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K-Ras4B is one the most frequently mutated proteins in cancer, yet mechanistic details of its activation such as its homodimerization on the membrane remain elusive. The structural determinants of K-Ras4B homodimerization have been debated with different conformations being proposed in the literature. Here, we perform microsecond all-atom Molecular Dynamics (MD) simulations on the K-Ras4B monomer in solution, the K-Ras4B monomer on the membrane, and two experimentally-based K-Ras4B dimer models of the α4-α5 interface to investigate the stability of these structures bound to GTP on a model cell membrane. We then evaluate the complexes for their propensity to form stable dimers on the plasma membrane in the presence and absence of Raf[RBD–CRD] effectors. We find that Raf[RBD-CRD] effectors enhance dimer stability, suggesting that the presence of effectors is necessary for K-Ras4B dimers stabilization on the cell membrane. Moreover, we observe, for the first time, a dynamic water channel at the K-Ras4B dimer interface, and identify putative allosteric connections in the K-Ras4B dimer interface. To discover novel K-Ras4B interfaces, we perform coarse-grained MD simulations in two dissociated K-Ras4B monomers on the membrane, which reveal that the dominant dimer interface is the α4-α5 interface. Finally, a druggability analysis is performed in the different K-Ras4B structures in the monomeric states. Strikingly, all known binding pockets of K-Ras4B are identified only in the structure that is membrane-bound, but not in the solution structure. Based on these results, we propose that modulating the protein-membrane interactions can be an alternative strategy for inhibiting K-Ras4B signaling.

Ras proteins are membrane-anchored GTPases that regulate key cellular signaling networks. It has been recently shown that different anionic lipid types can affect the properties of Ras in terms of dimerization/clustering on the cell membrane. To understand the effects of anionic lipids on key spatiotemporal properties of dimeric K-Ras4B, we perform all-atom molecular dynamics simulations of the dimer K-Ras4B in the presence and absence of Raf[RBD/CRD] effectors on two model anionic lipid membranes: one containing 78% mol DOPC, 20% mol DOPS, and 2% mol PIP2 and another one with enhanced concentration of anionic lipids containing 50% mol DOPC, 40% mol DOPS, and 10% mol PIP2. Analysis of our results unveils the orientational space of dimeric K-Ras4B and shows that the stability of the dimer is enhanced on the membrane containing a high concentration of anionic lipids in the absence of Raf effectors. This enhanced stability is also observed in the presence of Raf[RBD/CRD] effectors although it is not influenced by the concentration of anionic lipids in the membrane, but rather on the ability of Raf[CRD] to anchor to the membrane. We generate dominant K-Ras4B conformations by Markov state modeling and yield the population of states according to the K-Ras4B orientation on the membrane. For the membrane containing anionic lipids, we observe correlations between the diffusion of K-Ras4B and PIP2 and anchoring of anionic lipids to the Raf[CRD] domain. We conclude that the presence of effectors with the Raf[CRD] domain anchoring on the membrane as well as the membrane composition both influence the conformational stability of the K-Ras4B dimer, enabling the preservation of crucial interface interactions.

Wildfires produce large amounts of pyrogenic carbon (PyC), including charcoal, known for its chemical recalcitrance and sorption affinity for organic molecules. Wildfire-derived PyC can be transported to fluvial networks. Here it may alter the dissolved organic matter (DOM) concentration and composition as well as microbial biofilm functioning. Effects of PyC on carbon cycling in freshwater ecosystems remain poorly investigated. Employing in-stream flumes with a control versus treatment design (PyC pulse addition), we present evidence that field-aged PyC inputs to rivers can increase the dissolved organic carbon (DOC) concentration and alter the DOM composition. DOM fluorescence components were not affected by PyC. The in-stream DOM composition was altered due to leaching of pyrogenic DOM from PyC and possibly concurrent sorption of riverine DOM to PyC. Decreased DOM aromaticity indicated by a lower SUVA245 (−0.31 unit) and a higher pH (0.25 unit) was associated with changes in enzymatic activities in benthic biofilms, including a lower recalcitrance index (β-glucosidase/phenol oxidase), suggesting preferential usage of recalcitrant over readily available DOM by biofilms. The deposition of particulate PyC onto biofilms may further modulate the impacts of PyC due to direct contact with the biofilm matrix. This study highlights the importance of PyC for in-stream biogeochemical organic matter cycling in fire-affected watersheds., Pyrogenic carbon alters organic matter and pH in streams, affecting microbial functioning with potential implications for in-stream carbon cycling.

Sampling from the equilibrium distribution has always been a major problem in molecular simulations due to the very high dimensionality of the conformational space. Over several decades, many approaches have been used to overcome the problem. In particular, we focus on unbiased simulation methods such as parallel and adaptive sampling. Here, we recast adaptive sampling schemes on the basis of multi-armed bandits and develop a novel adaptive sampling algorithm under this framework, AdaptiveBandit. We test it on multiple simplified potentials and in a protein folding scenario. We find that this framework performs similarly to or better than previous methods in every type of test potential. Furthermore, it provides a novel framework to develop new sampling algorithms with better asymptotic characteristics.

