Rational Prediction of PROTAC-Compatible Protein-Protein Interfaces by Molecular Docking.

Proteolysis targeting chimeras (PROTACs) are heterobifunctional ligands that mediate the interaction between a protein target and an E3 ligase, resulting in a ternary complex, whose interaction with the ubiquitination machinery leads to target degradation. This technology is emerging as an exciting new avenue for therapeutic development, with several PROTACs currently undergoing clinical trials targeting cancer. Here, we describe a general and computationally efficient methodology combining restraint-based docking, energy-based rescoring, and a filter based on the minimal solvent-accessible surface distance to produce PROTAC-compatible PPIs suitable for when there is no a priori known PROTAC ligand. In a benchmark employing a manually curated data set of 13 ternary complex crystals, we achieved an accuracy of 92% when starting from bound structures and 77% when starting from unbound structures, respectively. Our method only requires that the ligand-bound structures of the monomeric forms of the E3 ligase and target proteins be given to run, making it general, accurate, and highly efficient, with the ability to impact early-stage PROTAC-based drug design campaigns where no structural information about the ternary complex structure is available.

Facilitating CG Simulations with MAD: The MArtini Database Server.

The MArtini Database (MAD - https://mad.ibcp.fr) is a web server designed for the sharing of structures and topologies of molecules parametrized with the Martini coarse-grained (CG) force field. MAD can also convert atomistic structures into CG structures and prepare complex systems (including proteins, lipids, etc.) for molecular dynamics (MD) simulations at the CG level. It is dedicated to the generation of input files for Martini 3, the most recent version of this popular CG force field. Specifically, the MAD server currently includes tools to submit or retrieve CG models of a wide range of molecules (lipids, carbohydrates, nanoparticles, etc.), transform atomistic protein structures into CG structures and topologies, with fine control on the process and assemble biomolecules into large systems, and deliver all files necessary to start simulations in the GROMACS MD engine.

The Det.Belt Server: A Tool to Visualize and Estimate Amphipathic Solvent Belts around Membrane Proteins.

Detergents wrap around membrane proteins to form a belt covering the hydrophobic part of the protein serving for membrane insertion and interaction with lipids. The number of detergent monomers forming this belt is usually unknown to investigators, unless dedicated detergent quantification is undertaken, which for many projects is difficult to setup. Yet, having an approximate knowledge of the amount of detergent forming the belt is extremely useful, to better grasp the protein of interest in interaction with its direct environment rather than picturing the membrane protein "naked". We created the Det.Belt server to dress up membrane proteins and represent in 3D the bulk made by detergent molecules wrapping in a belt. Many detergents are included in a database, allowing investigators to screen in silico the effect of different detergents around their membrane protein. The input number of detergents is changeable with fast recomputation of the belt for interactive usage. Metrics representing the belt are readily available together with scripts to render quality 3D images for publication. The Det.Belt server is a tool for biochemists to better grasp their sample.

Targeted-antibacterial-plasmids (TAPs) combining conjugation and CRISPR/Cas systems achieve strain-specific antibacterial activity.

The global emergence of drug-resistant bacteria leads to the loss of efficacy of our antibiotics arsenal and severely limits the success of currently available treatments. Here, we developed an innovative strategy based on targeted-antibacterial-plasmids (TAPs) that use bacterial conjugation to deliver CRISPR/Cas systems exerting a strain-specific antibacterial activity. TAPs are highly versatile as they can be directed against any specific genomic or plasmid DNA using the custom algorithm (CSTB) that identifies appropriate targeting spacer sequences. We demonstrate the ability of TAPs to induce strain-selective killing by introducing lethal double strand breaks (DSBs) into the targeted genomes. TAPs directed against a plasmid-born carbapenem resistance gene efficiently resensitise the strain to the drug. This work represents an essential step toward the development of an alternative to antibiotic treatments, which could be used for in situ microbiota modification to eradicate targeted resistant and/or pathogenic bacteria without affecting other non-targeted bacterial species.

