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Glycine receptors (GlyR) conduct inhibitory glycinergic neurotransmission in the spinal cord and the brainstem. They play an important role in muscle tone, motor coordination, respiration, and pain perception. However, the mechanism underlying GlyR activation remains unclear. There are five potential glycine binding sites in α1 GlyR, and different binding patterns may cause distinct activation or desensitization behaviors. In this study, we investigated the coupling of protein conformational changes and glycine binding events to elucidate the influence of binding patterns on the activation and desensitization processes of α1 GlyRs. Subsequently, we explored the energetic distinctions between the apical and lateral pathways during α1 GlyR conduction to identify the pivotal factors in the ion conduction pathway preference. Moreover, we predicted the mutational effects of the key residues and verified our predictions using electrophysiological experiments. For the mutants that can be activated by glycine, the predictions of the mutational directions were all correct. The strength of the mutational effects was assessed using Pearson’s correlation coefficient, yielding a value of −0.77 between the calculated highest energy barriers and experimental maximum current amplitudes. These findings contribute to our understanding of GlyR activation, identify the key residues of GlyRs, and provide guidance for mechanistic studies on other pLGICs.

Bioluminescence is a fascinating natural phenomenon, wherein organisms produce light through specific biochemical reactions. Among these organisms, Renillaluciferase (RLuc) derived from the sea pansy Renilla reniformisis notable for its blue light emission and has potential applications in bioluminescent tagging. Our study focuses on RLuc8, a variant of RLuc with eight amino acid substitutions. Recent studies have shown that the luminescent emitter coelenteramide can adopt multiple protonation states, which may be influenced by nearby residues at the enzyme’s active site, demonstrating a complex interplay between protein structure and bioluminescence. Herein, using the quantum mechanical consistent force field method and the semimacroscopic protein dipole-Langevin dipole method with linear response approximation, we show that the phenolate state of coelenteramide in RLuc8 is the primary light-emitting species in agreement with experimental results. Our calculations also suggest that the proton transfer (PT) from neutral coelenteramide to Asp162 plays a crucial role in the bioluminescence process. Additionally, we reproduced the observed emission maximum for the amide anion in RLuc8-D120A and the pyrazine anion in the presence of a Na+counterion in RLuc8-D162A, suggesting that these are the primary emitters. Furthermore, our calculations on the neutral emitter in the engineered AncFT-D160A enzyme, structurally akin to RLuc8-D162A but with a considerably blue-shifted emission peak, aligned with the observed data, possibly explaining the variance in emission peaks. Overall, this study demonstrates an effective approach to investigate chromophores’ bimolecular states while incorporating the PT process in emission spectra calculations, contributing valuable insights for future studies of PT in photoproteins.

The dysfunction and defects of ion channels are associated with many human diseases, especially for loss-of-function mutations in ion channels such as cystic fibrosis transmembrane conductance regulator mutations in cystic fibrosis. Understanding ion channels is of great current importance for both medical and fundamental purposes. Such an understanding should include the ability to predict mutational effects and describe functional and mechanistic effects. In this work, we introduce an approach to predict mutational effects based on kinetic information (including reaction barriers and transition state locations) obtained by studying the working mechanism of target proteins. Specifically, we take the Ca2+-activated chloride channel TMEM16A as an example and utilize the computational biology model to predict the mutational effects of key residues. Encouragingly, we verified our predictions through electrophysiological experiments, demonstrating a 94% prediction accuracy regarding mutational directions. The mutational strength assessed by Pearson’s correlation coefficient is −0.80 between our calculations and the experimental results. These findings suggest that the proposed methodology is reliable and can provide valuable guidance for revealing functional mechanisms and identifying key residues of the TMEM16A channel. The proposed approach can be extended to a broad scope of biophysical systems.

ABSTRACTIntroductionCollaborative computing has attracted great interest, enabling researchers worldwide to come together and work seamlessly. Its relevance has never been so apparent as during the recent pandemic given that it allows for the strengthening of scientific collaborations whilst avoiding physical interaction.This technology evaluation reviews the MEDIATE initiative, launched by the Exscalate4Cov consortium, which invites researchers to contribute their virtual screening simulations that are then combined with AI-based consensus approaches to provide robust and method-independent predictions. The best compounds are subsequently tested, and the biological results are then shared with the scientific community.Areas coveredIn this paper, the MEDIATE initiative is described. This shares compounds’ libraries and protein structures prepared to perform standardized virtual screenings. Preliminary analyses are also reported which provide encouraging results emphasizing the MEDIATE initiative’s capacity to identify active compounds.Expert opinionStructure-based virtual screening is well-suited for collaborative projects provided that the participating researchers work on the same input file. Until now, such a strategy was rarely pursued and most initiatives in the field were organized as challenges. The MEDIATE platform is focused on SARS-CoV-2 targets but can be seen as a prototype which can be utilized to perform collaborative virtual screening campaigns in any therapeutic field by sharing the appropriate input files.

