SLiMAn 2.0: meaningful navigation through peptide-protein interaction networks.

Among the myriad of protein-protein interactions occurring in living organisms, a substantial amount involves small linear motifs (SLiMs) recognized by structured domains. However, predictions of SLiM-based networks are tedious, due to the abundance of such motifs and a high portion of false positive hits. For this reason, a webserver SLiMAn (Short Linear Motif Analysis) was developed to focus the search on the most relevant SLiMs. Using SLiMAn, one can navigate into a given (meta-)interactome and tune a variety of parameters associated to each type of SLiMs in attempt to identify functional ELM motifs and their recognition domains. The IntAct and BioGRID databases bring experimental information, while IUPred and AlphaFold provide boundaries of folded and disordered regions. Post-translational modifications listed in PhosphoSite+ are highlighted. Links to PubMed accelerate scrutiny into the literature, to support (or not) putative pairings. Dedicated visualization features are also incorporated, such as Cytoscape for macromolecular networks and BINANA for intermolecular contacts within structural models generated by SCWRL 3.0. The use of SLiMAn 2.0 is illustrated on a simple example. It is freely available at https://sliman2.cbs.cnrs.fr.

Towards accurate high-throughput ligand affinity prediction by exploiting structural ensembles, docking metrics and ligand similarity.

MOTIVATION: Nowadays, virtual screening (VS) plays a major role in the process of drug development. Nonetheless, an accurate estimation of binding affinities, which is crucial at all stages, is not trivial and may require target-specific fine-tuning. Furthermore, drug design also requires improved predictions for putative secondary targets among which is Estrogen Receptor alpha (ERα). RESULTS: VS based on combinations of Structure-Based VS (SBVS) and Ligand-Based VS (LBVS) is gaining momentum to improve VS performances. In this study, we propose an integrated approach using ligand docking on multiple structural ensembles to reflect receptor flexibility. Then, we investigate the impact of the two different types of features (structure-based and ligand molecular descriptors) on affinity predictions using a random forest algorithm. We find that ligand-based features have lower predictive power (rP = 0.69, R2 = 0.47) than structure-based features (rP = 0.78, R2 = 0.60). Their combination maintains high accuracy (rP = 0.73, R2 = 0.50) on the internal test set, but it shows superior robustness on external datasets. Further improvement and extending the training dataset to include xenobiotics, leads to a novel high-throughput affinity prediction method for ERα ligands (rP = 0.85, R2 = 0.71). The presented prediction tool is provided to the community as a dedicated satellite of the @TOME server in which one can upload a ligand dataset in mol2 format and get ligand docked and affinity predicted. AVAILABILITY AND IMPLEMENTATION: http://edmon.cbs.cnrs.fr. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Structural and mechanistic insights into bisphenols action provide guidelines for risk assessment and discovery of bisphenol A substitutes.

Bisphenol A (BPA) is an industrial compound and a well known endocrine-disrupting chemical with estrogenic activity. The widespread exposure of individuals to BPA is suspected to affect a variety of physiological functions, including reproduction, development, and metabolism. Here we report that the mechanisms by which BPA and two congeners, bisphenol AF and bisphenol C (BPC), bind to and activate estrogen receptors (ER) α and ß differ from that used by 17ß-estradiol. We show that bisphenols act as partial agonists of ERs by activating the N-terminal activation function 1 regardless of their effect on the C-terminal activation function 2, which ranges from weak agonism (with BPA) to antagonism (with BPC). Crystallographic analysis of the interaction between bisphenols and ERs reveals two discrete binding modes, reflecting the different activities of compounds on ERs. BPA and 17ß-estradiol bind to ERs in a similar fashion, whereas, with a phenol ring pointing toward the activation helix H12, the orientation of BPC accounts for the marked antagonist character of this compound. Based on structural data, we developed a protocol for in silico evaluation of the interaction between bisphenols and ERs or other members of the nuclear hormone receptor family, such as estrogen-related receptor γ and androgen receptor, which are two known main targets of bisphenols. Overall, this study provides a wealth of tools and information that could be used for the development of BPA substitutes devoid of nuclear hormone receptor-mediated activity and more generally for environmental risk assessment.

Synthesis and structure-activity relationship studies of original cyclic diadenosine derivatives as nanomolar inhibitors of NAD kinase from pathogenic bacteria.

