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Extracellular cytokines are enriched in the tumor microenvironment and regulate various important properties of cancers, including autophagy. However, the precise molecular mechanisms underlying the link between autophagy and extracellular cytokines remain to be elucidated. In the present study, we demonstrate that IL-6 activates autophagy through the IL-6/JAK2/BECN1 pathway and promotes chemotherapy resistance in colorectal cancer (CRC). Mechanistically, IL-6 triggers the interaction between JAK2 and BECN1, where JAK2 phosphorylates BECN1 at Y333. We demonstrate that BECN1 Y333 phosphorylation is crucial for BECN1 activation and IL-6-induced autophagy by regulating PI3KC3 complex formation. Furthermore, we investigate BECN1 Y333 phosphorylation as a predictive marker for poor CRC prognosis and chemotherapy resistance. Combination treatment with autophagy inhibitors or pharmacological agents targeting the IL-6/JAK2/BECN1 signaling pathway may represent a potential strategy for CRC cancer therapy., IL-6 is an important cytokine in the tumour microenvironment, but its role in regulating autophagy in cancer cells is unclear. Here the authors show that IL-6 activates autophagy in colorectal cancer through the interaction between JAK2 and autophagy regulator, BECN1, which leads to chemotherapeutic resistance.

We present the results for CAPRI Round 50, the fourth joint CASP-CAPRI protein assembly prediction challenge. The Round comprised a total of twelve targets, including six dimers, three trimers, and three higher-order oligomers. Four of these were easy targets, for which good structural templates were available either for the full assembly, or for the main interfaces (of the higher-order oligomers). Eight were difficult targets for which only distantly related templates were found for the individual subunits. Twenty-five CAPRI groups including eight automatic servers submitted ~1250 models per target. Twenty groups including six servers participated in the CAPRI scoring challenge submitted ~190 models per target. The accuracy of the predicted models was evaluated using the classical CAPRI criteria. The prediction performance was measured by a weighted scoring scheme that takes into account the number of models of acceptable quality or higher submitted by each group as part of their five top-ranking models. Compared to the previous CASP-CAPRI challenge, top performing groups submitted such models for a larger fraction (70–75%) of the targets in this Round, but fewer of these models were of high accuracy. Scorer groups achieved stronger performance with more groups submitting correct models for 70–80% of the targets or achieving high accuracy predictions. Servers performed less well in general, except for the MDOCKPP and LZERD servers, who performed on par with human groups. In addition to these results, major advances in methodology are discussed, providing an informative overview of where the prediction of protein assemblies currently stands., Cancer Research UK, Grant/Award Number: FC001003; Changzhou Science and Technology Bureau, Grant/Award Number: CE20200503; Department of Energy and Climate Change, Grant/Award Numbers: DE-AR001213, DE-SC0020400, DE-SC0021303; H2020 European Institute of Innovation and Technology, Grant/Award Numbers: 675728, 777536, 823830; Institut national de recherche en informatique et en automatique (INRIA), Grant/Award Number: Cordi-S; Lietuvos Mokslo Taryba, Grant/Award Numbers: S-MIP-17-60, S-MIP-21-35; Medical Research Council, Grant/Award Number: FC001003; Japan Society for the Promotion of Science KAKENHI, Grant/Award Number: JP19J00950; Ministerio de Ciencia e Innovación, Grant/Award Number: PID2019-110167RB-I00; Narodowe Centrum Nauki, Grant/Award Numbers: UMO-2017/25/B/ST4/01026, UMO-2017/26/M/ST4/00044, UMO-2017/27/B/ST4/00926; National Institute of General Medical Sciences, Grant/Award Numbers: R21GM127952, R35GM118078, RM1135136, T32GM132024; National Institutes of Health, Grant/Award Numbers: R01GM074255, R01GM078221, R01GM093123, R01GM109980, R01GM133840, R01GN123055, R01HL142301, R35GM124952, R35GM136409; National Natural Science Foundation of China, Grant/Award Number: 81603152; National Science Foundation, Grant/Award Numbers: AF1645512, CCF1943008, CMMI1825941, DBI1759277, DBI1759934, DBI1917263, DBI20036350, IIS1763246, MCB1925643; NWO, Grant/Award Number: TOP-PUNT 718.015.001; Wellcome Trust, Grant/Award Number: FC001003

