Out-of-Equilibrium Biophysical Chemistry: The Case for Multidimensional, Integrated Single-Molecule Approaches

Experimental and computational investigation of the effect of Hsc70 structural variants on inhibiting amylin aggregation

The misfolding and aggregation of human islet amyloid polypeptide (hIAPP), also known as amylin, have been implicated in the pathogenesis of type 2 diabetes (T2D). Heat shock proteins, specifically, heat shock cognate 70 (Hsc70), are molecular chaperones that protect against hIAPP misfolding and inhibits its aggregation. Nevertheless, there is an incomplete understanding of the mechanistic interactions between Hsc70 domains and hIAPP, thus limiting their potential therapeutic role in diabetes. This study investigates the inhibitory capacities of different Hsc70 variants, aiming to identify the structural determinants that strike a balance between efficacy and cytotoxicity. Our experimental findings demonstrate that the ATPase activity of Hsc70 is not a pivotal factor for inhibiting hIAPP misfolding. We underscore the significance of the C-terminal substrate-binding domain of Hsc70 in inhibiting hIAPP aggregation, emphasizing that the removal of the lid subdomain diminishes the inhibitory effect of Hsc70. Additionally, we employed atomistic discrete molecular dynamics simulations to gain deeper insights into the interaction between Hsc70 variants and hIAPP. Integrating both experimental and computational findings, we propose a mechanism by which Hsc70's interaction with hIAPP monomers disrupts protein-protein connections, primarily by shielding the β-sheet edges of the Hsc70-β-sandwich. The distinctive conformational dynamics of the alpha helices of Hsc70 potentially enhance hIAPP binding by obstructing the exposed edges of the β-sandwich, particularly at the β5-β8 region along the alpha helix interface. This, in turn, inhibits fibril growth, and similar results were observed following hIAPP dimerization. Overall, this study elucidates the structural intricacies of Hsc70 crucial for impeding hIAPP aggregation, improving our understanding of the potential anti-aggregative properties of molecular chaperones in diabetes treatment.

Perspective: How Fast Dynamics Affect Slow Function in Protein Machines

Internal motions in proteins take place on a broad range of time- and space-scales. The potential roles of these dynamics in the biochemical functions of proteins have intrigued biophysicists for many years, and multiple mechanisms to couple motions to function have been proposed. Some of these mechanisms have relied on equilibrium concepts. For example, the modulation of dynamics was proposed to change the entropy of a protein, hence affecting processes such as binding. This so-called dynamic allostery scenario has been demonstrated in several recent experiments. Perhaps even more intriguing may be models that involve out-of-equilibrium operation, which by necessity require the input of energy. We discuss several recent experimental studies that expose such potential mechanisms for coupling dynamics and function. In Brownian ratchets, for example, directional motion is promoted by switching a protein between two free energy surfaces. An additional example involves the effect of microsecond domain-closure dynamics of an enzyme on its much slower chemical cycle. These observations lead us to propose a novel two-time-scale paradigm for the activity of protein machines: fast equilibrium fluctuations take place on the microsecond-millisecond time scale, while on a slower time scale, free energy is invested in order to push the system out of equilibrium and drive functional transitions. Motions on the two time scales affect each other and are essential for the overall function of these machines.

Learning continuous potentials from smFRET

Potential energy landscapes are useful models in describing events such as protein folding and binding. While single-molecule fluorescence resonance energy transfer (smFRET) experiments encode information on continuous potentials for the system probed, including rarely visited barriers between putative potential minima, this information is rarely decoded from the data. This is because existing analysis methods often model smFRET output assuming, from the onset, that the system probed evolves in a discretized state space to be analyzed within a hidden Markov model (HMM) paradigm. By contrast, here, we infer continuous potentials from smFRET data without discretely approximating the state space. We do so by operating within a Bayesian nonparametric paradigm by placing priors on the family of all possible potential curves. As our inference accounts for a number of required experimental features raising computational cost (such as incorporating discrete photon shot noise), the framework leverages a structured-kernel-interpolation Gaussian process prior to help curtail computational cost. We show that our structured-kernel-interpolation priors for potential energy reconstruction from smFRET analysis accurately infers the potential energy landscape from a smFRET binding experiment. We then illustrate advantages of structured-kernel-interpolation priors for potential energy reconstruction from smFRET over standard HMM approaches by providing information, such as barrier heights and friction coefficients, that is otherwise inaccessible to HMMs.

