PaperPlayer biorxiv biophysics

Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.10.16.342899v1?rss=1

Authors: Zimmer, M. H., Niesen, M. J., Miller, T. F.

Abstract:

Force-sensitive arrest peptides regulate protein biosynthesis by stalling the ribosome as they are translated. Synthesis can be resumed when the nascent arrest peptide experiences a pulling force of sufficient magnitude to break the stall. Efficient stalling is dependent on the specific identity of a large number of amino acids, including amino acids which are tens of angstroms away from the peptidyl transferase center (PTC). The mechanism of force-induced restart and the role of these essential amino acids far from the PTC is currently unknown. We use hundreds of independent molecular dynamics trajectories spanning over 120 s in combination with kinetic analysis to characterize the barriers along the force-induced restarting pathway for the arrest peptide SecM. We find that the essential amino acids far from the PTC play a major role in controlling the transduction of applied force. In successive states along the stall-breaking pathway, the applied force propagates up the nascent chain until it reaches the C-terminus of SecM and the PTC, inducing conformational changes that allow for restart of translation. A similar mechanism of force propagation through multiple states is observed in the VemP stall-breaking pathway, but secondary structure in VemP allows for heterogeneity in the order of transitions through intermediate states. Results from both arrest peptides explain how residues that are tens of angstroms away from the catalytic center of the ribosome impact stalling efficiency by mediating the response to an applied force and shielding the amino acids responsible for maintaining the stalled state of the PTC.

Copy rights belong to original authors. Visit the link for more info

Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.10.15.341792v1?rss=1

Authors: Isık, M., Rustenburg, A. S., Rizzi, A., Gunner, M. R., Mobley, D. L., Chodera, J. D.

Abstract:

The prediction of acid dissociation constants (pKa) is a prerequisite for predicting many other properties of a small molecule, such as its protein-ligand binding affinity, distribution coefficient (logD), membrane permeability, and solubility. The prediction of each of these properties requires knowledge of the relevant protonation states and solution free energy penalties of each state. The SAMPL6 pKa Challenge was the first time that a separate challenge was conducted for evaluating pKa predictions as part of the Statistical Assessment of Modeling of Proteins and Ligands (SAMPL) exercises. This challenge was motivated by significant inaccuracies observed in prior physical property prediction challenges, such as the SAMPL5 logD Challenge, caused by protonation state and pKa prediction issues. The goal of the pKa challenge was to assess the performance of contemporary pKa prediction methods for drug-like molecules. The challenge set was composed of 24 small molecules that resembled fragments of kinase inhibitors, a number of which were multiprotic. Eleven research groups contributed blind predictions for a total of 37 pKa distinct prediction methods. In addition to blinded submissions, four widely used pKa prediction methods were included in the analysis as reference methods. Collecting both microscopic and macroscopic pKa predictions allowed in-depth evaluation of pKa prediction performance. This article highlights deficiencies of typical pKa prediction evaluation approaches when the distinction between microscopic and macroscopic pKas is ignored; in particular, we suggest more stringent evaluation criteria for microscopic and macroscopic pKa predictions guided by the available experimental data. Top-performing submissions for macroscopic pKa predictions achieved RMSE of 0.7-1.0 pKa units and included both quantum chemical and empirical approaches, where the total number of extra or missing macroscopic pKas predicted by these submissions were fewer than 8 for 24 molecules. A large number of submissions had RMSE spanning 1-3 pKa units. Molecules with sulfur-containing heterocycles or iodo and bromo groups were less accurately predicted on average considering all methods evaluated. For a subset of molecules, we utilized experimentally-determined microstates based on NMR to evaluate the dominant tautomer predictions for each macroscopic state. Prediction of dominant tautomers was a major source of error for microscopic pKa predictions, especially errors in charged tautomers. The degree of inaccuracy in pKa predictions observed in this challenge is detrimental to the protein-ligand binding affinity predictions due to errors in dominant protonation state predictions and the calculation of free energy corrections for multiple protonation states. Underestimation of ligand pKa by 1 unit can lead to errors in binding free energy errors up to 1.2 kcal/mol. The SAMPL6 pKa Challenge demonstrated the need for improving pKa prediction methods for drug-like molecules, especially for challenging moieties and multiprotic molecules.

