Search

FeCl3 catalyzed aerobic oxidation of ketones always gives rise to the α-C-C cleavage product, having challenges to afford hydroxyl keto compounds. Here we report an effective control of the main product from keto acid to α-hydroxyl ketone, by reducing the concentration of FeCl3 catalyst, together with the employ of DMSO as the solvent. In addition, mechanistic investigations suggested the same FeCl3-coordinated peroxide intermediate for both hydroxylation and C-C cleavage routes, and emphasize the role of DMSO as both ligand and reductant. This work provides a new approach for selective aerobic oxidation under Lewis acid catalysis.

The aerobic α-hydroxylation of 2-Me-1-tetralone was investigated in imidazol-based ionic liquids (ILs), where reactions in 1-alkyl-3-methylimidazolium tetrafluoroborates were found to generate considerable products. By correlating the conversion at 2 h with viscosity, relative permittivity and the ET(30) value of ILs, we found that the local polarity in ILs represented by the ET(30) value or the chemical shift of α-proton at the substrate was the critical factor influencing the reaction rate. Furthermore, two-dimensional nuclear Overhauser effect spectroscopy (2D NOESY) was used to characterize the distribution of 2-Me-1-tetralone in ILs. As a result, the mesoscopic structures in ILs were recommended to have crucial influences on the distribution of the substrate in ILs, and the caused local polarity could affect the activation of 2-Me-1-tetralone. These findings revealed the solvent effects of ILs with different structures on the α-hydroxylation of 2-Me-1-tetralone, and may encourage the explorations of more types of aerobic oxidations in ILs.

The Coronavirus Disease 2019 (COVID-19), caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection, has created unprecedented public health and economic crises around the world. SARS-CoV-2 2'-O-methyltransferase (nsp16) adds a "cap" to viral RNA to maintain the stability of viral RNA, and inhibition of nsp16 activity may reduce viral proliferation, making this protein an attractive drug target. Here, we report the identification of several small molecule inhibitors of nsp16 by virtual screening. First, the nsp16-sinefungin complex (PDB ID: 6WKQ) was selected from the protein data bank. Asp6912, Cys6913, Asp6897 and Asp6928 were determined to be the key amino acids for sinefungin binding in the crystal structure of nsp16-sinefungin complex by molecular dynamics simulation. The complex structures in the stable binding trajectory of nsp16-sinefungin were than clustered through molecular dynamics RMSD analysis. Six clusters were generated, and six representative structures were selected to construct the pharmacophore based on the structure. These six pharmacophores were superimposed on the binding pocket to simplify and pick the common characteristics. The compounds obtained by the pharmacophore screening from Bionet and Chembiv databases were docked into the nsp16 active pocket. The candidate compounds were selected according to the molecular docking score and then screened by MM/GBSA. Finally, four candidate compounds were obtained. Four sets of 150ns molecular dynamics simulations were performed to determine whether candidate compounds could maintain stable interactions with key amino acids. The results of MD and MM/PBSA energy decomposition indicated that C1 and C2 could form a stable complex system with nsp16, and could form strong hydrogen bonds and salt bridges with the key amino acid Asp6897 and Asp6928. This study thus identifies and attempts to validate for the first time the potential inhibitory activities of C1 and C2 against nsp16, allowing the development of potent anti-COVID-19 drugs and unique treatment strategies.

Motivation Protein structure alignment is one of the fundamental problems in computational structure biology. A variety of algorithms have been developed to address this important issue in the past decade. However, due to their heuristic nature, current structure alignment methods may suffer from suboptimal alignment and/or over-fragmentation and thus lead to a biologically wrong alignment in some cases. To overcome these limitations, we have developed an accurate topology-independent and global structure alignment method through an FFT-based exhaustive search algorithm, which is referred to as FTAlign. Results Our FTAlign algorithm was extensively tested on six commonly used datasets and compared with seven state-of-the-art structure alignment approaches, TMalign, DeepAlign, Kpax, 3DCOMB, MICAN, SPalignNS and CLICK. It was shown that FTAlign outperformed the other methods in reproducing manually curated alignments and obtained a high success rate of 96.7 and 90.0% on two gold-standard benchmarks, MALIDUP and MALISAM, respectively. Moreover, FTAlign also achieved the overall best performance in terms of biologically meaningful structure overlap (SO) and TMscore on both the sequential alignment test sets including MALIDUP, MALISAM and 64 difficult cases from HOMSTRAD, and the non-sequential sets including MALIDUP-NS, MALISAM-NS, 199 topology-different cases, where FTAlign especially showed more advantage for non-sequential alignment. Despite its global search feature, FTAlign is also computationally efficient and can normally complete a pairwise alignment within one second. Availability and implementation http://huanglab.phys.hust.edu.cn/ftalign/.

