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Type IV pili (T4P) are distinctive dynamic filaments at the surface of many bacteria that can rapidly extend and retract and withstand strong forces. T4P are important virulence factors in many human pathogens, including Enterohemorrhagic Escherichia coli (EHEC). The structure of the EHEC T4P has been determined by integrating nuclear magnetic resonance (NMR) and cryo-electron microscopy data. To better understand pilus assembly, stability, and function, we performed a total of 108 ms all-atom molecular dynamics simulations of wild-type and mutant T4P. Extensive characterization of the conformational landscape of T4P in different conditions of temperature, pH, and ionic strength is complemented with targeted mutagenesis and biochemical analyses. Our simulations and NMR experiments reveal a conserved set of residues defining a calcium-binding site at the interface between three pilin subunits. Calcium binding enhances T4P stability ex vivo and in vitro, supporting the role of this binding site as a potential pocket for drug design., Competing Interests: Declaration of interest The authors declare no competing interests., (Copyright © 2021 The Authors. Published by Elsevier Ltd.. All rights reserved.)

Motivation: The structure of proteins is organized in a hierarchy among which the secondary structure elements, α -helix, β -strand and loop, are the basic bricks. The determination of secondary structure elements usually requires the knowledge of the whole structure. Nevertheless, in numerous experimental circumstances, the protein structure is partially known. The detection of secondary structures from these partial structures is hampered by the lack of information about connecting residues along the primary sequence., Results: We introduce a new methodology to estimate the secondary structure elements from the values of local distances and angles between the protein atoms. Our method uses a message passing neural network, named Sequoia, which allows the automatic prediction of secondary structure elements from the values of local distances and angles between the protein atoms. This neural network takes as input the topology of the given protein graph, where the vertices are protein residues, and the edges are weighted by values of distances and pseudo-dihedral angles generalizing the backbone angles ϕ and ψ . Any pair of residues, independently of its covalent bonds along the primary sequence of the protein, is tagged with this distance and angle information. Sequoia permits the automatic detection of the secondary structure elements, with an F 1-score larger than 80% for most of the cases, when α helices and β strands are predicted. In contrast to the approaches classically used in structural biology, such as DSSP, Sequoia is able to capture the variations of geometry at the interface of adjacent secondary structure element. Due to its general modeling frame, Sequoia is able to handle graphs containing only C α atoms, which is particularly useful on low resolution structural input and in the frame of electron microscopy development., Availability and Implementation: Sequoia source code can be found at https://github.com/Khalife/Sequoia with additional documentation., Supplementary Information: Supplementary data are available at Bioinformatics Advances online., (© The Author(s) 2021. Published by Oxford University Press.)

In the HTML version of this Letter, the affiliations for authors Andrew S. Azman, Dhirendra Kumar and Thandavarayan Ramamurthy were inverted (the PDF and print versions of the Letter were correct); the affiliations have been corrected online.

Yemen is currently experiencing, to our knowledge, the largest cholera epidemic in recent history. The first cases were declared in September 2016, and over 1.1 million cases and 2,300 deaths have since been reported 1 . Here we investigate the phylogenetic relationships, pathogenesis and determinants of antimicrobial resistance by sequencing the genomes of Vibrio cholerae isolates from the epidemic in Yemen and recent isolates from neighbouring regions. These 116 genomic sequences were placed within the phylogenetic context of a global collection of 1,087 isolates of the seventh pandemic V. cholerae serogroups O1 and O139 biotype El Tor 2-4 . We show that the isolates from Yemen that were collected during the two epidemiological waves of the epidemic 1 -the first between 28 September 2016 and 23 April 2017 (25,839 suspected cases) and the second beginning on 24 April 2017 (more than 1 million suspected cases)-are V. cholerae serotype Ogawa isolates from a single sublineage of the seventh pandemic V. cholerae O1 El Tor (7PET) lineage. Using genomic approaches, we link the epidemic in Yemen to global radiations of pandemic V. cholerae and show that this sublineage originated from South Asia and that it caused outbreaks in East Africa before appearing in Yemen. Furthermore, we show that the isolates from Yemen are susceptible to several antibiotics that are commonly used to treat cholera and to polymyxin B, resistance to which is used as a marker of the El Tor biotype.

