SPIDR: small-molecule peptide-influenced drug repurposing

Repurposing of Drugs for Cardiometabolic Disorders: An Out and Out Cumulation

Cardiometabolic disorders (CMD) is a constellation of metabolic predisposing factors for atherosclerosis such as insulin resistance (IR) or diabetes mellitus (DM), systemic hypertension, central obesity, and dyslipidemia. Cardiometabolic diseases (CMDs) continue to be the leading cause of mortality in both developed and developing nations, accounting for over 32% of all fatalities globally each year. Furthermore, dyslipidemia, angina, arrhythmia, heart failure, myocardial infarction (MI), and diabetes mellitus are the major causes of death, accounting for an estimated 19 million deaths in 2012. CVDs will kill more than 23 million individuals each year by 2030. Nonetheless, new drug development (NDD) in CMDs has been increasingly difficult in recent decades due to increased costs and a lower success rate. Drug repositioning in CMDs looks promising in this scenario for launching current medicines for new therapeutic indications. Repositioning is an ancient method that dates back to the 1960s and is mostly based on coincidental findings during medication trials. One significant advantage of repositioning is that the drug's safety profile is well known, lowering the odds of failure owing to undesirable toxic effects. Furthermore, repositioning takes less time and money than NDD. Given these facts, pharmaceutical corporations are becoming more interested in medication repositioning. In this follow-up, we discussed the notion of repositioning and provided some examples of repositioned medications in cardiometabolic disorders.

Recent Development of Small Molecules for SARS-CoV-2 and the Opportunity for Fragment-Based Drug Discovery

Abstract:

The ongoing pandemic of Covid-19 caused by SARS-CoV-2 is a major threat to global public health, drawing attention to develop new therapeutics for treatment. Much research work is focused on identifying or repurposing new small molecules to serve as potential inhibitors by interacting with viral or host-cell molecular targets and understanding the nature of the virus in the host cells. Identifying small molecules as potent inhibitors at an early stage is advantageous to make a molecule with higher potency and then find a lead compound for the development of drug discovery. Small molecules can show their inhibition property by targeting either SARS-CoV-2 main protease (Mpro) enzyme, papain-like protease (PLpro) enzyme, or helicase (Hel), or blocking the spike (S) protein angiotensin-converting enzyme 2 (ACE2) receptor. A very recent outbreak of a new variant (B.1.617.2—termed as Delta variant) of SARS-CoV-2 worldwide posed a greater challenge as it is resistant to clinically undergoing vaccine trials. Thus, the development of new drug molecules is of potential interest to combat SARS-CoV-2 disease, and for that, fragment-based drug discovery (FBDD) approach could be one of the ways to bring out an effective solution. Two cysteine protease enzymes would be an attractive choice of target for fragment-based drug discovery to tune the molecular structure at an early stage with suitable functionality. In this short review, the recent development of small-molecule as inhibitors against Covid-19 are discussed and the opportunity for FBDD is envisioned optimistically to provide an outlook regarding Covid-19 that may pave the way in the direction of the Covid-19 drug development paradigm.

Repositioning of Fungal-based Peptides as Modulators of Angiotensin-converting Enzyme-related Carboxypeptidase, SARS-coronavirus HR2 Domain, and Coronavirus Disease 2019 Main Protease

Background and Objectives

Angiotensin-converting enzyme-related carboxypeptidase, SARS-Coronavirus HR2 Domain, and COVID-19 main protease are essential for the cellular entry and replication of coronavirus in the host. This study investigated the putative inhibitory action of peptides form medicinal mushrooms, namely Pseudoplectania nigrella, Russula paludosa, and Clitocybe sinopica, towards selected proteins through computational studies.

Materials and Methods

The respective physicochemical properties of selected peptides were predicted using ProtParam tool, while the binding modes and binding free energy of selected peptides toward proteins were computed through HawkDock server. The structural flexibility and stability of docked protein-peptide complexes were assessed through iMODS server.

Results

The peptides showed an optimum binding afinity with the molecular targets; plectasin from P. nigrella showed the highest binding free energy compared to peptides from R. paludosa and C. sinopica. Besides, molecular dynamic simulations showed all fungal-based peptides could influence the flexibility and stability of selected proteins.

Conclusion

The study revealed fungal-based peptides could be explored as functional modulators of essential proteins that are involved in the cellular entry of coronavirus.

