Insights into the Molecular Mechanisms of Protein Platination from a Case Study:  The Reaction of Anticancer Platinum(II) Iminoethers with Horse Heart Cytochromec†

Biochemistry ◽  
2007 ◽  
Vol 46 (43) ◽  
pp. 12220-12230 ◽  
Author(s):  
Angela Casini ◽  
Chiara Gabbiani ◽  
Guido Mastrobuoni ◽  
Raffaella Zoe Pellicani ◽  
Francesco Paolo Intini ◽  
...  
Keyword(s):  
Molecular Mechanisms

scholarly journals Rehabilitation of Patients with Severe Disability after coVID-19 in Rehabilitation Department. Multiple Case Study

2021 ◽  
Vol 20 (3) ◽  
pp. 16-25
Author(s):  
Vladimir E. Vladimirsky ◽  
Evgeniy V. Vladimirsky ◽  
Anna N. Lunina ◽  
Anatoliy D. Fesyun ◽  
Andrey P. Rachin ◽  
...  
Keyword(s):  
Physical Activity ◽  
Cardiovascular System ◽  
Molecular Mechanisms ◽  
Physical Exertion ◽  
Metabolic Demand ◽  
Scientific Publications ◽  
Beneficial Effects ◽  
Physical Exercises ◽  
Multiple Case

The review analyzes the data of scientific publications on the effects of molecular mechanisms initiated by physical exertion on thefunction of the cardiovascular system and the course of cardiac diseases. As practice and a number of evidence-based studies haveshown, the beneficial effects of physical activity on the outcomes of diseases in a number of cardiac nosologies are comparable todrug treatment. Numerous mechanisms mediate the benefits of regular exercise for optimal cardiovascular function. Exercises causewidespread changes in numerous cells, tissues, and organs in response to increased metabolic demand, including adaptation of thecardiovascular system. Physical exercises, which include various types of aerobic exercises of varying intensity and duration, is animportant component of the therapeutic treatment of patients with cardiovascular diseases. Knowledge of the molecular basis ofthe physical activity impact on the cardiovascular system makes it possible to use biochemical markers to assess the effectiveness ofrehabilitation programs.

scholarly journals Highlighting Human Enzymes Active in Different Metabolic Pathways and Diseases: The Case Study of EC 1.2.3.1 and EC 2.3.1.9

Biomedicines ◽  
2020 ◽  
Vol 8 (8) ◽  
pp. 250
Author(s):  
Giulia Babbi ◽  
Davide Baldazzi ◽  
Castrense Savojardo ◽  
Martelli Pier Luigi ◽  
Rita Casadio
Keyword(s):  
Metabolic Pathways ◽  
Molecular Mechanisms ◽  
Mitochondrial Gene ◽  
Enzyme Commission Number ◽  
Multiple Roles ◽  
Acetyl Coa ◽  
Functional Activities ◽  
Interaction Sites ◽  
Semialdehyde Dehydrogenase

Enzymes are key proteins performing the basic functional activities in cells. In humans, enzymes can be also responsible for diseases, and the molecular mechanisms underlying the genotype to phenotype relationship are under investigation for diagnosis and medical care. Here, we focus on highlighting enzymes that are active in different metabolic pathways and become relevant hubs in protein interaction networks. We perform a statistics to derive our present knowledge on human metabolic pathways (the Kyoto Encyclopaedia of Genes and Genomes (KEGG)), and we found that activity aldehyde dehydrogenase (NAD(+)), described by Enzyme Commission number EC 1.2.1.3, and activity acetyl-CoA C-acetyltransferase (EC 2.3.1.9) are the ones most frequently involved. By associating functional activities (EC numbers) to enzyme proteins, we found the proteins most frequently involved in metabolic pathways. With our analysis, we found that these proteins are endowed with the highest numbers of interaction partners when compared to all the enzymes in the pathways and with the highest numbers of predicted interaction sites. As specific enzyme protein test cases, we focus on Alpha-Aminoadipic Semialdehyde Dehydrogenase (ALDH7A1, EC 2.3.1.9) and Acetyl-CoA acetyltransferase, cytosolic and mitochondrial (gene products of ACAT2 and ACAT1, respectively; EC 2.3.1.9). With computational approaches we show that it is possible, by starting from the enzyme structure, to highlight clues of their multiple roles in different pathways and of putative mechanisms promoting the association of genes to disease.

scholarly journals Molecular mechanisms of superoxide production by complex III: A bacterial versus human mitochondrial comparative case study

