A three-level regulatory mechanism of the aldo-keto reductase subfamily AKR12D.

Modulation of protein function through allosteric regulation is central in biology, but biomacromolecular systems involving multiple subunits and ligands may exhibit complex regulatory mechanisms at different levels, which remain poorly understood. Here, we discover an aldo-keto reductase termed AKRtyl and present its three-level regulatory mechanism. Specifically, by combining steady-state and transient kinetics, X-ray crystallography and molecular dynamics simulation, we demonstrate that AKRtyl exhibits a positive synergy mediated by an unusual Monod-Wyman-Changeux (MWC) paradigm of allosteric regulation at low concentrations of the cofactor NADPH, but an inhibitory effect at high concentrations is observed. While the substrate tylosin binds at a remote allosteric site with positive cooperativity. We further reveal that these regulatory mechanisms are conserved in AKR12D subfamily, and that substrate cooperativity is common in AKRs across three kingdoms of life. This work provides an intriguing example for understanding complex allosteric regulatory networks.

Global fungal-host interactome mapping identifies host targets of candidalysin.

Candidalysin, a cytolytic peptide toxin secreted by the human fungal pathogen Candida albicans, is critical for fungal pathogenesis. Yet, its intracellular targets have not been extensively mapped. Here, we performed a high-throughput enhanced yeast two-hybrid (HT-eY2H) screen to map the interactome of all eight Ece1 peptides with their direct human protein targets and identified a list of potential interacting proteins, some of which were shared between the peptides. CCNH, a regulatory subunit of the CDK-activating kinase (CAK) complex involved in DNA damage repair, was identified as one of the host targets of candidalysin. Mechanistic studies revealed that candidalysin triggers a significantly increased double-strand DNA breaks (DSBs), as evidenced by the formation of γ-H2AX foci and colocalization of CCNH and γ-H2AX. Importantly, candidalysin binds directly to CCNH to activate CAK to inhibit DNA damage repair pathway. Loss of CCNH alleviates DSBs formation under candidalysin treatment. Depletion of candidalysin-encoding gene fails to induce DSBs and stimulates CCNH upregulation in a murine model of oropharyngeal candidiasis. Collectively, our study reveals that a secreted fungal toxin acts to hijack the canonical DNA damage repair pathway by targeting CCNH and to promote fungal infection.

ASD2023: towards the integrating landscapes of allosteric knowledgebase.

Allosteric regulation, induced by perturbations at an allosteric site topographically distinct from the orthosteric site, is one of the most direct and efficient ways to fine-tune macromolecular function. The Allosteric Database (ASD; accessible online at http://mdl.shsmu.edu.cn/ASD) has been systematically developed since 2009 to provide comprehensive information on allosteric regulation. In recent years, allostery has seen sustained growth and wide-ranging applications in life sciences, from basic research to new therapeutics development, while also elucidating emerging obstacles across allosteric research stages. To overcome these challenges and maintain high-quality data center services, novel features were curated in the ASD2023 update: (i) 66 589 potential allosteric sites, covering > 80% of the human proteome and constituting the human allosteric pocketome; (ii) 748 allosteric protein-protein interaction (PPI) modulators with clear mechanisms, aiding protein machine studies and PPI-targeted drug discovery; (iii) 'Allosteric Hit-to-Lead,' a pioneering dataset providing panoramic views from 87 well-defined allosteric hits to 6565 leads and (iv) 456 dualsteric modulators for exploring the simultaneous regulation of allosteric and orthosteric sites. Meanwhile, ASD2023 maintains a significant growth of foundational allosteric data. Based on these efforts, the allosteric knowledgebase is progressively evolving towards an integrated landscape, facilitating advancements in allosteric target identification, mechanistic exploration and drug discovery.

Encoding Molecular Docking for Quantum Computers.

Molecular docking is important in drug discovery but is burdensome for classical computers. Here, we introduce Grid Point Matching (GPM) and Feature Atom Matching (FAM) to accelerate pose sampling in molecular docking by encoding the problem into quadratic unconstrained binary optimization (QUBO) models so that it could be solved by quantum computers like the coherent Ising machine (CIM). As a result, GPM shows a sampling power close to that of Glide SP, a method performing an extensive search. Moreover, it is estimated to be 1000 times faster on the CIM than on classical computers. Our methods could boost virtual drug screening of small molecules and peptides in future.

Designing drugs and chemical probes with the dualsteric approach.

Traditionally, drugs are monovalent, targeting only one site on the protein surface. This includes orthosteric and allosteric drugs, which bind the protein at orthosteric and allosteric sites, respectively. Orthosteric drugs are good in potency, whereas allosteric drugs have better selectivity and are solutions to classically undruggable targets. However, it would be difficult to simultaneously reach high potency and selectivity when targeting only one site. Also, both kinds of monovalent drugs suffer from mutation-caused drug resistance. To overcome these obstacles, dualsteric modulators have been proposed in the past twenty years. Compared to orthosteric or allosteric drugs, dualsteric modulators are bivalent (or bitopic) with two pharmacophores. Each of the two pharmacophores bind the protein at the orthosteric and an allosteric site, which could bring the modulator with special properties beyond monovalent drugs. In this study, we comprehensively review the current development of dualsteric modulators. Our main effort reason and illustrate the aims to apply the dualsteric approach, including a "double win" of potency and selectivity, overcoming mutation-caused drug resistance, developments of function-biased modulators, and design of partial agonists. Moreover, the strengths of the dualsteric technique also led to its application outside pharmacy, including the design of highly sensitive fluorescent tracers and usage as molecular rulers. Besides, we also introduced drug targets, designing strategies, and validation methods of dualsteric modulators. Finally, we detail the conclusions and perspectives.

