PaperPlayer biorxiv biophysics

Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.07.29.227389v1?rss=1

Authors: Paris, K. A., Santiago, U., Camacho, C. J.

Abstract:

Cell surface receptor engagement is a critical aspect of viral infection. At low pH, binding of SARS-CoV and its ACE2 receptor has a tight interaction that catalyzes the fusion of the spike and endosomal membranes followed by genome release. Largely overlooked has been the role of neutral pH in the respiratory tract, where we find that SARS-CoV stabilizes a transition state that enhances the off-rate from its receptor. An alternative pH-switch is found in CoV-2-like coronaviruses of tropical pangolins, but with a reversed phenotype where the tight interaction with ACE2 is at neutral pH. We show that a single point mutation in pangolin-CoV, unique to CoV-2, that deletes the last His residue in their receptor binding domain perpetuates this tight interaction independent of pH. This tight bond, not present in previous respiratory syndromes, implies that CoV-2 stays bound to the highly expressed ACE2 receptors in the nasal cavity about 100 times longer than CoV. This finding supports the unfamiliar pathology of CoV-2, observed virus retention in upper respiratory tract1, longer incubation times and extended periods of shedding. Implications to combat pandemics that, like SARS-CoV-2, export evolutionarily successful strains via higher transmission rates due to retention in nasal epithelium and their evolutionary origin are discussed.

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Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.07.28.225938v1?rss=1

Authors: Cookis, T., Mattos, C.

Abstract:

The interaction between Ras and Raf-kinase through the Ras-binding (RBD) and cysteine-rich domains (CRD) of Raf is essential for signaling through the mitogen-activated protein kinase (MAPK) pathway, yet the molecular mechanism leading to Raf activation has remained elusive. We present the 2.8 [A] crystal structure of the HRas/CRaf-RBD_CRD complex showing the Ras/Raf interface as a continuous surface on Ras. In the Ras dimer, with helices roughly perpendicular to the membrane, the CRD is located between the two Ras protomers and far from the membrane, where its dynamic nature in the Ras binding pocket is expected to accommodate BRaf and CRaf heterodimers. Our structure and its analysis by MD simulations, combined with work in the literature, result in a molecular model in which Ras binding is involved in the release of Raf autoinhibition while the Ras/Raf complex dimerizes to promote a platform for signal amplification, with Raf-CRD poised to have direct and allosteric effects on both the Ras active site and the dimerization interface.

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Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.07.28.225755v1?rss=1

Authors: Lopez, G., Nicolas, A., Roman, G., Godinez, R., Castro, M. A.

Abstract:

With an aperiodic, self-similar distribution of two-dimensional arrangement of atrial cells, it is possible to simulate such phenomena as Fibrillation, Fluttering, and a sequence of Fibrillation-Fluttering. The topology of a network of cells may facilitate the initiation and development of arrhythmias such as Fluttering and Fibrillation. Using a GPU parallel architecture, two basic cell topologies were considered in this simulation, an aperiodic, fractal distribution of connections among 462 cells, and a chessboard-like geometry of 60x60 and 600x600 cells. With a complex set of initial conditions, it is possible to produce tissue behavior that may be identified with arrhythmias. Finally, we found several sets of initial conditions that show how a mesh of cells may exhibit Fibrillation that evolves into Fluttering.

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Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.07.28.225664v1?rss=1

Authors: Rajarathnam, K., Sepuru, K. M., Nair, V., Prakash, P., Gorfe, A.

Abstract:

Chemokines are unusual class-A GPCR agonists because of their large size (~10 kDa) and binding at two distinct receptor sites: N-terminal domain (Site-I, unique to chemokines) and a groove defined by extracellular loop/transmembrane helices (Site-II, shared with all small molecule class-A ligands). Whereas binding at Site-II triggers receptor activation, the role of Site-I is not known. Structures and sequence analysis reveal that the receptor N-terminal domains (N-domains) are flexible and contain intrinsic disorder. Using a hybrid NMR-MD approach, we characterized the role of Site-I interactions for the CXCL8-CXCR1 pair. NMR data indicate that the CXCR1 N-domain becomes structured on binding and that the binding interface is extensive with 30% of CXCL8 residues participating in this initial interaction. MD simulations indicate that CXCL8 bound at Site-I undergoes extensive reorganization on engaging Site-II with several residues initially engaged at Site-I also engaging Site-II. We conclude that structural plasticity of Site-I interactions plays an active role in driving ligand recognition by a chemokine receptor.

