Sergei Grudinin

Bio: Sergei Grudinin is an academic researcher from University of Grenoble. The author has contributed to research in topics: Protein structure prediction & CASP. The author has an hindex of 22, co-authored 79 publications receiving 1521 citations. Previous affiliations of Sergei Grudinin include Centre national de la recherche scientifique & Forschungszentrum Jülich.

Mechanism of transmembrane signaling by sensor histidine kinases

Abstract: One of the major and essential classes of transmembrane (TM) receptors, present in all domains of life, is sensor histidine kinases (HKs), parts of two-component signaling systems (TCS). The structural mechanisms of transmembrane signaling by these sensors are poorly understood. We present here crystal structures of the periplasmic sensor domain, the TM domain and the cytoplasmic HAMP domain of the Escherichia coli nitrate/nitrite sensor HK NarQ in the ligand-bound and mutated ligand-free states. The structures reveal that the ligand binding induces significant rearrangements and piston-like shifts of TM helices. The HAMP domain protomers undergo lever-like motions and convert the piston-like motions into helical rotations. Our findings provide the structural framework for complete understanding of TM TCS signaling and for development of antimicrobial treatments targeting TCS.

Prediction of homoprotein and heteroprotein complexes by protein docking and template-based modeling: A CASP-CAPRI experiment.

Abstract: We present the results for CAPRI Round 30, the first joint CASP-CAPRI experiment, which brought together experts from the protein structure prediction and protein-protein docking communities. The Round comprised 25 targets from amongst those submitted for the CASP11 prediction experiment of 2014. The targets included mostly homodimers, a few homotetramers, and two heterodimers, and comprised protein chains that could readily be modeled using templates from the Protein Data Bank. On average 24 CAPRI groups and 7 CASP groups submitted docking predictions for each target, and 12 CAPRI groups per target participated in the CAPRI scoring experiment. In total more than 9500 models were assessed against the 3D structures of the corresponding target complexes. Results show that the prediction of homodimer assemblies by homology modeling techniques and docking calculations is quite successful for targets featuring large enough subunit interfaces to represent stable associations. Targets with ambiguous or inaccurate oligomeric state assignments, often featuring crystal contact-sized interfaces, represented a confounding factor. For those, a much poorer prediction performance was achieved, while nonetheless often providing helpful clues on the correct oligomeric state of the protein. The prediction performance was very poor for genuine tetrameric targets, where the inaccuracy of the homology-built subunit models and the smaller pair-wise interfaces severely limited the ability to derive the correct assembly mode. Our analysis also shows that docking procedures tend to perform better than standard homology modeling techniques and that highly accurate models of the protein components are not always required to identify their association modes with acceptable accuracy. Proteins 2016; 84(Suppl 1):323-348. © 2016 Wiley Periodicals, Inc.

Community-wide Evaluation of Methods for Predicting the Effect of Mutations on Protein-Protein Interactions

Abstract: Community-wide blind prediction experiments such as CAPRI and CASP provide an objective measure of the current state of predictive methodology. Here we describe a community-wide assessment of methods to predict the effects of mutations on protein-protein interactions. Twenty-two groups predicted the effects of comprehensive saturation mutagenesis for two designed influenza hemagglutinin binders and the results were compared with experimental yeast display enrichment data obtained using deep sequencing. The most successful methods explicitly considered the effects of mutation on monomer stability in addition to binding affinity, carried out explicit side-chain sampling and backbone relaxation, evaluated packing, electrostatic, and solvation effects, and correctly identified around a third of the beneficial mutations. Much room for improvement remains for even the best techniques, and large-scale fitness landscapes should continue to provide an excellent test bed for continued evaluation of both existing and new prediction methodologies.

Deep convolutional networks for quality assessment of protein folds.

