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

Identification of potential inhibitors targeting DNA adenine methyltransferase of Klebsiella pneumoniae for antimicrobial resistance management: a structure-based molecular docking study

Background: Klebsiella pneumoniae is an important opportunistic pathogen that frequently causes nosocomial infections. Notably, this bacterium has emerged as a major problem in hospital settings because of its acquisition of resistance to carbapenems. The majority of antibiotics act by targeting crucial pathways within bacterial cells. However, due to the development of resistance mechanisms, the efficiency of these antibiotics has decreased. Therefore, this study focused on a putative protein (DNA adenine methyltransferase; Dam) found in K. pneumoniae that encompasses a DNA methylation protein domain, indicating a novel potential target for pharmacological intervention. DNA methylation affects bacterial virulence attenuation.Methods: In the unavailability of a 3D structure for Dam protein in protein database, a 3D model was generated using SWISS-MODEL server and validated using computational tools. Following that, screening was performed against the Dam protein using a set of 2706 phytochemicals obtained from the ZINC database using PyRx0.8. ProTox-II platform was used to predict the physicochemical properties and various toxicity endpoints.Results: Among the screened compounds, ZINC4214775, ZINC4095704, and ZINC4136964 had higher binding affinity for the Dam and interacted with its active site residues. The computational analyses of these three identified hits indicate that their predicted properties were within an acceptable range for evaluating toxicity. In addition, a toxicity radar chart showed that these hits were within an acceptable range.Conclusions: These compounds have the potential to act as Dam inhibitors and could be investigated further for managing antimicrobial resistance in K. pneumoniae

Sampling of conformational ensemble for virtual screening using molecular dynamics simulations and normal mode analysis

Aim: Molecular dynamics simulations and normal mode analysis are well-established approaches to generate receptor conformational ensembles (RCEs) for ligand docking and virtual screening. Here, we report new fast molecular dynamics-based and normal mode analysis-based protocols combined with conformational pocket classifications to efficiently generate RCEs. Materials \& methods: We assessed our protocols on two well-characterized protein targets showing local active site flexibility, dihydrofolate reductase and large collective movements, CDK2. The performance of the RCEs was validated by distinguishing known ligands of dihydrofolate reductase and CDK2 among a dataset of diverse chemical decoys. Results \& discussion: Our results show that different simulation protocols can be efficient for generation of RCEs depending on different kind of protein flexibility

Comprehensive in silico analysis of the TDP-43 protein variants related to Amyotrophic Lateral Sclerosis and Frontotemporal Dementia: Abrangente na análise silicoanalítica das variantes proteicas TDP-43 relacionadas à Esclerose Lateral Amiotrófica e Demência Frontotempor

Amyotrophic lateral sclerosis (ALS) is a highly disabling neurodegenerative disorder characterized by the progressive loss of voluntary motor activity. ALS is currently the most frequent adult-onset motor neuron disorder, which is associated with a major economic burden. Two drugs have already been approved to treat ALS, but they confer a limited survival benefit. In turn, frontotemporal dementia (FTD) is an early-onset and fatal dementia characterized by deficits in behavior, language, and executive function. FTD is the most frequent cause of pre-senile dementia after Alzheimer's. Currently, FTD has no cure and the available treatments are merely symptomatic. Missense mutations in TDP-43, a nuclear RNA/DNA-binding protein, are among the main causes associated with ALS and FTD. Nonetheless, most of these mutations are not yet characterized. To date, no complete three-dimensional structure has already been determined for TDP-43. In this work, we characterized the impact of missense mutations in TDP-43 using prediction algorithms, evolutionary conservation analysis, and molecular dynamics simulations (MD). We also performed structural modeling and validation of the TDP-43 protein. Two hundred and seven TDP-43 mutations were compiled from the databases and literature. The predictive analysis pointed to a moderate rate of deleterious and destabilizing mutations. Furthermore, most mutations occur at evolutionarily variable positions. Combining the predictive analyses into a penalty system, our findings suggested that the uncharacterized mutations Y43C, D201Y, F211S, I222T, K224N, A260D, P262T, and A321D are considered the most-likely deleterious, thus being important targets for future investigation. This work also provided an accurate, complete, and unprecedented three-dimensional structure for TDP-43 that can be used to identify and optimize potential drug candidates. At last, our MD findings pointed to a noticeable flexibility increase in functional domains upon K263E, G335D, M337V, and Q343R variants, which may cause non-native interactions and impaired TDP-43 recognition, ultimately leading to protein aggregation