Copy rights belong to original authors. Visit the link for more info

Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.10.15.340935v1?rss=1

Authors: Ma, S., Henderson, J. A., Shen, J.

Abstract:

Renin is a pepsin-like aspartyl protease and an important drug target for the treatment of hypertension; despite three decades' research, its pH-dependent structure-function relationship remains poorly understood. Here we employed the continuous constant pH molecular dynamics (CpHMD) simulations to decipher the acid/base roles of renin's catalytic dyad and the conformational dynamics of the flap, which is a common structural feature among aspartyl proteases. The calculated pKa's suggest that the catalytic Asp38 and Asp226 serve as the general base and acid, respectively, in agreement with experiment and supporting the hypothesis that renin's neutral optimum pH is due to the substrate-induced pKa shifts of the aspartic dyad. The CpHMD data confirmed our previous hypothesis that hydrogen bond formation is the major determinant of the dyad pKa order. Additionally, our simulations showed that renin's flap remains open regardless of pH, although a Tyr-inhibited state is occasionally formed above pH 5. These findings are discussed in comparison to the related aspartyl proteases, including beta-secretases 1 and 2, capthepsin D, and plasmepsin II. Our work represents a first step towards a systematic understanding of the pH-dependent structure-dynamics-function relationships of pepsin-like aspartyl proteases that play important roles in biology and human disease states.

Copy rights belong to original authors. Visit the link for more info

Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.10.15.340885v1?rss=1

Authors: Filius, M., Kim, S. H., Severins, I., Joo, C.

Abstract:

Single-molecule FRET is a versatile tool to study nucleic acids and proteins at the nanometer scale. However, currently, only a couple of FRET pairs can be reliably measured on a single object. The limited number of available FRET pair fluorophores and complicated data analysis makes it challenging to apply single-molecule FRET for structural analysis of biomolecules. Currently, only a couple of FRET pairs can be reliably measured on a single object. Here we present an approach that allows for the determination of multiple distances between FRET pairs in a single object. We use programmable, transient binding between short DNA strands to resolve the FRET efficiency of multiple fluorophore pairs. By allowing only a single FRET pair to be formed at a time, we can determine the FRET efficiency and pair distance with sub-nanometer resolution. We determine the distance between other pairs by sequentially exchanging DNA strands. We name this multiplexing approach FRET X for FRET via DNA eXchange. We envision that our FRET X technology will be a tool for the high-resolution structural analysis of biomolecules and other nano-structures.

Copy rights belong to original authors. Visit the link for more info

Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.10.14.339416v1?rss=1

Authors: Hedde, P. N., Cinco, R., Malacrida, L., Kamaid, A., Gratton, E.

Abstract:

Hyperspectral imaging is highly sought after in many fields including mineralogy and geology, environment and agriculture, astronomy and, importantly, biomedical imaging and biological fluorescence. We developed ultrafast phasor-based hyperspectral snapshot microscopy based on sine/cosine interference filters to overcome the limitations of conventional hyperspectral imaging methods. Current approaches rely on slow spatial or spectral scanning limiting their application in living biological tissues, while faster snapshot methods such as image mapping spectrometry and multispectral interferometry are limited in spatial and/or spectral resolution, are computationally demanding, and devices are very expensive to manufacture. Leveraging light sheet microscopy, phasor-based hyperspectral snapshot microscopy improved imaging speed 10-100 fold and enabled previously elusive hyperspectral metabolic imaging of live, three-dimensional mouse tissues. As a fit-free method that does not require any a priori information, the phasor approach could also spectrally resolve subtle differences between cell types in the developing zebrafish retina and spectrally separate and track multiple organelles in 3D cultured cells. The sine/cosine snapshot method is adaptable to any microscope or imaging device thus making hyperspectral imaging broadly available to researchers and the public.