Abstract: Motivation The computational prediction of a protein structure from its sequence generally relies on a method to assess the quality of protein models. Most assessment methods rank candidate models using heavily engineered structural features, defined as complex functions of the atomic coordinates. However, very few methods have attempted to learn these features directly from the data. Results We show that deep convolutional networks can be used to predict the ranking of model structures solely on the basis of their raw three-dimensional atomic densities, without any feature tuning. We develop a deep neural network that performs on par with state-of-the-art algorithms from the literature. The network is trained on decoys from the CASP7 to CASP10 datasets and its performance is tested on the CASP11 dataset. Additional testing on decoys from the CASP12, CAMEO and 3DRobot datasets confirms that the network performs consistently well across a variety of protein structures. While the network learns to assess structural decoys globally and does not rely on any predefined features, it can be analyzed to show that it implicitly identifies regions that deviate from the native structure. Availability and implementation The code and the datasets are available at https://github.com/lamoureux-lab/3DCNN_MQA. Supplementary information Supplementary data are available at Bioinformatics online.

Pepsi‐SAXS: an adaptive method for rapid and accurate computation of small‐angle X‐ray scattering profiles

Abstract: A new method called Pepsi-SAXS is presented that calculates small-angle X-ray scattering profiles from atomistic models. The method is based on the multipole expansion scheme and is significantly faster compared with other tested methods. In particular, using the Nyquist–Shannon–Kotelnikov sampling theorem, the multipole expansion order is adapted to the size of the model and the resolution of the experimental data. It is argued that by using the adaptive expansion order, this method has the same quadratic dependence on the number of atoms in the model as the Debye-based approach, but with a much smaller prefactor in the computational complexity. The method has been systematically validated on a large set of over 50 models collected from the BioIsis and SASBDB databases. Using a laptop, it was demonstrated that Pepsi-SAXS is about seven, 29 and 36 times faster compared with CRYSOL, FoXS and the three-dimensional Zernike method in SAStbx, respectively, when tested on data from the BioIsis database, and is about five, 21 and 25 times faster compared with CRYSOL, FoXS and SAStbx, respectively, when tested on data from SASBDB. On average, Pepsi-SAXS demonstrates comparable accuracy in terms of χ2 to CRYSOL and FoXS when tested on BioIsis and SASBDB profiles. Together with a small allowed variation of adjustable parameters, this demonstrates the effectiveness of the method. Pepsi-SAXS is available at http:// team.inria.fr/nano-d/software/pepsi-saxs.

Water as an Active Constituent in Cell Biology

Abstract: When Szent-Gyorgyi called water the “matrix of life”,1 he was echoing an old sentiment. Paracelsus in the 16th century said that “water was the matrix of the world and of all its creatures.”2 But Paracelsus’s notion of a matrixsan active substance imbued with fecund, life-giving propertiess was quite different from the picture that, until very recently, molecular biologists have tended to hold of water’s role in the chemistry of life. Although acknowledging that liquid water has some unusual and important physical and chemical propertiessits potency as a solvent, its ability to form hydrogen bonds, its amphoteric naturesbiologists have regarded it essentially as the backdrop on which life’s molecular components are arrayed. It used to be common practice, for example, to perform computer simulations of biomolecules in a vacuum. Partly this was because the computational intensity of simulating a polypeptide chain was challenging even without accounting for solvent molecules too, but it also reflected the prevailing notion that water does little more than temper or moderate the basic physicochemical interactions responsible for molecular biology. What Gerstein and Levitt said 9 years ago remains true today: “When scientists publish models of biological molecules in journals, they usually draw their models in bright colors and place them against a plain, black background”.3 Curiously, this neglect of water as an active component of the cell went hand in hand with the assumption that life could not exist without it. That was basically an empirical conclusion derived from our experience of life on Earth: environments without liquid water cannot sustain life, and special strategies are needed to cope with situations in which, because of extremes of either heat or cold, the liquid is scarce.4-6 The recent confirmation that there is at least one world rich in organic molecules on which rivers and perhaps shallow seas or bogs are filled with nonaqueous fluidsthe liquid hydrocarbons of Titan7smight now bring some focus, even urgency, to the question of whether water is indeed a * E-mail: p.ball@nature.com. Philip Ball is a science writer and a consultant editor for Nature, where he worked as an editor for physical sciences for more than 10 years. He holds a Ph.D. in physics from the University of Bristol, where he worked on the statistical mechanics of phase transitions in the liquid state. His book H2O: A Biography of Water (Weidenfeld & Nicolson, 1999) was a survey of the current state of knowledge about the behavior of water in situations ranging from planetary geomorphology to cell biology. He frequently writes about aspects of water science for both the popular and the technical media.