Motivation An important step in structure-based drug design consists in the prediction of druggable binding sites. Several algorithms for detecting binding cavities, those likely to bind to a small drug compound, have been developed over the years by clever exploitation of geometric, chemical and evolutionary features of the protein. Results Here we present a novel knowledge-based approach that uses state-of-the-art convolutional neural networks, where the algorithm is learned by examples. In total, 7622 proteins from the scPDB database of binding sites have been evaluated using both a distance and a volumetric overlap approach. Our machine-learning based method demonstrates superior performance to two other competitive algorithmic strategies. Availability and implementation DeepSite is freely available at www.playmolecule.org. Users can submit either a PDB ID or PDB file for pocket detection to our NVIDIA GPU-equipped servers through a WebGL graphical interface. Supplementary information Supplementary data are available at Bioinformatics online.

Fast and accurate molecular force field (FF) parameterization is still an unsolved problem. Accurate FF are not generally available for all molecules, like novel druglike molecules. While methods based on quantum mechanics (QM) exist to parameterize them with better accuracy, they are computationally expensive and slow, which limits applicability to a small number of molecules. Here, we present an automated FF parameterization method which can utilize either density functional theory (DFT) calculations or approximate QM energies produced by different neural network potentials (NNPs), to obtain improved parameters for molecules. We demonstrate that for the case of torchani-ANI-1x NNP, we can parameterize small molecules in a fraction of time compared with an equivalent parameterization using DFT QM calculations while producing more accurate parameters than FF (GAFF2). We expect our method to be of critical importance in computational structure-based drug discovery (SBDD). The current version is available at PlayMolecule ( www.playmolecule.org ) and implemented in HTMD, allowing to parameterize molecules with different QM and NNP options.

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Recent advances in molecular simulations have allowed scientists to investigate slower biological processes than ever before. Together with these advances came an explosion of data that has transformed a traditionally computing-bound into a data-bound problem. Here, we present HTMD, a programmable, extensible platform written in Python that aims to solve the data generation and analysis problem as well as increase reproducibility by providing a complete workspace for simulation-based discovery. So far, HTMD includes system building for CHARMM and AMBER force fields, projection methods, clustering, molecular simulation production, adaptive sampling, an Amazon cloud interface, Markov state models, and visualization. As a result, a single, short HTMD script can lead from a PDB structure to useful quantities such as relaxation time scales, equilibrium populations, metastable conformations, and kinetic rates. In this paper, we focus on the adaptive sampling and Markov state modeling features.

The recent increase in the number of X-ray crystal structures of G-protein coupled receptors (GPCRs) has been enabling for structure-based drug design (SBDD) efforts. These structures have revealed that GPCRs are highly dynamic macromolecules whose function is dependent on their intrinsic flexibility. Unfortunately, the use of static structures to understand ligand binding can potentially be misleading, especially in systems with an inherently high degree of conformational flexibility. Here, we show that docking a set of dopamine D3 receptor compounds into the existing eticlopride-bound dopamine D3 receptor (D3R) X-ray crystal structure resulted in poses that were not consistent with results obtained from site-directed mutagenesis experiments. We overcame the limitations of static docking by using large-scale high-throughput molecular dynamics (MD) simulations and Markov state models (MSMs) to determine an alternative pose consistent with the mutation data. The new pose maintains critical interactions observed in the D3R/eticlopride X-ray crystal structure and suggests that a cryptic pocket forms due to the shift of a highly conserved residue, F6.52. Our study highlights the importance of GPCR dynamics to understand ligand binding and provides new opportunities for drug discovery. N.F. acknowledges support from Generalitat de Catalunya (FI-Agaur). GDF acknowledges support from MINECO (BIO2014-53095-P) and FEDER. We also thank all the volunteers of GPUGRID who donated GPU computing time to the project.

HTMD is a programmable scientific platform intended to facilitate simulation-based research in molecular systems. This paper presents the functionalities of HTMD for the preparation of a molecular dynamics simulation starting from PDB structures, building the system using well-known force fields, and applying standardized protocols for running the simulations. We demonstrate the framework's flexibility for high-throughput molecular simulations by applying a preparation, building, and simulation protocol with multiple force-fields on all of the seven hundred eukaryotic membrane proteins resolved to-date from the orientation of proteins in membranes (OPM) database. All of the systems are available on www.playmolecule.org .

High-throughput molecular dynamics (MD) simulations are a computational method consisting of using multiple short trajectories, instead of few long ones, to cover slow biological time scales. Compared to long trajectories this method offers the possibility to start the simulations in successive batches, building a knowledgeable model of the available data to inform subsequent new simulations iteratively. Here, we demonstrate an automatic, iterative, on-the-fly method for learning and sampling molecular simulations in the context of ligand binding for the case of trypsin-benzamidine binding. The method uses Markov state models to learn a simplified model of the simulations and decide where best to sample from, achieving a converged binding affinity in approximately one microsecond, 1 order of magnitude faster than classical sampling. This method demonstrates for the first time the potential of adaptive sampling schemes in the case of ligand binding.