Epidemics caused by viral infections pose a significant global threat. Cytoskeletal vimentin is a major intermediate filament (IF) protein, and is involved in numerous functions, including cell signaling, epithelial–mesenchymal transition, intracellular organization and cell migration. Vimentin has important roles for the life cycle of particular viruses; it can act as a co-receptor to enable effective virus invasion and guide efficient transport of the virus to the replication site. Furthermore, vimentin has been shown to rearrange into cage-like structures that facilitate virus replication, and to recruit viral components to the location of assembly and egress. Surprisingly, vimentin can also inhibit virus entry or egress, as well as participate in host-cell defense. Although vimentin can facilitate viral infection, how this function is regulated is still poorly understood. In particular, information is lacking on its interaction sites, regulation of expression, post-translational modifications and cooperation with other host factors. This Review recapitulates the different functions of vimentin in the virus life cycle and discusses how they influence host-cell tropism, virulence of the pathogens and the consequent pathological outcomes. These insights into vimentin–virus interactions emphasize the importance of cytoskeletal functions in viral cell biology and their potential for the identification of novel antiviral targets.

The emerging coronavirus (CoV) pandemic is threatening the public health all over the world. Cytoskeleton is an intricate network involved in controlling cell shape, cargo transport, signal transduction, and cell division. Infection biology studies have illuminated essential roles for cytoskeleton in mediating the outcome of host‒virus interactions. In this review, we discuss the dynamic interactions between actin filaments, microtubules, intermediate filaments, and CoVs. In one round of viral life cycle, CoVs surf along filopodia on the host membrane to the entry sites, utilize specific intermediate filament protein as co-receptor to enter target cells, hijack microtubules for transportation to replication and assembly sites, and promote actin filaments polymerization to provide forces for egress. During CoV infection, disruption of host cytoskeleton homeostasis and modification state is tightly connected to pathological processes, such as defective cytokinesis, demyelinating, cilia loss, and neuron necrosis. There are increasing mechanistic studies on cytoskeleton upon CoV infection, such as viral protein‒cytoskeleton interaction, changes in the expression and post-translation modification, related signaling pathways, and incorporation with other host factors. Collectively, these insights provide new concepts for fundamental virology and the control of CoV infection.

Virus infection has drawn extensive attention since it causes serious or even deadly diseases, consequently inducing a series of social and public health problems. Caveolin-1 is the most important structural protein of caveolae, a membrane invagination widely known for its role in endocytosis and subsequent cytoplasmic transportation. Caveolae/caveolin-1 is tightly associated with a wide range of biological processes, including cholesterol homeostasis, cell mechano-sensing, tumorigenesis, and signal transduction. Intriguingly, the versatile roles of caveolae/caveolin-1 in virus infections have increasingly been appreciated. Over the past few decades, more and more viruses have been identified to invade host cells via caveolae-mediated endocytosis, although other known pathways have been explored. The subsequent post-entry events, including trafficking, replication, assembly, and egress of a large number of viruses, are caveolae/caveolin-1-dependent. Deprivation of caveolae/caveolin-1 by drug application or gene editing leads to abnormalities in viral uptake, viral protein expression, or virion release, whereas the underlying mechanisms remain elusive and must be explored holistically to provide potential novel antiviral targets and strategies. This review recapitulates our current knowledge on how caveolae/caveolin-1 functions in every step of the viral infection cycle and various relevant signaling pathways, hoping to provide a new perspective for future viral cell biology research.

Summary Actin stress fibers guide cell migration and morphogenesis. During centripetal flow, actin transverse arcs fuse accompanied by the formation of myosin II stacks to generate mechanosensitive actomyosin bundles. However, whether myosin II stack formation plays a role in cell mechano-sensing has remained elusive. Myosin-18B is a “glue” molecule for assembling myosin II stacks. By examining actin networks and traction forces, we find that cells abolishing myosin-18B resemble Ca2+∕calmodulin-dependent kinase kinase 2 (CaMKK2)-defective cells. Inhibition of CaMKK2 activity reverses the strong actin network to thin filaments in myosin-18B-overexpressing cells. Moreover, AMP-activated protein kinase (AMPK) activation is able to relieve the thin stress fibers by myosin-18B knockout. Importantly, lack of myosin-18B compromises AMPK-vasodilator-stimulated phosphoprotein and RhoA-myosin signaling, thereby leading to defective persistent migration, which can be rescued only by full-length and C-extension-less myosin-18B. Together, these results reveal a critical role of myosin-18B in the mechanosensitive regulation of migrating cells., Graphical Abstract, Highlights • Myosin-18B knockout cells resemble cells dampening mechano-sensing signaling pathway • Myosin-18B depletion decreases the phosphorylation level of AMPK-VASP and MLC • Myosin-18B knockout cells show compromised persistent migration • The N-extension and coiled-coil domain of myosin-18B is indispensable in cell migration, Biological Sciences; Cell Biology; Functional Aspects of Cell Biology