Evaluation of CONSRANK-Like Scoring Functions for Rescoring Ensembles of Protein-Protein Docking Poses.

Scoring is a challenging step in protein-protein docking, where typically thousands of solutions are generated. In this study, we ought to investigate the contribution of consensus-rescoring, as introduced by Oliva et al. (2013) with the CONSRANK method, where the set of solutions is used to build statistics in order to identify recurrent solutions. We explore several ways to perform consensus-based rescoring on the ZDOCK decoy set for Benchmark 4. We show that the information of the interface size is critical for successful rescoring in this context, but that consensus rescoring in itself performs less well than traditional physics-based evaluation. The results of physics-based and consensus-based rescoring are partially overlapping, supporting the use of a combination of these approaches.

Identification and visualization of protein binding regions with the ArDock server.

ArDock (ardock.ibcp.fr) is a structural bioinformatics web server for the prediction and the visualization of potential interaction regions at protein surfaces. ArDock ranks the surface residues of a protein according to their tendency to form interfaces in a set of predefined docking experiments between the query protein and a set of arbitrary protein probes. The ArDock methodology is derived from large scale cross-docking studies where it was observed that randomly chosen proteins tend to dock in a non-random way at protein surfaces. The method predicts interaction site of the protein, or alternate interfaces in the case of proteins with multiple interaction modes. The server takes a protein structure as input and computes a score for each surface residue. Its output focuses on the interactive visualization of results and on interoperability with other services.

Non-interacting proteins may resemble interacting proteins: prevalence and implications.

The vast majority of proteins do not form functional interactions in physiological conditions. We have considered several sets of protein pairs from S. cerevisiae with no functional interaction reported, denoted as non-interacting pairs, and compared their 3D structures to available experimental complexes. We identified some non-interacting pairs with significant structural similarity with experimental complexes, indicating that, even though they do not form functional interactions, they have compatible structures. We estimate that up to 8.7% of non-interacting protein pairs could have compatible structures. This number of interactions exceeds the number of functional interactions (around 0.2% of the total interactions) by a factor 40. Network analysis suggests that the interactions formed by non-interacting pairs with compatible structures could be particularly hazardous to the protein-protein interaction network. From a structural point of view, these interactions display no aberrant structural characteristics, and are even predicted as relatively stable and enriched in potential physical interactors, suggesting a major role of regulation to prevent them.

Implication of Terminal Residues at Protein-Protein and Protein-DNA Interfaces.

Terminal residues of protein chains are charged and more flexible than other residues since they are constrained only on one side. Do they play a particular role in protein-protein and protein-DNA interfaces? To answer this question, we considered large sets of non-redundant protein-protein and protein-DNA complexes and analyzed the status of terminal residues and their involvement in interfaces. In protein-protein complexes, we found that more than half of terminal residues (62%) are either modified by attachment of a tag peptide (10%) or have missing coordinates in the analyzed structures (52%). Terminal residues are almost exclusively located at the surface of proteins (94%). Contrary to charged residues, they are not over or under-represented in protein-protein interfaces, but strongly prefer the peripheral region of interfaces when present at the interface (83% of terminal residues). The almost exclusive location of terminal residues at the surface of the proteins or in the rim regions of interfaces explains that experimental methods relying on tail hybridization can be successfully applied without disrupting the complexes under study. Concerning conformational rearrangement in protein-protein complexes, despite their expected flexibility, terminal residues adopt similar locations between the free and bound forms of the docking benchmark. In protein-DNA complexes, N-terminal residues are twice more frequent than C-terminal residues at interfaces. Both N-terminal and C-terminal residues are under-represented in interfaces, in contrast to positively charged residues, which are strongly favored. When located in protein-DNA interfaces, terminal residues prefer the periphery. N-terminal and C-terminal residues thus have particular properties with regard to interfaces, which cannot be reduced to their charged nature.