Nicotinamide adenine dinucleotide kinases (NAD kinases) are essential and ubiquitous enzymes involved in the production of NADP(H) which is an essential cofactor in many metabolic pathways. Targeting NAD kinase (NADK), a rate limiting enzyme of NADP biosynthesis pathway, represents a new promising approach to treat bacterial infections. Previously, we have produced the first NADK inhibitor active against staphylococcal infection. From this linear di-adenosine derivative, namely NKI1, we designed macrocyclic analogues. Here, we describe the synthesis and evaluation of an original series of cyclic diadenosine derivatives as NADK inhibitors of two pathogenic bacteria, Listeria monocytogenes and Staphylococcus aureus. The nature and length of the link between the two adenosine units were examined leading to sub-micromolar inhibitors of NADK1 from L. monocytogenes, including its most potent in vitro inhibitor reported so far (with a 300-fold improvement compared to NKI1).

Structure-Based and Knowledge-Informed Design of B-Raf Inhibitors Devoid of Deleterious PXR Binding.

Dabrafenib is an anticancer drug currently used in the clinics, alone or in combination. However, dabrafenib was recently shown to potently activate the human nuclear receptor pregnane X receptor (PXR). PXR activation increases the clearance of various chemicals and drugs, including dabrafenib itself. It may also enhance cell proliferation and tumor aggressiveness. Therefore, there is a need for rational design of a potent protein kinase B-Raf inhibitor devoid of binding to the secondary target PXR and resisting rapid metabolism. By determining the crystal structure of dabrafenib bound to PXR and analyzing its mode of binding to both PXR and its primary target, B-Raf-V600E, we were able to derive new compounds with nanomolar activity against B-Raf and no detectable affinity for PXR. The crystal structure of B-Raf in complex with our lead compound revealed a subdomain swapping of the activation loop with potentially important functional implications for a prolonged inhibition of B-Raf-V600E.

@TOME-2: a new pipeline for comparative modeling of protein-ligand complexes.

@TOME 2.0 is new web pipeline dedicated to protein structure modeling and small ligand docking based on comparative analyses. @TOME 2.0 allows fold recognition, template selection, structural alignment editing, structure comparisons, 3D-model building and evaluation. These tasks are routinely used in sequence analyses for structure prediction. In our pipeline the necessary software is efficiently interconnected in an original manner to accelerate all the processes. Furthermore, we have also connected comparative docking of small ligands that is performed using protein-protein superposition. The input is a simple protein sequence in one-letter code with no comment. The resulting 3D model, protein-ligand complexes and structural alignments can be visualized through dedicated Web interfaces or can be downloaded for further studies. These original features will aid in the functional annotation of proteins and the selection of templates for molecular modeling and virtual screening. Several examples are described to highlight some of the new functionalities provided by this pipeline. The server and its documentation are freely available at http://abcis.cbs.cnrs.fr/AT2/

Exploring the conformational space of a receptor for drug design: An ERα case study.

Protein flexibility is challenging for both experimentalists and modellers, especially in the field of drug design. Estrogen Receptor alpha (ERα) is an extensively studied Nuclear Receptor (NR) and a well-known therapeutic target with an important role in development and physiology. It is also a frequent off-target in standard toxicity tests for endocrine disruption. Here, we aim to evaluate the degree to which the conformational space and macromolecular flexibility of this well-characterized drug target can be described. Our approach exploits hundreds of crystallographic structures by means of molecular dynamics simulations and of virtual screening. The analysis of hundreds of crystal structures confirms the presence of two main conformational states, known as 'agonist' and 'antagonist', that mainly differ by the orientation of the C-terminal helix H12 which serves to close the binding pocket. ERα also shows some loop flexibility, as well as variable side-chain orientations in its active site. We scrutinized the extent to which standard molecular dynamics simulations or crystallographic refinement as ensemble recapitulate most of the variability features seen by the array of available crystal structures. In parallel, we investigated on the kind and extent of flexibility that are required to achieve convincing docking for all high-affinity ERα ligands present in BindingDB. Using either only one conformation with a few side-chains set flexible, or static structure ensembles in parallel during docking led to good quality and similar pose predictions. These results suggest that the several hundreds of crystal structures already known can properly describe the whole conformational universe of ERα's ligand binding domain. This opens the road for better drug design and affinity computation.

From Substrate to Fragments to Inhibitor Active In Vivo against Staphylococcus aureus.

