Atoms to Phenotypes: Molecular Design Principles of Cellular Energy Metabolism.

We report a 100-million atom-scale model of an entire cell organelle, a photosynthetic chromatophore vesicle from a purple bacterium, that reveals the cascade of energy conversion steps culminating in the generation of ATP from sunlight. Molecular dynamics simulations of this vesicle elucidate how the integral membrane complexes influence local curvature to tune photoexcitation of pigments. Brownian dynamics of small molecules within the chromatophore probe the mechanisms of directional charge transport under various pH and salinity conditions. Reproducing phenotypic properties from atomistic details, a kinetic model evinces that low-light adaptations of the bacterium emerge as a spontaneous outcome of optimizing the balance between the chromatophore's structural integrity and robust energy conversion. Parallels are drawn with the more universal mitochondrial bioenergetic machinery, from whence molecular-scale insights into the mechanism of cellular aging are inferred. Together, our integrative method and spectroscopic experiments pave the way to first-principles modeling of whole living cells.

Impact of the Unstirred Water Layer on the Permeation of Small-Molecule Drugs.

Over the last two decades, numerous molecular dynamics (MD) simulation-based investigations have attempted to predict the membrane permeability to small-molecule drugs as indicators of their bioavailability, a majority of which utilize the inhomogeneous solubility diffusion (ISD) model. However, MD-based membrane permeability is routinely 3-4 orders of magnitude larger than the values measured with the intestinal perfusion technique. There have been contentious discussions on the sources of the large discrepancies, and the two indisputable, potentially dominant ones are the fixed protonation state of the permeant and the neglect of the unstirred water layer (UWL). Employing six small-molecule drugs of different biopharmaceutical classification system classes, the current MD study relies on the ISD model but introduces the (de)protonation of the permeant by characterizing the permeation free energy of both neutral and charged states. In addition, the role of the UWL as a potential resistance against permeation is explored. The new MD protocol closely mimics the nature of small-molecule permeation and yields estimates that agree well with in vivo intestinal permeability.

Classifying Protein-Protein Binding Affinity with Free-Energy Calculations and Machine Learning Approaches.

Understanding the intricate phenomenon of neuronal wiring in the brain is of great interest in neuroscience. In the fruit fly, Drosophila melanogaster, the Dpr-DIP interactome has been identified to play an important role in this process. However, experimental data suggest that a merely limited subset of complexes, essentially 57 out of a total of 231, exhibit strong binding affinity. In this work, we sought to identify the residue-level molecular basis underlying the difference in binding affinity using a state-of-the-art methodology consisting of standard binding free-energy calculations with a geometrical route and machine learning (ML) techniques. We determined the binding affinity for two complexes using statistical mechanics simulations, achieving an excellent reproduction of the experimental data. Moreover, we predicted the binding free energy for two additional low-affinity complexes, devoid of experimental estimation, while simultaneously identifying key residues for the binding. Furthermore, through the use of ML algorithms, linear discriminant analysis, and random forest, we achieved remarkable accuracy, as high as 0.99, in discerning between strong (cognate) and weak (noncognate) binders. The presented ML approach encompasses easily transferable input features, enabling its broad application to any interactome while facilitating the identification of pivotal residues critical for binding interactions. The predictive power of the generated model was probed on similar protein families from 13 diverse species. Our ML model exhibited commendable performance on these additional data sets, showcasing its reliability and robustness across the species barrier.

Conformational transitions of the serotonin 5-HT3 receptor.

