Prediction of protein-protein interaction types using machine learning approaches

Prediction and analysis of protein-protein interactions (PPIs) is an important problem in life science research because of the fundamental roles of PPIs in many biological processes in living cells. One of the important problems surrounding PPIs is the identification and prediction of different types of complexes, which are characterized by properties such as type and numbers of proteins that interact, stability of the proteins, and also duration of the interactions. This thesis focuses on studying the temporal and stability aspects of the PPIs mostly using structural data. We have addressed the problem of predicting obligate and non-obligate protein complexes, as well as those aspects related to transient versus permanent because of the importance of non-obligate and transient complexes as therapeutic targets for drug discovery and development. We have presented a computational model to predict-protein interaction types using our proposed physicochemical features of desolvation and electrostatic energies and also structural and sequence domain-based features. To achieve a comprehensive comparison and demonstrate the strength of our proposed features to predict PPI types, we have also computed a wide range of previously used properties for prediction including physical features of interface area, chemical features of hydrophobicity and amino acid composition, physicochemical features of solvent-accessible surface area (SASA) and atomic contact vectors (ACV). After extracting the main features of the complexes, a variety of machine learning approaches have been used to predict PPI types. The prediction is performed via several state-of-the-art classification techniques, including linear dimensionality reduction (LDR), support vector machine (SVM), naive Bayes (NB) and k-nearest neighbor (k-NN). Moreover, several feature selection algorithms including gain ratio (GR), information gain (IG), chi-square (Chi2) and minimum redundancy maximum relevance (mRMR) are applied on the available datasets to obtain more discriminative and relevant properties to distinguish between these two types of complexes Our computational results on different datasets confirm that using our proposed physicochemical features of desolvation and electrostatic energies lead to significant improvements on prediction performance. Moreover, using structural and sequence domains of CATH and Pfam and doing biological analysis help us to achieve a better insight on obligate and non-obligate complexes and their interactions

New indolinone 1, 2, 3-triazole derivatives: Design, synthesis and anti-Alzheimer activity evaluation

Novel 1,2,3-triazole indolinone derivatives have been synthesized. All the titled compounds were characterized by 1H NMR, 13C NMR, MS and IR spectral data. The in vitro AChE and BChE inhibitory activity of all the compounds were evaluated.  Introduction: Alzheimer disease is the most frequent cause of dementia, which is very common in elder population with high morbidity. Treatment of this disease is one of the most promising targets in medicinal chemistry researches. Design and synthesis of novel 1,2,3 diazole indolinone derivatives as cholinesterase inhibitor (ChEI), are investigated in this study. Indolinone derivatives with 1,2,3-triazole moiety have been recently reported as potential AChE and BuChE inhibitors. There is also, a growing interest in evaluating the biological activity of these compounds and their derivatives to investigate their role in the prevention of neurodegenerative diseases. Methods and Results: The target compounds were prepared via the 1-methyl-3-((prop-2-yn-1-yloxy)imino)indolin-2-one as an intermediate in click reaction with substituted benzyl halides in water and DMF as solvent in room temperature. All the synthesized compounds were characterized by 1H NMR, 13C NMR, MS and IR spectral data. The in vitro AChE and BuChE inhibitory activity of all the compounds were evaluated. Conclusions: In conclusion, various novel 1,2,3-triazole indolinone derivatives were designed, synthesized, and evaluated against AChE and BChE. All these results clearly confirmed the efficacy of the corresponding compounds for further drug discovery developments

The effect of C-peptide on diabetic nephropathy: A review of molecular mechanisms

C-peptide is a small peptide connecting two chains of proinsulin molecule and is dissociated before the release of insulin. It is secreted in an equimolar amount to insulin from the pancreatic beta-cells into the circulation. Recent evidence demonstrates that it has other physiologic activities beyond its structural function. C-peptide modulates intracellular signaling pathways in various pathophysiologic states and, could potentially be a new therapeutic target for different disorders including diabetic complications. There is growing evidence that c-peptide has modulatory effects on the molecular mechanisms involved in the development of diabetic nephropathy. Although we have little direct evidence, pharmacological properties of c-peptide suggest that it can provide potent renoprotective effects especially, in a c-peptide deficient milieu as in type 1 diabetes mellitus. In this review, we describe possible molecular mechanisms by which c-peptide may improve renal efficiency in a diabetic milieu

