Current Protein and Peptide Science - Volume 21, Issue 11, 2020

Accumulating evidence demonstrates that miRNAs serve as critical biomarkers in various complex human diseases. Thus, identifying potential miRNA-disease associations has become a hot research topic for providing a better understanding of disease pathology, including cell carcinoma, cell proliferation and mevalonate pathway. Recently, based on various biological datasets, more and more computational prediction methods have been designed to uncover disease-related miRNAs for further experimental validation. Due to the fact that different limitations exist in previous computational methods, we proposed the model of Decision Template-based MiRNA-Disease Association prediction (DTMDA) to prioritize potential related miRNAs for diseases of interest. By integrating miRNA functional similarity network, miRNA Gaussian interaction profile kernel similarity network, two disease semantic similarity networks and disease Gaussian interaction profile kernel similarity network, we trained five multi-label K nearest neighbors-based core classifiers.

Reconstruction of gene regulatory networks (GRN) plays an important role in understanding the complexity, functionality and pathways of biological systems, which could support the design of new drugs for diseases. Because differential equation models are flexible androbust, these models have been utilized to identify biochemical reactions and gene regulatory networks. This paper investigates the differential equation models for reverse engineering gene regulatory networks. We introduce three kinds of differential equation models, including ordinary differential equation (ODE), time-delayed differential equation (TDDE) and stochastic differential equation (SDE). ODE models include linear ODE, nonlinear ODE and S-system model. We also discuss the evolutionary algorithms, which are utilized to search the optimal structures and parameters of differential equation models. This investigation could provide a comprehensive understanding of differential equation models, and lead to the discovery of novel differential equation models.

Accumulating evidence demonstrate that miRNAs can be treated as critical biomarkers in various complex human diseases. Thus, the identifications on potential miRNA-disease associations have become a hotpot for providing better understanding of disease pathology in this field. Recently, with various biological datasets, increasingly computational prediction approaches have been designed to uncover disease-related miRNAs for further experimental validation. To improve the prediction accuracy, several algorithms integrated miRNA similarities of known miRNA-disease associations to enhance the miRNA functional similarity network and disease similarities of known miRNA-disease associations to enhance the disease semantic similarity network. It is anticipated that machine learning methods would become an effective biological resource for clinical experimental guidance.

Many studies have shown that the spatial distribution of genes within a single chromosome exhibits distinct patterns. However, little is known about the characteristics of inter-chromosomal distribution of genes (including protein-coding genes, processed transcripts and pseudogenes) in different genomes. In this study, we explored these issues using the available genomic data of both human and model organisms. Moreover, we also analyzed the distribution pattern of protein-coding genes that have been associated with 14 common diseases and the insert/deletion mutations and single nucleotide polymorphisms detected by whole genome sequencing in an acute promyelocyte leukemia patient. We obtained the following novel findings. Firstly, inter-chromosomal distribution of genes displays a nonstochastic pattern and the gene densities in different chromosomes are heterogeneous. This kind of heterogeneity is observed in genomes of both lower and higher species. Secondly, protein-coding genes involved in certain biological processes tend to be enriched in one or a few chromosomes. Our findings have added new insights into our understanding of the spatial distribution of genome and disease- related genes across chromosomes. These results could be useful in improving the efficiency of disease-associated gene screening studies by targeting specific chromosomes.

Recently, an increasing number of biological and clinical reports have demonstrated that imbalance of microbial community has the ability to play important roles among several complex diseases concerning human health. Having a good knowledge of discovering potential of microbe-disease relationships, which provides the ability to having a better understanding of some issues, including disease pathology, further boosts disease diagnostics and prognostics, has been taken into account. Nevertheless, a few computational approaches can meet the need of huge scale of microbe-disease association discovery. In this work, we proposed the EHAI model, which is Enhanced Human microbe- disease Association Identification. EHAI employed the microbe-disease associations, and then Gaussian interaction profile kernel similarity has been utilized to enhance the basic microbe-disease association. Actually, some known microbe-disease associations and a large amount of associations are still unavailable among the datasets. The ‘super-microbe’ and ‘super-disease’ were employed to enhance the model. Computational results demonstrated that such super-classes have the ability to be helpful to the performance of EHAI. Therefore, it is anticipated that EHAI can be treated as an important biological tool in this field.