Protein-protein docking is an important computational tool for predicting protein-protein interactions. With the rapid development of proteomics projects, more and more experimental binding information ranging from mutagenesis data to three-dimensional structures of protein complexes are becoming available. Therefore, how to appropriately incorporate the biological information into traditional ab initio docking has been an important issue and challenge in the field of protein-protein docking. To address these challenges, we have developed a Hybrid DOCKing protocol of template-based and template-free approaches, referred to as HDOCK. The basic procedure of HDOCK is to model the structures of individual components based on the template complex by a template-based method if a template is available; otherwise, the component structures will be modeled based on monomer proteins by regular homology modeling. Then, the complex structure of the component models is predicted by traditional protein-protein docking. With the HDOCK protocol, we have participated in the CPARI experiment for rounds 28-35. Out of the 25 CASP-CAPRI targets for oligomer modeling, our HDOCK protocol predicted correct models for 16 targets, ranking one of the top algorithms in this challenge. Our docking method also made correct predictions on other CAPRI challenges such as protein-peptide binding for 6 out of 8 targets and water predictions for 2 out of 2 targets. The advantage of our hybrid docking approach over pure template-based docking was further confirmed by a comparative evaluation on 20 CASP-CAPRI targets. Proteins 2017; 85:497-512. © 2016 Wiley Periodicals, Inc.

Summary A structural database of peptide–protein interactions is important for drug discovery targeting peptide-mediated interactions. Although some peptide databases, especially for special types of peptides, have been developed, a comprehensive database of cleaned peptide–protein complex structures is still not available. Such cleaned structures are valuable for docking and scoring studies in structure-based drug design. Here, we have developed PepBDB—a curated Peptide Binding DataBase of biological complex structures from the Protein Data Bank (PDB). PepBDB presents not only cleaned structures but also extensive information about biological peptide–protein interactions, and allows users to search the database with a variety of options and interactively visualize the search results. Availability and implementation PepBDB is available at http://huanglab.phys.hust.edu.cn/pepbdb/.

Protein-protein docking is an important computational tool for predicting protein-protein interactions. With the rapid development of proteomics projects, more and more experimental binding information ranging from mutagenesis data to three-dimensional structures of protein complexes are becoming available. Therefore, how to appropriately incorporate the biological information into traditional ab initio docking has been an important issue and challenge in the field of protein-protein docking. To address these challenges, we have developed a Hybrid DOCKing protocol of template-based and template-free approaches, referred to as HDOCK. The basic procedure of HDOCK is to model the structures of individual components based on the template complex by a template-based method if a template is available; otherwise, the component structures will be modeled based on monomer proteins by regular homology modeling. Then, the complex structure of the component models is predicted by traditional protein-protein docking. With the HDOCK protocol, we have participated in the CPARI experiment for rounds 28-35. Out of the 25 CASP-CAPRI targets for oligomer modeling, our HDOCK protocol predicted correct models for 16 targets, ranking one of the top algorithms in this challenge. Our docking method also made correct predictions on other CAPRI challenges such as protein-peptide binding for 6 out of 8 targets and water predictions for 2 out of 2 targets. The advantage of our hybrid docking approach over pure template-based docking was further confirmed by a comparative evaluation on 20 CASP-CAPRI targets. Proteins 2017; 85:497-512. © 2016 Wiley Periodicals, Inc.

RNA–RNA interactions play fundamental roles in gene and cell regulation. Therefore, accurate prediction of RNA–RNA interactions is critical to determine their complex structures and understand the molecular mechanism of the interactions. Here, we have developed a physics-based double-iterative strategy to determine the effective potentials for RNA–RNA interactions based on a training set of 97 diverse RNA–RNA complexes. The double-iterative strategy circumvented the reference state problem in knowledge-based scoring functions by updating the potentials through iteration and also overcame the decoy-dependent limitation in previous iterative methods by constructing the decoys iteratively. The derived scoring function, which is referred to as DITScoreRR, was evaluated on an RNA–RNA docking benchmark of 60 test cases and compared with three other scoring functions. It was shown that for bound docking, our scoring function DITScoreRR obtained the excellent success rates of 90% and 98.3% in binding mode predictions when the top 1 and 10 predictions were considered, compared to 63.3% and 71.7% for van der Waals interactions, 45.0% and 65.0% for ITScorePP, and 11.7% and 26.7% for ZDOCK 2.1, respectively. For unbound docking, DITScoreRR achieved the good success rates of 53.3% and 71.7% in binding mode predictions when the top 1 and 10 predictions were considered, compared to 13.3% and 28.3% for van der Waals interactions, 11.7% and 26.7% for our ITScorePP, and 3.3% and 6.7% for ZDOCK 2.1, respectively. DITScoreRR also performed significantly better in ranking decoys and obtained significantly higher score-RMSD correlations than the other three scoring functions. DITScoreRR will be of great value for the prediction and design of RNA structures and RNA–RNA complexes.