The serotonin 5-HT3 receptor is a pentameric ligand-gated ion channel (pLGIC). It belongs to a large family of receptors that function as allosteric signal transducers across the plasma membrane1,2; upon binding of neurotransmitter molecules to extracellular sites, the receptors undergo complex conformational transitions that result in transient opening of a pore permeable to ions. 5-HT3 receptors are therapeutic targets for emesis and nausea, irritable bowel syndrome and depression3. In spite of several reported pLGIC structures4-8, no clear unifying view has emerged on the conformational transitions involved in channel gating. Here we report four cryo-electron microscopy structures of the full-length mouse 5-HT3 receptor in complex with the anti-emetic drug tropisetron, with serotonin, and with serotonin and a positive allosteric modulator, at resolutions ranging from 3.2 Å to 4.5 Å. The tropisetron-bound structure resembles those obtained with an inhibitory nanobody5 or without ligand9. The other structures include an 'open' state and two ligand-bound states. We present computational insights into the dynamics of the structures, their pore hydration and free-energy profiles, and characterize movements at the gate level and cation accessibility in the pore. Together, these data deepen our understanding of the gating mechanism of pLGICs and capture ligand binding in unprecedented detail.

Predictions from First-Principles of Membrane Permeability to Small Molecules: How Useful Are They in Practice?

Predicting from first-principles the rate of passive permeation of small molecules across the biological membrane represents a promising strategy for screening lead compounds upstream in the drug-discovery and development pipeline. One popular avenue for the estimation of permeation rates rests on computer simulations in conjunction with the inhomogeneous solubility-diffusion model, which requires the determination of the free-energy change and position-dependent diffusivity of the substrate along the translocation pathway through the lipid bilayer. In this Perspective, we will clarify the physical meaning of the membrane permeability inferred from such computer simulations, and how theoretical predictions actually relate to what is commonly measured experimentally. We will also examine why these calculations remain both technically challenging and overly computationally expensive, which has hitherto precluded their routine use in nonacademic settings. We finally synopsize possible research directions to meet these challenges, increase the predictive power of physics-based rates of passive permeation, and, by ricochet, improve their practical usefulness.

Achieving Accurate Standard Protein-Protein Binding Free Energy Calculations through the Geometrical Route and Ergodic Sampling.

A new strategy for the prediction of binding free energies of protein-protein complexes is reported in the present article. By combining an ergodic-sampling algorithm with the so-called "geometrical route", which introduces a series of geometrical restraints as a preamble to the physical separation of the two partners, we achieve accurate binding free energy calculations for medium-sized protein-protein complexes within the microsecond timescale. The ergodic-sampling algorithm, namely, Gaussian-accelerated molecular dynamics (GaMD), implicitly helps explore the conformational change of the two binding partners as they associate reversibly by raising the energy wells. Therefore, independent simulations capturing the isomerization of proteins are no longer needed, reducing both the computational cost and human effort. Numerical applications indicate errors on the order of 0.1 kcal/mol for the Abl-SH3 domain binding a decapeptide, of 2.6 kcal/mol for the barnase-barstar complex, and of 0.2 kcal/mol for human leukocyte elastase binding the third domain of the turkey ovomucoid inhibitor. Compared with the classical geometrical route, which resorts to collective variables to describe the isomerization of proteins, our new strategy possesses remarkable convergence properties and robustness for protein-protein complexes owing to improved ergodic sampling. We are confident that the strategy presented in this study will have a broad range of applications, helping us understand recognition-association phenomena in the areas of physical, biological, and medicinal chemistry.

Accurate Description of Solvent-Exposed Salt Bridges with a Non-polarizable Force Field Incorporating Solvent Effects.

The strength of salt bridges resulting from the interaction of cations and anions is modulated by their environment. However, polarization of the solvent molecules by the charged moieties makes the accurate description of cation-anion interactions in an aqueous solution by means of a pairwise additive potential energy function and classical combination rules particularly challenging. In this contribution, aiming at improving the representation of solvent-exposed salt-bridge interactions with an all-atom non-polarizable force field, we put forth here a parametrization strategy. First, the interaction of a cation and an anion is characterized by hybrid quantum mechanical/molecular mechanics (QM/MM) potential of mean force (PMF) calculations, whereby constantly exchanging solvent molecules around the ions are treated at the quantum mechanical level. The Lennard-Jones (LJ) parameters describing the salt-bridge ion pairs are then optimized to match the reference QM/MM PMFs through the so-called nonbonded FIX, or NBFIX, feature of the CHARMM force field. We apply the new set of parameters, coined CHARMM36m-SBFIX, to the calculation of association constants for the ammonium-acetate and guanidinium-acetate complexes, the osmotic pressures for glycine zwitterions, guanidinium, and acetate ions, and to the simulation of both folded and intrinsically disordered proteins. Our findings indicate that CHARMM36m-SBFIX improves the description of solvent-exposed salt-bridge interactions, both structurally and thermodynamically. However, application of this force field to the standard binding free-energy calculation of a protein-ligand complex featuring solvent-excluded salt-bridge interactions leads to a poor reproduction of the experimental value, suggesting that the parameters optimized in an aqueous solution cannot be readily transferred to describe solvent-excluded salt-bridge interactions. Put together, owing to their sensitivity to the environment, modeling salt-bridge interactions by means of a single, universal set of LJ parameters remains a daunting theoretical challenge.

