scholarly journals Towards Increasing the Clinical Relevance of In Silico Methods to Predict Pathogenic Missense Variants

2016 ◽  
Vol 12 (5) ◽  
pp. e1004725 ◽  
Author(s):  
David L. Masica ◽  
Rachel Karchin
Keyword(s):  
In Silico ◽  
Clinical Relevance ◽  
Missense Variants ◽  
In Silico Methods

Elucidation of Phosphodiesterase-1 Inhibitory Effect of Some Selected Natural Polyphenolics Using In Vitro and In Silico Methods

2016 ◽  
Vol 17 (4) ◽  
pp. 412-417 ◽  
Author(s):  
Abdur Rauf ◽  
Ilkay Erdogan Orhan ◽  
Abdulselam Ertas ◽  
Hamdi Temel ◽  
Taibi Ben Hadda ◽  
...  
Keyword(s):  
In Silico ◽  
Inhibitory Effect ◽  
In Silico Methods

Prediction of Drug-Drug Interactions Related to Inhibition or Induction of Drug-Metabolizing Enzymes

2019 ◽  
Vol 19 (5) ◽  
pp. 319-336 ◽  
Author(s):  
Alexander V. Dmitriev ◽  
Alexey A. Lagunin ◽  
Dmitry А. Karasev ◽  
Anastasia V. Rudik ◽  
Pavel V. Pogodin ◽  
...  
Keyword(s):  
In Silico ◽  
Computational Models ◽  
Field Analysis ◽  
Pharmaceutical Chemistry ◽  
Sources Of Information ◽  
Metabolizing Enzymes ◽  
Physiologically Based Pharmacokinetic ◽  
Pharmacophore Models ◽  
Drug Metabolising Enzymes ◽  
In Silico Methods

Drug-drug interaction (DDI) is the phenomenon of alteration of the pharmacological activity of a drug(s) when another drug(s) is co-administered in cases of so-called polypharmacy. There are three types of DDIs: pharmacokinetic (PK), pharmacodynamic, and pharmaceutical. PK is the most frequent type of DDI, which often appears as a result of the inhibition or induction of drug-metabolising enzymes (DME). In this review, we summarise in silico methods that may be applied for the prediction of the inhibition or induction of DMEs and describe appropriate computational methods for DDI prediction, showing the current situation and perspectives of these approaches in medicinal and pharmaceutical chemistry. We review sources of information on DDI, which can be used in pharmaceutical investigations and medicinal practice and/or for the creation of computational models. The problem of the inaccuracy and redundancy of these data are discussed. We provide information on the state-of-the-art physiologically- based pharmacokinetic modelling (PBPK) approaches and DME-based in silico methods. In the section on ligand-based methods, we describe pharmacophore models, molecular field analysis, quantitative structure-activity relationships (QSAR), and similarity analysis applied to the prediction of DDI related to the inhibition or induction of DME. In conclusion, we discuss the problems of DDI severity assessment, mention factors that influence severity, and highlight the issues, perspectives and practical using of in silico methods.

An Update of In Silico Tools for the Prediction of Pathogenesis in Missense Variants

2011 ◽  
Vol 6 (2) ◽  
pp. 185-198
Author(s):  
Alejandro j. Brea-Fernandez ◽  
Marta Ferro ◽  
Ceres Fernandez-Rozadilla ◽  
Ana Blanco ◽  
Laura Fachal ◽  
...  
Keyword(s):  
In Silico ◽  
Missense Variants ◽  
In Silico Tools

scholarly journals Energetics of LOHC: Structure-Property Relationships from Network of Thermochemical Experiments and in Silico Methods

Hydrogen ◽  
2021 ◽  
Vol 2 (1) ◽  
pp. 101-121
Author(s):  
Sergey P. Verevkin ◽  
Vladimir N. Emel’yanenko ◽  
Riko Siewert ◽  
Aleksey A. Pimerzin
Keyword(s):  
In Silico ◽  
Quantum Chemical ◽  
Structure Property ◽  
Chemical Methods ◽  
Key Technology ◽  
Structure Property Relationships ◽  
Quantum Chemical Methods ◽  
Dehydrogenation Reactions ◽  
Storage Technologies ◽  
In Silico Methods

The storage of hydrogen is the key technology for a sustainable future. We developed an in silico procedure, which is based on the combination of experimental and quantum-chemical methods. This method was used to evaluate energetic parameters for hydrogenation/dehydrogenation reactions of various pyrazine derivatives as a seminal liquid organic hydrogen carriers (LOHC), that are involved in the hydrogen storage technologies. With this in silico tool, the tempo of the reliable search for suitable LOHC candidates will accelerate dramatically, leading to the design and development of efficient materials for various niche applications.

scholarly journals Predicting Absorption-Distribution Properties of Neuroprotective Phosphine-Borane Compounds Using In Silico Modeling and Machine Learning