Comparative analysis of Leishmania exoproteomes: implication for host-pathogen interactions.

Leishmaniasis is a vector-borne disease caused by the protozoa Leishmania. We have analyzed and compared the sequences of three experimental exoproteomes of Leishmania promastigotes from different species to determine their specific features and to identify new candidate proteins involved in interactions of Leishmania with the host. The exoproteomes differ from the proteomes by a decrease in the average molecular weight per protein, in disordered amino acid residues and in basic proteins. The exoproteome of the visceral species is significantly enriched in sites predicted to be phosphorylated as well as in features frequently associated with molecular interactions (intrinsic disorder, number of disordered binding regions per protein, interaction and/or trafficking motifs) compared to the other species. The visceral species might thus have a larger interaction repertoire with the host than the other species. Less than 10% of the exoproteomes contain heparin-binding and RGD sequences, and ~30% the host targeting signal RXLXE/D/Q. These latter proteins might thus be exported inside the host cell during the intracellular stage of the infection. Furthermore we have identified nine protein families conserved in the three exoproteomes with specific combinations of Pfam domains and selected eleven proteins containing at least three interaction and/or trafficking motifs including two splicing factors, phosphomannomutase, 2,3-bisphosphoglycerate-independent phosphoglycerate mutase, the paraflagellar rod protein-1D and a putative helicase. Their role in host-Leishmania interactions warrants further investigation but the putative ATP-dependent DEAD/H RNA helicase, which contains numerous interaction motifs, a host targeting signal and two disordered regions, is a very promising candidate.

A new reference implementation of the PSICQUIC web service.

The Proteomics Standard Initiative Common QUery InterfaCe (PSICQUIC) specification was created by the Human Proteome Organization Proteomics Standards Initiative (HUPO-PSI) to enable computational access to molecular-interaction data resources by means of a standard Web Service and query language. Currently providing >150 million binary interaction evidences from 28 servers globally, the PSICQUIC interface allows the concurrent search of multiple molecular-interaction information resources using a single query. Here, we present an extension of the PSICQUIC specification (version 1.3), which has been released to be compliant with the enhanced standards in molecular interactions. The new release also includes a new reference implementation of the PSICQUIC server available to the data providers. It offers augmented web service capabilities and improves the user experience. PSICQUIC has been running for almost 5 years, with a user base growing from only 4 data providers to 28 (April 2013) allowing access to 151 310 109 binary interactions. The power of this web service is shown in PSICQUIC View web application, an example of how to simultaneously query, browse and download results from the different PSICQUIC servers. This application is free and open to all users with no login requirement (http://www.ebi.ac.uk/Tools/webservices/psicquic/view/main.xhtml).

BioJS: an open source JavaScript framework for biological data visualization.

SUMMARY: BioJS is an open-source project whose main objective is the visualization of biological data in JavaScript. BioJS provides an easy-to-use consistent framework for bioinformatics application programmers. It follows a community-driven standard specification that includes a collection of components purposely designed to require a very simple configuration and installation. In addition to the programming framework, BioJS provides a centralized repository of components available for reutilization by the bioinformatics community. AVAILABILITY AND IMPLEMENTATION: http://code.google.com/p/biojs/. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Automatic modeling of mammalian olfactory receptors and docking of odorants.

We present a procedure that (i) automates the homology modeling of mammalian olfactory receptors (ORs) based on the six three-dimensional (3D) structures of G protein-coupled receptors (GPCRs) available so far and (ii) performs the docking of odorants on these models, using the concept of colony energy to score the complexes. ORs exhibit low-sequence similarities with other GPCR and current alignment methods often fail to provide a reliable alignment. Here, we use a fold recognition technique to obtain a robust initial alignment. We then apply our procedure to a human OR that we have previously functionally characterized. The analysis of the resulting in silico complexes, supported by receptor mutagenesis and functional assays in a heterologous expression system, suggests that antagonists dock in the upper part of the binding pocket whereas agonists dock in the narrow lower part. We propose that the potency of agonists in activating receptors depends on their ability to establish tight interactions with the floor of the binding pocket. We developed a web site that allows the user to upload a GPCR sequence, choose a ligand in a library and obtain the 3D structure of the free receptor and ligand-receptor complex (http://genome.jouy.inra.fr/GPCRautomodel).