Standard Binding Free Energy and Membrane Desorption Mechanism for a Phospholipase C.

Peripheral membrane proteins (PMPs) bind temporarily to cellular membranes and play important roles in signaling, lipid metabolism, and membrane trafficking. Obtaining accurate membrane-PMP affinities using experimental techniques is more challenging than for protein-ligand affinities in an aqueous solution. At the theoretical level, calculation of the standard protein-membrane binding free energy using molecular dynamics simulations remains a daunting challenge owing to the size of the biological objects at play, the slow lipid diffusion, and the large variation in configurational entropy that accompanies the binding process. To overcome these challenges, we used a computational framework relying on a series of potential-of-mean-force (PMF) calculations including a set of geometrical restraints on collective variables. This methodology allowed us to determine the standard binding free energy of a PMP to a phospholipid bilayer using an all-atom force field. Bacillus thuringiensis phosphatidylinositol-specific phospholipase C (BtPI-PLC) was chosen due to its importance as a virulence factor and owing to the host of experimental affinity data available. We computed a standard binding free energy of -8.2 ± 1.4 kcal/mol in reasonable agreement with the reported experimental values (-6.6 ± 0.2 kcal/mol). In light of the 2.3-µs separation PMF calculation, we investigated the mechanism whereby BtPI-PLC disengages from interactions with the lipid bilayer during separation. We describe how a short amphipathic helix engages in transitory interactions to ease the passage of its hydrophobes through the interfacial region upon desorption from the bilayer.

MLCV: Bridging Machine-Learning-Based Dimensionality Reduction and Free-Energy Calculation.

Importance-sampling algorithms leaning on the definition of a model reaction coordinate (RC) are widely employed to probe processes relevant to chemistry and biology alike, spanning time scales not amenable to common, brute-force molecular dynamics (MD) simulations. In practice, the model RC often consists of a handful of collective variables (CVs) chosen on the basis of chemical intuition. However, constructing manually a low-dimensional RC model to describe an intricate geometrical transformation for the purpose of free-energy calculations and analyses remains a daunting challenge due to the inherent complexity of the conformational transitions at play. To solve this issue, remarkable progress has been made in employing machine-learning techniques, such as autoencoders, to extract the low-dimensional RC model from a large set of CVs. Implementation of the differentiable, nonlinear machine-learned CVs in common MD engines to perform free-energy calculations is, however, particularly cumbersome. To address this issue, we present here a user-friendly tool (called MLCV) that facilitates the use of machine-learned CVs in importance-sampling simulations through the popular Colvars module. Our approach is critically probed with three case examples consisting of small peptides, showcasing that through hard-coded neural network in Colvars, deep-learning and enhanced-sampling can be effectively bridged with MD simulations. The MLCV code is versatile, applicable to all the CVs available in Colvars, and can be connected to any kind of dense neural networks. We believe that MLCV provides an effective, powerful, and user-friendly platform accessible to experts and nonexperts alike for machine-learning (ML)-guided CV discovery and enhanced-sampling simulations to unveil the molecular mechanisms underlying complex biochemical processes.

Structure, substrate binding, and symmetry of the mitochondrial ADP/ATP carrier in its matrix-open state.