Molecules ◽  
2021 ◽  
Vol 26 (9) ◽  
pp. 2505
Author(s):  
Raheem Remtulla ◽  
Sanjoy Kumar Das ◽  
Leonard A. Levin
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
In Silico ◽  
Disulfide Bonds ◽  
Oral Absorption ◽  
In Vivo Studies ◽  
Drug Candidates ◽  
In Silico Methods

Phosphine-borane complexes are novel chemical entities with preclinical efficacy in neuronal and ophthalmic disease models. In vitro and in vivo studies showed that the metabolites of these compounds are capable of cleaving disulfide bonds implicated in the downstream effects of axonal injury. A difficulty in using standard in silico methods for studying these drugs is that most computational tools are not designed for borane-containing compounds. Using in silico and machine learning methodologies, the absorption-distribution properties of these unique compounds were assessed. Features examined with in silico methods included cellular permeability, octanol-water partition coefficient, blood-brain barrier permeability, oral absorption and serum protein binding. The resultant neural networks demonstrated an appropriate level of accuracy and were comparable to existing in silico methodologies. Specifically, they were able to reliably predict pharmacokinetic features of known boron-containing compounds. These methods predicted that phosphine-borane compounds and their metabolites meet the necessary pharmacokinetic features for orally active drug candidates. This study showed that the combination of standard in silico predictive and machine learning models with neural networks is effective in predicting pharmacokinetic features of novel boron-containing compounds as neuroprotective drugs.

scholarly journals Glucocorticoid resistance conferring mutation in the C-terminus of GR alters the receptor conformational dynamics

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Anna Kaziales ◽  
Florian Rührnößl ◽  
Klaus Richter
Keyword(s):  
Glucocorticoid Receptor ◽  
Steroid Hormones ◽  
In Silico ◽  
Conformational Dynamics ◽  
Glucocorticoid Resistance ◽  
Physiological Processes ◽  
C Terminus ◽  
Hsp90 Chaperone ◽  
In Silico Methods

AbstractThe glucocorticoid receptor is a key regulator of essential physiological processes, which under the control of the Hsp90 chaperone machinery, binds to steroid hormones and steroid-like molecules and in a rather complicated and elusive response, regulates a set of glucocorticoid responsive genes. We here examine a human glucocorticoid receptor variant, harboring a point mutation in the last C-terminal residues, L773P, that was associated to Primary Generalized Glucocorticoid Resistance, a condition originating from decreased affinity to hormone, impairing one or multiple aspects of GR action. Using in vitro and in silico methods, we assign the conformational consequences of this mutation to particular GR elements and report on the altered receptor properties regarding its binding to dexamethasone, a NCOA-2 coactivator-derived peptide, DNA, and importantly, its interaction with the chaperone machinery of Hsp90.

scholarly journals Antimicrobial Effects of Potential Probiotics of Bacillus spp. Isolated from Human Microbiota: In Vitro and In Silico Methods

2021 ◽  
Vol 9 (8) ◽  
pp. 1615
Author(s):  
Alfonso Torres-Sánchez ◽  
Jesús Pardo-Cacho ◽  
Ana López-Moreno ◽  
Ángel Ruiz-Moreno ◽  
Klara Cerk ◽  
...  
Keyword(s):  
Gut Microbiota ◽  
In Silico ◽  
Ecological Impacts ◽  
Bacillus Species ◽  
Human Gut ◽  
Contaminant Exposure ◽  
Antimicrobial Effects ◽  
Human Gut Microbiota ◽  
Bacillus Spp ◽  
In Silico Methods

The variable taxa components of human gut microbiota seem to have an enormous biotechnological potential that is not yet well explored. To investigate the usefulness and applications of its biocompounds and/or bioactive substances would have a dual impact, allowing us to better understand the ecology of these microbiota consortia and to obtain resources for extended uses. Our research team has obtained a catalogue of isolated and typified strains from microbiota showing resistance to dietary contaminants and obesogens. Special attention was paid to cultivable Bacillus species as potential next-generation probiotics (NGP) together with their antimicrobial production and ecological impacts. The objective of the present work focused on bioinformatic genome data mining and phenotypic analyses for antimicrobial production. In silico methods were applied over the phylogenetically closest type strain genomes of the microbiota Bacillus spp. isolates and standardized antimicrobial production procedures were used. The main results showed partial and complete gene identification and presence of polyketide (PK) clusters on the whole genome sequences (WGS) analysed. Moreover, specific antimicrobial effects against B. cereus, B. circulans, Staphylococcus aureus, Streptococcus pyogenes, Escherichia coli, Serratia marcescens, Klebsiella spp., Pseudomonas spp., and Salmonella spp. confirmed their capacity of antimicrobial production. In conclusion, Bacillus strains isolated from human gut microbiota and taxonomic group, resistant to Bisphenols as xenobiotics type endocrine disruptors, showed parallel PKS biosynthesis and a phenotypic antimicrobial effect. This could modulate the composition of human gut microbiota and therefore its functionalities, becoming a predominant group when high contaminant exposure conditions are present.

Sign in / Sign up

Export Citation Format

Share Document