In Silico Analysis of Protein–Protein Interactions Between Estrogen Receptor and Fungal Laccase

Background: Retinitis pigmentosa is an eye hereditary disease caused by photoreceptor death. One of the biggest problem is represented by its genetic heterogeneity, which has not yet allowed us to found all causative genes and how known ones could influence each other, leading to retinitis etiopathogenesis. Objective: To propose the possible relation between the “functional cluster” of vision dark adaptation, made of five phototransductional genes (RCVRN, GNB1, GNGT1, GRK7 and ARRB1), and retinitis pigmentosa onset. Methods: A bioinformatic approach was exploited: the starting point was searching through online database as PubMed and EMBASE to acquire information about the state of art of these gene. This step was followed by an in-silico analysis, performed by softwares as Cytoscape and Genecards Suite Plus, articulated in three phases: I) identification of common pathways and genes involved in; II) collection of previously detected genes; III) deep analysis of intersected genes and implication into etiopathogenesis of analzyed disease. Results: The whole in-silico analysis showed that all five gene products cooperate during phototransductional activation, expecially in the dark adaptation. Interestingly, the most exciting aspect regards the direct relation with several known retinitis pigmentosa causative genes, in form of protein interactions or other pathway correlations. Conclusion: Pathway analysis permitted us to hypothesize a possible role of analyzed genes in retinitis pigmentosa etiopathogenesis, also considering the key activity of their encoded proteins. Next step will be validating our hypotesis with functional assays to ensure the real meaning of this possible association, leading to new potential retinitis pigmentosa causative genes.

Download Full-text

In Silico Analysis Predicting Effects of Deleterious SNPs of Human RASSF5 Gene on its Structure and Functions

10.1101/2020.06.05.952952 ◽

2020 ◽

Author(s):

Md. Shahadat Hossain ◽

Arpita Singha Roy ◽

Md. Sajedul Islam

Keyword(s):

Protein Interactions ◽

In Silico ◽

Structural Changes ◽

Interaction Analysis ◽

In Silico Analysis ◽

Therapeutic Interventions ◽

Protein Protein Interactions ◽

Post Translational Modification ◽

Protein Protein Interaction ◽

The Stability

AbstractRas association domain-containing protein 5 (RASSF5), one of the prospective biomarkers for tumors, generally plays a crucial role as a tumor suppressor. As deleterious effects can result from functional differences through SNPs, we sought to analyze the most deleterious SNPs of RASSF5 as well as predict the structural changes associated with the mutants that hamper the normal protein-protein interactions. We adopted both sequence and structure based approaches to analyze the SNPs of RASSF5 protein. We also analyzed the putative post translational modification sites as well as the altered protein-protein interactions that encompass various cascades of signals. Out of all the SNPs obtained from the NCBI database, only 25 were considered as highly deleterious by six in silico SNP prediction tools. Among them, upon analyzing the effect of these nsSNPs on the stability of the protein, we found 17 SNPs that decrease the stability. Significant deviation in the energy minimization score was observed in P350R, F321L, and R277W. Besides this, docking analysis confirmed that P350R, A319V, F321L, and R277W reduce the binding affinity of the protein with H-Ras, where P350R shows the most remarkable deviation. Protein-protein interaction analysis revealed that RASSF5 acts as a hub connecting two clusters consisting of 18 proteins and alteration in the RASSF5 may lead to disassociation of several signal cascades. Thus, based on these analyses, our study suggests that the reported functional SNPs may serve as potential targets for different proteomic studies, diagnosis and therapeutic interventions.

Download Full-text

Molecular markers associated with the outcome of tamoxifen treatment in estrogen receptor-positive breast cancer patients: scoping review and in silico analysis

Discover Oncology ◽

10.1007/s12672-021-00432-7 ◽

2021 ◽

Vol 12 (1) ◽

Author(s):

Maiquidieli Dal Berto ◽

Giovana Tavares dos Santos ◽

Aniúsca Vieira dos Santos ◽

Andrew Oliveira Silva ◽

José Eduardo Vargas ◽

...

