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Molecular Informatics

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https://www.readbyqxmd.com/read/29984527/high-throughput-docking-and-molecular-dynamics-simulations-towards-the-identification-of-novel-peptidomimetic-inhibitors-against-cdc7
#1
Farahnaz Rezaei Makhouri, Jahan B Ghasemi
Inhibition protein-protein interactions (PPIs) using small molecules, that interfere with the formation of these complexes, modulates critical regulatory pathways and has therapeutic significance. DBF4-dependent kinase CDC7 is the S-phase checkpoint pathway target, which plays an important role for a proper response to DNA damage and replicative stress in multiple organisms. Overexpression of CDC7 and its protein regulator DBF4 is highly neurotoxic and promotes cancer and neurodegeneration. In the present study, virtual screening of inhibitor scaffolds mimicking DBF4 pharmacophoric properties was carried out and evaluation of their potential inhibitory activity toward CDC7 was performed using high-throughput docking and molecular dynamics simulations...
July 9, 2018: Molecular Informatics
https://www.readbyqxmd.com/read/29971949/predictive-models-for-homo-and-lumo-energies-of-n-donor-heterocycles-as-ligands-for-lanthanides-separation
#2
Vitaly P Solov'ev, Yuri A Ustynyuk, Nelly I Zhokhova, Kirill V Karpov
Quantum chemical calculations combined with QSPR methodology reveal challenging perspectives for the solution of a number of fundamental and applied problems. In this work, we performed the PM7 and DFT calculations and QSPR modeling of HOMO and LUMO energies for polydentate N-heterocyclic ligands promising for the extraction separation of lanthanides because these values are related to the ligands selectivity in the respect to the target cations. Data for QSPR modeling comprised the PM7 calculated HOMO and LUMO energies of N-donor heterocycles, including several types of both known and virtual undescribed polydentate ligands...
July 4, 2018: Molecular Informatics
https://www.readbyqxmd.com/read/29927068/docking-of-covalent-ligands-challenges-and-approaches
#3
REVIEW
Christoph Sotriffer
Covalent ligands have recently regained considerable attention in drug discovery. The rational design of such ligands, however, is still faced with particular challenges, mostly related to the fact that covalent bond formation is a quantum mechanical phenomenon which cannot adequately be handled by the force fields or empirical approaches typically used for noncovalent protein-ligand interactions. Although the necessity for quantum chemical approaches is clear, they cannot yet routinely be applied on large data sets of ligands or for a broader exploration of binding modes in docking calculations...
June 21, 2018: Molecular Informatics
https://www.readbyqxmd.com/read/29901257/machine-learning-classification-models-to-improve-the-docking-based-screening-a-case-of-pi3k-tankyrase-inhibitors
#4
Vladimir P Berishvili, Andrew E Voronkov, Eugene V Radchenko, Vladimir A Palyulin
One of the major challenges in the current drug discovery is the improvement of the docking-based virtual screening performance. It is especially important in the rational design of compounds with desired polypharmacology or selectivity profiles. To address this problem, we present a methodology for the development of target-specific scoring functions possessing high screening power. These scoring functions were built using the machine learning methods for the dual target inhibitors of PI3Kα and tankyrase, promising targets for colorectal cancer therapy...
June 14, 2018: Molecular Informatics
https://www.readbyqxmd.com/read/29882343/development-of-ligand-based-big-data-deep-neural-network-models-for-virtual-screening-of-large-compound-libraries
#5
Tao Xiao, Xingxing Qi, Yuzong Chen, Yuyang Jiang
High-performance ligand-based virtual screening (VS) models have been developed using various computational methods, including the deep neural network (DNN) method. There are high expectations for exploration of the advanced capabilities of DNN to improve VS performance, and this capability has been optimally achieved using large data training datasets. However, their ability to screen large compound libraries has not been evaluated. There is a need for developing and evaluating ligand-based large data DNN VS models for large compound libraries...