This study analyzes the origin of enzyme catalysis by focusing on the reaction of orotidine 5′-phosphate decarboxylase (ODCase). This reaction involves an enormous catalytic effect of 23 kcal/mol that has been attributed to reactant state destabilization associated with the use of binding energy through the so-called Circe effect. However, our early studies and subsequent key experiments have shown that the presumed effect of the binding energy (namely, the strain exerted by a bond to a phosphate group) does not contribute to the catalysis. In this study, we perform quantitative empirical valence bond calculations that reproduce the catalytic effect of ODCase and the effect of removing the phosphate side chain. The calculations demonstrate that the effect of the phosphate is due to a change in reorganization energy and should not be described as an induced fit effect. Similarly, we show that the overall catalytic effect is due to electrostatic transition state stabilization, which again reflects the smaller reorganization energy in the enzyme than in water. We also elaborate on the problems with the induced fit proposal, including the fact that it does not serve to tell us what the actual origin of the action of the catalytic effect is. In addition to the above points, we use this paper to discuss misconceptions about the meaning of the preorganization effect, as well as other misunderstandings of what is being done in consistent calculations of enzyme catalysis.

A variety of diseases are associated with tyrosine kinase enzymes that activate many proteins via signal transduction cascades. The similar ATP-binding pockets of these tyrosine kinases make it extremely difficult to design selective covalent inhibitors. The present study explores the contribution of the chemical reaction steps to the selectivity of the commercialized inhibitor acalabrutinib over the Bruton’s tyrosine kinase (BTK) and the interleukin-2-inducible T-cell kinase (ITK). Ab initio and empirical valence bond (EVB) simulations of the two kinases indicate that the most favorable reaction path involves a water-assisted mechanism of the 2-butynamide reactive group of acalabrutinib. BTK reacts with acalabrutinib with a substantially lower barrier than ITK, according to our calculated free-energy profile and kinetic simulations. Such a difference is due to the microenvironment of the active site, as further supported by a sequence-based analysis of specificity determinants for several commercialized inhibitors. Our study involves a new approach of simulating directly the IC50 and inactivation efficiency keff, instead of using the standard formulas. This new strategy is particularly important in studies of covalent inhibitors with a very exothermic bonding step. Overall, our results demonstrate the importance of understanding the chemical reaction steps in designing selective covalent inhibitors for tyrosine kinases.

The COVID-19 pandemic has been a public health emergency with continuously evolving deadly variants around the globe. Among many preventive and therapeutic strategies, the design of covalent inhibitors targeting the main protease (Mpro) of SARS-CoV-2 that causes COVID-19 has been one of the hotly pursued areas. Currently, about 30% of marketed drugs that target enzymes are covalent inhibitors. Such inhibitors have been shown in recent years to have many advantages that counteract past reservation of their potential off-target activities, which can be minimized by modulation of the electrophilic warhead and simultaneous optimization of nearby noncovalent interactions. This process can be greatly accelerated by exploration of binding affinities using computational models, which are not well-established yet due to the requirement of capturing the chemical nature of covalent bond formation. Here, we present a robust computational method for effective prediction of absolute binding free energies (ABFEs) of covalent inhibitors. This is done by integrating the protein dipoles Langevin dipoles method (in the PDLD/S-LRA/β version) with quantum mechanical calculations of the energetics of the reaction of the warhead and its amino acid target, in water. This approach evaluates the combined effects of the covalent and noncovalent contributions. The applicability of the method is illustrated by predicting the ABFEs of covalent inhibitors of SARS-CoV-2 Mproand the 20S proteasome. Our results are found to be reliable in predicting ABFEs for cases where the warheads are significantly different. This computational protocol might be a powerful tool for designing effective covalent inhibitors especially for SARS-CoV-2 Mproand for targeted protein degradation.

The cleavage of protein inside cell membranes regulates pathological pathways and is a subject of major interest. Thus, the nature of the coupling between the physical environment and the function of such proteins has recently attracted significant experimental and theoretical efforts. However, it is difficult to determine the nature of this coupling uniquely by experimental and theoretical studies unless one can separate the chemical and the environmental factors. This work describes calculations of the activation barriers of the intramembrane rhomboid protease in neutral and charged lipid bilayers and in detergent micelle, trying to explore the environmental effect. The calculations of the chemical barrier are done using the empirical valence bond (EVB) method. Additionally, the renormalization method captures the energetics and dynamical effects of the conformational change. The simulations indicate that the physical environment around the rhomboid protease is not a major factor in changing the chemical catalysis and that the conformational and substrate dynamics do not exhibit long-time coupling. General issues about the action of membrane-embedded enzymes are also considered.