The ClusPro web server for protein-protein docking.

Abstract: The ClusPro server (https://cluspro.org) is a widely used tool for protein-protein docking. The server provides a simple home page for basic use, requiring only two files in Protein Data Bank (PDB) format. However, ClusPro also offers a number of advanced options to modify the search; these include the removal of unstructured protein regions, application of attraction or repulsion, accounting for pairwise distance restraints, construction of homo-multimers, consideration of small-angle X-ray scattering (SAXS) data, and location of heparin-binding sites. Six different energy functions can be used, depending on the type of protein. Docking with each energy parameter set results in ten models defined by centers of highly populated clusters of low-energy docked structures. This protocol describes the use of the various options, the construction of auxiliary restraints files, the selection of the energy parameters, and the analysis of the results. Although the server is heavily used, runs are generally completed in <4 h.

Multimodal fast optical interrogation of neural circuitry

Abstract: Our understanding of the cellular implementation of systems-level neural processes like action, thought and emotion has been limited by the availability of tools to interrogate specific classes of neural cells within intact, living brain tissue. Here we identify and develop an archaeal light-driven chloride pump (NpHR) from Natronomonas pharaonis for temporally precise optical inhibition of neural activity. NpHR allows either knockout of single action potentials, or sustained blockade of spiking. NpHR is compatible with ChR2, the previous optical excitation technology we have described, in that the two opposing probes operate at similar light powers but with well-separated action spectra. NpHR, like ChR2, functions in mammals without exogenous cofactors, and the two probes can be integrated with calcium imaging in mammalian brain tissue for bidirectional optical modulation and readout of neural activity. Likewise, NpHR and ChR2 can be targeted together to Caenorhabditis elegans muscle and cholinergic motor neurons to control locomotion bidirectionally. NpHR and ChR2 form a complete system for multimodal, high-speed, genetically targeted, all-optical interrogation of living neural circuits.

Iterative model-building, structure refinement, and density modification with the PHENIX AutoBuild Wizard

Abstract: Iterative model-building, structure refinement, and density modification with the PHENIX AutoBuild Wizard Thomas C. Terwilliger a* , Ralf W. Grosse-Kunstleve b , Pavel V. Afonine b , Nigel W. Moriarty b , Peter Zwart b , Li-Wei Hung a , Randy J. Read c , Paul D. Adams b* a b Los Alamos National Laboratory, Mailstop M888, Los Alamos, NM 87545, USA Lawrence Berkeley National Laboratory, One Cyclotron Road, Bldg 64R0121, Berkeley, CA 94720, USA. c Department of Haematology, University of Cambridge, Cambridge CB2 0XY, UK. * Email: terwill@lanl.gov or PDAdams@lbl.gov Running title: The PHENIX AutoBuild Wizard Abstract The PHENIX AutoBuild Wizard is a highly automated tool for iterative model- building, structure refinement and density modification using RESOLVE or TEXTAL model- building, RESOLVE statistical density modification, and phenix.refine structure refinement. Recent advances in the AutoBuild Wizard and phenix.refine include automated detection and application of NCS from models as they are built, extensive model completion algorithms, and automated solvent molecule picking. Model completion algorithms in the AutoBuild Wizard include loop-building, crossovers between chains in different models of a structure, and side-chain optimization. The AutoBuild Wizard has been applied to a set of 48 structures at resolutions ranging from 1.1 A to 3.2 A, resulting in a mean R-factor of 0.24 and a mean free R factor of 0.29. The R-factor of the final model is dependent on the quality of the starting electron density, and relatively independent of resolution. Keywords: Model building; model completion; macromolecular models; Protein Data Bank; structure refinement; PHENIX Introduction Iterative model-building and refinement is a powerful approach to obtaining a complete and accurate macromolecular model. The approach consists of cycles of building an atomic model based on an electron density map for a macromolecular structure, refining the structure, using the refined structure as a basis for improving the map, and building a new model. This type of approach has been carried out in a semi-automated fashion for many years, with manual model-building iterating with automated refinement (Jensen, 1997). More recently, with the development first of ARP/wARP (Perrakis et al., 1999), and later other procedures including RESOLVE iterative model-building and refinement (Terwilliger,