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

Structure-Guided Engineering Unveils Deeper Substrate Channel in Processive Endoglucanase EG5C-1 Contributing to Enhanced Catalytic Efficiency and Processivity

Processive endoglucanases have generated significant interest due to their bifunctionality in the degradation of cellulose and low product inhibition. However, enhancing their catalytic efficiency through engineering remains a formidable challenge. To address this bottleneck, our engineering efforts targeted loop regions located in the substrate channel of processive endoglucanase EG5C-1. Guided by a comparative analysis of characteristic structural features of the substrate channels in cellobiohydrolase, endoglucanase, and processive endoglucanase, a highly active triple mutant CM6 (N105H/T205S/D233L) was generated that had a 5.1- and 4.7-fold increase in catalytic efficiency toward soluble substrate carboxymethyl cellulose-Na and insoluble substrate phosphoric acid-swollen cellulose (PASC), compared with wild-type EG5C-1. Furthermore, this mutant exhibited greater processivity compared to EG5C-1. Molecular dynamics simulations unveiled that the mutations in the loop regions reshaped the substrate channel, leading to a deeper cleft, resembling the closed channel configuration of cellobiohydrolases. The increased compactness of the substrate channel induced changes in the substrate binding mode and substrate deformation, thereby enhancing both binding affinity and catalytic efficiency. Moreover, metadynamics simulations demonstrated that the processive velocity of cellulose chain through the binding channel in mutant CM6 surpassed that observed in EG5C-1.

Elucidation of the noncovalent interactions driving enzyme activity guides branching enzyme engineering for α-glucan modification

Branching enzymes (BEs) confer to α-glucans, the primary energy-storage reservoir in nature, a variety of features, like slow digestion. The full catalytic cycle of BEs can be divided in six steps, namely two covalent catalytic steps involving glycosylation and transglycosylation, and four noncatalytic steps involving substrate binding and transfers (SBTs). Despite the ever-growing wealth of biochemical and structural information on BEs, clear mechanistic insights into SBTs from an industrial-performance perspective are still missing. Here, we report a Rhodothermus profundi BE (RpBE) endowed with twice as much enzymatic activity as the Rhodothermus obamensis BE currently used in industry. Furthermore, we focus on the SBTs for RpBE by means of large-scale computations supported by experiment. Engineering of the crucial positions responsible for the initial substrate-binding step improves enzymatic activity significantly, while offering a possibility to customize product types. In addition, we show that the high-efficiency substrate-transfer steps preceding glycosylation and transglycosylation are the main reason for the remarkable enzymatic activity of RpBE, suggestive of engineering directions for the BE family.

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.

Do antifreeze proteins generally possess the potential to promote ice growth?

The binding of antifreeze proteins (AFPs) to ice needs to be mediated by interfacial water molecules. Our previous study of the effect of AFPs on the dynamics of the interfacial water of freezing at its initial stage has shown that AFPs can promote the growth of ice before binding to it. However, whether different AFPs can promote the freezing of water molecules on the basal and the prismatic surfaces of ice still needs further study. In the present contribution, five representative natural AFPs with different structures and different activities that can be adsorbed on the basal and/or prismatic surfaces of ice are investigated at the atomic level. Our results show that the phenomenon of promoting the growth of ice crystals is not universal. Only hyperactive AFPs (hypAFPs) can promote the growth of the basal plane of ice, while moderately active AFPs cannot. Moreover, this significant promotion is not observed on the prismatic plane regardless of their activity. Further analysis indicates that this promotion may result from the thicker ice/water interface of the basal plane, and the synergy of hypAFPs with ice crystals.