Antibiotic resistance is a worldwide threat due to the decreasing supply of new antimicrobials. Novel targets and innovative strategies are urgently needed to generate pathbreaking drug compounds. NAD kinase (NADK) is essential for growth in most bacteria, as it supports critical metabolic pathways. Here, we report the discovery of a new class of antibacterials that targets bacterial NADK. We generated a series of small synthetic adenine derivatives to screen those harboring promising substituents in order to guide efficient fragment linking. This led to NKI1, a new lead compound inhibiting NADK that showed in vitro bactericidal activity against Staphylococcus aureus. In a murine model of infection, NKI1 restricted survival of the bacteria, including methicillin-resistant S. aureus. Collectively, these findings identify bacterial NADK as a potential drug target and NKI1 as a lead compound in the treatment of staphylococcal infections.

Insights into Substrate and Inhibitor Selectivity among Human GLUT Transporters through Comparative Modeling and Molecular Docking.

The solute carrier 2 family is composed of 14 transporters, which are members of the major facilitator superfamily. Despite their high physiological importance, there are still many open questions concerning their function and specificity, and in some cases, their physiological substrate is still unknown. To understand the determinants of the substrate and inhibitor specificity, we modeled all human glucose transport carriers (GLUTs) and simulated their interaction with known ligands. Comparative modeling was performed with the @TOME-2 pipeline, employing multiple templates and providing an ensemble of models for each GLUT. We analyzed models in both outward-occluded and inward-open conformations, to compare exofacial and endofacial binding sites throughout the family and understand differences in susceptibility of GLUTs to the inhibitor cytochalasin B. Finally, we employed molecular docking and bioinformatics to identify residues likely critical for recognition of myo-inositol by GLUT13 and urate by GLUT9. These results provide insights into the molecular basis for the specificity for these substrates. In addition, we suggested a potential recognition site of glucosamine by GLUT11 to be evaluated in future experiments.

In Silico Predictions of Endocrine Disruptors Properties.

Endocrine-disrupting chemicals (EDCs) are a broad class of molecules present in our environment that are suspected to cause adverse effects in the endocrine system by interfering with the synthesis, transport, degradation, or action of endogenous ligands. The characterization of the harmful interaction between environmental compounds and their potential cellular targets and the development of robust in vivo, in vitro, and in silico screening methods are important for assessment of the toxic potential of large numbers of chemicals. In this context, computer-aided technologies that will allow for activity prediction of endocrine disruptors and environmental risk assessments are being developed. These technologies must be able to cope with diverse data and connect chemistry at the atomic level with the biological activity at the cellular, organ, and organism levels. Quantitative structure-activity relationship methods became popular for toxicity issues. They correlate the chemical structure of compounds with biological activity through a number of molecular descriptors (e.g., molecular weight and parameters to account for hydrophobicity, topology, or electronic properties). Chemical structure analysis is a first step; however, modeling intermolecular interactions and cellular behavior will also be essential. The increasing number of three-dimensional crystal structures of EDCs' targets has provided a wealth of structural information that can be used to predict their interactions with EDCs using docking and scoring procedures. In the present review, we have described the various computer-assisted approaches that use ligands and targets properties to predict endocrine disruptor activities.

Comprehensive classification of the plant non-specific lipid transfer protein superfamily towards its sequence-structure-function analysis.

BACKGROUND: Non-specific Lipid Transfer Proteins (nsLTPs) are widely distributed in the plant kingdom and constitute a superfamily of related proteins. Several hundreds of different nsLTP sequences-and counting-have been characterized so far, but their biological functions remain unclear. It has been clear for years that they present a certain interest for agronomic and nutritional issues. Deciphering their functions means collecting and analyzing a variety of data from gene sequence to protein structure, from cellular localization to the physiological role. As a huge and growing number of new protein sequences are available nowadays, extracting meaningful knowledge from sequence-structure-function relationships calls for the development of new tools and approaches. As nsLTPs show high evolutionary divergence, but a conserved common right handed superhelix structural fold, and as they are involved in a large number of key roles in plant development and defense, they are a stimulating case study for validating such an approach. METHODS: In this study, we comprehensively investigated 797 nsLTP protein sequences, including a phylogenetic analysis on canonical protein sequences, three-dimensional structure modeling and functional annotation using several well-established bioinformatics programs. Additionally, two integrative methodologies using original tools were developed. The first was a new method for the detection of (i) conserved amino acid residues involved in structure stabilization and (ii) residues potentially involved in ligand interaction. The second was a structure-function classification based on the evolutionary trace display method using a new tree visualization interface. We also present a new tool for visualizing phylogenetic trees. RESULTS: Following this new protocol, an updated classification of the nsLTP superfamily was established and a new functional hypothesis for key residues is suggested. Lastly, this work allows a better representation of the diversity of plant nsLTPs in terms of sequence, structure and function.