Numerous biophysical approaches provide information about residues spatial proximity in proteins. However, correct assignment of the protein fold from this proximity information is not straightforward if the spatially close protein residues are not assigned to residues in the primary sequence. Here, we propose an algorithm to assign such residue numbers by ordering the columns and lines of the raw protein contact matrix directly obtained from proximity information between unassigned amino acids. The ordering problem is formatted as the search of a trail within a graph connecting protein residues through the nonzero contact values. The algorithm performs in two steps: (i) finding the longest trail of the graph using an original dynamic programming algorithm, (ii) clustering the individual ordered matrices using a self-organizing map (SOM) approach. The combination of the dynamic programming and self-organizing map approaches constitutes a quite innovative point of the present work. The algorithm was validated on a set of about 900 proteins, representative of the sizes and proportions of secondary structures observed in the Protein Data Bank. The algorithm was revealed to be efficient for noise levels up to 40%, obtaining average gaps of about 20% at maximum between ordered and initial matrices. The proposed approach paves the ways toward a method of fold prediction from noisy proximity information, as TM scores larger than 0.5 have been obtained for ten randomly chosen proteins, in the case of a noise level of 10%. The methods has been also validated on two experimental cases, on which it performed satisfactorily.

The determination of the conformational states corresponding to diverse functional roles of ligand gated ion channels is subject of intense investigation with various techniques, from X-rays structure determination to electrophysiology and computational modeling. Even with a certain number of structures becoming recently available, only few major structural features distinguishing conductive open channel from the non conductive resting protein have been highlighted, while high-resolution details are still missing. The characterization of the desensitized conformation(s) is even more complex, and only few specific characteristics have been identified. Furthermore, experimental data provide conflicting information for different ion channels, adding further complexity to the topic. Desensitization is defined as the transition of the agonist-bound open channel into an ion channel configuration inactive even in the presence of agonists. In this work, we analyze a conformation corresponding to a non conductive state obtained via molecular dynamics simulations of a homology model of the human α7 nicotinic receptor complexed with agonists. We highlight some characteristics that could associate it to a desensitized state. The obtained structure is assessed against experimental data for other ligand gated ion channels that have been putatively associated to active, inactive and desensitized conditions., (Copyright © 2017 Elsevier B.V. All rights reserved.)

Poly(ADP-ribose) polymerases (PARPs) play a key role in DNA damage repair. PARP inhibitors act as chemo- and radio- sensitizers and thus potentiate the cytotoxicity of DNA damaging agents. Although PARP inhibitors are currently investigated as chemotherapeutic agents, their cross-reactivity with other members of the PARP family remains unclear. Here, we apply Proteochemometric Modelling (PCM) to model the activity of 181 compounds on 12 human PARPs. We demonstrate that PCM (R0 (2) test =0.65-0.69; RMSEtest =0.95-1.01 °C) displays higher performance on the test set (interpolation) than Family QSAR and Family QSAM (Tukey's HSD, α 0.05), and outperforms Inductive Transfer knowledge among targets (Tukey's HSD, α 0.05). We benchmark the predictive signal of 8 amino acid and 11 full-protein sequence descriptors, obtaining that all of them (except for SOCN) perform at the same level of statistical significance (Tukey's HSD, α 0.05). The extrapolation power of PCM to new compounds (RMSE=1.02±0.80 °C) and targets (RMSE=1.03±0.50 °C) is comparable to interpolation, although the extrapolation ability is not uniform across the chemical and the target space. For this reason, we also provide confidence intervals calculated with conformal prediction. In addition, we present the R package conformal, which permits the calculation of confidence intervals for regression and classification caret models., (© 2015 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.)

