NO EXCESSIVE CRUSTAL GROWTH IN THE CENTRAL ASIAN OROGENIC BELT: FURTHER EVIDENCE FROM FIELD RELATIONSHIPS AND ISOTOPIC DATA

We provide new field observations and isotopic data for key areas of the Central Asian Orogenic Belt (CAOB), reiterating that no excessive crustal growth occurred during its ca. 800 Ma long orogenic evolution. Many Precambrian blocks (microcontinents) identified in the belt are exotic and are most likely derived from the northern margin of Gondwana, including the Tarim craton.We provide new field observations and isotopic data for key areas of the Central Asian Orogenic Belt (CAOB), reiterating that no excessive crustal growth occurred during its ca. 800 Ma long orogenic evolution. Many Precambrian blocks (microcontinents) identified in the belt are exotic and are most likely derived from the northern margin of Gondwana, including the Tarim craton

EARLY NEOPROTEROZOIC CRUST FORMATON IN THE DZABKHAN MICROCONTINENT, CENTRAL ASIAN OROGENIC BELT

The Dzabkhan microcontinent was defined by [Mossakovsky et al., 1994] as a cratonic terrane with an early Precambrian basement that combines highgrade metamorphic complexes of the Songino, Dzabkhan, Otgon, Baidarik, Ider and Jargalant Blocks. However, early Precambrian ages have so far only been recognized in the Baidarik and Ider blocks [Kozakov et al., 2007, 2011; Kröner et al., 2015].The Dzabkhan microcontinent was defined by [Mossakovsky et al., 1994] as a cratonic terrane with an early Precambrian basement that combines highgrade metamorphic complexes of the Songino, Dzabkhan, Otgon, Baidarik, Ider and Jargalant Blocks. However, early Precambrian ages have so far only been recognized in the Baidarik and Ider blocks [Kozakov et al., 2007, 2011; Kröner et al., 2015]

Protein docking prediction using predicted protein-protein interface

<p>Abstract</p> <p>Background</p> <p>Many important cellular processes are carried out by protein complexes. To provide physical pictures of interacting proteins, many computational protein-protein prediction methods have been developed in the past. However, it is still difficult to identify the correct docking complex structure within top ranks among alternative conformations.</p> <p>Results</p> <p>We present a novel protein docking algorithm that utilizes imperfect protein-protein binding interface prediction for guiding protein docking. Since the accuracy of protein binding site prediction varies depending on cases, the challenge is to develop a method which does not deteriorate but improves docking results by using a binding site prediction which may not be 100% accurate. The algorithm, named PI-LZerD (using Predicted Interface with Local 3D Zernike descriptor-based Docking algorithm), is based on a pair wise protein docking prediction algorithm, LZerD, which we have developed earlier. PI-LZerD starts from performing docking prediction using the provided protein-protein binding interface prediction as constraints, which is followed by the second round of docking with updated docking interface information to further improve docking conformation. Benchmark results on bound and unbound cases show that PI-LZerD consistently improves the docking prediction accuracy as compared with docking without using binding site prediction or using the binding site prediction as post-filtering.</p> <p>Conclusion</p> <p>We have developed PI-LZerD, a pairwise docking algorithm, which uses imperfect protein-protein binding interface prediction to improve docking accuracy. PI-LZerD consistently showed better prediction accuracy over alternative methods in the series of benchmark experiments including docking using actual docking interface site predictions as well as unbound docking cases.</p

Scoring docking conformations using predicted protein interfaces

BACKGROUND: Since proteins function by interacting with other molecules, analysis of protein-protein interactions is essential for comprehending biological processes. Whereas understanding of atomic interactions within a complex is especially useful for drug design, limitations of experimental techniques have restricted their practical use. Despite progress in docking predictions, there is still room for improvement. In this study, we contribute to this topic by proposing T-PioDock, a framework for detection of a native-like docked complex 3D structure. T-PioDock supports the identification of near-native conformations from 3D models that docking software produced by scoring those models using binding interfaces predicted by the interface predictor, Template based Protein Interface Prediction (T-PIP). RESULTS: First, exhaustive evaluation of interface predictors demonstrates that T-PIP, whose predictions are customised to target complexity, is a state-of-the-art method. Second, comparative study between T-PioDock and other state-of-the-art scoring methods establishes T-PioDock as the best performing approach. Moreover, there is good correlation between T-PioDock performance and quality of docking models, which suggests that progress in docking will lead to even better results at recognising near-native conformations. CONCLUSION: Accurate identification of near-native conformations remains a challenging task. Although availability of 3D complexes will benefit from template-based methods such as T-PioDock, we have identified specific limitations which need to be addressed. First, docking software are still not able to produce native like models for every target. Second, current interface predictors do not explicitly consider pairwise residue interactions between proteins and their interacting partners which leaves ambiguity when assessing quality of complex conformations

Barrier-to-autointegration factor 1 (Banf1) regulates poly [ADP-ribose] polymerase 1 (PARP1) activity following oxidative DNA damage