2013 ◽  
Vol 1827 (11-12) ◽  
pp. 1332-1339 ◽  
Author(s):  
Pascal Lanciano ◽  
Bahia Khalfaoui-Hassani ◽  
Nur Selamoglu ◽  
Anna Ghelli ◽  
Michela Rugolo ◽  
...  
Keyword(s):  
Molecular Mechanisms ◽  
Superoxide Production ◽  
Complex Iii ◽  
Comparative Case Study

Molecular mechanisms in bio-geo-interactions: From a case study to general mechanisms

Geochemistry ◽  
2005 ◽  
Vol 65 ◽  
pp. 7-27 ◽  
Author(s):  
Erika Kothe ◽  
Hans Bergmann ◽  
Georg Büchel
Keyword(s):  
Molecular Mechanisms

scholarly journals Molecular mechanisms and hormonal regulation underpinning morphological dormancy: a case study using Apium graveolens (Apiaceae)

2021 ◽  
Author(s):  
Matthew Walker ◽  
Marta Pérez ◽  
Tina Steinbrecher ◽  
Frances Gawthrop ◽  
Iva Pavlović ◽  
...  
Keyword(s):  
Hormonal Regulation ◽  
Molecular Mechanisms ◽  
Apium Graveolens ◽  
Morphological Dormancy

scholarly journals An integrative machine learning approach to discovering multi-level molecular mechanisms of obesity using data from monozygotic twin pairs

2019 ◽  
Author(s):  
Milla Kibble ◽  
Suleiman A. Khan ◽  
Muhammad Ammad-ud-din ◽  
Sailalitha Bollepalli ◽  
Teemu Palviainen ◽  
...  
Keyword(s):  
Machine Learning ◽  
Molecular Mechanisms ◽  
Monozygotic Twin ◽  
R Package ◽  
Data Sets ◽  
Learning Approaches ◽  
Machine Learning Approach ◽  
Using Data ◽  
Genomic Methylation

AbstractWe combined clinical, cytokine, genomic, methylation and dietary data from 43 young adult monozygotic twin pairs (aged 22 – 36, 53% female), where 25 of the twin pairs were substantially weight discordant (delta BMI > 3kg/ m2). These measurements were originally taken as part of the TwinFat study, a substudy of The Finnish Twin Cohort study. These five large multivariate data sets (comprising 42, 71, 1587, 1605 and 63 variables, respectively) were jointly analysed using an integrative machine learning method called Group Factor Analysis (GFA) to offer new hypotheses into the multi-molecular-level interactions associated with the development of obesity. New potential links between cytokines and weight gain are identified, as well as associations between dietary, inflammatory and epigenetic factors. This encouraging case study aims to enthuse the research community to boldly attempt new machine learning approaches which have the potential to yield novel and unintuitive hypotheses. The source code of the GFA method is publically available as the R package GFA.

scholarly journals Navigating the Human Metabolome for Biomarker Identification and Design of Pharmaceutical Molecules

2011 ◽  
Vol 2011 ◽  
pp. 1-19 ◽  
Author(s):  
Irene Kouskoumvekaki ◽  
Gianni Panagiotou
Keyword(s):  
Drug Discovery ◽  
Molecular Mechanisms ◽  
Structural Similarity ◽  
Disease Diagnosis ◽  
Data Sets ◽  
Learning Tools ◽  
Metabolomics Data ◽  
Pharmaceutical Molecules ◽  
Close Relatedness

Metabolomics is a rapidly evolving discipline that involves the systematic study of endogenous small molecules that characterize the metabolic pathways of biological systems. The study of metabolism at a global level has the potential to contribute significantly to biomedical research, clinical medical practice, as well as drug discovery. In this paper, we present the most up-to-date metabolite and metabolic pathway resources, and we summarize the statistical, and machine-learning tools used for the analysis of data from clinical metabolomics. Through specific applications on cancer, diabetes, neurological and other diseases, we demonstrate how these tools can facilitate diagnosis and identification of potential biomarkers for use within disease diagnosis. Additionally, we discuss the increasing importance of the integration of metabolomics data in drug discovery. On a case-study based on the Human Metabolome Database (HMDB) and the Chinese Natural Product Database (CNPD), we demonstrate the close relatedness of the two data sets of compounds, and we further illustrate how structural similarity with human metabolites could assist in the design of novel pharmaceuticals and the elucidation of the molecular mechanisms of medicinal plants.

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