Developing Hybrid All-Atom and Ultra-Coarse-Grained Models to Investigate Taxol-Binding and Dynein Interactions on Microtubules.

Simulating the conformations and functions of biological macromolecules by using all-atom (AA) models is a challenging task due to expensive computational costs. One possible strategy to solve this problem is to develop hybrid all-atom and ultra-coarse-grained (AA/UCG) models of the biological macromolecules. In the AA/UCG scheme, the interest regions are described by AA models, while the other regions are described in the UCG representation. In this study, we develop the hybrid AA/UCG models and apply them to investigate the conformational changes of microtubule-bound tubulins. The simulation results of the hybrid models elucidated the mechanism of why the taxol molecules selectively bound microtubules but not tubulin dimers. In addition, we also explore the interactions of the microtubules and dyneins. Our study shows that the hybrid AA/UCG model has great application potential in studying the function of complex biological systems.

AlloReverse: multiscale understanding among hierarchical allosteric regulations.

Increasing data in allostery are requiring analysis of coupling relationships among different allosteric sites on a single protein. Here, based on our previous efforts on reversed allosteric communication theory, we have developed AlloReverse, a web server for multiscale analysis of multiple allosteric regulations. AlloReverse integrates protein dynamics and machine learning to discover allosteric residues, allosteric sites and regulation pathways. Especially, AlloReverse could reveal hierarchical relationships between different pathways and couplings among allosteric sites, offering a whole map of allostery. The web server shows a good performance in re-emerging known allostery. Moreover, we applied AlloReverse to explore global allostery on CDC42 and SIRT3. AlloReverse predicted novel allosteric sites and allosteric residues in both systems, and the functionality of sites was validated experimentally. It also suggests a possible scheme for combined therapy or bivalent drugs on SIRT3. Taken together, AlloReverse is a novel workflow providing a complete regulation map and is believed to aid target identification, drug design and understanding of biological mechanisms. AlloReverse is freely available to all users at https://mdl.shsmu.edu.cn/AlloReverse/ or http://www.allostery.net/AlloReverse/.

Spatiotemporal and global profiling of DNA-protein interactions enables discovery of low-affinity transcription factors.

Precise dissection of DNA-protein interactions is essential for elucidating the recognition basis, dynamics and gene regulation mechanism. However, global profiling of weak and dynamic DNA-protein interactions remains a long-standing challenge. Here, we establish the light-induced lysine (K) enabled crosslinking (LIKE-XL) strategy for spatiotemporal and global profiling of DNA-protein interactions. Harnessing unique abilities to capture weak and transient DNA-protein interactions, we demonstrate that LIKE-XL enables the discovery of low-affinity transcription-factor/DNA interactions via sequence-specific DNA baits, determining the binding sites for transcription factors that have been previously unknown. More importantly, we successfully decipher the dynamics of the transcription factor subproteome in response to drug treatment in a time-resolved manner, and find downstream target transcription factors from drug perturbations, providing insight into their dynamic transcriptional networks. The LIKE-XL strategy offers a complementary method to expand the DNA-protein profiling toolbox and map accurate DNA-protein interactomes that were previously inaccessible via non-covalent strategies, for better understanding of protein function in health and disease.

DeepAlloDriver: a deep learning-based strategy to predict cancer driver mutations.

Driver mutations can contribute to the initial processes of cancer, and their identification is crucial for understanding tumorigenesis as well as for molecular drug discovery and development. Allostery regulates protein function away from the functional regions at an allosteric site. In addition to the known effects of mutations around functional sites, mutations at allosteric sites have been associated with protein structure, dynamics, and energy communication. As a result, identifying driver mutations at allosteric sites will be beneficial for deciphering the mechanisms of cancer and developing allosteric drugs. In this study, we provided a platform called DeepAlloDriver to predict driver mutations using a deep learning method that exhibited >93% accuracy and precision. Using this server, we found that a missense mutation in RRAS2 (Gln72 to Leu) might serve as an allosteric driver of tumorigenesis, revealing the mechanism of the mutation in knock-in mice and cancer patients. Overall, DeepAlloDriver would facilitate the elucidation of the mechanisms underlying cancer progression and help prioritize cancer therapeutic targets. The web server is freely available at: https://mdl.shsmu.edu.cn/DeepAlloDriver.

Research and Evaluation of the Allosteric Protein-Specific Force Field Based on a Pre-Training Deep Learning Model.

