically displaced from the protein hydration shell; by identifying protein regions that relinquish their waters more readily than others, we are then able to uncover the most hydrophobic protein patches. Surprisingly, such patches contain a large fraction of polar/charged atoms and have chemical compositions that are similar to the more hydrophilic protein patches. Importantly, we also find a striking correspondence between the most hydrophobic protein patches and regions that mediate protein interactions. Our work thus establishes a computational framework for characterizing the emergent hydrophobicity of amphiphilic solutes, such as proteins, which display nanoscale heterogeneity, and for uncovering their interaction interfaces.

The interactions of a protein, its phase behavior, and ultimately, its ability to function, are all influenced by the interactions between the protein and its hydration waters. Here we study proteins with a variety of sizes, shapes, chemistries, and biological functions, and characterize their interactions with their hydration waters using molecular simulation and enhanced sampling techniques. We find that akin to extended hydrophobic surfaces, proteins situate their hydration waters at the edge of a dewetting transition, making them susceptible to unfavorable perturbations. We also find that the strength of the unfavorable potential needed to trigger dewetting is roughly the same, regardless of the protein being studied, and depends only the width of the hydration shell being perturbed. Our findings establish a framework for systematically classifying protein patches according to how favorably they interact with water.