Protein-protein association is fundamental to many life processes. However, a microscopic model describing the structures and kinetics during association and dissociation is lacking on account of the long lifetimes of associated states, which have prevented efficient sampling by direct molecular dynamics (MD) simulations. Here we demonstrate protein-protein association and dissociation in atomistic resolution for the ribonuclease barnase and its inhibitor barstar by combining adaptive high-throughput MD simulations and hidden Markov modelling. The model reveals experimentally consistent intermediate structures, energetics and kinetics on timescales from microseconds to hours. A variety of flexibly attached intermediates and misbound states funnel down to a transition state and a native basin consisting of the loosely bound near-native state and the tightly bound crystallographic state. These results offer a deeper level of insight into macromolecular recognition and our approach opens the door for understanding and manipulating a wide range of macromolecular association processes.

Living with fire is a challenge for human communities because they are influenced by socio-economic, political, ecological and climatic processes at various spatial and temporal scales. Over the course of 2 days, the authors discussed how communities could live with fire challenges at local, national and transnational scales. Exploiting our diverse, international and interdisciplinary expertise, we outline generalizable properties of fire-adaptive communities in varied settings where cultural knowledge of fire is rich and diverse. At the national scale, we discussed policy and management challenges for countries that have diminishing fire knowledge, but for whom global climate change will bring new fire problems. Finally, we assessed major fire challenges that transcend national political boundaries, including the health burden of smoke plumes and the climate consequences of wildfires. It is clear that to best address the broad range of fire problems, a holistic wildfire scholarship must develop common agreement in working terms and build across disciplines. We must also communicate our understanding of fire and its importance to the media, politicians and the general public. This article is part of the themed issue ‘The interaction of fire and mankind’.

Small molecules used in fragment-based drug discovery form multiple, promiscuous binding complexes difficult to capture experimentally. Here, we identify such binding poses and their associated energetics and kinetics using molecular dynamics simulations on AmpC β-lactamase. Only one of the crystallographic binding poses was found to be thermodynamically favorable; however, the ligand shows several binding poses within the pocket. This study demonstrates free-binding molecular simulations in the context of fragment-to-lead development and its potential application in drug design.

Purpose To evaluate the effectiveness of fractionated stereotactic radiotherapy (SFRT) in the treatment of optic nerve sheath meningioma (ONSM). Methods and Materials Between 1993 and 2005, 109 patients (113 eyes) with primary ( n = 37) or secondary ( n = 76) ONSM were treated according to a prospective protocol with SFRT to a median dose of 54 Gy. All patients underwent radiographic, ophthalmologic, and endocrine analysis before and after SFRT. Radiographic response, visual control, and late side effects were endpoints of the analysis. Results Median time to last clinical, radiographic, and ophthalmologic follow up was 30.2 months ( n = 113), 42.7 months ( n = 108), and 53.7 months ( n = 91), respectively. Regression of the tumor was observed in 5 eyes and progression in 4 eyes, whereas 104 remained stable. Visual acuity improved in 12, deteriorated in 11, and remained stable in 68 eyes. Mean visual field defects reduced from 33.6% ( n = 90) to 17.8% ( n = 56) in ipsilateral and from 10% ( n = 94) to 6.7% ( n = 62) in contralateral eyes. Ocular motility improved in 23, remained stable in 65, and deteriorated in 3 eyes. Radiographic tumor control was 100% at 3 years and 98% at 5 years. Visual acuity was preserved in 94.8% after 3 years and in 90.9% after 5 years. Endocrine function was normal in 90.8% after 3 years and in 81.3% after 5 years. Conclusions SFRT represents a highly effective treatment for ONSM. Interdisciplinary counseling of the patients is recommended. Because of the high rate of preservation of visual acuity we consider SFRT the standard approach for the treatment of ONSM. Prolonged observation is warranted to more accurately assess late visual impairment. Moderate de-escalation of the radiation dose might improve the preservation of visual acuity and pituitary gland function.

Adverse effects of soil water repellency (hydrophobicity) are of concern during or following rainfall or irrigation, and are often preceded by conditions of high atmospheric relative humidity (RH). Assessments of repellency are, however, commonly conducted on air-dried samples at ambient laboratory conditions. This study explores the effects of differing antecedent RHs (32-98%) on the water repellency of air-dried soils of wide ranging characteristics under laboratory conditions using water drop penetration time (WDPT) and ethanol-percentage tests. Most samples exhibited considerably higher water repellency after exposure (< 1 d) to 98% RH compared with lower RHs, typical of ambient laboratory conditions. This work suggests that previous studies mayhave incorrectly classified some soils, likely to exhibit water repellency in the field, as wettable, and that tests carried out following exposure of samples to high RH provide assessments that best reflect critical field conditions.