Cosolvent-Based Protein Pharmacophore for Ligand Enrichment in Virtual Screening

Virtual screening of large compound databases, looking for potential ligands of a target protein, is a major tool in computer-aided drug discovery. Throughout the years, different techniques such as similarity searching, pharmacophore matching, or molecular docking have been applied with the aim of finding hit compounds showing appreciable affinity. Molecular dynamics simulations in mixed solvents have been shown to identify hot spots relevant for protein-drug interaction, and implementations based on this knowledge were developed to improve pharmacophore matching of small molecules, binding free-energy estimations, and docking performance in terms of pose prediction. Here, we proved in a retrospective manner that cosolvent-derived pharmacophores from molecular dynamics (solvent sites) improve the performance of docking-based virtual screening campaigns. We applied a biased docking scheme based on solvent sites to nine relevant target proteins that have a set of known ligands or actives and compounds that are, presumably, nonbinders (decoys). Our results show improvement in virtual screening performance compared to traditional docking programs both at a global level, with up to 35% increase in areas under the receiver operating characteristic curve, and in early stages, with up to a 7-fold increase in enrichment factors at 1%. However, the improvement in pose prediction of actives was less profound. The presented application makes use of the AutoDock Bias method and is the only cosolvent-derived pharmacophore technique that employs its knowledge both in the ligand conformational search algorithm and the final affinity scoring for virtual screening purposes.Fil: Arcon, Juan Pablo. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Química Biológica de la Facultad de Ciencias Exactas y Naturales. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Instituto de Química Biológica de la Facultad de Ciencias Exactas y Naturales; ArgentinaFil: Defelipe, Lucas Alfredo. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Química Biológica de la Facultad de Ciencias Exactas y Naturales. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Instituto de Química Biológica de la Facultad de Ciencias Exactas y Naturales; ArgentinaFil: Lopez, Elias Daniel. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Química Biológica de la Facultad de Ciencias Exactas y Naturales. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Instituto de Química Biológica de la Facultad de Ciencias Exactas y Naturales; ArgentinaFil: Burastero, Osvaldo. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Química Biológica de la Facultad de Ciencias Exactas y Naturales. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Instituto de Química Biológica de la Facultad de Ciencias Exactas y Naturales; ArgentinaFil: Modenutti, Carlos Pablo. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Química Biológica de la Facultad de Ciencias Exactas y Naturales. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Instituto de Química Biológica de la Facultad de Ciencias Exactas y Naturales; ArgentinaFil: Barril, Xavier. Universidad de Barcelona; EspañaFil: Marti, Marcelo Adrian. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Química Biológica de la Facultad de Ciencias Exactas y Naturales. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Instituto de Química Biológica de la Facultad de Ciencias Exactas y Naturales; ArgentinaFil: Turjanski, Adrian. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Química Biológica de la Facultad de Ciencias Exactas y Naturales. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Instituto de Química Biológica de la Facultad de Ciencias Exactas y Naturales; Argentin

Structure/activity virtual screening and in vitro testing of small molecule inhibitors of 8-hydroxy-5-deazaflavin:NADPH oxidoreductase from gut methanogenic bacteria

Abstract Virtual screening techniques and in vitro binding/inhibitory assays were used to search within a set of more than 8,000 naturally occurring small ligands for candidate inhibitors of 8-hydroxy-5-deazaflavin:NADPH oxidoreductase (FNO) from Methanobrevibacter smithii, the enzyme that catalyses the bidirectional electron transfer between NADP+ and F420H2 during the intestinal production of CH4 from CO2. In silico screening using molecular docking classified the ligand-enzyme complexes in the range between − 4.9 and − 10.5 kcal/mol. Molecular flexibility, the number of H-bond acceptors and donors, the extent of hydrophobic interactions, and the exposure to the solvent were the major discriminants in determining the affinity of the ligands for FNO. In vitro studies on a group of these ligands selected from the most populated/representative clusters provided quantitative kinetic, equilibrium, and structural information on ligands' behaviour, in optimal agreement with the predictive computational results