June 8, 2018: Molecular Informatics
https://www.readbyqxmd.com/read/29797496/polypharmacological-drug-target-inference-for-chemogenomics
#6
Petra Schneider, Gisbert Schneider
Pharmacological drug actions are often caused by multi-target effects. While most of the currently approved synthetic drugs were designed to interact with a single 'on-target', these chemical agents often interact with additional 'off-targets'. Understanding and rationalizing these multiple interactions will be indispensable for the design of future precision medicines. We employed computational predictions of drug-target interactions to analyze functional drug-drug relationships. 900 approved drugs were represented in terms of their predicted activity fingerprints, considering 1158 potential target activities...
May 24, 2018: Molecular Informatics
https://www.readbyqxmd.com/read/29791068/cdpbc-a-software-application-for-estimation-of-concentration-dependent-plasma-binding-capacity-of-small-molecule
#7
Om Prakash, Upendra Nath Dwivedi
Drug-plasma binding (DPB) is an important aspect during pharmacokinetics (PK) studies. DPB of small molecule cannot be evaluated through computational means. Here we present CDPBC; a standalone application for evaluation of small molecule for its capacity (concentration dependent) of binding with plasma proteins. This application is freely available at URL (https://github.com/undwive di/CDPBC.git). The application is enriched with evaluation of five major proteins of plasma. Input for application is a docked complex against the suggested PDBs of plasma proteins...
May 23, 2018: Molecular Informatics
https://www.readbyqxmd.com/read/29774657/cheminformatics-in-drug-discovery-an-industrial-perspective
#8
REVIEW
Hongming Chen, Thierry Kogej, Ola Engkvist
Cheminformatics has established itself as a core discipline within large scale drug discovery operations. It would be impossible to handle the amount of data generated today in a small molecule drug discovery project without persons skilled in cheminformatics. In addition, due to increased emphasis on "Big Data", machine learning and artificial intelligence, not only in the society in general, but also in drug discovery, it is expected that the cheminformatics field will be even more important in the future...
May 18, 2018: Molecular Informatics
https://www.readbyqxmd.com/read/29756682/design-strategy-of-multi-electron-transfer-catalysts-based-on-a-bioinformatic-analysis-of-oxygen-evolution-and-reduction-enzymes
#9
Hideshi Ooka, Kazuhito Hashimoto, Ryuhei Nakamura
Understanding the design strategy of photosynthetic and respiratory enzymes is important to develop efficient artificial catalysts for oxygen evolution and reduction reactions. Here, based on a bioinformatic analysis of cyanobacterial oxygen evolution and reduction enzymes (photosystem II: PS II and cytochrome c oxidase: COX, respectively), the gene encoding the catalytic D1 subunit of PS II was found to be expressed individually across 38 phylogenetically diverse strains, which is in contrast to the operon structure of the genes encoding major COX subunits...
May 14, 2018: Molecular Informatics
https://www.readbyqxmd.com/read/29749713/antimalarial-mode-of-action-amma-database-data-selection-verification-and-chemical-space-analysis
#10
Pavel Sidorov, Elisabeth Davioud-Charvet, Gilles Marcou, Dragos Horvath, Alexandre Varnek
This paper presents the effort of collecting and curating a data set of 15461 molecules tested against the malaria parasite, with robust activity and mode of action annotations. The set is compiled from in-house experimental data and the public ChEMBL database subsets. We illustrate the usefulness of the dataset by building QSAR models for antimalarial activity and QSPR models for modes of actions, as well as by the analysis of the chemical space with the Generative Topographic Mapping method. The GTM models perform well in prediction of both activity and mode of actions, on par with the classical SVM methods...
May 11, 2018: Molecular Informatics
https://www.readbyqxmd.com/read/29733509/cheminformatics-driven-development-of-novel-therapies-for-drug-resistant-prostate-cancer
#11
Fuqiang Ban, Kush Dalal, Eric LeBlanc, Hélène Morin, Paul S Rennie, Artem Cherkasov
Androgen receptor (AR) is a master regulator of prostate cancer (PCa), and therefore is a pivotal drug target for the treatment of PCa including its castration-resistance form (CRPC). The development of acquired resistance is a major challenge in the use of the current antiandrogens. The recent advancements in inhibiting AR activity with small molecules specifically designed to target areas distinct from the receptor's androgen binding site are carefully discussed. Our new classes of AR inhibitors of AF2 and BF3 functional sites and DBD domains designed using cheminformatics techniques are promising to circumvent various AR-dependent resistance mechanisms...