Modeling of mammalian olfactory receptors and docking of odorants.

Olfactory receptors (ORs) belong to the superfamily of G protein-coupled receptors (GPCRs), the second largest class of genes after those related to immunity, and account for about 3 % of mammalian genomes. ORs are present in all multicellular organisms and represent more than half the GPCRs in mammalian species (e.g., the mouse OR repertoire contains >1,000 functional genes). ORs are mainly expressed in the olfactory epithelium where they detect odorant molecules, but they are also expressed in a number of other cells, such as sperm cells, although their functions in these cells remain mostly unknown. It has recently been reported that ORs are present in tumoral tissues where they are expressed at different levels than in healthy tissues. A specific OR is over-expressed in prostate cancer cells, and activation of this OR has been shown to inhibit the proliferation of these cells. Odorant stimulation of some of these receptors results in inhibition of cell proliferation. Even though their biological role has not yet been elucidated, these receptors might constitute new targets for diagnosis and therapeutics. It is important to understand the activation mechanism of these receptors at the molecular level, in particular to be able to predict which ligands are likely to activate a particular receptor ('deorphanization') or to design antagonists for a given receptor. In this review, we describe the in silico methodologies used to model the three-dimensional (3D) structure of ORs (in the more general framework of GPCR modeling) and to dock ligands into these 3D structures.

Statistical significance of threading scores.

We present a general method for assessing threading score significance. The threading score of a protein sequence, thread onto a given structure, should be compared with the threading score distribution of a random amino-acid sequence, of the same length, thread on the same structure; small p-values point significantly high scores. We claim that, due to general protein contact map properties, this reference distribution is a Weibull extreme value distribution whose parameters depend on the threading method, the structure, the length of the query and the random sequence simulation model used. These parameters can be estimated off-line with simulated sequence samples, for different sequence lengths. They can further be interpolated at the exact length of a query, enabling the quick computation of the p-value.

A large decoy set of protein-protein complexes produced by flexible docking.

Computational methods are needed to help characterize the structure and function of protein-protein complexes. To develop and improve such methods, standard test problems are essential. One important test is to identify experimental structures from among large sets of decoys. Here, a flexible docking procedure was used to produce such a large ensemble of decoy complexes. In addition to their use for structure prediction, they can serve as a proxy for the nonspecific, protein-protein complexes that occur transiently in the cell, which are hard to characterize experimentally, yet biochemically important. For 202 homodimers and 41 heterodimers with known X-ray structures, we produced an average of 1217 decoys each. The structures were characterized in detail. The decoys have rather large protein-protein interfaces, with at least 45 residue-residue contacts for every 100 contacts found in the experimental complex. They have limited intramonomer deformation and limited intermonomer steric conflicts. The decoys thoroughly sample each monomer's surface, with all the surface amino acids being part of at least one decoy interface. The decoys with the lowest intramonomer deformation were analyzed separately, as proxies for nonspecific protein-protein complexes. Their interfaces are less hydrophobic than the experimental ones, with an amino acid composition similar to the overall surface composition. They have a poorer shape complementarity and a weaker association energy, but are no more fragmented than the experimental interfaces, with 2.1 distinct patches of interacting residues on average, compared to 2.6 for the experimental interfaces. The decoys should be useful for testing and parameterizing docking methods and scoring functions; they are freely available as PDB files at http://biology.polytechnique.fr/decoys.

Prins fluorination cyclisations: Preparation of 4-fluoro-pyran and -piperidine heterocycles.