Mechanism and biomass association of glucuronoyl esterase: an α/β hydrolase with potential in biomass conversion

Glucuronoyl esterases (GEs) are α/β serine hydrolases and a relatively new addition in the toolbox to reduce the recalcitrance of lignocellulose, the biggest obstacle in cost-effective utilization of this important renewable resource. While biochemical and structural characterization of GEs have progressed greatly recently, there have yet been no mechanistic studies shedding light onto the rate-limiting steps relevant for biomass conversion. The bacterial GE OtCE15A possesses a classical yet distinctive catalytic machinery, with easily identifiable catalytic Ser/His completed by two acidic residues (Glu and Asp) rather than one as in the classical triad, and an Arg side chain participating in the oxyanion hole. By QM/MM calculations, we identified deacylation as the decisive step in catalysis, and quantified the role of Asp, Glu and Arg, showing the latter to be particularly important. The results agree well with experimental and structural data. We further calculated the free-energy barrier of post-catalysis dissociation from a complex natural substrate, suggesting that in industrial settings non-catalytic processes may constitute the rate-limiting step, and pointing to future directions for enzyme engineering in biomass utilization.

Zong and coworkers combine computational and experimental methods to decipher in detail the mechanism of action of glucuronoyl esterases, enzymes with significant biotechnological potential for decoupling lignin from polysaccharides in biomass.

Stimuli-Mediated Specific Isolation of Exosomes from Blood Plasma for High-Throughput Profiling of Cancer Biomarkers

Exosomes, ranging from 30-150 nm in diameter, have emerged as promising non-invasive biomarkers for the diagnosis and prognosis of numerous diseases. However, current research on exosomes is largely restricted by the lack of an efficient method to isolate exosomes from real samples. Herein, the first stimuli-mediated enrichment and purification system to selectively and efficiently extract exosomes from clinical plasma for high-throughput profiling of exosomal mRNAs as cancer biomarkers is presented. This novel isolation system relies on specific installation of the stimuli-responsive copolymers onto exosomal phospholipid bilayers, by which the enrichment and purification are exclusively achieved for exosomes rather than the non-vesicle counterparts co-existing in real samples. The stimuli-mediated isolation system outperforms conventional methods such as ultracentrifugation and polyethylene glycol-based precipitation in terms of isolation yield, purity, and retained bioactivity. The high performance of the isolation system is demonstrated by enriching exosomes from 77 blood plasma samples and validated the clinical potentials in profiling exosomal mRNAs for cancer diagnosis and discrimination with high accuracy. This simple isolation system can boost the development of extracellular vesicle research, not limited to exosomes, in both basic and clinical settings.

Computer-aided design of molecular machines: techniques, paradigms and difficulties

With their development in the past decade, molecular machines, which achieve specific tasks by responding to external stimuli, have gradually come to be regarded as powerful tools for a wide range of applications, rather than interesting molecular toys. This conceptual change in turn motivates scientists to design molecular machines with complex architectures. Due to the lack of general principles bridging the functions and the chemical structures of molecular machines, experience-based design becomes difficult with the increase of size and complexity of the architectures. Computer-aided molecular-machine design, therefore, has attracted widespread attention on account of its ability to model and investigate complex molecular architectures without too much time and expense required for synthetic experiments. Using leading-edge numerical-simulation techniques, the mechanisms underlying achieving tasks through response to external stimuli of a large number of existing molecular machines have been successfully explored. Based on the experience of studying existing molecular machines, generalized methodologies of predicting the properties and working principles of molecular candidates have been established, paving the way for de novo computer-aided design of molecular machines. In this perspective, we introduce cutting-edge techniques that have been applied for investigating and designing molecular machines. We show paradigms of computer-aided design of molecular machines, which can serve as guidelines for the investigation of new supramolecular architectures. Moreover, we discuss the limitations and possible future developments of current techniques and methodologies in the field of computer-aided design of molecular machines.

Regulation of aquaporin-3 water permeability by hyaluronan

Hyaluronan (HA) is a major component in the extracellular matrix and is responsible for maintaining the water content of the skin. However, the function and moisturizing mechanism at the atomic level of HA remain only partially understood. Investigating the interactions of HA and other skin components can help us understand how the former moisturizes the skin. Considering that aquaporin-3 (AQP3) is a protein responsible for transmembrane water transport in the human skin, we have, therefore, investigated the interactions of AQP3 and HA with different molecular weights using molecular dynamics simulations in the present work. Our results indicate that HA can adsorb onto AQP3 and decrease water mobility around the latter. In addition, the permeation rate of water through AQP3 can also be decreased by HA, and this phenomenon is particularly obvious for small molecular HA. Moreover, we found that large molecular HA can link two adjacent membranes in the extracellular matrix, increasing the adhesion between the membranes in the periplasm. The results of the present study indicate that HA is a natural regulator of AQP3, revealing the synergetic function of HA and AQP3 in the extracellular matrix of the skin.

