Influence of Stereochemistry in a Local Approach for Calculating Protein Conformations.

Protein structure prediction is generally based on the use of local conformational information coupled with long-range distance restraints. Such restraints can be derived from the knowledge of a template structure or the analysis of protein sequence alignment in the framework of models arising from the physics of disordered systems. The accuracy of approaches based on sequence alignment, however, is limited in the case where the number of aligned sequences is small.

Conformational Space of the Translocation Domain of Botulinum Toxin: Atomistic Modeling and Mesoscopic Description of the Coiled-Coil Helix Bundle.

The toxicity of botulinum multi-domain neurotoxins (BoNTs) arises from a sequence of molecular events, in which the translocation of the catalytic domain through the membrane of a neurotransmitter vesicle plays a key role. A recent structural study of the translocation domain of BoNTs suggests that the interaction with the membrane is driven by the transition of an α helical switch towards a β hairpin. Atomistic simulations in conjunction with the mesoscopic model are used to investigate the consequences of this proposition for the toxin-membrane interaction.

New Crystal Form of Human Neuropilin-1 b1 Fragment with Six Electrostatic Mutations Complexed with KDKPPR Peptide Ligand.

Neuropilin 1 (NRP1), a cell-surface co-receptor of a number of growth factors and other signaling molecules, has long been the focus of attention due to its association with the development and the progression of several types of cancer. For example, the KDKPPR peptide has recently been combined with a photosensitizer and a contrast agent to bind NRP1 for the detection and treatment by photodynamic therapy of glioblastoma, an aggressive brain cancer. The main therapeutic target is a pocket of the fragment b1 of NRP1 (NRP1-b1), in which vascular endothelial growth factors (VEGFs) bind.

Conformational Insights into the Control of CNF1 Toxin Activity by Peptidyl-Prolyl Isomerization: A Molecular Dynamics Perspective.

The cytotoxic necrotizing factor 1 (CNF1) toxin from uropathogenic constitutively activates Rho GTPases by catalyzing the deamidation of a critical glutamine residue located in the switch II (SWII). In crystallographic structures of the CNF1 catalytic domain (CNF1), surface-exposed P768 and P968 peptidyl-prolyl imide bonds (X-Pro) adopt an unusual conformation. Here, we show that mutation of each proline residue into glycine abrogates CNF1 in vitro deamidase activity, while mutant forms of CNF1 remain functional on RhoA in cells.

Tandem domain structure determination based on a systematic enumeration of conformations.

Protein structure determination is undergoing a change of perspective due to the larger importance taken in biology by the disordered regions of biomolecules. In such cases, the convergence criterion is more difficult to set up and the size of the conformational space is a obstacle to exhaustive exploration. A pipeline is proposed here to exhaustively sample protein conformations using backbone angle limits obtained by nuclear magnetic resonance (NMR), and then to determine the populations of conformations.

Analyzing the Relationship Between the Activation of the Edema Factor and Its Interaction With Calmodulin.

Molecular dynamics (MD) simulations have been recorded on the complex between the edema factor (EF) of and calmodulin (CaM), starting from a structure with the orthosteric inhibitor adefovir bound in the EF catalytic site. The starting structure has been destabilized by alternately suppressing different co-factors, such as adefovir ligand or ions, revealing several long-distance correlations between the conformation of CaM, the geometry of the CaM/EF interface, the enzymatic site and the overall organization of the complex. An allosteric communication between CaM/EF interface and the EF catalytic site, highlighted by these correlations, was confirmed by several bioinformatics approaches from the literature.

Systematic Exploration of Protein Conformational Space Using a Distance Geometry Approach.

The optimization approaches classically used during the determination of protein structure encounter various difficulties, especially when the size of the conformational space is large. Indeed, in such a case, algorithmic convergence criteria are more difficult to set up. Moreover, the size of the search space makes it difficult to achieve a complete exploration.

Structural Biology and Molecular Modeling to Analyze the Entry of Bacterial Toxins and Virulence Factors into Host Cells.

Understanding the functions and mechanisms of biological systems is an outstanding challenge. One way to overcome it is to combine together several approaches such as molecular modeling and experimental structural biology techniques. Indeed, the interplay between structural and dynamical properties of the system is crucial to unravel the function of molecular machinery's.

