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Allostery, the phenomenon by which the perturbation of a molecule at one site alters its behavior at a remote functional site, enables control over biomolecular function. Allosteric modulation is a promising avenue for drug discovery and is employed in the design of mechanical metamaterials. However, a general principle of allostery, i.e. a set of quantitative and transferable "ground rules", remained elusive. It is neither a set of structural motifs nor intrinsic motions. Focusing on elastic network models, we here show that an allosteric lever--a mode-coupling pattern induced by the perturbation--governs the directional, source-to-target, allosteric communication: a structural perturbation of an allosteric site couples the excitation of localized hard elastic modes with concerted long range soft-mode relaxation. Perturbations of non-allosteric sites instead couple hard and soft modes uniformly. The allosteric response is shown to be generally non-linear and non-reciprocal, and allows for minimal structural distortions to be efficiently transmitted to specific changes at distant sites. Allosteric levers exist in proteins and "pseudoproteins"--networks designed to display an allosteric response. Interestingly, protein sequences that constitute allosteric transmission channels are shown to be evolutionarily conserved. To illustrate how the results may be applied in drug design, we use them to successfully predict known allosteric sites in proteins.

We assess costs and efficiency of state-of-the-art high performance cloud computing compared to a traditional on-premises compute cluster. Our use case are atomistic simulations carried out with the GROMACS molecular dynamics (MD) toolkit with a focus on alchemical protein-ligand binding free energy calculations. We set up a compute cluster in the Amazon Web Services (AWS) cloud that incorporates various different instances with Intel, AMD, and ARM CPUs, some with GPU acceleration. Using representative biomolecular simulation systems we benchmark how GROMACS performs on individual instances and across multiple instances. Thereby we assess which instances deliver the highest performance and which are the most cost-efficient ones for our use case. We find that, in terms of total costs including hardware, personnel, room, energy and cooling, producing MD trajectories in the cloud can be as cost-efficient as an on-premises cluster given that optimal cloud instances are chosen. Further, we find that high-throughput ligand-screening for protein-ligand binding affinity estimation can be accelerated dramatically by using global cloud resources. For a ligand screening study consisting of 19,872 independent simulations, we used all hardware that was available in the cloud at the time of the study. The computations scaled-up to reach peak performances using more than 4,000 instances, 140,000 cores, and 3,000 GPUs simultaneously around the globe. Our simulation ensemble finished in about two days in the cloud, while weeks would be required to complete the task on a typical on-premises cluster consisting of several hundred nodes. We demonstrate that the costs of such and similar studies can be drastically reduced with a checkpoint-restart protocol that allows to use cheap Spot pricing and by using instance types with optimal cost-efficiency., Comment: 59 pages, 11 figures, 11 tables v2 fixed a typo in the abstract

Potassium channels play critical roles in many physiological processes, providing a selective permeation route for K+ ions in and out of a cell, by employing a carefully designed selectivity filter, evolutionarily conserved from viruses to mammals. The structure of the selectivity filter was determined at atomic resolution by x-ray crystallography, showing a tight coordination of desolvated K+ ions by the channel. However, the molecular mechanism of K+ ions permeation through potassium channels remains unclear, with structural, functional and computational studies often providing conflicting data and interpretations. In this review, we will present the proposed mechanisms, discuss their origins, and will critically assess them against all available data. General properties shared by all potassium channels are introduced first, followed by the introduction of two main mechanisms of ion permeation: soft and direct knock-on. Then, we will discuss critical computational and experimental studies that shaped the field. We will especially focus on molecular dynamics (MD) simulations, that provided mechanistic and energetic aspects of K+ permeation, but at the same time created long-standing controversies. Further challenges and possible solutions are presented as well., Comment: 48 pages, 3 figures

We identify hardware that is optimal to produce molecular dynamics trajectories on Linux compute clusters with the GROMACS 2018 simulation package. Therefore, we benchmark the GROMACS performance on a diverse set of compute nodes and relate it to the costs of the nodes, which may include their lifetime costs for energy and cooling. In agreement with our earlier investigation using GROMACS 4.6 on hardware of 2014, the performance to price ratio of consumer GPU nodes is considerably higher than that of CPU nodes. However, with GROMACS 2018, the optimal CPU to GPU processing power balance has shifted even more towards the GPU. Hence, nodes optimized for GROMACS 2018 and later versions enable a significantly higher performance to price ratio than nodes optimized for older GROMACS versions. Moreover, the shift towards GPU processing allows to cheaply upgrade old nodes with recent GPUs, yielding essentially the same performance as comparable brand-new hardware., Comment: 41 pages, 13 figures, 4 tables. This updated version includes the following improvements: - most notably, added benchmarks for two coarse grain MARTINI systems VES and BIG, resulting in a new Figure 13 - fixed typos - made text clearer in some places - added two more benchmarks for MEM and RIB systems (E3-1240v6 + RTX 2080 / 2080Ti)

A popular approach to analyze the dynamics of high-dimensional many-body systems, such as macromolecules, is to project the trajectories onto a space of slowly-varying collective variables, where subsequent analyses are made, such as clustering or estimation of free energy profiles or Markov state models (MSMs). However, existing "dynamical" dimension reduction methods, such as the time-lagged independent component analysis (TICA) are only valid if the dynamics obeys detailed balance (microscopic reversibility) and typically require long, equilibrated simulation trajectories. Here we develop a dimension reduction method for non-equilibrium dynamics based on the recently developed Variational Approach for Markov Processes (VAMP) by Wu and No\'e. VAMP is illustrated by obtaining a low-dimensional description of a single file ion diffusion model and by identifying long-lived states from molecular dynamics simulations of the KcsA channel protein in an external electrochemical potential. This analysis provides detailed insights into the coupling of conformational dynamics, the configuration of the selectivity filter, and the conductance of the channel. We recommend VAMP as a replacement for the less general TICA method.

The partial least squares algorithm for dependent data realisations is considered. Consequences of ignoring the dependence for the algorithm performance are studied both theoretically and in simulations. It is shown that ignoring certain non-stationary dependence structures leads to inconsistent estimation. A simple modification of the partial least squares algorithm for dependent data is proposed and consistency of corresponding estimators is shown. A real-data example on protein dynamics llustrates a superior predictive power of the method and the practical relevance of the problem.

The molecular dynamics simulation package GROMACS runs efficiently on a wide variety of hardware from commodity workstations to high performance computing clusters. Hardware features are well exploited with a combination of SIMD, multi-threading, and MPI-based SPMD/MPMD parallelism, while GPUs can be used as accelerators to compute interactions offloaded from the CPU. Here we evaluate which hardware produces trajectories with GROMACS 4.6 or 5.0 in the most economical way. We have assembled and benchmarked compute nodes with various CPU/GPU combinations to identify optimal compositions in terms of raw trajectory production rate, performance-to-price ratio, energy efficiency, and several other criteria. Though hardware prices are naturally subject to trends and fluctuations, general tendencies are clearly visible. Adding any type of GPU significantly boosts a node's simulation performance. For inexpensive consumer-class GPUs this improvement equally reflects in the performance-to-price ratio. Although memory issues in consumer-class GPUs could pass unnoticed since these cards do not support ECC memory, unreliable GPUs can be sorted out with memory checking tools. Apart from the obvious determinants for cost-efficiency like hardware expenses and raw performance, the energy consumption of a node is a major cost factor. Over the typical hardware lifetime until replacement of a few years, the costs for electrical power and cooling can become larger than the costs of the hardware itself. Taking that into account, nodes with a well-balanced ratio of CPU and consumer-class GPU resources produce the maximum amount of GROMACS trajectory over their lifetime.