Hyperactive Antifreeze Proteins Promote Ice Growth before Binding to It

The antifreeze mechanism of antifreeze proteins (AFPs) evolved by organisms has been widely studied. However, detailed knowledge of the synergy between AFPs and ice crystals still remains fragmentary. In the present contribution, the cooperative effect of the hyperactive insect antifreeze protein TmAFP and ice crystals on the interfacial water during the entire process of inhibiting ice growth is systematically investigated at the atomic level and compared with its low activity mutant and a nonantifreeze protein. The results indicate a significant synergy between TmAFP and ice crystals, which enables the TmAFP to promote the ice growth before adsorbing on the surfaces of the ice crystals, while the mutant and the nonantifreeze protein cannot promote the ice growth due to the lack of this synergy. When TmAFP approaches the ice surface, the interfacial water is induced by both the AFP and the ice crystals to form the anchored clathrate motif, which binds TmAFP to the ice surface, resulting in a local increase in the curvature of the ice surface, thereby inhibiting the growth of ice. In this study, three stages, namely, promotion, adsorption, and inhibition, are observed in the complete process of TmAFP inhibiting ice growth, and the synergistic mechanism between protein and ice crystals is revealed. The results are helpful for the design of antifreeze proteins and bioinspired antifreeze materials with superior performance.

Machine learning reveals sequence-function relationships in family 7 glycoside hydrolases

Family 7 glycoside hydrolases (GH7) are among the principal enzymes for cellulose degradation in nature and industrially. These enzymes are often bimodular, including a catalytic domain and carbohydrate-binding module (CBM) attached via a flexible linker, and exhibit an active site that binds cello-oligomers of up to ten glucosyl moieties. GH7 cellulases consist of two major subtypes: cellobiohydrolases (CBH) and endoglucanases (EG). Despite the critical importance of GH7 enzymes, there remain gaps in our understanding of how GH7 sequence and structure relate to function. Here, we employed machine learning to gain data-driven insights into relationships between sequence, structure, and function across the GH7 family. Machine-learning models, trained only on the number of residues in the active-site loops as features, were able to discriminate GH7 CBHs and EGs with up to 99% accuracy, demonstrating that the lengths of loops A4, B2, B3, and B4 strongly correlate with functional subtype across the GH7 family. Classification rules were derived such that specific residues at 42 different sequence positions each predicted the functional subtype with accuracies surpassing 87%. A random forest model trained on residues at 19 positions in the catalytic domain predicted the presence of a CBM with 89.5% accuracy. Our machine learning results recapitulate, as top-performing features, a substantial number of the sequence positions determined by previous experimental studies to play vital roles in GH7 activity. We surmise that the yet-to-be-explored sequence positions among the top-performing features also contribute to GH7 functional variation and may be exploited to understand and manipulate function.

Overcoming Free-Energy Barriers with a Seamless Combination of a Biasing Force and a Collective Variable-Independent Boost Potential

Amid collective-variable (CV)-based importance-sampling algorithms, a hybrid of the extended adaptive biasing force and the well-tempered metadynamics algorithms (WTM-eABF) has proven particularly cost-effective for exploring the rugged free-energy landscapes that underlie biological processes. However, as an inherently CV-based algorithm, this hybrid scheme does not explicitly accelerate sampling in the space orthogonal to the chosen CVs, thereby limiting its efficiency and accuracy, most notably in those cases where the slow degrees of freedom of the process at hand are not accounted for in the model transition coordinate. Here, inspired by Gaussian-accelerated molecular dynamics (GaMD), we introduce the same CV-independent harmonic boost potential into WTM-eABF, yielding a hybrid algorithm coined GaWTM-eABF. This algorithm leans on WTM-eABF to explore the transition coordinate with a GaMD-mollified potential and recovers the unbiased free-energy landscape through thermodynamic integration followed by proper reweighting. As illustrated in our numerical tests, GaWTM-eABF effectively overcomes the free-energy barriers in orthogonal space and correctly recovers the unbiased potential of mean force (PMF). Furthermore, applying both GaWTM-eABF and WTM-eABF to two biologically relevant processes, namely, the reversible folding of (i) deca-alanine and (ii) chignolin, our results indicate that GaWTM-eABF reduces the uncertainty in the PMF calculation and converges appreciably faster than WTM-eABF. Obviating the need of multiple-copy strategies, GaWTM-eABF is a robust, computationally efficient algorithm to surmount the free-energy barriers in orthogonal space and maps with utmost fidelity the free-energy landscape along selections of CVs. Moreover, our strategy that combines WTM-eABF with GaMD can be easily extended to other biasing-force algorithms.