Minimal NMR distance information for rigidity of protein graphs.

Nuclear Magnetic Resonance (NMR) experiments provide distances between nearby atoms of a protein molecule. The corresponding structure determination problem is to determine the 3D protein structure by exploiting such distances. We present a new order on the atoms of the protein, based on information from the chemistry of proteins and NMR experiments, which allows us to formulate the problem as a combinatorial search.

Conformational sampling of CpxA: Connecting HAMP motions to the histidine kinase function.

In the histidine kinase family, the HAMP and DHp domains are considered to play an important role into the transmission of signal arising from environmental conditions to the auto-phosphorylation site and to the binding site of response regulator. Several conformational motions inside HAMP have been proposed to transmit this signal: (i) the gearbox model, (ii) α helices rotations, pistons and scissoring, (iii) transition between ordered and disordered states. In the present work, we explore by temperature-accelerated molecular dynamics (TAMD), an enhanced sampling technique, the conformational space of the cytoplasmic region of histidine kinase CpxA.

Closed-Locked and Apo-Resting State Structures of the Human α7 Nicotinic Receptor: A Computational Study.

Nicotinic acetylcholine receptors, belonging to the Cys-loop superfamily of ligand-gated ion channels (LGICs), are membrane proteins present in neurons and at neuromuscular junctions. They are responsible for signal transmission, and their function is regulated by neurotransmitters, agonists, and antagonists drugs. A detailed knowledge of their conformational transition in response to ligand binding is critical to understanding the basis of ligand-receptor interaction, in view of new pharmacological approaches to control receptor activity.

re-TAMD: exploring interactions between H3 peptide and YEATS domain using enhanced sampling.

Background: Analysis of preferred binding regions of a ligand on a protein is important for detecting cryptic binding pockets and improving the ligand selectivity.

Result: The enhanced sampling approach TAMD has been adapted to allow a ligand to unbind from its native binding site and explore the protein surface. This so-called re-TAMD procedure was then used to explore the interaction between the N terminal peptide of histone H3 and the YEATS domain.

Molecular Modeling of the Catalytic Domain of CyaA Deepened the Knowledge of Its Functional Dynamics.

Although CyaA has been studied for over three decades and revealed itself to be a very good prototype for developing various biotechnological applications, only a little is known about its functional dynamics and about the conformational landscape of this protein. Molecular dynamics simulations helped to clarify the view on these points in the following way. First, the model of interaction between AC and calmodulin (CaM) has evolved from an interaction centered on the surface between C-CaM hydrophobic patch and the α helix H of AC, to a more balanced view, in which the C-terminal tail of AC along with the C-CaM Calcium loops play an important role.

Building Graphs To Describe Dynamics, Kinetics, and Energetics in the d-ALa:d-Lac Ligase VanA.

The d-Ala:d-Lac ligase, VanA, plays a critical role in the resistance of vancomycin. Indeed, it is involved in the synthesis of a peptidoglycan precursor, to which vancomycin cannot bind. The reaction catalyzed by VanA requires the opening of the so-called "ω-loop", so that the substrates can enter the active site.

Modification in hydrophobic packing of HAMP domain induces a destabilization of the auto-phosphorylation site in the histidine kinase CpxA.

The histidine kinases belong to the family of two-component systems, which serves in bacteria to couple environmental stimuli to adaptive responses. Most of the histidine kinases are homodimers, in which the HAMP and DHp domains assemble into an elongated helical region flanked by two CA domains. Recently, X-ray crystallographic structures of the cytoplasmic region of the Escherichia coli histidine kinase CpxA were determined and a phosphotransferase-defective mutant, M228V, located in HAMP, was identified.

Improving the prediction of organism-level toxicity through integration of chemical, protein target and cytotoxicity qHTS data.

Prediction of compound toxicity is essential because covering the vast chemical space requiring safety assessment using traditional experimentally-based, resource-intensive techniques is impossible. However, such prediction is nontrivial due to the complex causal relationship between compound structure and harm. Protein target annotations and experimental outcomes encode relevant bioactivity information complementary to chemicals' structures.

