Structure-Based Virtual Screening for Drug Discovery: a Problem-Centric Review

Structure-based virtual screening (SBVS) has been widely applied in early-stage drug discovery. From a problem-centric perspective, we reviewed the recent advances and applications in SBVS with a special focus on docking-based virtual screening. We emphasized the researchers’ practical efforts in real projects by understanding the ligand-target binding interactions as a premise. We also highlighted the recent progress in developing target-biased scoring functions by optimizing current generic scoring functions toward certain target classes, as well as in developing novel ones by means of machine learning techniques

Rapid and Accurate Prediction and Scoring of Water Molecules in Protein Binding Sites

Water plays a critical role in ligand-protein interactions. However, it is still challenging to predict accurately not only where water molecules prefer to bind, but also which of those water molecules might be displaceable. The latter is often seen as a route to optimizing affinity of potential drug candidates. Using a protocol we call WaterDock, we show that the freely available AutoDock Vina tool can be used to predict accurately the binding sites of water molecules. WaterDock was validated using data from X-ray crystallography, neutron diffraction and molecular dynamics simulations and correctly predicted 97% of the water molecules in the test set. In addition, we combined data-mining, heuristic and machine learning techniques to develop probabilistic water molecule classifiers. When applied to WaterDock predictions in the Astex Diverse Set of protein ligand complexes, we could identify whether a water molecule was conserved or displaced to an accuracy of 75%. A second model predicted whether water molecules were displaced by polar groups or by non-polar groups to an accuracy of 80%. These results should prove useful for anyone wishing to undertake rational design of new compounds where the displacement of water molecules is being considered as a route to improved affinity

Advances in the treatment of explicit water molecules in docking and binding free energy calculations

BACKGROUND: The inclusion of direct effects mediated by water during the ligand-receptor recognition is a hot-topic of modern computational chemistry applied to drug discovery and development. Docking or virtual screening with explicit hydration is still debatable, despite the successful cases that have been presented in the last years. Indeed, how to select the water molecules that will be included in the docking process or how the included waters should be treated remain open questions. OBJECTIVE: In this review, we will discuss some of the most recent methods that can be used in computational drug discovery and drug development when the effect of a single water, or of a small network of interacting waters, needs to be explicitly considered. RESULTS: Here, we analyse software to aid the selection, or to predict the position, of water molecules that are going to be explicitly considered in later docking studies. We also present software and protocols able to efficiently treat flexible water molecules during docking, including examples of applications. Finally, we discuss methods based on molecular dynamics simulations that can be used to integrate docking studies or to reliably and efficiently compute binding energies of ligands in presence of interfacial or bridging water molecules. CONCLUSIONS: Software applications aiding the design of new drugs that exploit water molecules, either as displaceable residues or as bridges to the receptor, are constantly being developed. Although further validation is needed, workflows that explicitly consider water will probably become a standard for computational drug discovery soon

Pharmacogenetic modeling of human cytochrome P450 2D6; On the force of variation in inducing toxicity

Understanding the way in which drugs are metabolized by CYP2D6 and hence the underlying mechanisms that define potential toxicity is crucial to avoid adverse reactions. The high occurrence of CYP2D6 polymorphs enhances the complexity of the toxicity assessment of a drug candidate and should be tackled from early drug discovery phase on. The research described in this PhD thesis has been performed to provide novel fundamental insights regarding the metabolic activity of CYP2D6 wild-type and several polymorphs using various state-of-the-art in silico techniques. The results of the CYP2D6-focused studies enhance our knowledge regarding the enzyme particularities, and can be used to accelerate the development of CYP2D6 modeling tools with more accurate and reliable predictions

Computational prediction of metabolism: sites, products, SAR, P450 enzyme dynamics, and mechanisms.

Metabolism of xenobiotics remains a central challenge for the discovery and development of drugs, cosmetics, nutritional supplements, and agrochemicals. Metabolic transformations are frequently related to the incidence of toxic effects that may result from the emergence of reactive species, the systemic accumulation of metabolites, or by induction of metabolic pathways. Experimental investigation of the metabolism of small organic molecules is particularly resource demanding; hence, computational methods are of considerable interest to complement experimental approaches. This review provides a broad overview of structure- and ligand-based computational methods for the prediction of xenobiotic metabolism. Current computational approaches to address xenobiotic metabolism are discussed from three major perspectives: (i) prediction of sites of metabolism (SOMs), (ii) elucidation of potential metabolites and their chemical structures, and (iii) prediction of direct and indirect effects of xenobiotics on metabolizing enzymes, where the focus is on the cytochrome P450 (CYP) superfamily of enzymes, the cardinal xenobiotics metabolizing enzymes. For each of these domains, a variety of approaches and their applications are systematically reviewed, including expert systems, data mining approaches, quantitative structure-activity relationships (QSARs), and machine learning-based methods, pharmacophore-based algorithms, shape-focused techniques, molecular interaction fields (MIFs), reactivity-focused techniques, protein-ligand docking, molecular dynamics (MD) simulations, and combinations of methods. Predictive metabolism is a developing area, and there is still enormous potential for improvement. However, it is clear that the combination of rapidly increasing amounts of available ligand- and structure-related experimental data (in particular, quantitative data) with novel and diverse simulation and modeling approaches is accelerating the development of effective tools for prediction of in vivo metabolism, which is reflected by the diverse and comprehensive data sources and methods for metabolism prediction reviewed here. This review attempts to survey the range and scope of computational methods applied to metabolism prediction and also to compare and contrast their applicability and performance.JK, MJW, JT, PJB, AB and RCG thank Unilever for funding

Busca racional por moléculas bioativas em modelos de diabetes, leucemia e tuberculose

Tese (doutorado) - Universidade Federal de Santa Catarina, Centro de Ciências Físicas e Matemáticas. Programa de Pós-Graduação em Química.Esta tese apresenta o desenho racional de moléculas bioativas baseadas na estrutura de fármacos (ou moléculas ativas) e está subdividida em três capítulos de acordo com cada patologia-alvo: 1) diabetes; 2) leucemia e 3) tuberculose. No capítulo 1, foram planejadas e sintetizadas sulfonamidas e sulfonil(tio)uréias, baseadas na estrutura da glibenclamida, para obtenção de potenciais agentes hipoglicemiantes, num total de 22 compostos, sendo 15 inéditos. A sulfonamida 5, mostrou a mais pronunciada atividade, contribuindo para a homeostase da glicose, uma vez que é equipotente a glibenclamida, mas sem provocar sobrecarga das células-? nos processos de secreção. No capítulo 2, foram sintetizadas quatro classes de compostos: acil-hidrazonas, oxadiazóis, imidas e tiazolidinonas baseadas nos fármacos colchicina e combretastatina A-4, num total de 62 moléculas, sendo 34 estruturas inéditas. 29 foi o composto mais potente, com atividade de 15nM frente às células da linhagem Jurkat e de 25nM em células REH, apresentando baixa toxicidade para células normais, e como mecanismo de ação, a inibição da tubulina. No capítulo 3, apresenta-se o primeiro estudo de screening virtual de bibliotecas de compostos na PtpB de M. tuberculosis, baseados na única estrutura de raio-X disponível PtpB:OMTS. Na primeira etapa, realizou-se o screening da biblioteca de produtos naturais com mais de 800 compostos resultando na identificação do composto mais ativo, competitivo e seletivo: Kuwanol E (Ki =1,6 µM). Em uma segunda etapa, foi analisada a biblioteca de compostos comerciais Drugs Now do banco de dados ZINC, com mais de 5 milhões de estruturas, resultando na seleção de 13 moléculas através de docking/rescoring