Bulletin of the American Physical Society
APS March Meeting 2020
Volume 65, Number 1
Monday–Friday, March 2–6, 2020; Denver, Colorado
Session R24: Physics of Protein Structure, Folding and DesignFocus
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Sponsoring Units: GSNP DBIO DPOLY Chair: Corey O'Hern, Yale University Room: 401 |
Thursday, March 5, 2020 8:00AM - 8:36AM |
R24.00001: The key differences in protein x-ray crystal and solution NMR structures Invited Speaker: John Treado The ability to determine the structure of a protein to near-atomic resolution using x-ray crystallography and NMR spectroscopy has been vital for advances in structural biology. However, it is unclear whether or not the structures obtained from these two methods are the same to within experimental uncertainties, or if these characterization methods influence the structures in some way. To address this important question, we compiled a dataset of paired high-resolution x-ray crystal structures and high-quality NMR structures to determine whether there are any systematic differences between the structures solved using the two experimental techniques. Backbone fluctuations of core Cα atoms reveal that core residues from x-ray crystal structures occupy smaller regions of configuration space than core residues in NMR structures. We also find that the core residues of NMR structures are more densely packed than core residues in x-ray crystal structures. We explain this result by preparing packings of amino-acid-shaped particles with thermalized packing-generation protocols, and find that packings with higher thermalization resemble cores in NMR structures, while in the limit of no thermalization we recover packings that resemble the cores of x-ray crystal structures. This result suggests that the differences between NMR and x-ray crystal structures are caused by the varying degree of thermal fluctuations in the two characterization methods. NMR solution structures are allowed to fluctuate more, which compacts the core, but allows for greater exploration of core configurations. In contrast, x-ray crystal structures occupy only well-defined, smaller regions of configuration space. |
Thursday, March 5, 2020 8:36AM - 8:48AM |
R24.00002: Decoy Detection of Computational Protein Designs Alex Grigas, Zhe Mei, John Treado, Zachary Levine, Lynne Regan, Corey Shane O'Hern Decoy detection is one way to reframe protein folding, not in terms of folding a protein, but in terms of differentiating a well-folded protein from a poorly folded one. An effective decoy scoring metric would both improve prediction methods and indicate how prediction methods fail. The O’Hern group has been making progress in understanding protein structure by focusing on the core regions of proteins, which are inaccessible to solvent. Core structure is uniquely specified by purely repulsive atomic interactions, as hard-sphere interactions are able to predict core structure. In this work, we apply this framework to the decoy detection problem and find that state-of-the-art protein predictions in the CASP11, 12 and 13 competitions often have core regions that are overpacked, due to overlapping residues. Additionally, cores in the predicted protein structures are often too small and too solvent-exposed, suggesting that the prediction methods do not properly capture hydrophobic collapse. Finally, by scoring CASP predictions based on its core structure, we can effectively distinguish between high- and low-quality computational protein designs. |
Thursday, March 5, 2020 8:48AM - 9:00AM |
R24.00003: Low-Force Elasticity Reveals Complex Structure of an Intrinsically Disordered Protein Hoang Truong, Ian L Morgan, Omar Saleh Intrinsically disordered proteins (IDPs) do not possess a well-defined three-dimensional structure, which presents a challenge to investigating their conformations. To overcome this challenge, we explore low-force polymeric elasticity as a means to quantify structural properties. Here, using a high-resolution single-molecule magnetic tweezer, we stretch a polypeptide construct derived from the neurofilament tail domains and study its conformations. At low forces, the construct behaves as an ideal coil instead of a self-avoiding coil. This is surprising given the IDP’s high net charge, suggesting the presence of weak long-range attractive interactions. In addition, the measured persistence length is longer than expected for a random polypeptide chain. This result suggests the IDP has residual structure, which stiffen the chain at low force. We further probe the response of long-range attraction and chain stiffening to changes in salts and denaturants. Our data reveals a rich elastic behavior and complex structure in a nominally disordered chain. |
Thursday, March 5, 2020 9:00AM - 9:12AM |
R24.00004: ProSPr: Protein Structure Prediction via Interatomic Distances Wendy Billings, Bryce E Hedelius, Todd Millecam, David Wingate, Dennis Della Corte Substantial progress has been made in the past several years towards the accurate prediction of protein tertiary structures from primary sequence, aided greatly by the integration of machine learning. Current success is based on two-stage protocols: first, the training of a deep convolutional neural network (CNN) to predict macromolecular structure restraints, and second, the use of these restraints to construct a folded three-dimensional structure of the target protein. Such a two-stage folding protocol was used by DeepMind in the recent Critical Assessment of Structure Prediction (CASP13), which outperformed all established groups. However, DeepMind has not expressed a plan to publish the code of their AlphaFold protocol. Here we present ProSPr, a network representing the first part of the AlphaFold pipeline for predicting interatomic distances, and demonstrate its abilities in the contact prediction task relative to other state-of-the-art methods. We also investigate and report on the roles of certain input features in prediction quality. ProSPr is made freely available to the scientific community both as source code and a Docker container, which we anticipate will encourage the development of better techniques for assembling protein structures from restraints. |
Thursday, March 5, 2020 9:12AM - 9:24AM |
R24.00005: Landscapes, Nonlinearity, and Biomolecular Energy Redistribution Justin Elenewski, Kirill Velizhanin, Michael Zwolak Although selective energy redistribution is critical to the function of numerous biomolecules and functional nanomaterials, the processes mediating these dynamics remain a poorly understood facet of nonequilibrium thermodynamics. In this talk, I will discuss how topological features, nonlinearities, and energy landscape architecture can collude to define biomolecular heat propagation [1]. Our exhaustive all-atom simulations and novel local-in-time and space analysis - which is equally applicable to both theory and experiment - permit the multiscale dissection of energy migration in biomolecules. Unlike transport through small-molecule systems, we find that nonlinearity dominates over coherent processes at even at short length- and time-scales. Leveraging these observations, I will demonstrate how vibrational energy transport can probe otherwise inaccessible aspects of macromolecular dynamics and the interactions that underlie biological function. |