The outbreak of a novel coronavirus, which was later formally named the severe acute respiratory coronavirus 2 (SARS-CoV-2), has caused a worldwide public health crisis. Previous studies showed that SARS-CoV-2 is highly homologous to SARS-CoV and infects humans through the binding of the spike protein to ACE2. Here, we have systematically studied the molecular mechanisms of human infection with SARS-CoV-2 and SARS-CoV by protein-protein docking and MD simulations. It was found that SARS-CoV-2 binds ACE2 with a higher affinity than SARS-CoV, which may partly explain that SARS-CoV-2 is much more infectious than SARS-CoV. In addition, the spike protein of SARS-CoV-2 has a significantly lower free energy than that of SARS-CoV, suggesting that SARS-CoV-2 is more stable and may survive a higher temperature than SARS-CoV. This provides insights into the evolution of SARS-CoV-2 because SARS-like coronaviruses have originated in bats. Our computation also suggested that the RBD-ACE2 binding for SARS-CoV-2 is much more temperature-sensitive than that for SARS-CoV. Thus, it is expected that SARS-CoV-2 would decrease its infection ability much faster than SARS-CoV when the temperature rises. These findings would be beneficial for the disease prevention and drug/vaccine development of SARS-CoV-2.

A major subclass of protein–protein interactions is formed by homo-oligomers with certain symmetry. Therefore, computational modeling of the symmetric protein complexes is important for understanding the molecular mechanism of related biological processes. Although several symmetric docking algorithms have been developed for Cn symmetry, few docking servers have been proposed for Dn symmetry. Here, we present HSYMDOCK, a web server of our hierarchical symmetric docking algorithm that supports both Cn and Dn symmetry. The HSYMDOCK server was extensively evaluated on three benchmarks of symmetric protein complexes, including the 20 CASP11–CAPRI30 homo-oligomer targets, the symmetric docking benchmark of 213 Cn targets and 35 Dn targets, and a nonredundant test set of 55 transmembrane proteins. It was shown that HSYMDOCK obtained a significantly better performance than other similar docking algorithms. The server supports both sequence and structure inputs for the monomer/subunit. Users have an option to provide the symmetry type of the complex, or the server can predict the symmetry type automatically. The docking process is fast and on average consumes 10∼20 min for a docking job. The HSYMDOCK web server is available at http://huanglab.phys.hust.edu.cn/hsymdock/.

Protein–protein and protein–DNA/RNA interactions play a fundamental role in a variety of biological processes. Determining the complex structures of these interactions is valuable, in which molecular docking has played an important role. To automatically make use of the binding information from the PDB in docking, here we have presented HDOCK, a novel web server of our hybrid docking algorithm of template-based modeling and free docking, in which cases with misleading templates can be rescued by the free docking protocol. The server supports protein–protein and protein–DNA/RNA docking and accepts both sequence and structure inputs for proteins. The docking process is fast and consumes about 10–20 min for a docking run. Tested on the cases with weakly homologous complexes of

The well-known mismatch repair (MMR) machinery, MutS/MutL, is absent in numerous Archaea and some Bacteria. Recent studies have shown that EndoMS/NucS has the ability to cleave double-stranded DNA (dsDNA) containing a mismatched base pair, which suggests a novel mismatch repair process. However, the recognition mechanism and the binding process of EndoMS/NucS in the MMR pathway remain unclear. In this study, we investigate the binding dynamics of EndoMS/NucS to mismatched dsDNA and its energy as a function of the angle between the two C-terminal domains of EndoMS/NucS, through molecular docking and extensive molecular dynamics (MD) simulations. It is found that there exists a half-open transition state corresponding to an energy barrier (at an activation angle of approximately 80 ∘ ) between the open state and the closed state, according to the energy curve. When the angle is larger than the activation angle, the C-terminal domains can move freely and tend to change to the open state (local energy minimum). Otherwise, the C-terminal domains will interact with the mismatched dsDNA directly and converge to the closed state at the global energy minimum. As such, this two-state system enables the exposed N-terminal domains of EndoMS/NucS to recognize mismatched dsDNA during the open state and then stabilize the binding of the C-terminal domains of EndoMS/NucS to the mismatched dsDNA during the closed state. We also investigate how the EndoMS/NucS recognizes and binds to mismatched dsDNA, as well as the effects of K + ions. The results provide insights into the recognition and binding mechanisms of EndoMS/NucS to mismatched dsDNA in the MMR pathway.

