Session C12: Physics of Proteins I

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Computational enzyme design has been an expanding frontier in scientific development and application. The ongoing challenges in this field center on the accurate modeling of an enzyme’s behavior when mutations are introduced. Ideally, the recognition of valuable mutations and discarding ineffectual ones is decisive so that in vitro work is drastically economized. Additionally, complimentary effects between mutations may be determined. This can lead to designed enzymes that were otherwise inaccessible through traditional. This modeling has become increasingly complex as more tools are developed to address the precise needs of enzymatic efficiency and efficacy. The rapid growth of this field means that it is easy to be unaware of developments that would meet the needs of interlaboratory efforts. Last year, a review detailing the most recent developments and focuses within computational enzyme design over the last 5 years was accepted. This talk draws from this review to discuss the significant developments of computational enzyme design within the last five years and unique solutions to long-standing issues.   

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Many biological processes, from intracellular transport to trans-membrane ion pumping, rely on non-equilibrium conformation dynamics to drive directional processes. Here, we study the dynamics of bacteriorhodopsin during its light-activated, multi-step catalytic cycle that results in vectorial H+ transport across a membrane. Applying single-molecule 2D fluorescence lifetime correlation spectroscopy (sm-2D-FLCS), we are able to monitor the dynamics of several intermediates of the reaction cycle from microseconds to seconds. The nature of the experiment allows forward and reverse transitions to be measured separately, which is crucial in determining the efficiency of protein motor action. Wild-type bacteriorhodopsin shows forward transitions are more favorable by several orders of magnitude, indicating efficient directional motion and successful ion transport. In contrast, common protein mutants that destabilize the H+ transfer chain are shown to have near identical forward and reverse dynamics, consistent with bidirectional H+ transport and no motor efficiency.   

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Understanding the various roles of intrinsically disordered proteins (IDPs) requires having detailed knowledge of their disordered conformations, which can be an experimental challenge. Here, using single-molecule magnetic tweezers, we probe the conformation of a polypeptide construct of the neurofilament subunit protein’s disordered tail by looking at its elastic properties. When stretched at subpicoNewton forces, the IDP behaves nearly like an ideal chain – which was surprising given its high net charge – suggesting local structures within the protein. In guanidinium, these local structures are unfolded as the protein extends in length and behaves as a swollen random coil. We observe gradual extension and swelling with increasing guanidinium concentration, indicating partial unfolding of these structures. Comparing this to the elastic response in other solvent conditions, such as salt and nonionic denaturant, reveals the nature of these local structures. In addition, applying higher forces (<100pN) provides more proof of these local structures by mechanically disrupting them. Overall, we show complex elastic behaviors that reveal local structures in an IDP, and we demonstrate a framework to study the conformations of IDPs through force stretching experiments.   

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Proteins are intrinsically dynamic and exist as an ensemble of conformations. Molecular dynamics simulations allow us to generate trajectories of proteins as they move around in a parameterized force field. These trajectories give us a deeper view of the conformations proteins take in solution than the static structures generated by experimental techniques such as X-ray crystallography. Many computational methods also exist for predicting three-dimensional structures for proteins with known amino acid sequences. Many of these methods use machine learning techniques to generate inter-residue distance predictions. Though the purpose of these distance predictions is to predict static structures that match experimentally determined protein structures, they may also contain information about the conformations the proteins adopt in solution. Analysis of protein trajectories and inter-residue distance predictions is helping us to better understand this relationship and may lead to the discovery of an efficient approach to determine dynamic characteristics from structure prediction data.   

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Proteins and many other systems are often conceptualized as networks to access analysis methods. Several approaches use molecular dynamics simulations of proteins to construct networks using correlational statistics. However, in the field of network science, it is well-established to solve the inverse problem for a network that can produce the observed correlations. We apply this inverse approach to two adhesion proteins, FimH and Siglec-8, to identify networks that are distinct from correlation networks and instead resemble a contact map. In the inverse networks, covalent interactions are stronger than hydrogen-bonds and non-bonding interactions. This pattern is not present in correlation networks. Moreover, interactions within the backbone dominate the inverse networks, while interactions between sidechains dominate the correlation networks. Due to the differences in the networks constructed by correlation and by solving the inverse problem, there are also differences in topological properties, community detection, and comparing connectivity. While more computationally expensive, solving the inverse problem can remove transitive correlations to produce networks with physically interpretable interactions.   

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Standard computations of exit times surround numerical routines to determine characteristics of the energy landscape in which CRISPR proteins participate, including stages of the binding process comprised of protein inspection of target sequences for complementarity, in which successful binding occurs through the formation of a stably bound complex. In this work, an IVP associated with a stochastically driven oscillator for the exit time will be presented, from which exit times for varying potential landscapes are obtained. To build upon previous formulations of potential landscape recovery from distributions of exit times in the landscape, we also will discuss novel numerical schemes from which fluctuations to the binding landscape can be realized. The scheme primarily relies on establishing relations between numerical approximations of exit times, in turn enabling us to reconstruct a potential landscape which is valuable for constructing probability measures to quantify likely configurations of different proteins within the Cas family throughout the binding process. Time permitting, comparisons will be established between other statistical mechanics approaches.   

