Improvements and new functionalities of UNRES server for coarse-grained modeling of protein structure, dynamics, and interactions

In this paper we report the improvements and extensions of the UNRES server (https://unres-server.chem.ug.edu.pl) for physics-based simulations with the coarse-grained UNRES model of polypeptide chains. The improvements include the replacement of the old code with the recently optimized one and adding the recent scale-consistent variant of the UNRES force field, which performs better in the modeling of proteins with the β and the α+β structures. The scope of applications of the package was extended to data-assisted simulations with restraints from nuclear magnetic resonance (NMR) and chemical crosslink mass-spectroscopy (XL-MS) measurements. NMR restraints can be input in the NMR Exchange Format (NEF), which has become a standard. Ambiguous NMR restraints are handled without expert intervention owing to a specially designed penalty function. The server can be used to run smaller jobs directly or to prepare input data to run larger production jobs by using standalone installations of UNRES

Assessment of chemical-crosslink-assisted protein structure modeling in CASP13

International audienceWith the advance of experimental procedures obtaining chemical crosslinking information is becoming a fast and routine practice. Information on crosslinks can greatly enhance the accuracy of protein structure modeling. Here, we review the current state of the art in modeling protein structures with the assistance of experimentally determined chemical crosslinks within the framework of the 13th meeting of Critical Assessment of Structure Prediction approaches. This largest‐to‐date blind assessment reveals benefits of using data assistance in difficult to model protein structure prediction cases. However, in a broader context, it also suggests that with the unprecedented advance in accuracy to predict contacts in recent years, experimental crosslinks will be useful only if their specificity and accuracy further improved and they are better integrated into computational workflows

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

New UNRES force field package with Fortan 90

UNRES is a coarse-grained model of polypeptide chains. Until now, each version of UNRES (UNRESPACK v.3.2 and earlier ones) has been written in Fortran 77. Due to the fact that Fortran 77 enables us to use only static arrays, the Fortran 77 version has significant memory problems, and consequently, UNRESPACK has had to be split into many programs. Our recent work was focused on creating a new UNRES package with Fortran 90 (UNRESPACK v.4.0), based on the previous Fortran 77 versions. Fortran 90 provides dynamic memory allocation, user defined data types, and structuring the code into modules which encompass subroutines, functions, and variables. Moreover, Fortran 90 adds internal functions and subroutines, providing greater flexibility. The whole code of UNRES with Fortran 90 has been restructured, so that it now consists of modules that can be assembled to create the main simulation program and companion programs. This approach enabled us to eliminate the redundancy of the code, while keeping all functions of the package

Photocatalytical Decomposition of Contaminants οn Thin Film Gas Sensors

Gas sensing materials have been prepared in a form of $TiO_{2}-SnO_{2}$ thin films by rf reactive sputtering from $Ti:SnO_{2}$ and $Sn:TiO_{2}$ targets. Material studies have been performed by scanning electron microscopy, atomic force microscopy, X-ray diffraction at grazing incidence, Mössbauer spectroscopy, X-ray photoelectron spectroscopy and optical spectrophotometry. Dynamic gas sensing responses have been recorded as reproducible changes in the electrical resistance upon introduction of hydrogen at a partial pressure of 100-6000 ppm over a wide temperature range 473-873 K. Contamination experiments have been carried out with the motor oil (40 vol.% solution in $CCl_{4}$) in order to study the effect of UV light illumination on the gas sensor response. Optical spectroscopy has been applied to monitor the photodecomposition of the test compound, bromothymol blue. The Electronic Nose, ALPHA MOS FOX 4000 has been used in order to differentiate between different groups of motor oil vapors

Gas sensing properties of TiO2 - SnO2 nanomaterials

Nanocomposites of TiO2/SnO2 for hydrogen and ammonia detection are compared with complex oxides that belong to TiO2/SnO2 system. Nanocomposites have been prepared by mechanical mixing of nanopowders with different specific surface area SSA=159 m2/g for TiO2 and SSA=21 m2/g for SnO2 as determined from BET measurements. Complex oxides have been synthesized by sol - gel method from organic precursor of TTIP and SnCl2*2H2O. The resulting SSA=91 m2/g has turned out to be larger for 50 mol % TiO2 + 50 mol % SnO2 sol-gel sample as compared with 65 m2/g for the nanocomposite of the same nominal chemical content. Nanocomposites consist of two separate phases of larger-grain (21-28 nm) cassiterite SnO2 and smaller-grain (8-11 nm) rutile TiO2, respectively, over a full compositional range. XRD and STEM suggest that a solid solution with some precipitation of SnO2 is formed for 50 mol. % TiO2 + 50 mol. % SnO2. For nanocomposites, TEM experiments reveal the presence of small, elongated TiO2 crystals and larger SnO2 crystals of irregular shape. For 50 mol.% TiO2 + 50 mol.% SnO2 sol - gel sample, spherical, homogeneously distributed grains are seen in TEM. Sensor responses exhibit a broad maximum over the compositional range at 20 - 50 mol.% of TiO2 mixed with SnO2. The electrical resistivity of 50 mol % TiO2 + 50 mol % SnO2 sol-gel sample is less affected than that of the nanocomposite of the same composition, by the exposure to H2 and NH2 at elevated temperatures