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.

The development of efficient and low-cost strategy for production of monoethylene glycol (MEG) through hydration of ethylene oxide (EO) at low H2O/EO molar ratios is an important industrial challenge. We...

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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.

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/.

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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.

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.