The DNA repair capacity of human cells declines with age, in a process that is not clearly understood. Mutation of the nuclear envelope protein barrier-to-autointegration factor 1 (Banf1) has previously been shown to cause a human progeroid disorder, Néstor–Guillermo progeria syndrome (NGPS). The underlying links between Banf1, DNA repair and the ageing process are unknown. Here, we report that Banf1 controls the DNA damage response to oxidative stress via regulation of poly [ADP-ribose] polymerase 1 (PARP1). Specifically, oxidative lesions promote direct binding of Banf1 to PARP1, a critical NAD-dependent DNA repair protein, leading to inhibition of PARP1 auto-ADP-ribosylation and defective repair of oxidative lesions, in cells with increased Banf1. Consistent with this, cells from patients with NGPS have defective PARP1 activity and impaired repair of oxidative lesions. These data support a model whereby Banf1 is crucial to reset oxidative-stress-induced PARP1 activity. Together, these data offer insight into Banf1-regulated, PARP1-directed repair of oxidative lesions

The Bactofilin Cytoskeleton Protein BacM of Myxococcus xanthus Forms an Extended β-Sheet Structure Likely Mediated by Hydrophobic Interactions

Bactofilins are novel cytoskeleton proteins that are widespread in Gram-negative bacteria. Myxococcus xanthus, an important predatory soil bacterium, possesses four bactofilins of which one, BacM (Mxan_7475) plays an important role in cell shape maintenance. Electron and fluorescence light microscopy, as well as studies using over-expressed, purified BacM, indicate that this protein polymerizes in vivo and in vitro into ~3 nm wide filaments that further associate into higher ordered fibers of about 10 nm. Here we use a multipronged approach combining secondary structure determination, molecular modeling, biochemistry, and genetics to identify and characterize critical molecular elements that enable BacM to polymerize. Our results indicate that the bactofilin-determining domain DUF583 folds into an extended β-sheet structure, and we hypothesize a left-handed β-helix with polymerization into 3 nm filaments primarily via patches of hydrophobic amino acid residues. These patches form the interface allowing head-to-tail polymerization during filament formation. Biochemical analyses of these processes show that folding and polymerization occur across a wide variety of conditions and even in the presence of chaotropic agents such as one molar urea. Together, these data suggest that bactofilins are comprised of a structure unique to cytoskeleton proteins, which enables robust polymerization

Characterization of a Novel Binding Protein for Fortilin/TCTP — Component of a Defense Mechanism against Viral Infection in Penaeus monodon

The Fortilin (also known as TCTP) in Penaeus monodon (PmFortilin) and Fortilin Binding Protein 1 (FBP1) have recently been shown to interact and to offer protection against the widespread White Spot Syndrome Virus infection. However, the mechanism is yet unknown. We investigated this interaction in detail by a number of in silico and in vitro analyses, including prediction of a binding site between PmFortilin/FBP1 and docking simulations. The basis of the modeling analyses was well-conserved PmFortilin orthologs, containing a Ca2+-binding domain at residues 76–110 representing a section of the helical domain, the translationally controlled tumor protein signature 1 and 2 (TCTP_1, TCTP_2) at residues 45–55 and 123–145, respectively. We found the pairs Cys59 and Cys76 formed a disulfide bond in the C-terminus of FBP1, which is a common structural feature in many exported proteins and the “x–G–K–K” pattern of the amidation site at the end of the C-terminus. This coincided with our previous work, where we found the “x–P–P–x” patterns of an antiviral peptide also to be located in the C-terminus of FBP1. The combined bioinformatics and in vitro results indicate that FBP1 is a transmembrane protein and FBP1 interact with N-terminal region of PmFortilin

Role of Interaction and Nucleoside Diphosphate Kinase B in Regulation of the Cystic Fibrosis Transmembrane Conductance Regulator Function by cAMP-Dependent Protein Kinase A

Cystic fibrosis results from mutations in the cystic fibrosis transmembrane conductance regulator (CFTR), a cAMP-dependent protein kinase A (PKA) and ATP-regulated chloride channel. Here, we demonstrate that nucleoside diphosphate kinase B (NDPK-B, NM23-H2) forms a functional complex with CFTR. In airway epithelia forskolin/IBMX significantly increases NDPK-B co-localisation with CFTR whereas PKA inhibitors attenuate complex formation. Furthermore, an NDPK-B derived peptide (but not its NDPK-A equivalent) disrupts the NDPK-B/CFTR complex in vitro (19-mers comprising amino acids 36-54 from NDPK-B or NDPK-A). Overlay (Far-Western) and Surface Plasmon Resonance (SPR) analysis both demonstrate that NDPK-B binds CFTR within its first nucleotide binding domain (NBD1, CFTR amino acids 351-727). Analysis of chloride currents reflective of CFTR or outwardly rectifying chloride channels (ORCC, DIDS-sensitive) showed that the 19-mer NDPK-B peptide (but not its NDPK-A equivalent) reduced both chloride conductances. Additionally, the NDPK-B (but not NDPK-A) peptide also attenuated acetylcholine-induced intestinal short circuit currents. In silico analysis of the NBD1/NDPK-B complex reveals an extended interaction surface between the two proteins. This binding zone is also target of the 19-mer NDPK-B peptide, thus confirming its capability to disrupt NDPK-B/CFTR complex. We propose that NDPK-B forms part of the complex that controls chloride currents in epithelia

Prediction of protein assemblies, the next frontier: The CASP14-CAPRI experiment

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: FC00100