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Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.07.28.225052v1?rss=1

Authors: Kilic, Z., Sgouralis, I., Presse, S.

Abstract:

The hidden Markov model (HMM) is a framework for time series analysis widely applied to single molecule experiments. It has traditionally been used to interpret signals generated by systems, such as single molecules, evolving in a discrete state space observed at discrete time levels dictated by the data acquisition rate. Within the HMM framework, originally developed for applications outside the Natural Sciences, such as speech recognition, transitions between states, such as molecular conformational states, are modeled as occurring at the end of each data acquisition period and are described using transition probabilities. Yet, while measurements are often performed at discrete time levels in the Natural Sciences, physical systems evolve in continuous time according to transition rates. It then follows that the modeling assumptions underlying the HMM are justified if the transition rates of a physical process from state to state are small as compared to the data acquisition rate. In other words, HMMs apply to slow kinetics. The problem is, as the transition rates are unknown in principle, it is unclear, a priori, whether the HMM applies to a particular system. For this reason, we must generalize HMMs for physical systems, such as single molecules, as these switch between discrete states in continuous time. We do so by exploiting recent mathematical tools developed in the context of inferring Markov jump processes and propose the hidden Markov jump process (HMJP). We explicitly show in what limit the HMJP reduces to the HMM. Resolving the discrete time discrepancy of the HMM has clear implications: we no longer need to assume that processes, such as molecular events, must occur on timescales slower than data acquisition and can learn transition rates even if these are on the same timescale or otherwise exceed data acquisition rates.

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Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.07.27.223826v1?rss=1

Authors: Mrazikova, K., Mlynsky, V., Kuhrova, P., Pokorna, P., Kruse, H., Krepl, M., Otyepka, M., Banas, P., Sponer, J.

Abstract:

Explicit solvent atomistic molecular dynamics (MD) simulations represent an established technique to study structural dynamics of RNA molecules and an important complement for diverse experimental methods. However, performance of molecular mechanical (MM) force fields (ffs) remains far from satisfactory even after decades of development, as apparent from a problematic structural description of some important RNA motifs. Actually, some of the smallest RNA molecules belong to the most challenging systems for MD simulations and, among them, the UNCG tetraloop is saliently difficult. We report a detailed analysis of UNCG MD simulations, depicting the sequence of events leading to the loss of the UNCG native state during MD simulations. We identify molecular interactions, backbone conformations and substates that are involved in the process. The total amount of MD simulation data analyzed in this work is close to 1.3 millisecond. Then, we unravel specific ff deficiencies using diverse quantum mechanical/molecular mechanical (QM/MM) and QM calculations. Comparison between the MM and QM methods shows discrepancies in the description of the 5`-flanking phosphate moiety and both signature sugar-base interactions. Our work indicates that poor behavior of the UNCG tetraloop in simulations is a complex issue that cannot be attributed to one dominant and straightforwardly correctable factor. Instead, there is a concerted effect of multiple ff inaccuracies that are coupled and amplifying each other. We attempted to improve the simulation behavior by some carefully-tailored interventions but the results are still far from satisfactory, underlying the difficulties in development of accurate nucleic acids ffs.

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Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.07.27.223487v1?rss=1

Authors: Paul, A., Samantray, S., Anteghini, M., Strodel, B.

Abstract:

The amlyoid-{beta} peptide (A{beta}) is closely linked to the development of Alzheimer's disease. Molecular dynamics (MD) simulations have become an indispensable tool for studying the behavior of this peptide at the (sub)molecular level, thereby providing insight into the molecular basis of Alzheimer's disease. General key aspects of MD simulations are the force field used for modeling the peptide or protein and its environment, which is important for accurate modeling of the system of interest, and the length of the simulations, which determines whether or not equilibrium is reached. In this study we address these points by analyzing 30-s MD simulations acquired for A{beta}40 using seven different force fields. We assess the convergence of these simulations based on the convergence of various structural properties and of NMR and fluorescence spectroscopic observables. Moreover, we calculate Markov state models for each of the seven MD simulations, which provide an unprecedented view of the thermodynamics and kinetics of the amyloid-{beta} peptide. This further allows us to provide answers for pertinent questions, like: Which force fields are suitable for modeling A{beta}? (a99SB-UCB and a99SB-ILDN/TIP4P-D); What does A{beta} peptide really look like? (mostly extended and disordered) and; How long does it take MD simulations of A{beta} to attain equilibrium? (20-30 s). We believe the analyses presented in this study will provide a useful reference guide for important questions relating to the structure and dynamics of A{beta} in particular, and by extension other similar disordered peptides.