The recruitment and transference of proteins through protein&ndash, protein interactions is a general process involved in various biological functions in cells. Despite the importance of this general process, the dynamic mechanism of how proteins are recruited and transferred from one interacting partner to another remains unclear. In this study, we investigated the dynamic mechanisms of recruitment and translocation of histone chaperone CIA/ASF1 for nucleosome disassembly by exploring the conformational space and the free energy profile of unbound DBD(CCG1) and CIA/ASF1-bound DBD(CCG1) systems through extensive molecular dynamics simulations. It was found that there exists three metastable conformational states for DBD(CCG1), an unbound closed state, a CIA/ASF1-bound half-open state, and an open state. The free energy landscape shows that the closed state and the half-open state are separated by a high free energy barrier, while the half-open state and the open state are connected with a moderate free energy increase. The high free energy barrier between the closed and half-open states explains why DBD(CCG1) can recruit CIA/ASF1 and remain in the binding state during the transportation. In addition, the asymmetric binding of CIA/ASF1 on DBD(CCG1) allows DBD(CCG1) to adopt the open state by moving one of its two domains, such that the exposed domain of DBD(CCG1) is able to recognize the acetylated histone H4 tails. As such, CIA/ASF1 has a chance to translocate from DBD(CCG1) to histone, which is also facilitated by the moderate energy increase from the bound half-open state to the open state of DBD(CCG1). These findings suggest that the recruitment and transference of histone chaperone CIA/ASF1 is highly favored by its interaction with DBD(CCG1) via conformational selection and asymmetric binding, which may represent a general mechanism of similar biological processes.

The electronic and phonon transport properties of quaternary tetradymite BiSbSeTe2 are investigated by using a first-principles approach and Boltzmann transport theory. Unlike the binary counterpart Bi2Te3, we obtain a pair of Rashba splitting bands induced by the absence of an inversion center. Such unique characteristics could lead to a large Seebeck coefficient even at a relatively higher carrier concentration. Besides, we find an ultralow lattice thermal conductivity of BiSbSeTe2, especially along the interlayer direction, which can be traced to the extremely small phonon relaxation time mainly induced by the mixed covalent bonds. As a consequence, a considerably large ZT value of ~2.0 can be obtained at 500 K, indicating that the unique lattice structure of BiSbSeTe2 caused by isoelectronic substitution could be an advantage to achieve high thermoelectric performance.

Molecular docking is a widely-used computational tool for the study of molecular recognition, which aims to predict the binding mode and binding affinity of a complex formed by two or more constituent molecules with known structures. An important type of molecular docking is protein-ligand docking because of its therapeutic applications in modern structure-based drug design. Here, we review the recent advances of protein flexibility, ligand sampling, and scoring functions—the three important aspects in protein-ligand docking. Challenges and possible future directions are discussed in the Conclusion.

In this study, we use the recently released 2012 Community Structure-Activity Resource (CSAR) data set to evaluate two knowledge-based scoring functions, ITScore and STScore, and a simple force-field-based potential (VDWScore). The CSAR data set contains 757 compounds, most with known affinities, and 57 crystal structures. With the help of the script files for docking preparation, we use the full CSAR data set to evaluate the performances of the scoring functions on binding affinity prediction and active/inactive compound discrimination. The CSAR subset that includes crystal structures is used as well, to evaluate the performances of the scoring functions on binding mode and affinity predictions. Within this structure subset, we investigate the importance of accurate ligand and protein conformational sampling and find that the binding affinity predictions are less sensitive to non-native ligand and protein conformations than the binding mode predictions. We also find the full CSAR data set to be more challenging in making binding mode predictions than the subset with structures. The script files used for preparing the CSAR data set for docking, including scripts for canonicalization of the ligand atoms, are offered freely to the academic community.