G-Protein-coupled receptors (GPCRs) belong to an important family of integral membrane receptor proteins that are essential for a variety of transmembrane signaling process, such as vision, olfaction, and hormone responses. They are also involved in many human diseases (Alzheimer’s, heart diseases, etc.) and are therefore common drug targets. Thus, understanding the details of the GPCR activation process is a task of major importance. Various types of crystal structures of GPCRs have been solved either at stable end-point states or at possible intermediate states. However, the detailed mechanism of the activation process is still poorly understood. For example, it is not completely clear when the nucleotide release from the G protein occurs and how the key residues on α5 contribute to the coupling process and further affect the binding specificity. In this work we show by free energy analysis that the guanosine diphosphate (GDP) molecule could be released from the Gsprotein when the binding cavity is half open. This occurs during the transition to the Gsopen state, which is the rate-determining step in the system conformational change. We also account for the experimentally observed slow-down effects by the change of the reaction barriers after mutations. Furthermore, we identify potential key residues on α5 and validated their significance by site-directed mutagenesis, which illustrates that computational works have predictive value even for complex biophysical systems. The methodology of the current work may be applied to other biophysical systems of interest.

Cysteine proteases play a major role in many life processes and are the target of key drugs. The reaction mechanism of these enzymes is a complex process, which involves several steps that are divided into two main groups: acylation and deacylation. In this work, we studied the energy profile for the acylation and a part of the deacylation reaction of three different enzymes, cruzain, papain, and the Q19A-mutated papain with the benzyloxycarbonyl-phenylalanylarginine-4-methylcoumaryl-7-amide (CBZ-FR-AMC) substrate. The calculations were performed using the EVB and PDLD/S-LRA methods. The overall agreement between the calculated and observed results is encouraging and indicates that we captured the correct reaction mechanism. Finally, our finding indicates that the minimum of the reaction profile, between the acylation and deacylation steps, should provide an excellent state for the binding of covalent inhibitors.

The development of reliable ways of predicting the binding free energies of covalent inhibitors is a challenge for computer-aided drug design. Such development is important, for example, in the fight against the SARS-CoV-2 virus, in which covalent inhibitors can provide a promising tool for blocking Mpro, the main protease of the virus. This work develops a reliable and practical protocol for evaluating the binding free energy of covalent inhibitors. Our protocol presents a major advance over other approaches that do not consider the chemical contribution of the binding free energy. Our strategy combines the empirical valence bond method for evaluating the reaction energy profile and the PDLD/S-LRA/β method for evaluating the noncovalent part of the binding process. This protocol has been used in the calculations of the binding free energy of an α-ketoamide inhibitor of Mpro. Encouragingly, our approach reproduces the observed binding free energy. Our study of covalent inhibitors of cysteine proteases indicates that in the choice of an effective warhead it is crucial to focus on the exothermicity of the point on the free energy surface of a peptide cleavage that connects the acylation and deacylation steps. Overall, we believe that our approach should provide a powerful and effective method for in silicodesign of covalent drugs.

The ATPase-catalysed conversion of ATP to ADP is a fundamental process in biology. During the hydrolysis of ATP, the a3ß3domain undergoes conformational changes while the central stalk (?/D) rotates unidirectionally. Experimental studies have suggested that different catalytic mechanisms operate depending on the type of ATPase, but the structural and energetic basis of these mechanisms remains unclear. In particular, it is not clear how the positions of the catalytic dwells influence the energy transduction. Here we show that the observed dwell positions, unidirectional rotation and movement against the applied torque are reflections of the free-energy surface of the systems. Instructively, we determine that the dwell positions do not substantially affect the stopping torque. Our results suggest that the three resting states and the pathways that connect them should not be treated equally. The current work demonstrates how the free-energy landscape determines the behaviour of different types of ATPases.

The COVID-19 caused by SARS-CoV-2 has spread globally and caused tremendous loss of lives and properties, and it is of utmost urgency to understand its propagation process and to find ways to slow down the epidemic. In this work, we used a coarse-grained model to calculate the binding free energy of SARS-CoV-2 or SARS-CoV to their human receptor ACE2. The investigation of the free energy contribution of the interacting residues indicates that the residues located outside the receptor binding domain are the source of the stronger binding of the novel virus. Thus, the current results suggest that the essential evolution of SARS-CoV-2 happens remotely from the binding domain at the spike protein trimeric body. Such evolution may facilitate the conformational change and the infection process that occurs after the virus is bound to ACE2. By studying the binding pattern between SARS-CoV antibody m396 and SARS-CoV-2, it is found that the remote energetic contribution is missing, which might explain the absence of cross-reactivity of such antibodies.