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A principle of the energy landscape theory of protein folding is that amino acid sequences that fold in biologically relevant times are characterized by a funnel-shaped landscape directed towards the native state. Some proteins, however, have recently been found to reversibly switch between two entirely different folds controlled by changes in the local environment. Do these so-called metamorphic proteins exhibit rugged energy landscapes with multiple deep funnels? We used a hybrid sequence-structure based all-atom model to simulate the folding and fold switching of the C-terminal domain (CTD) of the bacterial transcription factor RfaH, a prototypical metamorphic protein. The CTD adopts an α-helical hairpin within the free RfaH structure but undergoes a structural transformation into a β-barrel fold when detached from the rest of RfaH. For the isolated CTD, we found an energy landscape characterized by a single dominant funnel to the beta-barrel state. This suggests that protein folding funnels can be robust against sequence variants that encode for more than one fold. Our results indicate, moreover, that the encoding of dual folds mainly impacts the unfolded state of the CTD, which we found exhibits residual α-helical structure.   

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Collagens are the predominant proteins in vertebrates, forming diverse hierarchical structures to support cells and form connective tissues. Despite their mechanical importance, surprisingly little is established about the molecular encoding of mechanics. Here, we combine position-dependent flexibility analysis with single-molecule imaging by atomic force microscopy (AFM), and find that collagens exhibit variable flexibility along their backbones. We identify a region of high bending flexibility around the matrix metalloprotease (MMP) binding site in a fibril-forming collagen. This result represents the first demonstration of a unique mechanical signature of the MMP site, key for remodeling of the extracellular matrix during development and disease. By comparing collagens with continuous and discontinuous triple-helix-forming sequences, we find that the type of helix interruption correlates with local flexibility, providing a step towards a much-needed map between sequence, structure, and mechanics in these large proteins. Our results inform our understanding of collagen’s ability to adopt compact conformations during cellular secretion and suggest a physical mechanism by which higher-order structure may be regulated by the distinct molecular properties of different collagens.   

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expert   

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The thermal activation process by which a system passes from one local energy minimum to another by crossing an energy barrier is a recurring motif in physics, chemistry, and biology. For instance, biopolymer chains are typically modeled in terms of energy landscapes, with folded and unfolded configurations represented by two distinct wells separated by a barrier. The rate of transfer from the unfolded to folded state depends most importantly on the height of the barrier with respect to the temperature of the heat bath—but also in seemingly idiosyncratic ways on the details of the shape of the barrier. We consider the case of bias due to an external force, analogus to the pulling force applied in optical tweezer experiments on biopolymers. We identify universal behavior of the barrier crossing process and demonstrate that data collapse onto a universal curve can be achieved for simulated data over a wide variety of energy landscapes having barriers of different height and shape.   

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The ability of the multidrug and toxic compound extrusion (MATE) transporters to expel chemical compounds out of the cell can result in multidrug resistance. Recently, both the inward-facing and outward-facing molecular structures have been determined for PfMATE, the MATE transporter of the archaeon Pyrococcus furiosus. This has allowed a deeper understanding of the mechanisms by which PfMATE is able to perform conformational transitions between inward- and outward-facing structures. To investigate this process, we performed molecular dynamics simulations of different systems of inward- and outward-facing PfMATE in the native archaeal lipid bilayer. The targeted molecular dynamics (TMD) simulations of the conformational transition between inward- and outward-facing structures of PfMATE have allowed us to quantify structure conversions by calculating the free-energy profiles using potential of mean force (PMF) calculations. The results show that the residues Asp41/Asp184 in either the deprotonated state or Na⁺-bound state favor the outward-facing conformation, whereas the protonated Asp184, Glu163, and Glu331 favor the inward-facing conformation.   

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Structures of proteins within a family differ mostly locally while usually preserving the overall conformation. How such localized variations are linked to the evolution of protein gene and function is not generally understood, and so far, the deformations were analyzed by linear frameworks. Here, we employ a purely geometric, nonlinear measure of local conformational changes and study its statistics. In particular, we examine how the measure correlates with statistics of point mutations and coevolution in protein families.   

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In recent experiment, transport of mRNA–protein complex in neurons has been observed to follow a truncated Levy walk behavior. We have theoretically studied a random walk model based on majority rule. At a given instant, the moving direction of a cargo is determined by motor coordination mediated by a tug-of-war mechanism between two kinds of competing motor proteins. We have demonstrated that the run-time distribution P(t) for unidirectional transport of a cargo can be described by a truncated Levy walk P(t) ∝ t^−3/2e^−γ_ut with γ_u being the unbinding rate of a motor protein from microtubule. The mean squared displacement of a cargo changes from super-diffusive behavior 〈X²〉 ∝ t² for t < γ_u⁻¹ to normal diffusion 〈X²〉 ∝ t for t > γ_u⁻¹. By considering the correlation effect in binding of a motor protein to microtubule, we have shown that Levy walk behavior of P(t) ∝ t^−3/2 persists robustly against correlation simply adding a finite cutoff time γ_b/γ_c² with γ_c representing the amount of correlation.