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Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.07.28.225235v1?rss=1

Authors: Zhang, C., Boppart, S. A.

Abstract:

The metabolic properties of live cells are very susceptible to intra- or extra-cellular perturbations, making their measurements challenging tasks. We show that the dynamics of lipid droplets (LDs) carry information to measure the lipid metabolism of live cells. Coherent anti-Stokes Raman scattering microscopy was used to statistically quantify LD dynamics in living cells in a label-free manner. We introduce dynamic signatures of cells derived from the LD displacement, speed, travel length, and directionality, which allows for the detection of cellular changes induced by stimuli such as fluorescent labeling, temperature change, starvation, and chemical treatment. Histogram fittings of the dynamic signatures using lognormal distribution functions provide quantification of changes in cellular metabolic states. The LD dynamics also enable separation of subpopulations of LDs correlated with different functions. We demonstrate that LD dynamics are new markers to quantify the metabolic changes in live cells.

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Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.07.28.225490v1?rss=1

Authors: Gupta, C., Cava, J. K., Sarkar, D., Wilson, E. A., Vant, J., Murray, S., Singharoy, A., Karmaker, S. K.

Abstract:

Molecular dynamics (MD) simulations have emerged to become the back- bone of today's computational biophysics. Simulation tools such as, NAMD, AMBER and GROMACS have accumulated more than 100,000 users. Despite this remarkable success, now also bolstered by compatibility with graphics processor units (GPUs) and exascale computers, even the most scalable simulations cannot access biologically relevant timescales - the number of numerical integration steps necessary for solving differential equations in a million-to-billion-dimensional space is computationally in- tractable. Recent advancements in Deep Learning has made it such that patterns can be found in high dimensional data. In addition, Deep Learning have also been used for simulating physical dynamics. Here, we utilize LSTMs in order to predict future molecular dynamics from current and previous timesteps, and examine how this physics-guided learning can benefit researchers in computational biophysics. In particular, we test fully connected Feed-forward Neural Networks, Recurrent Neural Networks with LSTM / GRU memory cells with TensorFlow and PyTorch frame- works trained on data from NAMD simulations to predict conformational transitions on two different biological systems. We find that non-equilibrium MD is easier to train and performance improves under the assumption that each atom is independent of all other atoms in the system. Our study represents a case study for high-dimensional data that switches stochastically between fast and slow regimes. Applications of re- solving these sets will allow real-world applications in the interpretation of data from Atomic Force Microscopy experiments.

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Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.07.28.132373v1?rss=1

Authors: Kilic, Z., Sgouralis, I., Presse, S.

Abstract:

The time spent by a single RNA polymerase (RNAP) at specific locations along the DNA, termed "residence time", reports on the initiation, elongation and termination stages of transcription. At the single molecule level, this information can be obtained from dual ultra-stable optical trapping experiments, revealing a transcriptional elongation of RNAP interspersed with residence times of variable duration. Successfully discriminating between long and short residence times was used by previous approaches to learn about RNAP's transcription elongation dynamics. Here, we propose an approach based on the Bayesian sticky hidden Markov models that treats all residence times, for an E. Coli RNAP, on an equal footing without a priori discriminating between long and short residence times. In addition, our method has two additional advantages, we provide: full distributions around key point statistics; and directly treat the sequence-dependence of RNAP's elongation rate. By applying our approach to experimental data, we find: no emergent separation between long and short residence times warranted by the data; force dependent average residence time transcription elongation dynamics; limited effects of GreB on average backtracking durations and counts; and a slight drop in the average residence time as a function of applied force in RNaseA's presence.

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