Serine proteases, implicated in important physiological functions, have a high intra-family similarity, which leads to unwanted off-target effects of inhibitors with insufficient selectivity. However, the availability of sequence and structure data has now made it possible to develop approaches to design pharmacological agents that can discriminate successfully between their related binding sites. In this study, we have quantified the relationship between 12,625 distinct protease inhibitors and their bioactivity against 67 targets of the serine protease family (20,213 data points) in an integrative manner, using proteochemometric modelling (PCM). The benchmarking of 21 different target descriptors motivated the usage of specific binding pocket amino acid descriptors, which helped in the identification of active site residues and selective compound chemotypes affecting compound affinity and selectivity. PCM models performed better than alternative approaches (models trained using exclusively compound descriptors on all available data, QSAR) employed for comparison with R(2)/RMSE values of 0.64 ± 0.23/0.66 ± 0.20 vs. 0.35 ± 0.27/1.05 ± 0.27 log units, respectively. Moreover, the interpretation of the PCM model singled out various chemical substructures responsible for bioactivity and selectivity towards particular proteases (thrombin, trypsin and coagulation factor 10) in agreement with the literature. For instance, absence of a tertiary sulphonamide was identified to be responsible for decreased selective activity (by on average 0.27 ± 0.65 pChEMBL units) on FA10. Among the binding pocket residues, the amino acids (arginine, leucine and tyrosine) at positions 35, 39, 60, 93, 140 and 207 were observed as key contributing residues for selective affinity on these three targets.

Proteochemometrics (PCM) is an approach for bioactivity predictive modeling which models the relationship between protein and chemical information. Gaussian Processes (GP), based on Bayesian inference, provide the most objective estimation of the uncertainty of the predictions, thus permitting the evaluation of the applicability domain (AD) of the model. Furthermore, the experimental error on bioactivity measurements can be used as input for this probabilistic model. In this study, we apply GP implemented with a panel of kernels on three various (and multispecies) PCM datasets. The first dataset consisted of information from 8 human and rat adenosine receptors with 10,999 small molecule ligands and their binding affinity. The second consisted of the catalytic activity of four dengue virus NS3 proteases on 56 small peptides. Finally, we have gathered bioactivity information of small molecule ligands on 91 aminergic GPCRs from 9 different species, leading to a dataset of 24,593 datapoints with a matrix completeness of only 2.43%. GP models trained on these datasets are statistically sound, at the same level of statistical significance as Support Vector Machines (SVM), with [Formula: see text] values on the external dataset ranging from 0.68 to 0.92, and RMSEP values close to the experimental error. Furthermore, the best GP models obtained with the normalized polynomial and radial kernels provide intervals of confidence for the predictions in agreement with the cumulative Gaussian distribution. GP models were also interpreted on the basis of individual targets and of ligand descriptors. In the dengue dataset, the model interpretation in terms of the amino-acid positions in the tetra-peptide ligands gave biologically meaningful results.

Pentameric ligand gated ion channels (pLGICs) mediate signal transduction. The binding of an extracellular ligand is coupled to the transmembrane channel opening. So far, all known agonists bind at the interface between subunits in a topologically conserved "orthosteric site" whose amino acid composition defines the pharmacological specificity of pLGIC subtypes. A striking exception is the bacterial proton-activated GLIC protein, exhibiting an uncommon orthosteric binding site in terms of sequence and local architecture. Among a library of Gloeobacter violaceus metabolites, we identified a series of cinnamic acid derivatives, which antagonize the GLIC proton-elicited response. Structure-activity analysis shows a key contribution of the carboxylate moiety to GLIC inhibition. Molecular docking coupled to site-directed mutagenesis support that the binding pocket is located below the classical orthosteric site. These antagonists provide new tools to modulate conformation of GLIC, currently used as a prototypic pLGIC, and opens new avenues to study the signal transduction mechanism.

Edema Factor (EF) is a component of Bacillus anthracis toxin essential for virulence. Its adenylyl cyclase activity is induced by complexation with the ubiquitous eukaryotic cellular protein, calmodulin (CaM). EF and its complexes with CaM, nucleotides and/or ions, have been extensively characterized by X-ray crystallography. Those structural data allowed molecular simulations analysis of various aspects of EF action mechanism, including the delineation of EF and CaM domains through their association energetics, the impact of calcium binding on CaM, and the role of catalytic site ions. Furthermore, a transition path connecting the free inactive form to the CaM-complexed active form of EF was built to model the activation mechanism in an attempt to define an inhibition strategy. The cavities at the surface of EF were determined for each path intermediate to identify potential sites where the binding of a ligand could block activation. A non-catalytic cavity (allosteric) was found to shrink rapidly at early stages of the path and was chosen to perform virtual screening. Amongst 18 compounds selected in silico and tested in an enzymatic assay, 6 thiophen ureidoacid derivatives formed a new family of EF allosteric inhibitors with IC50 as low as 2 micromolars.