Thursday, March 5, 2020 9:24AM - 10:00AM |
R24.00006: Understanding the native fluctuations of protein cores Invited Speaker: Zhe Mei Understanding how thermal fluctuations affect protein structure is essential for characterizing the energy landscape of proteins, as well as determining the response to amino acid mutations. Protein structures obtained from liquid-state NMR, unlike those from x-ray crystallography, provide a number of model structures that satisfy the experimental constraints. Using a database of high-quality, paired NMR and x-ray crystal structures, we have shown that there are important differences between NMR structures and those solved by x-ray crystallography including differences in the root-mean-square deviations of the core Cα atomic positions, identities of the core amino acids, and packing fractions of core residues. In this work, we carry out all-atom molecular dynamics simulations to study the fluctuating conformational dynamics of wildtype globular proteins, as well as mutants, in aqueous solvent at room temperature. We study the fluctuating conformations using several metrics including the radius of gyration Rg, packing fraction, and Cα atomic positions. We find that most often the MD simulations sample conformations that are representative of the NMR and x-ray conformations, but ~40% of the sampled structures are not consistent with the experimental structures, with larger Rg and lower packing fractions. |
Thursday, March 5, 2020 10:00AM - 10:12AM |
R24.00007: Steric Equation of State for Monoclonal Antibodies from Low to High Concentrations Hassan Shahfar, Christopher J Roberts Steric interactions potentially play a significant role in packing behavior of proteins as one considers increasing concentrations, particularly for monoclonal antibodies (MAbs) due to their extended, anisotropic, and flexible structure. Steric contributions to the equation of state are key to balancing attractive contributions in crowded environments that result in phase separation. Steric protein-protein interactions are sensitive to the size and shape of proteins and can be considered as the minimal level of interactions to be captured in coarse-grained models.To date, all atom simulation was used to calculate only the role of steric only behavior of monoclonal antibodies on the second osmotic virial coefficients . A biased Monte Carlo algorithm (Mayer sampling) used to calculate the steric interactions up to the fifth virial coefficient using all atom simulations for representative MAb structures. The results are compared with different levels of coarse grained models to assess the accuracy of predictions of lower resolution models. The results show that, upon increasing concentration to approx. 10 volume percent, the deviation of the predictions of coarse grained models is pronounced, but can be mitigated by the choice of model. |
Thursday, March 5, 2020 10:12AM - 10:24AM |
R24.00008: Origins of Critical Phenomena in the Folding Phase Diagram of Proteins Andrei Gasic, Margaret Cheung Proteins are folded polymers that are able to respond to slight environmental perturbations to preformation their biological function while also keeping a quasi-unique conformation. Such properties may be exhibited by a physical system near a critical point. Recent experimental and computational findings demonstrate that protein folding transitions in the temperature (T), pressure (P), and crowding volume-fraction (φ) phase diagram have signatures of criticality, where distinct folding phases merge [A. G. Gasic et al., Phys. Rev. X (2019)]. Here we investigate the origin of this critical behavior using insight from polymer physics. Based on our theory, we show that the separation of T between the folding and collapse transition temperatures (TF and TΘ, respectively) lead to a critical transition. We derive the relationship for the correlation length and show divergence approaching the critical regime. Structure-based model simulations of a protein in a crowded environment are used to validate our predictions. Our study illustrates the importance of the crowded cellular environment for a protein biological function. |
Thursday, March 5, 2020 10:24AM - 10:36AM |
R24.00009: Dissimilar ligands bind in a similar fashion: guiding the ligand binding mode prediction Xianjin Xu, Xiaoqin Zou The Molecular Similarity Principle have achieved great successes in the field of drug design/discovery. Existing studies focus on similar ligands, while the behavior of dissimilar ligands remains hidden in darkness. In this study, a strategy was introduced for comparing the binding modes of ligands with different molecular structures for the first time. Our systematic analysis on a newly constructed dataset of protein-ligand complexes showed that ligands with similar structures tend to share a similar binding mode, which is consistent with the molecular similarity principle. More importantly, the results revealed that dissimilar ligands can also bind in a similar fashion. This finding would open a new avenue for rational drug design. Furthermore, a template-guided method was introduced for predicting protein-ligand complex structures. Encouragingly, even with the use of dissimilar ligands as templates, our method significantly outperformed bound docking of the molecular docking method. |
Thursday, March 5, 2020 10:36AM - 10:48AM |
R24.00010: An escape rate analysis for pulling experiments based on energy landscapes in two reaction coordinates Sudeep Adhikari, Kevin Stuart David Beach The structural dynamics of biopolymers such as proteins are described in the context of their conformational energy landscapes. In optical-tweezer pulling experiments, features of the energy landscapes are extracted from the observed distribution of the critical force at which the polymer unfolds, and typically the analysis is based on a one-dimensional (1D) reaction coordinate, the extension. But a 1D analysis is inadequate when various possible folded configurations are degenerate in the end-to-end length. We present an analytical framework for the well escape rate in the context of an effective 2D landscape. We test our analysis against simulated pulling experiments and verify our ability to extract meaningful well parameters. |
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