May 7, 2018: Molecular Informatics
https://www.readbyqxmd.com/read/29683269/multiple-machine-learning-based-chemoinformatics-models-for-identification-of-histone-acetyl-transferase-inhibitors
#12
Shagun Krishna, Sushil Kumar, Deependra Kumar Singh, Amar Deep Lakra, Dibyendu Banerjee, Mohammad Imran Siddiqi
The histone acetyl transferase (HAT) are involved in acetylation of histones that lead to transcription activation in numerous gene regulatory mechanisms. There are very few GCN5 HAT inhibitors reported despite of their role in cancer progression. In this study, we have utilized in-silico virtual screening approaches based on various machine learning algorithm to identify potent inhibitors of GCN5 HAT from commercially available Maybridge library. We have generated predictive chemoinformatics models based on k-Nearest neighbour, naïve Bayesian, Random Forest and Support Vector Machine...
April 23, 2018: Molecular Informatics
https://www.readbyqxmd.com/read/29673107/could-adenosine-recognize-its-receptors-with-a-stoichiometry-other-than-1-1
#13
Giuseppe Deganutti, Veronica Salmaso, Stefano Moro
One of the most largely accepted concepts in the G protein-coupled receptors (GPCRs) field is that the ligand, either agonist or antagonist, recognizes its receptor with a stoichiometry of 1 : 1. Recent experimental evidence, reporting ternary complexes formed by GPCR:orthosteric: allosteric ligands, has complicated the ligand-receptor 1 : 1 binding scenario. Molecular modeling simulations have been used to retrieve insights on the whole ligand-receptor recognition process, beyond information on the final bound state provided by experimental techniques...
April 19, 2018: Molecular Informatics
https://www.readbyqxmd.com/read/29377626/tox21-enricher-web-based-chemical-biological-functional-annotation-analysis-tool-based-on-tox21-toxicity-screening-platform
#14
Junguk Hur, Larson Danes, Jui-Hua Hsieh, Brett McGregor, Dakota Krout, Scott Auerbach
The US Toxicology Testing in the 21st Century (Tox21) program was established to develop more efficient and human-relevant toxicity assessment methods. The Tox21 program screens >10,000 chemicals using quantitative high-throughput screening (qHTS) of assays that measure effects on toxicity pathways. To date, more than 70 assays have yielded >12 million concentration-response curves. The patterns of activity across assays can be used to define similarity between chemicals. Assuming chemicals with similar activity profiles have similar toxicological properties, we may infer toxicological properties based on its neighbourhood...
May 2018: Molecular Informatics
https://www.readbyqxmd.com/read/29134756/development-of-predictive-qsar-models-of-4-thiazolidinones-antitrypanosomal-activity-using-modern-machine-learning-algorithms
#15
Anna Kryshchyshyn, Oleg Devinyak, Danylo Kaminskyy, Philippe Grellier, Roman Lesyk
This paper presents novel QSAR models for the prediction of antitrypanosomal activity among thiazolidines and related heterocycles. The performance of four machine learning algorithms: Random Forest regression, Stochastic gradient boosting, Multivariate adaptive regression splines and Gaussian processes regression have been studied in order to reach better levels of predictivity. The results for Random Forest and Gaussian processes regression are comparable and outperform other studied methods. The preliminary descriptor selection with Boruta method improved the outcome of machine learning methods...