Structural Prediction of Protein–Protein Interactions by Docking: Application to Biomedical Problems

A huge amount of genetic information is available thanks to the recent advances in sequencing technologies and the larger computational capabilities, but the interpretation of such genetic data at phenotypic level remains elusive. One of the reasons is that proteins are not acting alone, but are specifically interacting with other proteins and biomolecules, forming intricate interaction networks that are essential for the majority of cell processes and pathological conditions. Thus, characterizing such interaction networks is an important step in understanding how information flows from gene to phenotype. Indeed, structural characterization of protein–protein interactions at atomic resolution has many applications in biomedicine, from diagnosis and vaccine design, to drug discovery. However, despite the advances of experimental structural determination, the number of interactions for which there is available structural data is still very small. In this context, a complementary approach is computational modeling of protein interactions by docking, which is usually composed of two major phases: (i) sampling of the possible binding modes between the interacting molecules and (ii) scoring for the identification of the correct orientations. In addition, prediction of interface and hot-spot residues is very useful in order to guide and interpret mutagenesis experiments, as well as to understand functional and mechanistic aspects of the interaction. Computational docking is already being applied to specific biomedical problems within the context of personalized medicine, for instance, helping to interpret pathological mutations involved in protein–protein interactions, or providing modeled structural data for drug discovery targeting protein–protein interactions.Spanish Ministry of Economy grant number BIO2016-79960-R; D.B.B. is supported by a predoctoral fellowship from CONACyT; M.R. is supported by an FPI fellowship from the Severo Ochoa program. We are grateful to the Joint BSC-CRG-IRB Programme in Computational Biology.Peer ReviewedPostprint (author's final draft

IN SILICO PROBING OF ANTI-ARTHRITIC POTENTIAL OF TRADITIONALLY FERMENTED AYURVEDIC POLYHERBAL PRODUCT BALARISHTA REVEALS LUPEOL AND DESULPHOSINIGRIN AS EFFICIENT INTERACTING COMPONENTS WITH UREC

Objective: To assess the anti-arthritic properties of Balarishta, an Ayurvedic fermented poly herbal product used to combat the immunological disorder, Rheumatoid Arthritis which is an autoimmune disease triggered by Proteus urinary tract infection through in silico analysis and assay of antimicrobial activity. Methods: Antibacterial activity of Balarishta against Proteus mirabilis was assessed. Phytochemical analysis was performed by Gas Chromatography-Mass Spectroscopy. Urease interaction proteins were homology modeled based on template constraints and physicochemical parameters and stereo chemical nature of the proteins were analyzed. Rigid and flexible docking was done to study the hydrogen bond interaction patterns between active ingredients of Balarishta and urease interaction proteins. Results: In Balarishta, 42 bioactive metabolites were identified by Gas Chromatography-Mass Spectroscopy analysis. These metabolites were checked for strong binding affinities against urease subunits and urease accessory proteins of Proteus mirabilis in silico. ureC subunit exhibited high binding to the compound desulphosinigrin (-10.5217 Kcal/mol) followed by lupeol (-10.0308 Kcal/mol) with conserved residue interaction ranging from amino acid residues 308 – 327. Further, lupeol when bound to ureC had 4 hydrogen bonds as compared to desulphosinigrin with 6 hydrogen bonds. Free energy calculations based on flexible docking showed that lupeol had significant binding affinity for ureC with -9.2 Kcal/mol rather than -6.0 Kcal/mol for desulphosinigrin. Both binding has residue conservation - Cys 319, His 320 and His 321. The results corroborated with in vitro antibacterial activity. Conclusion: It is proposed that Balarishta would be efficient in arresting Rheumatoid Arthritis complicated urinary tract infections

Benchmarking of protein descriptor sets in proteochemometric modeling (part 2): modeling performance of 13 amino acid descriptor sets.