Copy rights belong to original authors. Visit the link for more info

Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.10.15.341610v1?rss=1

Authors: Roman, I. R., Roman, A. S., Large, E. W.

Abstract:

Music has a tempo (or frequency of the underlying beat) that musicians maintain throughout a performance. Musicians maintain this musical tempo on their own or paced by a metronome. Behavioral studies have found that each musician shows a spontaneous rate of movement, called spontaneous motor tempo (SMT), which can be measured when a musician spontaneously plays a simple melody. Data shows that a musician's SMT systematically influences how actions align with the musical tempo. In this study we present a model that captures this phenomenon. To develop our model, we review the results from three musical performance settings that have been previously published: (1) solo musical performance with a pacing metronome tempo that is different from the SMT, (2) solo musical performance without a metronome at a spontaneous tempo that is faster or slower than the SMT, and (3) duet musical performance between musician pairs with matching and mismatching SMTs. In the first setting, the asynchrony between the pacing metronome and the musician's tempo grew as a function of the difference between the metronome tempo and the musician's SMT. In the second setting, musicians drifted away from the initial spontaneous tempo toward the SMT. And in the third setting, the absolute asynchronies between performing musicians were smaller if their SMTs matched compared to when they did not. Based on these previous observations, we hypothesize that, while musicians can perform musical actions at a tempo different from their SMT, the SMT constantly acts as a pulling force. We developed a model to test our hypothesis. The model is an oscillatory dynamical system with Hebbian and elastic tempo learning that simulates music performance. We simulate an individual's SMT with the dynamical system's natural frequency. Hebbian learning lets the system's frequency adapt to match the stimulus frequency. The pulling force is simulated with an elasticity term that pulls the learned frequency toward the system's natural frequency. We used this model to simulate the three music performance settings, replicating behavioral results. Our model also lets us make predictions of musician's performance not yet tested. The present study offers a dynamical explanation of how an individual's SMT affects adaptive synchronization in realistic musical performance.

Copy rights belong to original authors. Visit the link for more info

Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.10.14.338277v1?rss=1

Authors: Dixit, P. D.

Abstract:

In modern biological physics, there is a great interest in building generative probabilistic models for ensembles of covarying binary variables. A popular approach is to use the maximum entropy principle. Here, one builds generative models that use as constraints lower level statistics estimated from the data. While extremely popular, maximum entropy models have conceptual as well as practical issues; they rely on the modelers' choice of constraints and are computationally expensive to infer when the number of variables is large (n > 100). Here, we address both these issues with Superstatistical Generative Model for Binary Data (SiGMoiD). SiGMoiD is a maximum entropy based framework where we imagine that the data as arising from superstatistical system; individual binary variables are coupled to the same bath whose intensive variables fluctuate from sample to sample. Moreover, instead of choosing the constraints, in SiGMoiD we choose only the number of constraints and let the algorithm infer them from the data itself. Notably, we show that SiGMoiD is orders of magnitude faster than current maximum entropy-based models and allows us to model collections of very large number of binary variables. We also discuss future directions.

Copy rights belong to original authors. Visit the link for more info

Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.10.14.340349v1?rss=1

Authors: Scott, K., Fraley, S., Rangamani, P.

Abstract:

YAP/TAZ is a master regulator of mechanotransduction whose functions rely on translocation from the cytoplasm to the nucleus in response to diverse physical cues. Substrate stiffness, substrate dimensionality, and cell shape are all input signals for YAP/TAZ, and through this pathway, regulate critical cellular functions and tissue homeostasis. Yet, the relative contributions of each biophysical signal and the mechanisms by which they synergistically regulate YAP/TAZ in realistic tissue microenvironments that provide multiplexed input signals remains unclear. For example, in simple 2D culture, YAP/TAZ nuclear localization correlates strongly with substrate stiffness, while in 3D environments, YAP/TAZ translocation can increase with stiffness, decrease with stiffness, or remain unchanged. Here, we develop a spatial model of YAP/TAZ translocation to enable quantitative analysis of the relationships between substrate stiffness, substrate dimensionality, and cell shape. Our model couples cytosolic stiffness to nuclear mechanics to replicate existing experimental trends, and extends beyond current data to predict that increasing substrate activation area through changes in culture dimensionality, while conserving cell volume, forces distinct shape changes that result in nonlinear effect on YAP/TAZ nuclear localization. Moreover, differences in substrate activation area versus total membrane area can account for counterintuitive trends in YAP/TAZ nuclear localization in 3D culture. Based on this multiscale investigation of the different system features of YAP/TAZ nuclear translocation, we predict that how a cell reads its environment is a complex information transfer function of multiple mechanical and biochemical factors. These predictions reveal design principles of cellular and tissue engineering for YAP/TAZ mechanotransduction.

Copy rights belong to original authors. Visit the link for more info

Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.10.14.338756v1?rss=1

Authors: Heyne, M., Shirian, J., Cohen, I., Peleg, Y., Radisky, E. S., Papo, N., Shifman, J. M.

Abstract:

Each protein-protein interaction (PPI) has evolved to possess binding affinity that is compatible with its cellular function. As such, cognate enzyme/inhibitor interactions frequently exhibit very high binding affinities, while structurally similar non-cognate PPIs possess substantially weaker binding affinities. To understand how slight differences in sequence and structure could lead to drastic changes in PPI binding free energy ({Delta}{Delta}Gbind), we study three homologous PPIs that span nine orders of magnitude in binding affinity and involve a serine protease interacting with an inhibitor BPTI. Using state-of-the-art methodology that combines protein randomization and affinity sorting coupled to next-generation sequencing and data normalization, we report quantitative binding landscapes consisting of {Delta}{Delta}Gbind values for the three PPIs, gleaned from tens of thousands of single and double mutations in the BPTI binding interface. We demonstrate that the three homologous PPIs possess drastically different binding landscapes and lie at different points in respect to the landscape maximum. Furthermore, the three PPIs demonstrate distinct patterns of coupling energies between two simultaneous mutations that depend not only on positions involved but also on the nature of the mutation. Interestingly, we find that in all three PPIs positive epistasis is frequently observed at hot-spot positions where mutations lead to loss of high affinity, while conversely negative epistasis is observed at cold-spot positions, where mutations lead to affinity enhancement. The new insights on PPI evolution revealed in this study will be invaluable in understanding evolution of other biological complexes and can greatly facilitate design of novel high-affinity protein inhibitors.

Copy rights belong to original authors. Visit the link for more info

Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.10.14.338822v1?rss=1

Authors: Deganutti, G., Barkan, K., Ladds, G., Reynolds, C. A.

Abstract:

Despite being a target for about one-third of approved drugs, G protein-coupled receptors (GPCRs) still represent a tremendous reservoir for therapeutic strategies against countless diseases. For example, several cardiovascular and central nervous systems conditions could benefit from clinical agents that activate the adenosine 1 receptor (A1R), however, the pursuit of A1R agonists for clinical use are usually impeded by both on- and off-target side effects. One of the possible strategies to overcome this issue is the development of positive allosteric modulators (PAMs) capable of selectively enhancing the effect of a specific receptor subtype and triggering functional selectivity (a phenomenon also referred to as bias). Intriguingly, besides enforcing the effect of agonists upon binding to an allosteric site, most of the A1R PAMs display intrinsic partial agonism and orthosteric competition with antagonists. To rationalize this behaviour, we simulated the binding of the prototypical PAMs PD81723 and VCP171, the antagonist 13B, and the bitopic agonist VCP746. We propose that a single PAM can bind several A1R sites rather than a unique allosteric pocket, reconciling the structure-activity relationship and the mutagenesis results.

Copy rights belong to original authors. Visit the link for more info