PL-PatchSurfer: A Novel Molecular Local Surface-Based Method for Exploring Protein-Ligand Interactions

Structure-based computational methods have been widely used in exploring protein-ligand interactions, including predicting the binding ligands of a given protein based on their structural complementarity. Compared to other protein and ligand representations, the advantages of a surface representation include reduced sensitivity to subtle changes in the pocket and ligand conformation and fast search speed. Here we developed a novel method named PL-PatchSurfer (Protein-Ligand PatchSurfer). PL-PatchSurfer represents the protein binding pocket and the ligand molecular surface as a combination of segmented surface patches. Each patch is characterized by its geometrical shape and the electrostatic potential, which are represented using the 3D Zernike descriptor (3DZD). We first tested PL-PatchSurfer on binding ligand prediction and found it outperformed the pocket-similarity based ligand prediction program. We then optimized the search algorithm of PL-PatchSurfer using the PDBbind dataset. Finally, we explored the utility of applying PL-PatchSurfer to a larger and more diverse dataset and showed that PL-PatchSurfer was able to provide a high early enrichment for most of the targets. To the best of our knowledge, PL-PatchSurfer is the first surface patch-based method that treats ligand complementarity at protein binding sites. We believe that using a surface patch approach to better understand protein-ligand interactions has the potential to significantly enhance the design of new ligands for a wide array of drug-targets

Molecular Dynamics in Protein Structure Quality Assessment and Refinement

Proteins are the active biomolecules of the cell. They perform metabolic action, give the cell structure, protect the cell from antigens, give the cell motility, and much more. The function of proteins are intrinsically linked to their structures, so it is therefore necessary to characterize the structure of a protein to fully understand its function and operation. In this research the application of computational methods, primarily molecular dynamics, towards protein structure determination, refinement, and quality assessment were studied. I applied molecular dynamics techniques to four major projects; the determination of relative error of atomic models deposited with electron microscopy maps in the EMDB, solving and refining atomics structure models for the PhageG major capsid proteins, the elucidation of the structure the protein USP7 and the binding pose of a of a candidate therapeutic drug, and the determination of relative stability of candidate protein folds to distinguish near native models from not. Each year an increasing number of protein structures have been solved using electron microscopy (EM). The influx of solved structure has proven to be a boon to the community, but it is necessary to note that the quality EM maps vary substantially. To understand to what extent atomic structure models generated from EM matched their respective maps, two computational structure refinement methods were used to examine how much structures could be refined. The deviation from the starting structure by refinement, as well as the disagreement between refined models produced by the two computational methods, scaled inversely with both the global and local map resolutions. The results suggested that the observed discrepancy between the deposited maps and refined models is due to the lack of resolvable structural data present in EM maps at low to moderate resolutions, and therefore these annotations must be used with caution in further applications. I also successfully implemented molecular dynamics as a method for protein structure quality assessment. Proteins tend towards shapes which minimize their energy. Experimentally, the stability of a protein can be measured through several techniques, one such technique includes the controlled application of tension to proteins in an atomic force microscopy (AFM) framework. This kind of tension-based approach is of interest as it probes the force required to unfold individual domains of a protein rather than a bulk characteristic like molting point or activity. It has been shown that key features observed in an AFM experiment can be well reproduced with molecular dynamics simulation, which has been applied to characterize the mechanisms of unfolding of proteins as well as ligand-protein interactions. Steered molecular dynamics (SMD) was applied to pull and unfold proteins and determine the force required to unfold them. The relative force required to unfold different models with the same sequence was used to estimate relative model accuracy. This follows from the hypothesis that the structural stability of a given model’s conformation would positively correlate with its accuracy, i.e. how close that model is to its native fold. It was found that near-native models could be successfully selected by comparing the forces required to unfold models, indicating that high unfolding forces indeed indicated high model stability, which in turn correlated with model accuracy

Energetic Coupling between Ligand Binding and Dimerization in <i>Escherichia coli</i> Phosphoglycerate Mutase