The mitochondrial ADP/ATP carrier (AAC) performs the first and last step in oxidative phosphorylation by exchanging ADP and ATP across the mitochondrial inner membrane. Its optimal function has been shown to be dependent on cardiolipins (CLs), unique phospholipids located almost exclusively in the mitochondrial membrane. In addition, AAC exhibits an enthralling threefold pseudosymmetry, a unique feature of members of the SLC25 family. Recently, its conformation poised for binding of ATP was solved by x-ray crystallography referred to as the matrix state. Binding of the substrate leads to conformational changes that export of ATP to the mitochondrial intermembrane space. In this contribution, we investigate the influence of CLs on the structure, substrate-binding properties, and structural symmetry of the matrix state, employing microsecond-scale molecular dynamics simulations. Our findings demonstrate that CLs play a minor stabilizing role on the AAC structure. The interdomain salt bridges and hydrogen bonds forming the cytoplasmic network and tyrosine braces, which ensure the integrity of the global AAC scaffold, highly benefit from the presence of CLs. Under these conditions, the carrier is found to be organized in a more compact structure in its interior, as revealed by analyses of the electrostatic potential, measure of the AAC cavity aperture, and the substrate-binding assays. Introducing a convenient structure-based symmetry metric, we quantified the structural threefold pseudosymmetry of AAC, not only for the crystallographic structure, but also for conformational states of the carrier explored in the molecular dynamics simulations. Our results suggest that CLs moderately contribute to preserve the pseudosymmetric structure of AAC.

BFEE2: Automated, Streamlined, and Accurate Absolute Binding Free-Energy Calculations.

Accurate absolute binding free-energy estimation in silico, following either an alchemical or a geometrical route, involves several subprocesses and requires the introduction of geometric restraints. Human intervention, for instance, to define the necessary collective variables, prepare the input files, monitor the simulation, and perform post-treatments is, however, tedious, cumbersome, and prone to errors. With the aim of automating and streamlining free-energy calculations, especially for nonexperts, version 2.0 of the binding free energy estimator (BFEE2) provides both standardized alchemical and geometrical workflows and obviates the need for extensive human intervention to guarantee complete reproducibility of the results. To achieve the largest gamut of protein-ligand and, more generally, of host-guest complexes, BFEE2 supports most academic force fields, such as CHARMM, Amber, OPLS, and GROMOS. Configurational files are generated in the NAMD and Gromacs formats, and all the post-treatments are performed in an automated fashion. Moreover, convergence of the free-energy calculation can be monitored from the intermediate files generated during the simulation. All in all, BFEE2 is a foolproof, versatile tool for accurate absolute binding free-energy calculations, assisting the end-user over a broad range of applications.

Charge Transfer and Chemo-Mechanical Coupling in Respiratory Complex I.

The mitochondrial respiratory chain, formed by five protein complexes, utilizes energy from catabolic processes to synthesize ATP. Complex I, the first and the largest protein complex of the chain, harvests electrons from NADH to reduce quinone, while pumping protons across the mitochondrial membrane. Detailed knowledge of the working principle of such coupled charge-transfer processes remains, however, fragmentary due to bottlenecks in understanding redox-driven conformational transitions and their interplay with the hydrated proton pathways. Complex I from Thermus thermophilus encases 16 subunits with nine iron-sulfur clusters, reduced by electrons from NADH. Here, employing the latest crystal structure of T. thermophilus complex I, we have used microsecond-scale molecular dynamics simulations to study the chemo-mechanical coupling between redox changes of the iron-sulfur clusters and conformational transitions across complex I. First, we identify the redox switches within complex I, which allosterically couple the dynamics of the quinone binding pocket to the site of NADH reduction. Second, our free-energy calculations reveal that the affinity of the quinone, specifically menaquinone, for the binding-site is higher than that of its reduced, menaquinol form-a design essential for menaquinol release. Remarkably, the barriers to diffusive menaquinone dynamics are lesser than that of the more ubiquitous ubiquinone, and the naphthoquinone headgroup of the former furnishes stronger binding interactions with the pocket, favoring menaquinone for charge transport in T. thermophilus. Our computations are consistent with experimentally validated mutations and hierarchize the key residues into three functional classes, identifying new mutation targets. Third, long-range hydrogen-bond networks connecting the quinone-binding site to the transmembrane subunits are found to be responsible for proton pumping. Put together, the simulations reveal the molecular design principles linking redox reactions to quinone turnover to proton translocation in complex I.