With the emergence of the novel severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2), the whole world is suffering from atypical pneumonia, which resulted in more than 559,047 deaths worldwide. In this time of crisis and urgency, the only hope comes from new candidate vaccines and potential antivirals. However, formulating new vaccines and synthesizing new antivirals are a laborious task. Therefore, considering the high infection rate and mortality due to COVID-19, utilization of previous information, and repurposing of existing drugs against valid viral targets have emerged as a novel drug discovery approach in this challenging time. The transmembrane spike (S) glycoprotein of coronaviruses (CoVs), which facilitates the virus’s entry into the host cells, exists in a homotrimeric form and is covered with N-linked glycans. S glycoprotein is known as the main target of antibodies having neutralizing potency and is also considered as an attractive target for therapeutic or vaccine development. Similarly, targeting of N-linked glycans of S glycoprotein envelope of CoV via carbohydrate-binding agents (CBAs) could serve as an attractive therapeutic approach for developing novel antivirals. CBAs from natural sources like lectins from plants, marine algae and prokaryotes and lectin mimics like Pradimicin-A (PRM-A) have shown antiviral activities against CoV and other enveloped viruses. However, the potential use of CBAs specifically lectins was limited due to unfavorable responses like immunogenicity, mitogenicity, hemagglutination, inflammatory activity, cellular toxicity, etc. Here, we reviewed the current scenario of CBAs as antivirals against CoVs, presented strategies to improve the efficacy of CBAs against CoVs; and studied the molecular interactions between CBAs (lectins and PRM-A) with Man9 by molecular docking for potential repurposing against CoVs in general, and SARSCoV- 2, in particular.

Immunotherapy emerges as a treatment strategy for breast cancer marker, diagnosis and treatment. In this review, monoclonal antibodies (mAbs)-based passive and peptide vaccines as active immunotherapy approaches like activation of B-cells and T-cells are studied. Passive immunotherapy is mAbs-based therapy effective against tumor cells, which acts by targeting HER2, IGF 1R, VEGF, BCSC and immune checkpoints. Neuropeptide Y (NPY) and GPCR are the areas of interest to target BC metastases for on-targeting therapeutic action. Neuropeptide S (NPS) or NPS receptor 1, acts as a biomarker for Neuroendocrine tumors (NET), mostly characterized by synaptophysin and chromogranin-A expression or Ki-67 proliferation index. The protein fusion technologies arise as a promising avenue in plant expression systems for increased recombinant Ab accumulation and cost-efficient purification. Recently, mAbs-based immunotherapy effectiveness is appreciated as a novel therapeutic combination of chemotherapy and immunotherapy to reduce the side effects and improve therapeutic responsiveness. Synthetic drug resistance will be overcome by mAbs-based therapy through several clinical trials and detection methods need to be optimized for accuracy and precision. Pharmacokinetic attributes need to be accessed for preferred receptor-agonist activity without ligand accumulation.

Topoisomerases are reported to resolve the topological problems of DNA during several cellular processes, such as DNA replication, transcription, recombination, and chromatin remodeling. Two types of topoisomerases (Topo I and II) accomplish their designated tasks by introducing single- or double-strand breaks within the duplex DNA molecules, and thus maintain the proper structural conditions of DNA to release the topological torsions, which is generated by unwinding of DNA to access coded information, in the course of replication, transcription, and other processes. Both the topoisomerases have been looked at as crucial targets against various types of cancers such as lung, melanoma, breast, and prostate cancers. Conceptually, targeting topoisomerases will disrupt both DNA replication and transcription, thereby leading to inhibition of cell division and consequently stopping the growth of actively dividing cancerous cells. Since the discovery of camptothecin (an alkaloid) as an inhibitor of Topo I in 1958, a number of derivatives of camptothecin were developed as potent inhibitors of Topo I. Two such derivatives of camptothecin, namely, topotecan and irinotecan, have been commonly used as US Food and Drug Administration (FDA) approved drugs against Topo I. Similarly, the first Topo II inhibitor, namely, etoposide, an analogue of podophyllotoxin, was developed in 1966 and got FDA approval as an anti-cancer drug in 1983. Subsequently, several other inhibitors of Topo II, such as doxorubicin, mitoxantrone, and teniposide, were developed. These drugs have been reported to cause accumulation of cytotoxic non-reversible DNA double-strand breaks (cleavable complex). Thus, the present review describes the anticancer potential of plant-derived secondary metabolites belonging to alkaloids, flavonoids and terpenoids directed against topoisomerases. Furthermore, in view of the recent advances made in the field of computer-aided drug design, the present review also discusses the use of computational approaches such as ADMET, molecular docking, molecular dynamics simulation and QSAR to assess and predict the safety, efficacy, potency and identification of these potent anti-cancerous therapeutic molecules.