Root mean-square deviation (RMSD) after roto-translational least-squares fitting is a measure of global structural similarity of macromolecules used commonly. On the other hand, experimental x-ray B-factors are used frequently to study local structural heterogeneity and dynamics in macromolecules by providing direct information about root mean-square fluctuations (RMSF) that can also be calculated from molecular dynamics simulations. We provide a mathematical derivation showing that, given a set of conservative assumptions, a root mean-square ensemble-average of an all-against-all distribution of pairwise RMSD for a single molecular species, (1/2), is directly related to average B-factors () and (1/2). We show this relationship and explore its limits of validity on a heterogeneous ensemble of structures taken from molecular dynamics simulations of villin headpiece generated using distributed-computing techniques and the Folding@Home cluster. Our results provide a basis for quantifying global structural diversity of macromolecules in crystals directly from x-ray experiments, and we show this on a large set of structures taken from the Protein Data Bank. In particular, we show that the ensemble-average pairwise backbone RMSD for a microscopic ensemble underlying a typical protein x-ray structure is approximately 1.1 A, under the assumption that the principal contribution to experimental B-factors is conformational variability.

Highlights d Mfn2 binds directly and specifically to phosphatidylserine (PS) d Hepatic Mfn2 deficiency causes a reduced transfer of PS from ER to mitochondria d Mfn2 ablation in liver causes a NASH-like phenotype and liver cancer d A defective transfer of PS from ER to mitochondria causes liver disease

Our current understanding of epidermal growth factor receptor (EGFR) autoinhibition is based on X-ray structural data of monomer and dimer receptor fragments and does not explain how mutations achieve ligand-independent phosphorylation. Using a repertoire of imaging technologies and simulations we reveal an extracellular head-to-head interaction through which ligand-free receptor polymer chains of various lengths assemble. The architecture of the head-to-head interaction prevents kinase-mediated dimerisation. The latter, afforded by mutation or intracellular treatments, splits the autoinhibited head-to-head polymers to form stalk-to-stalk flexible non-extended dimers structurally coupled across the plasma membrane to active asymmetric tyrosine kinase dimers, and extended dimers coupled to inactive symmetric kinase dimers. Contrary to the previously proposed main autoinhibitory function of the inactive symmetric kinase dimer, our data suggest that only dysregulated species bear populations of symmetric and asymmetric kinase dimers that coexist in equilibrium at the plasma membrane under the modulation of the C-terminal domain.

development, combining different levels of theory to increase accuracy, aiming to connect chemical and molecular changes to macroscopic observables. In this review, we outline biomolecular simulation methods and highlight examples of its application to investigate questions in biology. enzyme, membrane, molecular dynamics, multiscale, protein, QM/MM 1 | INTRODUCTION Biomolecular simulations are now making significant contributions to a wide variety of problems in drug discovery, drug development, biocatalysis, biotechnology, nanotechnology, chemical biology, and medicine. Biomolecular simulation is a rapidly growing field in scale and impact, increasingly demonstrating its worth in understanding mechanisms and analyzing activities, and contributing to the design of drugs and biocatalysts. Physics-based simulations complement experiments in building a molecular-level understanding of biology: They can test hypotheses and interpret and analyze experimental data in terms of interactions at the atomic level. Different types of simulation techniques have been developed, which are applicable to a range of different problems in biomolecular science. Simulations have already shown their worth in helping to analyze how enzymes catalyze biochemical reactions, and how proteins adopt their functional structures, for example, within cell membranes. They contribute to the design of drugs and catalysts, and in understanding the molecular basis of disease. Simulations have played a key role in developing the conceptual framework now at the heart of biomolecular science: that the dynamics of biological molecules is central to their function. Developing methods from chemical physics and computational science will open exciting new opportunities in biomolecular science, including in drug design and development, biotechnology, and biocatalysis. With high-performance computing resources, large-scale atomistic simulations of biological machines such as the ribosome, proton pumps and motors, membrane receptor complexes, and even whole viruses have become possible. Useful simulations of smaller systems can be carried out with desktop resources, thanks to developments allowing, for example, graphics processing units (GPUs) to be used. A particular challenge across the field is the integration of simulations crossing the span of length-and timescales as different types of simulation method are required for different types of problems. 1 Biomolecular systems pose fundamental scientific challenges (e.g., protein folding, enzyme catalysis, gene regulation, disease mechanisms, and antimicrobial resistance) and are at the heart of many advanced technological developments (drug discovery, biotechnology, biocatalysis, biomaterials, and genetic engineering). Biomolecular systems are inherently complex and pose significant challenges in modeling. An essential underlying paradigm is the need to consider biomolecular ensembles and their dynamics, rather than simply static biomolecular structures to understand and predict their behavior and propertie...

Biomolecular X-ray structures typically provide a static, time- and ensemble-averaged view of molecular ensembles in crystals. In the absence of rigid-body motions and lattice defects, B-factors are thought to accurately reflect the structural heterogeneity of such ensembles. In order to study the effects of averaging on B-factors, we employ molecular dynamics simulations to controllably manipulate microscopic heterogeneity of a crystal containing 216 copies of villin headpiece. Using average structure factors derived from simulation, we analyse how well this heterogeneity is captured by high-resolution molecular-replacement-based model refinement. We find that both isotropic and anisotropic refined B-factors often significantly deviate from their actual values known from simulation: even at high 1.0 Å resolution and Rfree of 5.9%, B-factors of some well-resolved atoms underestimate their actual values even sixfold. Our results suggest that conformational averaging and inadequate treatment of correlated motion considerably influence estimation of microscopic heterogeneity via B-factors, and invite caution in their interpretation.