Temperature Accelerated Molecular Dynamics with Soft-Ratcheting Criterion Orients Enhanced Sampling by Low-Resolution Information.

Many proteins exhibit an equilibrium between multiple conformations, some of them being characterized only by low-resolution information. Visiting all conformations is a demanding task for computational techniques performing enhanced but unfocused exploration of collective variable (CV) space. Otherwise, pulling a structure toward a target condition biases the exploration in a way difficult to assess.

Improved large-scale prediction of growth inhibition patterns using the NCI60 cancer cell line panel.

Motivation: Recent large-scale omics initiatives have catalogued the somatic alterations of cancer cell line panels along with their pharmacological response to hundreds of compounds. In this study, we have explored these data to advance computational approaches that enable more effective and targeted use of current and future anticancer therapeutics.

Results: We modelled the 50% growth inhibition bioassay end-point (GI50) of 17,142 compounds screened against 59 cancer cell lines from the NCI60 panel (941,831 data-points, matrix 93.

Chemically Aware Model Builder (camb): an R package for property and bioactivity modelling of small molecules.

Background: In silico predictive models have proved to be valuable for the optimisation of compound potency, selectivity and safety profiles in the drug discovery process.

Results: camb is an R package that provides an environment for the rapid generation of quantitative Structure-Property and Structure-Activity models for small molecules (including QSAR, QSPR, QSAM, PCM) and is aimed at both advanced and beginner R users. camb's capabilities include the standardisation of chemical structure representation, computation of 905 one-dimensional and 14 fingerprint type descriptors for small molecules, 8 types of amino acid descriptors, 13 whole protein sequence descriptors, filtering methods for feature selection, generation of predictive models (using an interface to the R package caret), as well as techniques to create model ensembles using techniques from the R package caretEnsemble).

A Structural Model of the Human α7 Nicotinic Receptor in an Open Conformation.

Nicotinic acetylcholine receptors (nAchRs) are ligand-gated ion channels that regulate chemical transmission at the neuromuscular junction. Structural information is available at low resolution from open and closed forms of an eukaryotic receptor, and at high resolution from other members of the same structural family, two prokaryotic orthologs and an eukaryotic GluCl channel. Structures of human channels however are still lacking.

Comparing the Influence of Simulated Experimental Errors on 12 Machine Learning Algorithms in Bioactivity Modeling Using 12 Diverse Data Sets.

To date, no systematic study has assessed the effect of random experimental errors on the predictive power of QSAR models. To address this shortage, we have benchmarked the noise sensitivity of 12 learning algorithms on 12 data sets (15,840 models in total), namely the following: Support Vector Machines (SVM) with radial and polynomial (Poly) kernels, Gaussian Process (GP) with radial and polynomial kernels, Relevant Vector Machines (radial kernel), Random Forest (RF), Gradient Boosting Machines (GBM), Bagged Regression Trees, Partial Least Squares, and k-Nearest Neighbors. Model performance on the test set was used as a proxy to monitor the relative noise sensitivity of these algorithms as a function of the level of simulated noise added to the bioactivities from the training set.

Proteochemometric modelling coupled to in silico target prediction: an integrated approach for the simultaneous prediction of polypharmacology and binding affinity/potency of small molecules.

The rampant increase of public bioactivity databases has fostered the development of computational chemogenomics methodologies to evaluate potential ligand-target interactions (polypharmacology) both in a qualitative and quantitative way. Bayesian target prediction algorithms predict the probability of an interaction between a compound and a panel of targets, thus assessing compound polypharmacology qualitatively, whereas structure-activity relationship techniques are able to provide quantitative bioactivity predictions. We propose an integrated drug discovery pipeline combining in silico target prediction and proteochemometric modelling (PCM) for the respective prediction of compound polypharmacology and potency/affinity.

Identification of binding sites and favorable ligand binding moieties by virtual screening and self-organizing map analysis.

Background: Identifying druggable cavities on a protein surface is a crucial step in structure based drug design. The cavities have to present suitable size and shape, as well as appropriate chemical complementarity with ligands.

Results: We present a novel cavity prediction method that analyzes results of virtual screening of specific ligands or fragment libraries by means of Self-Organizing Maps.