Dimerization of the Pragmin Pseudo-Kinase Regulates Protein Tyrosine Phosphorylation.

The pseudo-kinase and signaling protein Pragmin has been linked to cancer by regulating protein tyrosine phosphorylation via unknown mechanisms. Here we present the crystal structure of the Pragmin 906-1,368 amino acid C terminus, which encompasses its kinase domain. We show that Pragmin contains a classical protein-kinase fold devoid of catalytic activity, despite a conserved catalytic lysine (K997). By proteomics, we discovered that this pseudo-kinase uses the tyrosine kinase CSK to induce protein tyrosine phosphorylation in human cells. Interestingly, the protein-kinase domain is flanked by N- and C-terminal extensions forming an original dimerization domain that regulates Pragmin self-association and stimulates CSK activity. A1329E mutation in the C-terminal extension destabilizes Pragmin dimerization and reduces CSK activation. These results reveal a dimerization mechanism by which a pseudo-kinase can induce protein tyrosine phosphorylation. Further sequence-structure analysis identified an additional member (C19orf35) of the superfamily of dimeric Pragmin/SgK269/PEAK1 pseudo-kinases.

CRAACK: consensus program for NMR amino acid type assignment.

Protein peak spectrum assignment is a prerequisite of the nuclear magnetic resonance study of a molecule. We present here a computer tool which proposes the determination of the amino acid type from the values of the chemical shifts. This tool is based on two consensus algorithms based on several published typing algorithms and was trained and extensively tested against the Biological Magnetic Resonance Bank chemical shift data bank. The first one accomplishes the analysis with support vector machine technology, grouping related amino acids together, and presents a mean rate of success above 90% on the test set. The second one uses a classical consensus algorithm of vote. Furthermore, secondary structural prediction is available. This tool can be used for assisting manual assignment of peptides and proteins and can also be used as a step in an automated approach to assignment. This program has been called CRAACK and is publicly available at the following URL: http://abcis.cbs.cnrs.fr/craack.

Structure of a liganded type 2 non-specific lipid-transfer protein from wheat and the molecular basis of lipid binding.

In plants, a family of ubiquitous proteins named non-specific lipid-transfer proteins (ns-LTPs) facilitates the transfer of fatty acids, phospholipids and steroids between membranes. Recent data suggest that these secreted proteins play a key role in the formation of cuticular wax layers and in defence mechanisms against pathogens. In this study, X-ray crystallography has been used to examine the structural details of the interaction between a wheat type 2 ns-LTP and a lipid, L-alpha-palmitoyl-phosphatidyl glycerol. This crystal structure was solved ab initio at 1.12 A resolution by direct methods. The typical alpha-helical bundle fold of this protein is maintained by four disulfide bridges and delineates two hydrophobic cavities. The inner surface of the main cavity is lined by non-polar residues that provide a hydrophobic environment for the palmitoyl moiety of the lipid. The head-group region of this lipid protrudes from the surface and makes several polar interactions with a conserved patch of basic residues at the entrance of the pocket. The alkyl chain of a second lipid is bound within an adjacent smaller cavity. The structure shows that binding of the lipid tails to the protein involves extensive hydrophobic interactions.

Refined solution structure of a liganded type 2 wheat nonspecific lipid transfer protein.

The refined structure of a wheat type 2 nonspecific lipid transfer protein (ns-LTP2) liganded with l-alpha-palmitoylphosphatidylglycerol has been determined by NMR. The (15)N-labeled protein was produced in Pichia pastoris. Physicochemical conditions and ligandation were intensively screened to obtain the best NMR spectra quality. This ns-LTP2 is a 67-residue globular protein with a diameter of about 30 A. The structure is composed of five helices forming a right superhelix. The protein presents an inner cavity, which has been measured at 341 A(3). All of the helices display hydrophobic side chains oriented toward the cavity. The phospholipid is found in this cavity. Its fatty acid chain is completely inserted in the protein, the l-alpha-palmitoylphosphatidylglycerol glycerol moiety being located on a positively charged pocket on the surface of the protein. The superhelix structure of the protein is coiled around the fatty acid chain. The overall structure shows similarities with ns-LTP1. Nevertheless, large three-dimensional structural discrepancies are observed for the H3 and H4 alpha-helices, the C-terminal region, and the last turn of the H2 helix. The lipid is orthogonal to the orientation observed in ns-LTP1. The volume of the hydrophobic cavity appears to be in the same range as the one of ns-LTP1, despite the fact that ns-LTP2 is shorter by 24 residues.