Anti-inflammatory potentials of incretin-based therapies used in the management of diabetes

GLP-1 receptor agonists (GLP-1RA) and dipeptidyl peptidase 4 inhibitors (DPP-4i) are two classes of antidiabetic agents used in the management of diabetes based on incretin hormones. There is emerging evidence that they have anti-inflammatory effects. Since most long-term complications of diabetes have a background of chronic inflammation, these agents may be beneficial against diabetic complications not only due to their hypoglycemic potential but also via their anti-inflammatory effects. However, the exact molecular mechanisms by which GLP-1RAs and DPP-4i exert their anti-inflammatory effects are not clearly understood. In this review, we discuss the potential molecular pathways by which these incretin-based therapies exert their anti-inflammatory effects

Incretin-based therapies and renin-angiotensin system: Looking for new therapeutic potentials in the diabetic milieu

Incretin-based therapies include pharmacologic agents such as glucagon like peptide-1 receptor agonists and dipeptidyl peptidase-4 inhibitors which exert potent anti-hyperglycemic effects in the diabetic milieu. They are also shown to have extra-pancreatic effects. Renin-angiotensin system is part of the endocrine system which is widely distributed in the body and is closely involved in water and electrolyte homeostasis as well as renal and cardiovascular functions. Hence the renin-angiotensin system is the main target for treating patients with various renal and cardiovascular disorders. There is growing evidence that incretins have modulatory effects on renin-angiotensin system activity; thereby, can be promising therapeutic agents for the management of renal and cardiovascular disorders. But the exact molecular interactions between incretins and renin-angiotensin system are not clearly understood. In this current study, we have reviewed the possible molecular mechanisms by which incretins modulate renin-angiotensin system activity

Molecular mechanisms by which SGLT2 inhibitors can induce insulin sensitivity in diabetic milieu: A mechanistic review

Sodium-glucose co-transporter-2 inhibitors (SGLT2i) are a relatively newer class of anti-hyperglycemic medications that reduce blood glucose by inhibition of renal glucose re-uptake, thereby increasing urinary glucose excretion. Although glycosuria is the primary mechanism of action of these agents, there is some evidence suggesting they can reduce insulin resistance and induce peripheral insulin sensitivity. Identifying the molecular mechanisms by which these medications improve glucose homeostasis can help us to develop newer forms of SGLT2i with lesser side effects. We have reviewed the molecular mechanisms and signaling pathways by which SGLT2i therapy improve insulin sensitivity and ameliorates insulin resistance

Obesity and Insulin Resistance: A Review of Molecular Interactions

The prevalence of insulin resistance and diabetes mellitus is rising globally in epidemic proportions. Diabetes and its complications contribute to significant morbidity and mortality. An increase in sedentary lifestyle and consumption of a more energydense diet increased the incidence of obesity which is a significant risk factor for type 2 diabetes. Obesity acts as a potent upstream event that promotes molecular mechanisms involved in insulin resistance and diabetes mellitus. However, the exact molecular mechanisms between obesity and diabetes are not clearly understood. In the current study, we have reviewed the molecular interactions between obesity and type 2 diabetes

Hepatic benefits of sodium-glucose cotransporter 2 inhibitors in liver disorders

Diabetic patients are at higher risk of liver dysfunction compared with the normal population. Thus, using hypoglycemic agents to improve liver efficiency is important in these patients. Sodium-glucose cotransporters-2 inhibitors (SGLT2i) are newly developed antidiabetic drugs with potent glucose-lowering effects. However, recent limited evidence suggests that they have extra-glycemic benefits and may be able to exert protective effects on the liver. Hence, these drugs could serve as promising pharmacological agents with multiple benefits against different hepatic disorders. In this review, the current knowledge about the possible effects of SGLT2 inhibitors on different forms of liver complications and possible underlying mechanisms are discussed