The Prins reaction was investigated using BF3.OEt2 as a Lewis acid. It has been recently demonstrated, that if BF3.OEt2 is used in stoichiometric amounts then these reactions generate fluorinated products where the BF3.OEt2 contributes fluoride ion to quench the intermediate carbocations. In this study oxa- and aza-Prins reactions for the synthesis of 4-fluoro-pyrans and -piperidines were investigated. The products were obtained in good yields, but only with moderate diastereoselectivity. These Prins fluorination reactions can be accelerated under microwave conditions. The study extends the Prins fluorination methodology for the generation of the C-F bond in heterocycles.

Homology modelling of protein-protein complexes: a simple method and its possibilities and limitations.

BACKGROUND: Structure-based computational methods are needed to help identify and characterize protein-protein complexes and their function. For individual proteins, the most successful technique is homology modelling. We investigate a simple extension of this technique to protein-protein complexes. We consider a large set of complexes of known structures, involving pairs of single-domain proteins. The complexes are compared with each other to establish their sequence and structural similarities and the relation between the two. Compared to earlier studies, a simpler dataset, a simpler structural alignment procedure, and an additional energy criterion are used. Next, we compare the Xray structures to models obtained by threading the native sequence onto other, homologous complexes. An elementary requirement for a successful energy function is to rank the native structure above any threaded structure. We use the DFIREbeta energy function, whose quality and complexity are typical of the models used today. Finally, we compare near-native models to distinctly non-native models. RESULTS: If weakly stable complexes are excluded (defined by a binding energy cutoff), as well as a few unusual complexes, a simple homology principle holds: complexes that share more than 35% sequence identity share similar structures and interaction modes; this principle was less clearcut in earlier studies. The energy function was then tested for its ability to identify experimental structures among sets of decoys, produced by a simple threading procedure. On average, the experimental structure is ranked above 92% of the alternate structures. Thus, discrimination of the native structure is good but not perfect. The discrimination of near-native structures is fair. Typically, a single, alternate, non-native binding mode exists that has a native-like energy. Some of the associated failures may correspond to genuine, alternate binding modes and/or native complexes that are artefacts of the crystal environment. In other cases, additional model filtering with more sophisticated tools is needed. CONCLUSION: The results suggest that the simple modelling procedure applied here could help identify and characterize protein-protein complexes. The next step is to apply it on a genomic scale.

Recognizing protein-protein interfaces with empirical potentials and reduced amino acid alphabets.

BACKGROUND: In structural genomics, an important goal is the detection and classification of protein-protein interactions, given the structures of the interacting partners. We have developed empirical energy functions to identify native structures of protein-protein complexes among sets of decoy structures. To understand the role of amino acid diversity, we parameterized a series of functions, using a hierarchy of amino acid alphabets of increasing complexity, with 2, 3, 4, 6, and 20 amino acid groups. Compared to previous work, we used the simplest possible functional form, with residue-residue interactions and a stepwise distance-dependence. We used increased computational resources, however, constructing 290,000 decoys for 219 protein-protein complexes, with a realistic docking protocol where the protein partners are flexible and interact through a molecular mechanics energy function. The energy parameters were optimized to correctly assign as many native complexes as possible. To resolve the multiple minimum problem in parameter space, over 64000 starting parameter guesses were tried for each energy function. The optimized functions were tested by cross validation on subsets of our native and decoy structures, by blind tests on series of native and decoy structures available on the Web, and on models for 13 complexes submitted to the CAPRI structure prediction experiment. RESULTS: Performance is similar to several other statistical potentials of the same complexity. For example, the CAPRI target structure is correctly ranked ahead of 90% of its decoys in 6 cases out of 13. The hierarchy of amino acid alphabets leads to a coherent hierarchy of energy functions, with qualitatively similar parameters for similar amino acid types at all levels. Most remarkably, the performance with six amino acid classes is equivalent to that of the most detailed, 20-class energy function. CONCLUSION: This suggests that six carefully chosen amino acid classes are sufficient to encode specificity in protein-protein interactions, and provide a starting point to develop more complicated energy functions.