Modulation of membrane permeability by carbon dioxide.

Promoting drug delivery across the biological membrane is a common strategy to improve bioavailability. Inspired by the observation that carbonated alcoholic beverages can increase the absorption rate of ethanol, we speculate that carbon dioxide (CO2 ) molecules could also enhance membrane permeability to drugs. In the present work, we have investigated the effect of CO2 on the permeability of a model membrane formed by 1-palmitoyl-2-oleoyl-sn-glycero-3-phosphocholine lipids to three drug-like molecules, namely, ethanol, 2',3'-dideoxyadenosine, and trimethoprim. The free-energy and fractional-diffusivity profiles underlying membrane translocation were obtained from µs-timescale simulations and combined in the framework of the fractional solubility-diffusion model. We find that addition of CO2 in the lipid environment results in an increase of the membrane permeability to the three substrates. Further analysis of the permeation events reveals that CO2 expands and loosens the membrane, which, in turn, facilitates permeation of the drug-like molecules. © 2019 Wiley Periodicals, Inc.

Taming Rugged Free Energy Landscapes Using an Average Force.

The observation of complex structural transitions in biological and abiological molecular objects within time scales amenable to molecular dynamics (MD) simulations is often hampered by significant free energy barriers associated with entangled movements. Importance-sampling algorithms, a powerful class of numerical schemes for the investigation of rare events, have been widely used to extend simulations beyond the time scale common to MD. However, probing processes spanning milliseconds through microsecond molecular simulations still constitutes in practice a daunting challenge because of the difficulty of taming the ruggedness of multidimensional free energy surfaces by means of naive transition coordinates. To address this limitation, in recent years we have elaborated importance-sampling methods relying on an adaptive biasing force (ABF). In this Account, we review recent developments of algorithms aimed at mapping rugged free energy landscapes that correspond to complex processes of physical, chemical, and biological relevance. Through these developments, we have broadened the spectrum of applications of the popular ABF algorithm while improving its computational efficiency, notably for multidimensional free energy calculations. One major algorithmic advance, coined meta-eABF, merges the key features of metadynamics and an extended Lagrangian variant of ABF (eABF) by simultaneously shaving the barriers and flooding the valleys of the free energy landscape, and it possesses a convergence rate up to 5-fold greater than those of other importance-sampling algorithms. Through faster convergence and enhanced ergodic properties, meta-eABF represents a significant step forward in the simulation of millisecond-time-scale events. Here we introduce extensions of the algorithm, notably its well-tempered and replica-exchange variants, which further boost the sampling efficiency while gaining in numerical stability, thus allowing quantum-mechanical/molecular-mechanical free energy calculations to be performed at a lower cost. As a paradigm to bridge microsecond simulations to millisecond events by means of free energy calculations, we have applied the ABF family of algorithms to decompose complex movements in molecular objects of biological and abiological nature. We show here how water lubricates the shuttling of an amide-based rotaxane by altering the mechanism that underlies the concerted translation and isomerization of the macrocycle. Introducing novel collective variables in a computational workflow for the rigorous determination of standard binding free energies, we predict with utmost accuracy the thermodynamics of protein-ligand reversible association. Because of their simplicity, versatility, and robust mathematical foundations, the algorithms of the ABF family represent an appealing option for the theoretical investigation of a broad range of problems relevant to physics, chemistry, and biology.

Finding an Optimal Pathway on a Multidimensional Free-Energy Landscape.