Background While a large body of work exists on comparing and benchmarking descriptors of molecular structures, a similar comparison of protein descriptor sets is lacking. Hence, in the current work a total of 13 amino acid descriptor sets have been benchmarked with respect to their ability of establishing bioactivity models. The descriptor sets included in the study are Z-scales (3 variants), VHSE, T-scales, ST-scales, MS-WHIM, FASGAI, BLOSUM, a novel protein descriptor set (termed ProtFP (4 variants)), and in addition we created and benchmarked three pairs of descriptor combinations. Prediction performance was evaluated in seven structure-activity benchmarks which comprise Angiotensin Converting Enzyme (ACE) dipeptidic inhibitor data, and three proteochemometric data sets, namely (1) GPCR ligands modeled against a GPCR panel, (2) enzyme inhibitors (NNRTIs) with associated bioactivities against a set of HIV enzyme mutants, and (3) enzyme inhibitors (PIs) with associated bioactivities on a large set of HIV enzyme mutants. Results The amino acid descriptor sets compared here show similar performance ( 0.3 log units RMSE difference and >0.7 difference in MCC). Combining different descriptor sets generally leads to better modeling performance than utilizing individual sets. The best performers were Z-scales (3) combined with ProtFP (Feature), or Z-Scales (3) combined with an average Z-Scale value for each target, while ProtFP (PCA8), ST-Scales, and ProtFP (Feature) rank last. Conclusions While amino acid descriptor sets capture different aspects of amino acids their ability to be used for bioactivity modeling is still – on average – surprisingly similar. Still, combining sets describing complementary information consistently leads to small but consistent improvement in modeling performance (average MCC 0.01 better, average RMSE 0.01 log units lower). Finally, performance differences exist between the targets compared thereby underlining that choosing an appropriate descriptor set is of fundamental for bioactivity modeling, both from the ligand- as well as the protein side

Homology modeling and in silico characterization of synaptotagmin 1 (SYT1) protein from Arabidopsis thaliana (L.) Heynh.

Synaptotagmins are a group of C2 domain containing proteins which playimportant roles in vesicle trafficking in synaptic vesicles of animals. With the advent ofplant genome sequencing, many synaptotagmins are also discovered from plants and theyhave been found to perform many crucial physiological roles in plants. The model plant Arabidopsis thaliana (L.) Heynh. contains five synaptotagmins of which SYT1 isresponsible for many important functions including maintenance of plasma membraneintegrity and cell viability. In this study, the three dimensional structure of this proteinhas been developed in silico by homology modeling method to collect knowledge aboutits structure-function relationship. Additionally, the interaction of calcium ion with thisprotein was studied. The structures of C2 domains are somewhat different from the known animal synaptotagmins. The protein contains many phosphorylation sites whichindicate that SYT1 is part of one or more signaling cascades

eMatchSite: Sequence Order-Independent Structure Alignments of Ligand Binding Pockets in Protein Models

© 2014 Michal Brylinski. Detecting similarities between ligand binding sites in the absence of global homology between target proteins has been recognized as one of the critical components of modern drug discovery. Local binding site alignments can be constructed using sequence order-independent techniques, however, to achieve a high accuracy, many current algorithms for binding site comparison require high-quality experimental protein structures, preferably in the bound conformational state. This, in turn, complicates proteome scale applications, where only various quality structure models are available for the majority of gene products. To improve the state-of-the-art, we developed eMatchSite, a new method for constructing sequence order-independent alignments of ligand binding sites in protein models. Large-scale benchmarking calculations using adenine-binding pockets in crystal structures demonstrate that eMatchSite generates accurate alignments for almost three times more protein pairs than SOIPPA. More importantly, eMatchSite offers a high tolerance to structural distortions in ligand binding regions in protein models. For example, the percentage of correctly aligned pairs of adenine-binding sites in weakly homologous protein models is only 4–9% lower than those aligned using crystal structures. This represents a significant improvement over other algorithms, e.g. the performance of eMatchSite in recognizing similar binding sites is 6% and 13% higher than that of SiteEngine using high- and moderate-quality protein models, respectively. Constructing biologically correct alignments using predicted ligand binding sites in protein models opens up the possibility to investigate drug-protein interaction networks for complete proteomes with prospective systems-level applications in polypharmacology and rational drug repositioning. eMatchSite is freely available to the academic community as a web-server and a stand-alone software distribution at http://www.brylinski.org/ematchsite