In solution or solid-state, determining the three-dimensional structure of biomolecules by Nuclear -Magnetic Resonance (NMR) normally requires the collection of distance information. The interpretation of the spectra containing this distance information is a critical step in an NMR structure determination. In this chapter, we present the Ambiguous Restraints for Iterative Assignment (ARIA) program for automated cross-peak assignment and determination of macromolecular structure from solution and solid-state NMR experiments. While the program was initially designed for the assignment of nuclear Overhauser effect (NOE) resonances, it has been extended to the interpretation of magic-angle spinning (MAS) solid-state NMR data. This chapter first details the concepts and procedures carried out by the program. Then, we describe both the general strategy for structure determination with ARIA 2.3 and practical aspects of the technique. ARIA 2.3 includes all recent developments. such as an extended integration of the Collaborative Computing Project for the NMR community (CCPN), the incorporation of the log-harmonic distance restraint potential and an automated treatment of symmetric oligomers.

Numerous high-resolution crystallographic structures of the acetylcholine binding protein (AChBP), a molluscan cholinergic protein, homologous to the extracellular domain of nicotinic acetylcholine receptors, are available. This offers opportunities to model the interaction between various ligands and the acetylcholine binding site. Herein we present a study of the interplay between ligand binding and motions of the C-loop capping the binding site. Nicotinic agonists and antagonists were docked on AChBP X-ray structures. It is shown that the studied agonists and antagonists can be discriminated according to their higher affinities for structures respectively obtained in the presence of agonists or antagonists, highlighting the fact that AChBP structures retain a pharmacological footprint of the compound used in crystallography experiments. A detailed analysis of the binding site cavities suggests that this property is mainly related to the shape of the cavities., (Copyright © 2011 Elsevier Inc. All rights reserved.)

The function of bio-macromolecules is determined by both their 3D structure and conformational dynamics. These molecules are inherently flexible systems displaying a broad range of dynamics on time-scales from picoseconds to seconds. Nuclear Magnetic Resonance (NMR) spectroscopy has emerged as the method of choice for studying both protein structure and dynamics in solution. Typically, NMR experiments are sensitive both to structural features and to dynamics, and hence the measured data contain information on both. Despite major progress in both experimental approaches and computational methods, obtaining a consistent view of structure and dynamics from experimental NMR data remains a challenge. Molecular dynamics simulations have emerged as an indispensable tool in the analysis of NMR data.

The use of generous distance bounds has been the hallmark of NMR structure determination. However, bounds necessitate the estimation of data quality before the calculation, reduce the information content, introduce human bias, and allow for major errors in the structures. Here, we propose a new rapid structure calculation scheme based on Bayesian analysis. The minimization of an extended energy function, including a new type of distance restraint and a term depending on the data quality, results in an estimation of the data quality in addition to coordinates. This allows for the determination of the optimal weight on the experimental information. The resulting structures are of better quality and closer to the X-ray crystal structure of the same molecule. With the new calculation approach, the analysis of discrepancies from the target distances becomes meaningful. The strategy may be useful in other applications-for example, in homology modeling.

In a wide variety of proteins, insolubility presents a challenge to structural biology, as X-ray crystallography and liquid-state NMR are unsuitable. Indeed, no general approach is available as of today for studying the three-dimensional structures of membrane proteins and protein fibrils. We here demonstrate, at the example of the microcrystalline model protein Crh, how high-resolution 3D structures can be derived from magic-angle spinning solid-state NMR distance restraints for fully labeled protein samples. First, we show that proton-mediated rare-spin correlation spectra, as well as carbon-13 spin diffusion experiments, provide enough short, medium, and long-range structural restraints to obtain high-resolution structures of this 2 x 10.4 kDa dimeric protein. Nevertheless, the large number of 13C/15N spins present in this protein, combined with solid-state NMR line widths of about 0.5-1 ppm, induces substantial ambiguities in resonance assignments, preventing 3D structure determination by using distance restraints uniquely assigned on the basis of their chemical shifts. In the second part, we thus demonstrate that an automated iterative assignment algorithm implemented in a dedicated solid-state NMR version of the program ARIA permits to resolve the majority of ambiguities and to calculate a de novo 3D structure from highly ambiguous solid-state NMR data, using a unique fully labeled protein sample. We present, using distance restraints obtained through the iterative assignment process, as well as dihedral angle restraints predicted from chemical shifts, the 3D structure of the fully labeled Crh dimer refined at a root-mean-square deviation of 1.33 A.