An ad-hoc, yet widely adopted approach to investigate complex molecular objects in motion using importance-sampling schemes involves two steps, namely (i) mapping the multidimensional free-energy landscape that characterizes the movements in the molecular object at hand and (ii) finding the most probable transition path connecting basins of the free-energy hyperplane. To achieve this goal, we turn to an importance-sampling algorithm, coined well-tempered metadynamics-extended adaptive biasing force (WTM-eABF), aimed at mapping rugged free-energy landscapes, combined with a path-searching algorithm, which we call multidimensional lowest energy (MULE), to identify the underlying minimum free-energy pathway in the collective-variable space of interest. First, the well-tempered feature of the importance-sampling scheme confers to the latter an asymptotic convergence, while the overall algorithm inherits the advantage of high sampling efficiency of its predecessor, meta-eABF, making its performance less sensitive to user-defined parameters. Second, the Dijkstra algorithm implemented in MULE is able to identify with utmost efficiency a pathway that satisfies minimum free energy of activation among all the possible routes in the multidimensional free-energy landscape. Numerical simulations of three molecular assemblies indicate that association of WTM-eABF and MULE constitutes a reliable, efficient and robust approach for exploring coupled movements in complex molecular objects. On account of its ease of use and intrinsic performance, we expect WTM-eABF and MULE to become a tool of choice for both experts and nonexperts interested in the thermodynamics and the kinetics of processes relevant to chemistry and biology.

Boosting Free-Energy Perturbation Calculations with GPU-Accelerated NAMD.

Harnessing the power of graphics processing units (GPUs) to accelerate molecular dynamics (MD) simulations in the context of free-energy calculations has been a longstanding effort toward the development of versatile, high-performance MD engines. We report a new GPU-based implementation in NAMD of free-energy perturbation (FEP), one of the oldest, most popular importance-sampling approaches for the determination of free-energy differences that underlie alchemical transformations. Compared to the CPU implementation available since 2001 in NAMD, our benchmarks indicate that the new implementation of FEP in traditional GPU code is about four times faster, without any noticeable loss of accuracy, thereby paving the way toward more affordable free-energy calculations on large biological objects. Moreover, we have extended this new FEP implementation to a code path highly optimized for a single-GPU node, which proves to be up to nearly 30 times faster than the CPU implementation. Through optimized GPU performance, the present developments provide the community with a cost-effective solution for conducting FEP calculations. The new FEP-enabled code has been released with NAMD 3.0.

Perturbations of Native Membrane Protein Structure in Alkyl Phosphocholine Detergents: A Critical Assessment of NMR and Biophysical Studies.

Membrane proteins perform a host of vital cellular functions. Deciphering the molecular mechanisms whereby they fulfill these functions requires detailed biophysical and structural investigations. Detergents have proven pivotal to extract the protein from its native surroundings. Yet, they provide a milieu that departs significantly from that of the biological membrane, to the extent that the structure, the dynamics, and the interactions of membrane proteins in detergents may considerably vary, as compared to the native environment. Understanding the impact of detergents on membrane proteins is, therefore, crucial to assess the biological relevance of results obtained in detergents. Here, we review the strengths and weaknesses of alkyl phosphocholines (or foscholines), the most widely used detergent in solution-NMR studies of membrane proteins. While this class of detergents is often successful for membrane protein solubilization, a growing list of examples points to destabilizing and denaturing properties, in particular for α-helical membrane proteins. Our comprehensive analysis stresses the importance of stringent controls when working with this class of detergents and when analyzing the structure and dynamics of membrane proteins in alkyl phosphocholine detergents.

Scalable molecular dynamics on CPU and GPU architectures with NAMD.

NAMDis a molecular dynamics program designed for high-performance simulations of very large biological objects on CPU- and GPU-based architectures. NAMD offers scalable performance on petascale parallel supercomputers consisting of hundreds of thousands of cores, as well as on inexpensive commodity clusters commonly found in academic environments. It is written in C++ and leans on Charm++ parallel objects for optimal performance on low-latency architectures. NAMD is a versatile, multipurpose code that gathers state-of-the-art algorithms to carry out simulations in apt thermodynamic ensembles, using the widely popular CHARMM, AMBER, OPLS, and GROMOS biomolecular force fields. Here, we review the main features of NAMD that allow both equilibrium and enhanced-sampling molecular dynamics simulations with numerical efficiency. We describe the underlying concepts utilized by NAMD and their implementation, most notably for handling long-range electrostatics; controlling the temperature, pressure, and pH; applying external potentials on tailored grids; leveraging massively parallel resources in multiple-copy simulations; and hybrid quantum-mechanical/molecular-mechanical descriptions. We detail the variety of options offered by NAMD for enhanced-sampling simulations aimed at determining free-energy differences of either alchemical or geometrical transformations and outline their applicability to specific problems. Last, we discuss the roadmap for the development of NAMD and our current efforts toward achieving optimal performance on GPU-based architectures, for pushing back the limitations that have prevented biologically realistic billion-atom objects to be fruitfully simulated, and for making large-scale simulations less expensive and easier to set up, run, and analyze. NAMD is distributed free of charge with its source code at www.ks.uiuc.edu.