May 2018: Molecular Informatics
https://www.readbyqxmd.com/read/29116686/protocols-for-the-design-of-kinase-focused-compound-libraries
#16
Edgar Jacoby, Berthold Wroblowski, Christophe Buyck, Jean-Marc Neefs, Christophe Meyer, Maxwell D Cummings, Herman van Vlijmen
Protocols for the design of kinase-focused compound libraries are presented. Kinase-focused compound libraries can be differentiated based on the design goal. Depending on whether the library should be a discovery library specific for one particular kinase, a general discovery library for multiple distinct kinase projects, or even phenotypic screening, there exists today a variety of in silico methods to design candidate compound libraries. We address the following scenarios: 1) Datamining of SAR databases and kinase focused vendor catalogues; 2) Predictions and virtual screening; 3) Structure-based design of combinatorial kinase inhibitors; 4) Design of covalent kinase inhibitors; 5) Design of macrocyclic kinase inhibitors; and 6) Design of allosteric kinase inhibitors and activators...
May 2018: Molecular Informatics
https://www.readbyqxmd.com/read/29024508/importance-of-an-orchestrate-participation-of-each-individual-residue-present-at-a-catalytic-site
#17
Santanu Das
GTP hydrolysis is indispensable to keep a living cell healthy. Nature has evolved so many enzymes to enhance the slow GTP hydrolysis. Rab GTPases are evolved to regulate vesicle trafficking. GTPase activating proteins (GAPs) accelerates their intrinsic slow GTP hydrolysis in order to maintain the sustainability between cellular events. Any malfunction/interference in this hydrolysis disrupts normal cellular events and causes severe diseases. In this study, GTP hydrolysis mechanism of Rab33B catalyzed by TBC-domain GAP protein Gyp1p has been decoded using extensive ab initio QM/MM metadynamics simulations...
May 2018: Molecular Informatics
https://www.readbyqxmd.com/read/29112332/unbinding-of-kinesin-from-microtubule-in-the-strongly-bound-states-enhances-under-assisting-forces
#18
Hamidreza Khataee, Solmaz Naseri, Yongmin Zhong, Alan Wee-Chung Liew
The ability to predict the cellular dynamics of intracellular transport has enormous potential to impact human health. A key transporter is kinesin-1, an ATP-driven molecular motor that shuttles cellular cargos along microtubules (MTs). The dynamics of kinesins depends critically on their unbinding rate from MT, which varies depending on the force direction applied on the motor, i.e. the force-unbinding rate relation is asymmetric. However, it remains unclear how changing the force direction from resisting (applied against the motion direction) to assisting (applied in the motion direction) alters the kinesin's unbinding and stepping...
April 2018: Molecular Informatics
https://www.readbyqxmd.com/read/29106077/in-silico-studies-of-mammalian-%C3%AE-alad-interactions-with-selenides-and-selenoxides
#19
Pablo Andrei Nogara, João Batista Teixeira Rocha
Previous studies have shown that the mammalian δ-aminolevulinic acid dehydratase (δ-ALAD) is inhibited by selenides and selenoxides, which can involve thiol oxidation. However, the precise molecular interaction of selenides and selenoxides with the active center of the enzyme is unknown. Here, we try to explain the interaction of selenides and the respective selenoxides with human δ-ALAD by in silico molecular docking. The in silico data indicated that Se atoms of selenoxides have higher electrophilic character than their respective selenides...
April 2018: Molecular Informatics
https://www.readbyqxmd.com/read/29106044/an-improved-binary-differential-evolution-algorithm-for-feature-selection-in-molecular-signatures
#20
X S Zhao, L L Bao, Q Ning, J C Ji, X W Zhao
The discovery of biomarkers from high-dimensional data is a very challenging task in cancer diagnoses. On the one hand, biomarker discovery is the so-called high-dimensional small-sample problem. On the other hand, these data are redundant and noisy. In recent years, biomarker discovery from high-throughput biological data has become an increasingly important emerging topic in the field of bioinformatics. In this study, we propose a binary differential evolution algorithm for feature selection. Firstly, we suggest using a two-stage approach, where three filter methods including the Fisher score, T-statistics, and Information gain are used to generate the feature pool for input to differential evolution (DE)...
April 2018: Molecular Informatics
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