MTCP1 (for Mature-T-Cell Proliferation) was the first gene unequivocally identified in the group of uncommon leukemias with a mature phenotype. The three-dimensional solution structure of the human p8MTCP1 protein encoded by the MTCP1 oncogene has been previously determined by homonuclear proton two-dimensional NMR methods at 600 MHz: it consists of an original scaffold comprising three alpha-helices, associated with a new cysteine motif. Two of the helices are covalently paired by two disulfide bridges, forming an alpha-hairpin which resembles an antiparallel coiled-coil. The third helix is orientated roughly parallel to the plane defined by the alpha-antiparallel motif and appears less well defined. In order to gain more insight into the details of this new scaffold, we uniformly labeled with nitrogen-15 a mutant of this protein (C12A-p8MTCP1) in which the unbound cysteine at position 12 has been replaced by an alanine residue, thus allowing reproducibly high yields of recombinant protein. The refined structure benefits from 211 additional NOEs, extracted from 15N-edited 3D experiments, and from a nearly complete set of phi angular restraints allowing the estimation of the helical content of the structured part of the protein. Moreover, measurements of 15N spin relaxation times and heteronuclear 15N¿1H¿NOEs provided additional insights into the dynamics of the protein backbone. The analysis of the linear correlation between J(0) and J(omega) was used to interpret relaxation parameters. It appears that the apparent relative disorder seen in helix III is not simply due to a lack of experimental constraints, but associated with substantial contributions of sub-nanosecond motions in this segment.

We describe a quantitative processing method which gives access to the longitudinal and transverse cross-relaxation rates from off-resonance ROESY intensities. This method takes advantage of the dependence of the off-resonance ROESY experiments at any mixing time and any spin-lock angle θ on two relaxation matrices, the longitudinal and the transverse ones. This allows one to take into account multistep magnetization transfers even if the measurements are performed only at one or two mixing times. The ratio of the longitudinal to transverse cross-relaxation rates can then be used as a local indicator of the internal dynamics, without assuming a structure or a model of motion. After validation of this processing method by numerical simulations, it is applied to the analysis of the dynamics of the peptide ranalexin dissolved in pure water and in water/TFE., (Copyright 1999 Academic Press.)

The Gifa program is designed for processing, displaying and analysing 1D, 2D and 3D NMR data sets. It has been constructed in a modular fashion, based on three independent modules: a set of commands that perform all the basic processing operations such as apodisation functions, a complete set of Fourier Transforms, phasing and baseline correction, peak-picking and line fitting, linear prediction and maximum entropy processing; a set of command language primitives that permit the execution of complex macro commands; and a set of graphic commands that permit to build a complete graphic user interface, allowing the user to interact easily with the program. We have tried to create a versatile program that can be easily extended according to the user's requirements and that is adapted to a novice as well as an experienced user. The program runs on any UNIX computer, with or without graphic display, in interactive or batch mode.

A data processing approach is proposed for reducing the t(1) noise observed in multidimensional NMR spectra. This method is based on the use of the Cadzow procedure [Cadzow, J.A. (1988) IEEE Trans. Acous. Speech Signal Proc., 36, 49-62], and is demonstrated to be efficient for simulated cases as well as real experiments.

A method for quantification of distances between amide hydrogens using only the 3D NOESY-HMQC experiment recorded on a (15)N-labelled protein is presented. This method is based on an approximate expression of the NOE intensities between amide hydrogens obtained from continuum modelling of the non-amide spins; this expression is used in a distance calculation algorithm. The algorithm has been named CROWD, standing for Continuum approximation of Relaxati On path Ways between Dilute spins. This approximation as well as the CROWD algorithm are tested on a simulated case; the CROWD algorithm is then applied to experimental data, measured on a fragment of bacteriorhodopsin.