Binding properties of the quaternary assembly protein SPAG1.

In cells, many constituents are able to assemble resulting in large macromolecular machineries possessing very specific biological and physiological functions, e.g. ribosome, spliceosome and proteasome. Assembly of such entities is commonly mediated by transient protein factors. SPAG1 is a multidomain protein, known to participate in the assembly of both the inner and outer dynein arms. These arms are required for the function of sensitive and motile cells. Together with RUVBL1, RUVBL2 and PIH1D2, SPAG1 is a key element of R2SP, a protein complex assisting the quaternary assembly of specific protein clients in a tissue-specific manner and associating with heat shock proteins (HSPs) and regulators. In this study, we have investigated the role of TPR domains of SPAG1 in the recruitment of HSP chaperones by combining biochemical assays, ITC, NMR spectroscopy and molecular dynamics (MD) simulations. First, we propose that only two, out of the three TPR domains, are able to recruit the protein chaperones HSP70 and HSP90. We then focused on one of these TPR domains and elucidated its 3D structure using NMR spectroscopy. Relying on an NMR-driven docking approach and MD simulations, we deciphered its binding interface with the C-terminal tails of both HSP70 and HSP90. Finally, we addressed the biological function of SPAG1 and specifically demonstrated that a SPAG1 sub-fragment, containing a putative P-loop motif, cannot efficiently bind and hydrolyze GTP in vitro Our data challenge the interpretation of SPAG1 possessing GTPase activity. We propose instead that SPAG1 regulates nucleotide hydrolysis activity of the HSP and RUVBL1/2 partners.

Lysine Mutation of the Claw-Arm-Like Loop Accelerates Catalysis by Cellobiohydrolases.

Searching for viable strategies to accelerate the catalytic cycle of glycoside hydrolase family 7 (GH7) cellobiohydrolase I (CBHI)-the workhorse cellulose-degrading enzymes, we have performed a total of 12-µs molecular dynamics simulations on GH7 CBHI, which brought to light a new mechanism for cellobiose expulsion, coined "claw-arm" action. The loop flanking the product binding site plays the role of a flexible "arm" extending toward cellobiose, and residue Thr389 of this loop acts as a "claw" that captures cellobiose. Five mutations of residue Thr389 were considered to enhance the loop-cellobiose interaction. The lysine mutant was found to significantly accelerate cellobiose expulsion and facilitate polysaccharide-chain translocation. Lysine mutation of Thr393 in Talaromyces emersonii CBHI (TeCel7A) performed similarly. Lysine approaches the catalytic area and stabilizes the Michaelis complex, potentially affecting glycosylation, the rate-limiting step of the catalytic cycle. QM/MM calculations indicate that lysine replacement diminishes the barrier against proton transfer, the crucial step of glycosylation, by 2.3 kcal/mol. Experimental validation was performed using the full-length wild-type (WT) of TeCel7A and its mutants, recombinantly expressed in Pichia pastoris, to degrade the substrates. Compared with the WT, the lysine mutant revealed an associated higher enzymatic reaction rate. Furthermore, cellobiose yield was also increased by lysine mutation, indicating that dissociation of the enzyme from cellulose was accelerated, which largely stems from the enhanced flexibility of the "arm". The present work is envisioned to help design strategies for improving enzymatic activity, while decreasing enzyme cost.