Accuracy of Alternate Nonpolarizable Force Fields for the Determination of Protein-Ligand Binding Affinities Dominated by Cation-π Interactions

Modifying pair-specific Lennard-Jones parameters through the nonbonded FIX (NBFIX) feature of the CHARMM36 force field has proven cost-effective for improving the description of cation-π interactions in biological objects by means of pairwise additive potential energy functions. Here, two sets of newly optimized CHARMM36 force-field parameters including NBFIX corrections, coined CHARMM36m-NBF and CHARMM36-WYF, and the original force fields, namely CHARMM36m and Amber ff14SB, are used to determine the standard binding free energies of seven protein-ligand complexes containing cation-π interactions. Compared with precise experimental measurements, our results indicate that the uncorrected, original force fields significantly underestimate the binding free energies, with a mean error of 5.3 kcal/mol, while the mean errors of CHARMM36m-NBF and CHARMM36-WYF amount to 0.8 and 2.1 kcal/mol, respectively. The present study cogently demonstrates that the use of modified parameters jointly with NBFIX corrections dramatically increases the accuracy of the standard binding free energy of protein-ligand complexes dominated by cation-π interactions, most notably with CHARMM36m-NBF.

Computational modeling of carbohydrate processing enzymes reactions

Carbohydrate processing enzymes are of biocatalytic interest. Glycoside hydrolases and the recently discovered lytic polysaccharide monooxygenase for their use in biomass degradation to obtain biofuels or valued chemical entities. Glycosyltransferases or engineered glycosidases and phosphorylases for the synthesis of carbohydrates and glycosylated products. Quantum mechanics-molecular mechanics (QM/MM) methods are highly contributing to establish their different chemical reaction mechanisms. Other computational methods are also used to study enzyme conformational changes, ligand pathways, and processivity, e.g. for processive glycosidases like cellobiohydrolases. There is still a long road to travel to fully understand the role of conformational dynamics in enzyme activity and also to disclose the variety of reaction mechanisms these enzymes employ. Additionally, computational tools for enzyme engineering are beginning to be applied to evaluate substrate specificity or aid in the design of new biocatalysts with increased thermostability or tailored activity, a growing field where molecular modeling is finding its way.

Molecular dynamics study of the recognition of ATP by nucleic acid aptamers

Despite their great success in recognizing small molecules in vitro, nucleic acid aptamers are rarely used in clinical settings. This is partially due to the lack of structure-based mechanistic information. In this work, atomistic molecular dynamics simulations are used to study the static and dynamic supramolecular structures relevant to the process of the wild-type (wt) nucleic acid aptamer recognition and binding of ATP. The effects brought about by mutation of key residues in the recognition site are also explored. The simulations reveal that the aptamer displays a high degree of rigidity and is structurally very little affected by the binding of ATP. Interaction energy decomposition shows that dispersion forces from π-stacking between ATP and the G6 and A23 nucleobases in the aptamer binding site plays a more important role in stabilizing the supramolecular complex, compared to hydrogen-bond interaction between ATP and G22. Moreover, metadynamics simulations show that during the association process, water molecules act as essential bridges connecting ATP with G22, which favors the dynamic stability of the complex. The calculations carried out on three mutated aptamer structures confirm the crucial role of the hydrogen bonds and π-stacking interactions for the binding affinity of the ATP nucleic acid aptamer.

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.