Keyword(s):

Breast Cancer ◽

Estrogen Receptor ◽

Molecular Markers ◽

Scoping Review ◽

In Silico ◽

In Silico Analysis ◽

Tamoxifen Treatment ◽

Cellular Processes ◽

Estrogen Receptor Positive ◽

Silico Analysis

AbstractTamoxifen (TMX) is used as adjuvant therapy for estrogen receptor-positive (ER+) breast cancer cases due to its affinity and inhibitory effects. However, about 30% of cases show drug resistance, resulting in recurrence and metastasis, the leading causes of death. A literature review can help to elucidate the main cellular processes involved in TMX resistance. A scoping review was performed to find clinical studies investigating the association of expression of molecular markers profiles with long-term outcomes in ER+ patients treated with TMX. In silico analysis was performed to assess the interrelationship among the selected markers, evaluating the joint involvement with the biological processes. Forty-five studies were selected according to the inclusion and exclusion criteria. After clustering and gene ontology analysis, 23 molecular markers were significantly associated, forming three clusters of strong correlation with cell cycle regulation, signal transduction of proliferative stimuli, and hormone response involved in morphogenesis and differentiation of mammary gland. Also, it was found that overexpression of markers in selected clusters is a significant indicator of poor overall survival. The proposed review offered a better understanding of independent data from the literature, revealing an integrative network of markers involved in cellular processes that could modulate the response of TMX. Analysis of these mechanisms and their molecular components could improve the effectiveness of TMX.

Download Full-text

Target related in silico analysis of Bergenin and tuberculosis management

Biomedicine ◽

10.51248/.v40i4.322 ◽

2021 ◽

Vol 40 (4) ◽

pp. 474-481

Author(s):

Virupaksha A. Bastikar ◽

Alpana Bastikar ◽

Pramodkumar P. Gupta ◽

Sandeep R. Pai ◽

Santosh S. Chhajed

Keyword(s):

Protein Interactions ◽

In Silico ◽

Biological Process ◽

In Silico Analysis ◽

Enrichment Analysis ◽

Binding Mode ◽

Health Concern ◽

Silico Analysis

Introduction and Aim: Tuberculosis (TB) is a global health concern, claiming two million lives every year. Although an oldest known human infectious disease, researcher is falling short of giving out an effective and reliable vaccine or therapy. The current antimycobacterial drugs include Isoniazid, Ethambutol, Rifampicin and Pyrazinemamide available in market, but most of these are known to have certain adverse effects. Hence there is an increase in demand for natural products with anti-tuberculosis activity with no or limited side effects. Indian traditional systems of medicine have a plethora of promising plants for treatment of tuberculosis, of which Bergenin is the most well established and extensively used compound. The main aim of this research was to investigate the role of Bergenin as an anti-tuberculosis agent with the help of in-silico analysis and protein interaction studies. Materials and Methods: In the present study 04 known 3-dimensional crystallized anti-tubercular drug target is considered and retrieved from PDB. Drug Isoniazid, Ethambutol, Rifampicin, Pyrazineamide and phytochemical Bergenin were retrieved, sketched and geometrically optimized. Molecular docking is carried to understand the binding mode and its core interactions. ADMET properties were calculated in assessment of the toxicity. Protein-protein interactions and enrichment analysis is carried out to understand the biological process involved with rpsA protein. Results: In the present study other than Rifampicin, Bergenin reported with better binding energy and similar pharmacophoric interaction pattern as compared to all the 04 indigenous inhibitors. The PPI network and enrichment analysis predicts the plausible biological process involved with rpsA protein and can be further targeted in treatment of tuberculosis. Conclusion: The results showed that Bergenin was better than and competent with the existing drugs and can be used as an anti-tuberculosis agent if studied in-vitro and in-vivo for its activity.

Download Full-text