Protein-RNA interactions play an important role in many biological processes. The ability to predict the molecular structures of protein-RNA complexes from docking would be valuable for understanding the underlying chemical mechanisms. We have developed a novel nonredundant benchmark dataset for protein-RNA docking and scoring. The diverse dataset of 72 targets consists of 52 unbound-unbound test complexes, and 20 unbound-bound test complexes. Here, unbound-unbound complexes refer to cases in which both binding partners of the cocrystallized complex are either in apo form or in a conformation taken from a different protein-RNA complex, whereas unbound-bound complexes are cases in which only one of the two binding partners has another experimentally determined conformation. The dataset is classified into three categories according to the interface root mean square deviation and the percentage of native contacts in the unbound structures: 49 easy, 16 medium, and 7 difficult targets. The bound and unbound cases of the benchmark dataset are expected to benefit the development and improvement of docking and scoring algorithms for the docking community. All the easy-to-view structures are freely available to the public at http://zoulab.dalton.missouri.edu/RNAbenchmark/.

International audience; The CAPRI (Critical Assessment of Predicted Interactions) and CASP (Critical Assessment of protein Structure Prediction) experiments have demonstrated the power of community-wide tests of methodology in assessing the current state of the art and spurring progress in the very challenging areas of protein docking and structure prediction. We sought to bring the power of community-wide experiments to bear on a very challenging protein design problem that provides a complementary but equally fundamental test of current understanding of protein-binding thermodynamics. We have generated a number of designed protein-protein interfaces with very favorable computed binding energies but which do not appear to be formed in experiments, suggesting that there may be important physical chemistry missing in the energy calculations. A total of 28 research groups took up the challenge of determining what is missing: we provided structures of 87 designed complexes and 120 naturally occurring complexes and asked participants to identify energetic contributions and/or structural features that distinguish between the two sets. The community found that electrostatics and solvation terms partially distinguish the designs from the natural complexes, largely due to the nonpolar character of the designed interactions. Beyond this polarity difference, the community found that the designed binding surfaces were, on average, structurally less embedded in the designed monomers, suggesting that backbone conformational rigidity at the designed surface is important for realization of the designed function. These results can be used to improve computational design strategies, but there is still much to be learned; for example, one designed complex, which does form in experiments, was classified by all metrics as a nonbinder.

A hierarchical approach has been developed for protein-protein docking. In the first step, a Fast Fourier Transform (FFT)-based docking algorithm is used to globally sample all putative binding modes, in which the protein is represented by a reduced model, that is, each side chain on the protein surface is represented by its center of mass. Compared to conventional FFT docking with all-atom models, the FFT docking method with a reduced model is expected to generate more hits because it allows larger side-chain flexibility. Next, the filtered binding modes (normally several thousands) are refined by an iteratively derived knowledge-based scoring function ITScorePP and by considering backbone/loop flexibility using an ensemble docking algorithm. The distance-dependent potentials of ITScorePP were extracted by a physics-based iterative method, which circumvents the long-standing reference state problem in the knowledge-based approaches. With this hierarchical protocol, we have participated in the CAPRI experiments for Rounds 15-19 of 11 targets (T32-T42). In the predictor experiments, we achieved correct binding modes for six targets: three are with high accuracy (T40 for both distinct binding modes, T41, and T42), two are with medium accuracy (T34 and T37), and one is acceptable (T32). In the scorer experiments, of the seven target complexes that contain at least one acceptable mode submitted by the CAPRI predictor groups, we obtained correct binding modes for four targets: three are with high accuracy (T37, T40, and T41) and one is with medium accuracy (T34), suggesting good accuracy and robustness of ITScorePP.

Protein-RNA interactions play important roles in many biological processes. Given the high cost and technique difficulties in experimental methods, computationally predicting the binding complexes from individual protein and RNA structures is pressingly needed, in which a reliable scoring function is one of the critical components. Here, we have developed a knowledge-based scoring function, referred to as ITScore-PR, for protein-RNA binding mode prediction by using a statistical mechanics-based iterative method. The pairwise distance-dependent atomic interaction potentials of ITScore-PR were derived from experimentally determined protein–RNA complex structures. For validation, we have compared ITScore-PR with 10 other scoring methods on four diverse test sets. For bound docking, ITScore-PR achieved a success rate of up to 86% if the top prediction was considered and up to 94% if the top 10 predictions were considered, respectively. For truly unbound docking, the respective success rates of ITScore-PR were up to 24 and 46%. ITScore-PR can be used stand-alone or easily implemented in other docking programs for protein–RNA recognition.