Current Protein and Peptide Science - Volume 22, Issue 3, 2021

Drug reposition, or repurposing, has become a promising strategy in therapeutics due to its advantages in several aspects of drug therapy. General drug development is expensive and can take more than 10 years to go through the designing, development, and necessary approval steps. However, established drugs have already overcome these steps and thus a potential candidate may be already available decreasing the risks and costs involved. In case of viral diseases, virus invades the cells of host organism and provoke biochemical changes in it that lead to tissue damage, alternations in normal physiological functions and sometimes death. Inside the cell, the virus finds the machinery necessary for its multiplication, as for instance the protein quality control system, which involves chaperones and Hsps (heat shock proteins) that, in addition to physiological functions, help in the stabilization of viral proteins. Recently, many inhibitors of Hsp90 have been developed as therapeutic strategies against diseases such as the Hsp90 inhibitors used in anticancer therapy. Several shreds of evidence indicate that these inhibitors can also be used as therapeutic strategies against viruses. Therefore, since a drug treatment for COVID-19 is urgently needed, this review aims to discuss the potential use of Hsp90 inhibitors in the treatment of this globally threatening disease.

Anticancer peptides (ACPs) eliminate pathogenic bacteria and kill tumor cells, showing no hemolysis and no damages to normal human cells. This unique ability explores the possibility of ACPs as therapeutic delivery and its potential applications in clinical therapy. Identifying ACPs is one of the most fundamental and central problems in new antitumor drug research. During the past decades, a number of machine learning-based prediction tools have been developed to solve this important task. However, the predictions produced by various tools are difficult to quantify and compare. Therefore, in this article, a comprehensive review of existing machine learning methods for ACPs prediction and fair comparison of the predictors is provided. To evaluate current prediction tools, a comparative study was conducted and analyzed the existing ACPs predictor from the 10 public works of literature. The comparative results obtained suggest that the Support Vector Machine-based model with features combination provided significant improvement in the overall performance when compared to the other machine learning method-based prediction models.

Antimicrobial peptides (AMPs) are natural polypeptides with antimicrobial activities and are found in most organisms. AMPs are evolutionarily conservative components that belong to the innate immune system and show potent activity against bacteria, fungi, viruses and in some cases display antitumor activity. Thus, AMPs are major candidates in the development of new antibacterial reagents. In the last few decades, AMPs have attracted significant attention from the research community. During the early stages of the development of this research field, AMPs were experimentally identified, which is an expensive and time-consuming procedure. Therefore, research and development (R) of fast, highly efficient computational tools for predicting AMPs has enabled the rapid identification and analysis of new AMPs from a wide range of organisms. Moreover, these computational tools have allowed researchers to better understand the activities of AMPs, which has promoted R of antibacterial drugs. In this review, we systematically summarize AMP prediction tools and their corresponding algorithms used.

Flavonoids are plant products abundant in every day diets and are claimed to be beneficial for the human health. After absorption, flavonoids are transported by the serum albumin (SA), the most abundant carrier blood protein, through the formation of flavonoids-SA complex. This review deals with the current state of knowledge on the flavonoids-SA complex forthe past 10 years, mainly involving multi-spectroscopic techniques and molecular dynamics simulation studies to explore the binding mechanism, thermodynamics and structural aspects of flavonoids, binding to SA. Especially, the novel methods such as capillary electrophoresis, high performance affinity chromatography approach, native mass spectrometry, and microscale thermophoresis, used in the characterization of the interaction between flavonoids and SA as well as flavonoid-based fluorescent probe for the SA measurement, are also included in this review.

Angina pectoris, associated with coronary artery disease, a cardiovascular disease where the pain is caused by adverse oxygen supply in the myocardium, results in contractility and discomfort in the chest. Inflammasomes, triggered by stimuli due to infection and cellular stress, have been identified to play a vital role in the progression of cardiovascular disorders and, thus, causing various symptoms like angina pectoris. Nlrp3 inflammasome, a key contributor in the pathogenesis of angina pectoris, requires activation and primary signaling for the commencement of inflammation. Nlrp3 inflammasome elicits out an inflammatory response by the emission of pro-inflammatory cytokines by ROS (reactive oxygen species) production, mobilization of K⁺ efflux and Ca²⁺ and by activation of lysosome destabilization that eventually causes pyroptosis, a programmed cell death process. Thus, inflammasome is considered to be one of the factors involved in the progression of coronary artery diseases and has an intricate role in the development of angina pectoris.

Traditional network-based computational methods have shown good results in drug analysis and prediction. However, these methods are time-consuming and lack universality, and it is difficult to exploit the auxiliary information of nodes and edges. Network embedding provides a promising way for alleviating the above problems by transforming the network into a low-dimensional space while preserving network structure and auxiliary information. This thus facilitates the application of machine learning algorithms for subsequent processing. Network embedding has been introduced into drug analysis and prediction in the last few years, and has shown superior performance over traditional methods. However, there is no systematic review of this issue. This article offers a comprehensive survey of the primary network embedding methods and their applications in drug analysis and prediction. The network embedding technologies applied in homogeneous network and heterogeneous network are investigated and compared, including matrix decomposition, random walk, and deep learning. Especially, the Graph neural network (GNN) methods in deep learning are highlighted. Furthermore, the applications of network embedding in drug similarity estimation, drug-target interaction prediction, adverse drug reactions prediction, protein function and therapeutic peptides prediction are discussed. Several future potential research directions are also discussed.

Drug Repurposing (DR) is an alternative to the traditional drug discovery process. It is cost and time effective,with high returns and low-risk process that can tackle the increasing need for interventions for varied diseases and new outbreaks. Repurposing of old drugs for other diseases has gained wider attention, as there have been several old drugs approved by the FDA for new diseases. In the global emergency of COVID-19 pandemic, this is one of the strategies implemented in the repurposing of old anti-infective, anti-rheumatic and anti-thrombotic drugs. The goal of the current review is to elaborate the process of DR, its advantages, repurposed drugs for a plethora of disorders, and the evolution of related academic publications. Further, detailed are the computational approaches: literature mining and semantic inference, network-based drug repositioning, signature matching, retrospective clinical analysis, molecular docking and experimental phenotypic screening. We discuss the legal and economic potential barriers in DR, existent collaborative models and recommendations for overcoming these hurdles and leveraging the complete potential of DR in finding new indications.