The proteasome is a key protease in the eukaryotic cells which is responsible for various important cellular processes such as the control of the cell cycle, immune responses, protein homeostasis, inflammation, apoptosis, and the response to proteotoxic stress. Acting as a major molecular machine for protein degradation, proteasome first identifies damaged or obsolete regulatory proteins by attaching ubiquitin chains and subsequently utilizes conserved pore loops of the heterohexameric ring of AAA+ (ATPases associated with diverse cellular activities) to pull and mechanically unfold and translocate the misfolded protein to the active site for proteolysis. A detailed knowledge of the reaction mechanism for this proteasomal proteolysis is of central importance, both for fundamental understanding and for drug discovery. The present study investigates the mechanism of the proteolysis by the proteasome with full consideration of the protein’s flexibility and its impact on the reaction free energy. Major attention is paid to the role of the protein electrostatics in determining the activation barriers. The reaction mechanism is studied by considering a small artificial fluorogenic peptide substrate (Suc-LLVY-AMC) and evaluating the activation barriers and reaction free energies for the acylation and deacylation steps, by using the empirical valence bond method. Our results shed light on the proteolysis mechanism and thus should be important for further studies of the proteasome action.

Simulating the nature of voltage-activated systems is a problem of major current interest, ranging from the action of voltage-gated ion channels to energy storage batteries. However, fully microscopic converging molecular simulations of external voltage effects present a major challenge, and macroscopic models are associated with major uncertainties about the dielectric treatment and the underlying physical basis. Recently we developed a coarse-grained (CG) model that represents explicitly the electrodes, the electrolytes, and the membrane/protein system. The CG model provides a semimacroscopic way of capturing the microscopic physics of voltage-activated systems. Our method was originally validated by reproducing macroscopic and analytical results for key test cases and then used in modeling voltage-activated ion channels and related problems. In this work, we further establish the reliability of the CG voltage model by comparing it to the results of Monte Carlo (MC) simulations with a microscopic electrolyte model. The comparison explores different aspects of membrane, electrolyte, and electrode systems ranging from the Gouy–Chapman model to the determination of the electrolyte charge distribution in the solution between two electrodes (without and with a separating membrane), as well as the evaluation of gating charges. Overall the agreement is very impressive. This provides confidence in the CG model and also shows that the MC model can be used in realistic simulation of voltage activation of membrane proteins with sufficient computer time.

Enzymes, as paramount protein catalysts, occupy a central role in fostering remarkable progress across numerous fields. However, the intricacy of sequence-function relationships continues to obscure our grasp of enzyme behaviors and curtails our capabilities in rational enzyme engineering. Generative artificial intelligence (AI), known for its proficiency in handling intricate data distributions, holds the potential to offer novel perspectives in enzyme research. Generative models could discern elusive patterns within the vast sequence space and uncover new functional enzyme sequences. This review highlights the recent advancements in employing generative AI for enzyme sequence analysis. We delve into the impact of generative AI in predicting mutation effects on enzyme fitness, catalytic activity and stability, rationalizing the laboratory evolution of de novoenzymes, and decoding protein sequence semantics and their application in enzyme engineering. Notably, the prediction of catalytic activity and stability of enzymes using natural protein sequences serves as a vital link, indicating how enzyme catalysis shapes enzyme evolution. Overall, we foresee that the integration of generative AI into enzyme studies will remarkably enhance our knowledge of enzymes and expedite the creation of superior biocatalysts.This Review underscores the latest advances in applying generative AI to the analysis of enzyme sequences, which has deepened our insight into enzyme evolution and catalysis, thereby advancing enzyme engineering.

Increasing the accuracy of the evaluation of ligand-binding energies is one of the most important tasks of current computational biology. Here we explore the accuracy of free energy perturbation (FEP) approaches by comparing the performance of a “regular” FEP method to the one using replica exchange to enhance the sampling on a well-defined benchmark. The examination was limited to the so-called alchemical perturbations which are restricted to a fragment of the drug, and therefore, the calculation is a relative one rather than the absolute binding energy of the drug. Overall, our calculations reach the 1 kcal/mol accuracy limit. It is also shown that the accurate prediction of the position of water molecules around the binding pocket is important for FEP calculations. Interestingly, the replica exchange method does not significantly improve the accuracy of binding energies, suggesting that we reach the limit where the force field quality is a critical factor for accurate calculations.

SARS-CoV-2 is the virus that causes the global pandemic of COVID-19. The main protease (Mpro) of SARS-CoV-2 is essential for viral infection and is one of the major therapeutic targets for COVID-19. Here, we report the design, synthesis, and biological characterization of a novel heterobifunctional small molecule that could effectively induce the degradation of SARS-CoV-2 Mproand its drug-resistant mutants in HEK 293T cells, thus demonstrating a new alternative strategy for intervening with proteins important for this novel coronavirus.