Challenges in Inferring the Directionality of Active Molecular Processes from Single-Molecule Fluorescence Resonance Energy Transfer Trajectories

We discuss some of the practical challenges that one faces in using stochastic thermodynamics to infer directionality of molecular machines from experimental single-molecule trajectories. Because of the limited spatiotemporal resolution of single-molecule experiments and because both forward and backward transitions between the same pairs of states cannot always be detected, differentiating between the forward and backward directions of, e.g., an ATP-consuming molecular machine that operates periodically, turns out to be a nontrivial task. Using a simple extension of a Markov-state model that is commonly employed to analyze single-molecule transition-path measurements, we illustrate how irreversibility can be hidden from such measurements but in some cases can be uncovered when non-Markov effects in low-dimensional single-molecule trajectories are considered.

Frustrations of supported catalytic clusters under operando conditions predicted by a simple lattice model

The energy landscape with a number of close minima separated by low barriers is a well-known issue in computational heterogeneous catalysis. In the framework of the emerging out-of-equilibrium material science, the navigation through such involved landscapes is associated with the functionality of materials. Current advancements in the cluster catalysis has brought and continues to bring essential nuances to the topic. One of them is the possibility of frustration of the catalytic centre under operando conditions. However, this conjecture is difficult to check either experimentally or theoretically. As a step in this direction, as-simple-as-possible lattice model is used to estimate how the supposed frustrations may couple with the elementary reaction and manifest themselves at the macroscopic scale.

Mechanism of lesion verification by the human XPD helicase in nucleotide excision repair

In nucleotide excision repair (NER), the xeroderma pigmentosum D helicase (XPD) scans DNA searching for bulky lesions, stalls when encountering such damage to verify its presence, and allows repair to proceed. Structural studies have shown XPD bound to its single-stranded DNA substrate, but molecular and dynamic characterization of how XPD translocates on undamaged DNA and how it stalls to verify lesions remains poorly understood. Here, we have performed extensive all-atom MD simulations of human XPD bound to undamaged and damaged ssDNA, containing a mutagenic pyrimidine (6-4) pyrimidone UV photoproduct (6-4PP), near the XPD pore entrance. We characterize how XPD responds to the presence of the DNA lesion, delineating the atomistic-scale mechanism that it utilizes to discriminate between damaged and undamaged nucleotides. We identify key amino acid residues, including FeS residues R112, R196, H135, K128, Arch residues E377 and R380, and ATPase lobe 1 residues 215-221, that are involved in damage verification and show how movements of Arch and ATPase lobe 1 domains relative to the FeS domain modulate these interactions. These structural and dynamic molecular depictions of XPD helicase activity with unmodified DNA and its inhibition by the lesion elucidate how the lesion is verified by inducing XPD stalling.

Cell and Tissue Nanomechanics: From Early Development to Carcinogenesis

Cell and tissue nanomechanics, being inspired by progress in high-resolution physical mapping, has recently burst into biomedical research, discovering not only new characteristics of normal and diseased tissues, but also unveiling previously unknown mechanisms of pathological processes. Some parallels can be drawn between early development and carcinogenesis. Early embryogenesis, up to the blastocyst stage, requires a soft microenvironment and internal mechanical signals induced by the contractility of the cortical actomyosin cytoskeleton, stimulating quick cell divisions. During further development from the blastocyst implantation to placenta formation, decidua stiffness is increased ten-fold when compared to non-pregnant endometrium. Organogenesis is mediated by mechanosignaling inspired by intercellular junction formation with the involvement of mechanotransduction from the extracellular matrix (ECM). Carcinogenesis dramatically changes the mechanical properties of cells and their microenvironment, generally reproducing the structural properties and molecular organization of embryonic tissues, but with a higher stiffness of the ECM and higher cellular softness and fluidity. These changes are associated with the complete rearrangement of the entire tissue skeleton involving the ECM, cytoskeleton, and the nuclear scaffold, all integrated with each other in a joint network. The important changes occur in the cancer stem-cell niche responsible for tumor promotion and metastatic growth. We expect that the promising concept based on the natural selection of cancer cells fixing the most invasive phenotypes and genotypes by reciprocal regulation through ECM-mediated nanomechanical feedback loop can be exploited to create new therapeutic strategies for cancer treatment.