Optimal assignment methods for ligand-based virtual screening

<p>Abstract</p> <p>Background</p> <p>Ligand-based virtual screening experiments are an important task in the early drug discovery stage. An ambitious aim in each experiment is to disclose active structures based on new scaffolds. To perform these "scaffold-hoppings" for individual problems and targets, a plethora of different similarity methods based on diverse techniques were published in the last years. The optimal assignment approach on molecular graphs, a successful method in the field of quantitative structure-activity relationships, has not been tested as a ligand-based virtual screening method so far.</p> <p>Results</p> <p>We evaluated two already published and two new optimal assignment methods on various data sets. To emphasize the "scaffold-hopping" ability, we used the information of chemotype clustering analyses in our evaluation metrics. Comparisons with literature results show an improved early recognition performance and comparable results over the complete data set. A new method based on two different assignment steps shows an increased "scaffold-hopping" behavior together with a good early recognition performance.</p> <p>Conclusion</p> <p>The presented methods show a good combination of chemotype discovery and enrichment of active structures. Additionally, the optimal assignment on molecular graphs has the advantage to investigate and interpret the mappings, allowing precise modifications of internal parameters of the similarity measure for specific targets. All methods have low computation times which make them applicable to screen large data sets.</p

Systematic Computational Analysis of Structure-Activity Relationships

The exploration of structure–activity relationships (SARs) of small bioactive molecules is a central task in medicinal chemistry. Typically, SARs are analyzed on a case-by-case basis for series of closely related molecules. Classical methods that explore SARs include quantitative SAR (QSAR) modeling and molecular similarity analysis. These methods conceptually rely on the similarity–property principle which states that similar molecules should also have similar biological activity. Although this principle is intuitive and supported by a wealth of observations, it is well-recognized that SARs can have fundamentally different character. Small chemical modifications of active molecules often dramatically alter biological responses, giving rise to “activity cliffs” and “discontinuous” SARs. By contrast, structurally diverse molecules can have similar activity, a situation that is indicative of “continuous” SARs. The combination of continuous and discontinuous components characterizes “heterogeneous” SARs, a phenotype that is frequently encountered in medicinal chemistry. This thesis focuses on the systematic computational analysis of SARs present in sets of active molecules. Approaches to quantitatively describe, classify, and compare SARs at multiple levels of detail are introduced. Initially, a comparative study of crystallographic enzyme–inhibitor complexes is presented that relates two-dimensional and three-dimensional inhibitor similarity and potency to each other. The analysis reveals the presence of systematic and in part unexpected relationships between molecular similarity and potency and explains why apparently inconsistent SARs can coexist in compound activity classes. For the systematic characterization of complex SARs, a numerical function termed SAR Index (SARI) is developed that quantitatively describes continuous and discontinuous SAR components present in sets of active molecules. On the basis of two-dimensional molecular similarity and potency, SARI distinguishes between the three basic SAR categories described above. Heterogeneous SARs are further divided into two previously unobserved subtypes that are distinguished by the way they combine different SAR features. SARI profiling of various enzyme inhibitor classes demonstrates the prevalence of heterogeneous SARs for many classes. Furthermore, control calculations are conducted in order to assess the influence of molecular representation and data set size on SARI scoring. It is shown that SARI scores remain largely stable in response to variation of these critical parameters. Based on the SARI formalism, a methodology is developed to study multiple global and local SAR components of compound activity classes. The approach combines graphical analysis of Network-like Similarity Graphs (NSGs) and SARI score calculations at multiple levels of detail. Compound classes of different global SAR character are found to produce distinct network topologies. Local SAR features are studied in subsets of similar compounds and systematically related to global SAR character. Furthermore, key compounds are identified that are major determinants of local and global SAR characteristics. The approach is also applied to study structure–selectivity relationships (SSRs). Compound selectivity often results from potency differences for multiple targets and presents a critical factor in lead optimization projects. Here, SSRs are explored for sets of compounds that are active against pairs of related targets. For this purpose, the molecular network approach is adapted to the evaluation of SSRs. Results show that SSRs can be quantitatively described and categorized in analogy to single-target SARs. In addition, local SSR environments are identified and compared to SAR features. Within these environments, key compounds are identified that determine characteristic features of single-target SARs and dual-target SSRs. Comparison of similar compounds that have significantly different selectivity reveals chemical modifications that render compounds target-selective. Furthermore, a methodology is introduced to study SAR contributions from functional groups and substitution sites in series of analogous molecules. Analog series are systematically organized according to substitution sites in a hierarchical data structure termed Combinatorial Analog Graph (CAG), and the SARI scoring scheme is applied to evaluate SAR contributions of variable functional groups at specific substitution sites. Combinations of sites that determine SARs within analog series and make large contributions to SAR discontinuity are identified. These sites are prime targets for further chemical modification. In addition to determining key substitution patterns, CAG analysis also identifies substitution sites that have not been thoroughly explored