Virtual Screening of Plant Volatile Compounds Reveals a High Affinity of Hylamorpha elegans (Coleoptera: Scarabaeidae) Odorant-Binding Proteins for Sesquiterpenes From Its Native Host

Indexación: Web of ScienceHylamorpha elegans (Burmeister) is a native Chilean scarab beetle considered to be a relevant agricultural pest to pasture and cereal and small fruit crops. Because of their cryptic habits, control with conventional methods is difficult; therefore, alternative and environmentally friendly control strategies are highly desirable. The study of proteins that participate in the recognition of odorants, such as odorant-binding proteins (OBPs), offers interesting opportunities to identify new compounds with the potential to modify pest behavior and computational screening of compounds, which is commonly used in drug discovery, may help to accelerate the discovery of new semiochemicals. Here, we report the discovery of four OBPs in H. elegans as well as six new volatiles released by its native host Nothofagus obliqua (Mirbel). Molecular docking performed between OBPs and new and previously reported volatiles from N. obliqua revealed the best binding energy values for sesquiterpenic compounds. Despite remarkable divergence at the amino acid level, three of the four OBPs evaluated exhibited the best interaction energy for the same ligands. Molecular dynamics investigation reinforced the importance of sesquiterpenes, showing that hydrophobic residues of the OBPs interacted most frequently with the tested ligands, and binding free energy calculations demonstrated van der Waals and hydrophobic interactions to be the most important. Altogether, the results suggest that sesquiterpenes are interesting candidates for in vitro and in vivo assays to assess their potential application in pest management strategies.http://jinsectscience.oxfordjournals.org/content/16/1/3

eRepo-ORP: Exploring the Opportunity Space to Combat Orphan Diseases with Existing Drugs

© 2017 About 7000 rare, or orphan, diseases affect more than 350 million people worldwide. Although these conditions collectively pose significant health care problems, drug companies seldom develop drugs for orphan diseases due to extremely limited individual markets. Consequently, developing new treatments for often life-threatening orphan diseases is primarily contingent on financial incentives from governments, special research grants, and private philanthropy. Computer-aided drug repositioning is a cheaper and faster alternative to traditional drug discovery offering a promising venue for orphan drug research. Here, we present eRepo-ORP, a comprehensive resource constructed by a large-scale repositioning of existing drugs to orphan diseases with a collection of structural bioinformatics tools, including eThread, eFindSite, and eMatchSite. Specifically, a systematic exploration of 320,856 possible links between known drugs in DrugBank and orphan proteins obtained from Orphanet reveals as many as 18,145 candidates for repurposing. In order to illustrate how potential therapeutics for rare diseases can be identified with eRepo-ORP, we discuss the repositioning of a kinase inhibitor for Ras-associated autoimmune leukoproliferative disease. The eRepo-ORP data set is available through the Open Science Framework at https://osf.io/qdjup/

Proteins and their interacting partners: an introduction to protein–ligand binding site prediction methods

Elucidating the biological and biochemical roles of proteins, and subsequently determining their interacting partners, can be difficult and time consuming using in vitro and/or in vivo methods, and consequently the majority of newly sequenced proteins will have unknown structures and functions. However, in silico methods for predicting protein–ligand binding sites and protein biochemical functions offer an alternative practical solution. The characterisation of protein–ligand binding sites is essential for investigating new functional roles, which can impact the major biological research spheres of health, food, and energy security. In this review we discuss the role in silico methods play in 3D modelling of protein–ligand binding sites, along with their role in predicting biochemical functionality. In addition, we describe in detail some of the key alternative in silico prediction approaches that are available, as well as discussing the Critical Assessment of Techniques for Protein Structure Prediction (CASP) and the Continuous Automated Model EvaluatiOn (CAMEO) projects, and their impact on developments in the field. Furthermore, we discuss the importance of protein function prediction methods for tackling 21st century problems

Binding site matching in rational drug design: Algorithms and applications

© 2018 The Author(s) 2018. Published by Oxford University Press. All rights reserved. Interactions between proteins and small molecules are critical for biological functions. These interactions often occur in small cavities within protein structures, known as ligand-binding pockets. Understanding the physicochemical qualities of binding pockets is essential to improve not only our basic knowledge of biological systems, but also drug development procedures. In order to quantify similarities among pockets in terms of their geometries and chemical properties, either bound ligands can be compared to one another or binding sites can be matched directly. Both perspectives routinely take advantage of computational methods including various techniques to represent and compare small molecules as well as local protein structures. In this review, we survey 12 tools widely used to match pockets. These methods are divided into five categories based on the algorithm implemented to construct binding-site alignments. In addition to the comprehensive analysis of their algorithms, test sets and the performance of each method are described. We also discuss general pharmacological applications of computational pocket matching in drug repurposing, polypharmacology and side effects. Reflecting on the importance of these techniques in drug discovery, in the end, we elaborate on the development of more accurate meta-predictors, the incorporation of protein flexibility and the integration of powerful artificial intelligence technologies such as deep learning