Allosteric modulators are important regulation elements that bind the allosteric site beyond the active site, leading to the changes in dynamic and/or thermodynamic properties of the protein. Allosteric modulators have been a considerable interest as potential drugs with high selectivity and safety. However, current experimental methods have limitations to identify allosteric sites. Therefore, molecular dynamics simulation based on empirical force field becomes an important complement of experimental methods. Moreover, the precision and efficiency of current force fields need improvement. Deep learning and reweighting methods were used to train allosteric protein-specific precise force field (named APSF). Multiple allosteric proteins were used to evaluate the performance of APSF. The results indicate that APSF can capture different types of allosteric pockets and sample multiple energy-minimum reference conformations of allosteric proteins. At the same time, the efficiency of conformation sampling for APSF is higher than that for ff14SB. These findings confirm that the newly developed force field APSF can be effectively used to identify the allosteric pocket that can be further used to screen potential allosteric drugs based on these pockets.

Explaining and Predicting Allostery with Allosteric Database and Modern Analytical Techniques.

Allostery is a phenomenon that the protein activity is regulated when a non-functional site on it is bounded. This phenomenon is important in life process and disease therapy. However, it is difficult to study allostery due to the lack of knowledge. Facing this demand, we have created Allosteric Database (ASD) 10 years before to collect numerous kinds of allosteric data. In this review, we will introduce the 4 categories of data in ASD. For each category, we further reviewed how researchers applied ASD data to conduct studies. We focused on their research topics, analytical methods and conclusions. Several discoveries of new drug targets and allosteric modulators driven by ASD are also summarized. We hope this review could inspire researchers with new utilities of ASD data.

Reaction Mechanism of CO2 and Styrene Oxide Catalyzed by Ionic Liquids: A Combined DFT Calculation and Experimental Study.

Bioactive compound 3-aryl-2-oxazolidinone could be synthesized by a green method mixing carbon dioxide, aniline, and ethylene oxide. Our group previously proposed a parallel mechanism for this conversion catalyzed by ionic liquids. Recently, a new study on a similar reaction system of styrene oxide, carbon dioxide, and aniline under the catalysis of K3PO4 gave a different serial mechanism. In order to explore the mechanism of reaction, we conducted a combined theoretical and experimental study on a one-pot conversion of styrene oxide, carbon dioxide, and aniline. In experiments, two isomer products, 3,5-diphenyl-l,3-oxazolidin-2-one and 3,4-diphenyl-l,3-oxazolidin-2-one, were observed. The computational results show that the parallel mechanism is more favored in thermodynamics and in kinetics due to the instability of isocyanate and hardness of its generation. Hence, we believe the previous parallel mechanism is more reasonable under our catalysts and conditions.

ß-cyclodextrin modified quantum dots as pseudo-stationary phase for direct enantioseparation based on capillary electrophoresis with laser-induced fluorescence detection.

In recent years, quantum dots (QDs) have attracted a tremendous amount of attention due to their compelling features. In this work, a kind of composite QDs based on ß-cyclodextrin (ß-CD) and its derivatives modification was prepared, and for the first time utilized to separate and determine enantiomers in the combined system of capillary electrophoresis with laser-induced fluorescence detection (CE-LIF). By taking advantages of the inclusion complexation of ß-CD and the fluorescence property of QD core, the composite QDs were added into the running buffer as pseudo-stationary phase. The resultant CE-LIF method accomplished enantioseparation for six groups of model analytes without need of capillary preparation and analyte derivatization. The effects of composite QDs concentration, the pH value and concentration of the running buffer on resolution have been investigated individually. The RSDs of interday and intraday repeatability were in the range of 2.7-8.1%, 0.7-3.9%, and 1.5-3.8% for the peak area, migration time and resolution, respectively. The theoretical calculation results of the binding energies and binding constant further validated the interaction mechanism of composite QDs and target analytes. Furthermore, this developed method was successfully applied to the analysis of the active components (catechin and epicatechin) in Chinese herb Catechu, and the recoveries were in the range of 92.2-108%. The experimental results suggested that the preparation strategy of the composite QDs is appropriate for enantioseparation of more enantiomers by adjusting the modifiers on the surface of QDs, which is particularly promising for electrophoretic enantioseparation based on fluorescence detection, especially for those analytes lacking proper derivative functional groups.

Coarse-Grained Simulation of Mechanical Properties of Single Microtubules With Micrometer Length.

Microtubules are one of the most important components in the cytoskeleton and play a vital role in maintaining the shape and function of cells. Because single microtubules are some micrometers long, it is difficult to simulate such a large system using an all-atom model. In this work, we use the newly developed convolutional and K-means coarse-graining (CK-CG) method to establish an ultra-coarse-grained (UCG) model of a single microtubule, on the basis of the low electron microscopy density data of microtubules. We discuss the rationale of the micro-coarse-grained microtubule models of different resolutions and explore microtubule models up to 12-micron length. We use the devised microtubule model to quantify mechanical properties of microtubules of different lengths. Our model allows mesoscopic simulations of micrometer-level biomaterials and can be further used to study important biological processes related to microtubule function.