Energetic coupling of two molecular events in a protein molecule is ubiquitous in biochemical reactions mediated by proteins, such as catalysis and signal transduction. Here, we investigate energetic coupling between ligand binding and folding of a dimer using a model system that shows three-state equilibrium unfolding of an exceptional quality. The homodimeric <i>Escherichia coli</i> cofactor-dependent phosphoglycerate mutase (dPGM) was found to be stabilized by ATP in a proteome-wide screen, although dPGM does not require or utilize ATP for enzymatic function. We investigated the effect of ATP on the thermodynamic stability of dPGM using equilibrium unfolding. We found that, in the absence of ATP, dPGM populates a partially unfolded, monomeric intermediate during equilibrium unfolding. However, addition of 1.0 mM ATP drastically reduces the population of the intermediate by selectively stabilizing the native dimer. Using a computational ligand docking method, we predicted ATP binds to the active site of the enzyme using the triphosphate group. By performing equilibrium unfolding and isothermal titration calorimetry with active-site variants of dPGM, we confirmed that active-site residues are involved in ATP binding. Our findings show that ATP promotes dimerization of the protein by binding to the active site, which is distal from the dimer interface. This cooperativity suggests an energetic coupling between the active site and the dimer interface. We also propose a structural link to explain how ligand binding to the active site is energetically coupled with dimerization

Conotoxin Prediction: New Features to Increase Prediction Accuracy

Conotoxins are toxic, disulfide-bond-rich peptides from cone snail venom that target a wide range of receptors and ion channels with multiple pathophysiological effects. Conotoxins have extraordinary potential for medical therapeutics that include cancer, microbial infections, epilepsy, autoimmune diseases, neurological conditions, and cardiovascular disorders. Despite the potential for these compounds in novel therapeutic treatment development, the process of identifying and characterizing the toxicities of conotoxins is difficult, costly, and time-consuming. This challenge requires a series of diverse, complex, and labor-intensive biological, toxicological, and analytical techniques for effective characterization. While recent attempts, using machine learning based solely on primary amino acid sequences to predict biological toxins (e.g., conotoxins and animal venoms), have improved toxin identification, these methods are limited due to peptide conformational flexibility and the high frequency of cysteines present in toxin sequences. This results in an enumerable set of disulfide-bridged foldamers with different conformations of the same primary amino acid sequence that affect function and toxicity levels. Consequently, a given peptide may be toxic when its cysteine residues form a particular disulfide-bond pattern, while alternative bonding patterns (isoforms) or its reduced form (free cysteines with no disulfide bridges) may have little or no toxicological effects. Similarly, the same disulfide-bond pattern may be possible for other peptide sequences and result in different conformations that all exhibit varying toxicities to the same receptor or to different receptors. We present here new features, when combined with primary sequence features to train machine learning algorithms to predict conotoxins, that significantly increase prediction accuracy

From chemical platform molecules to new biosolvents: Design engineering as a substitution methodology

International audienceThe substitution of conventional solvents, in line with regulation changes, requires the use of appropriate methodologies able to generate candidate molecules. Starting from the widely used trial and error approach, we developed two improved, time- and cost-saving methodologies, involving the prediction of molecule properties and reverse design. Reverse design is an innovative methodology to design biosolvents through a virtual laboratory: stages of generation of molecular structures and properties prediction are integrated into a computer-aided molecular design tool providing solutions that meet targeted specifi cations. These two substitution methodologies were applied in a case study aiming at replacing acetone and methyl ethyl ketone for the solubilization of epoxy resin prepolymers. The generation of performing biosolvents was carried ot from furfural as a bio-based platform molecule, thanks to the prediction of different relevant properties (physico-chemical, safety, and environmental characteristics). The reverse design succeeded in ranking these solvent candidates according to their capacity to match the required specifi cations

Al-Qaeda\u27s Operational Evolution: Behavioral and Organizational Perspectives

Al‐Qaeda is widely regarded by the military, law enforcement, diplomatic, and intelligence communities as being the foremost threat to U.S. national security and safety. The nature of this threat, however, has changed since al‐Qaeda first emerged in the late 1980s. This article describes the emergence of a new form of transnational terrorism and details al‐Qaeda\u27s progression from being an organization to an ideological movement. Drawing on a theory of social movements, we analyze its trajectory and the levels of influence. We also offer a behavioral perspective in explaining how al‐Qaeda has adapted as a learning organization with new leadership, tactics, and patterns of recruitment and training