Artificial intelligence to deep learning: machine intelligence approach for drug discovery

Drug designing and development is an important area of research for pharmaceutical companies and chemical scientists. However, low efficacy, off-target delivery, time consumption, and high cost impose a hurdle and challenges that impact drug design and discovery. Further, complex and big data from genomics, proteomics, microarray data, and clinical trials also impose an obstacle in the drug discovery pipeline. Artificial intelligence and machine learning technology play a crucial role in drug discovery and development. In other words, artificial neural networks and deep learning algorithms have modernized the area. Machine learning and deep learning algorithms have been implemented in several drug discovery processes such as peptide synthesis, structure-based virtual screening, ligand-based virtual screening, toxicity prediction, drug monitoring and release, pharmacophore modeling, quantitative structure-activity relationship, drug repositioning, polypharmacology, and physiochemical activity. Evidence from the past strengthens the implementation of artificial intelligence and deep learning in this field. Moreover, novel data mining, curation, and management techniques provided critical support to recently developed modeling algorithms. In summary, artificial intelligence and deep learning advancements provide an excellent opportunity for rational drug design and discovery process, which will eventually impact mankind. The primary concern associated with drug design and development is time consumption and production cost. Further, inefficiency, inaccurate target delivery, and inappropriate dosage are other hurdles that inhibit the process of drug delivery and development. With advancements in technology, computer-aided drug design integrating artificial intelligence algorithms can eliminate the challenges and hurdles of traditional drug design and development. Artificial intelligence is referred to as superset comprising machine learning, whereas machine learning comprises supervised learning, unsupervised learning, and reinforcement learning. Further, deep learning, a subset of machine learning, has been extensively implemented in drug design and development. The artificial neural network, deep neural network, support vector machines, classification and regression, generative adversarial networks, symbolic learning, and meta-learning are examples of the algorithms applied to the drug design and discovery process. Artificial intelligence has been applied to different areas of drug design and development process, such as from peptide synthesis to molecule design, virtual screening to molecular docking, quantitative structure-activity relationship to drug repositioning, protein misfolding to protein-protein interactions, and molecular pathway identification to polypharmacology. Artificial intelligence principles have been applied to the classification of active and inactive, monitoring drug release, pre-clinical and clinical development, primary and secondary drug screening, biomarker development, pharmaceutical manufacturing, bioactivity identification and physiochemical properties, prediction of toxicity, and identification of mode of action.

Repurposing Drugs to Treat Heart and Brain Illness

Drug development is a complicated, slow and expensive process with high failure rates. One strategy to mitigate these factors is to recycle existing drugs with viable safety profiles and have gained Food and Drug Administration approval following extensive clinical trials. Cardiovascular and neurodegenerative diseases are difficult to treat, and there exist few effective therapeutics, necessitating the development of new, more efficacious drugs. Recent scientific studies have led to a mechanistic understanding of heart and brain disease progression, which has led researchers to assess myriad drugs for their potential as pharmacological treatments for these ailments. The focus of this review is to survey strategies for the selection of drug repurposing candidates and provide representative case studies where drug repurposing strategies were used to discover therapeutics for cardiovascular and neurodegenerative diseases, with a focus on anti-inflammatory processes where new drug alternatives are needed.

Ribbon α-Conotoxin KTM Exhibits Potent Inhibition of Nicotinic Acetylcholine Receptors

KTM is a 16 amino acid peptide with the sequence WCCSYPGCYWSSSKWC. Here, we present the nuclear magnetic resonance (NMR) structure and bioactivity of this rationally designed α-conotoxin (α-CTx) that demonstrates potent inhibition of rat α3β2-nicotinic acetylcholine receptors (rα3β2-nAChRs). Two bioassays were used to test the efficacy of KTM. First, a qualitative PC12 cell-based assay confirmed that KTM acts as a nAChR antagonist. Second, bioactivity evaluation by two-electrode voltage clamp electrophysiology was used to measure the inhibition of rα3β2-nAChRs by KTM (IC50 = 0.19 ± 0.02 nM), and inhibition of the same nAChR isoform by α-CTx MII (IC50 = 0.35 ± 0.8 nM). The three-dimensional structure of KTM was determined by NMR spectroscopy, and the final set of 20 structures derived from 32 distance restraints, four dihedral angle constraints, and two disulfide bond constraints overlapped with a mean global backbone root-mean-square deviation (RMSD) of 1.7 ± 0.5 Å. The structure of KTM did not adopt the disulfide fold of α-CTx MII for which it was designed, but instead adopted a flexible ribbon backbone and disulfide connectivity of C2-C16 and C3-C8 with an estimated 12.5% α-helical content. In contrast, α-CTx MII, which has a native fold of C2-C8 and C3-C16, has an estimated 38.1% α-helical secondary structure. KTM is the first reported instance of a Framework I (CC-C-C) α-CTx with ribbon connectivity to display sub-nanomolar inhibitory potency of rα3β2-nAChR subtypes.

Qualitative Assay to Detect Dopamine Release by Ligand Action on Nicotinic Acetylcholine Receptors

A pheochromocytoma of the rat adrenal medulla derived (a.k.a. PC12) cell-based assay for dopamine measurement by luminescence detection was customized for the qualitative evaluation of agonists and antagonists of nicotinic acetylcholine receptors (nAChRs). The assay mechanism begins with ligand binding to transmembrane nAChRs, altering ion flow into the cell and inducing dopamine release from the cell. Following release, dopamine is oxidized by monoamine oxidase generating hydrogen peroxide that catalyzes a chemiluminescence reaction involving luminol and horseradish peroxidase, thus producing a detectable response. Results are presented for the action of nAChR agonists (acetylcholine, nicotine, and cytisine), and antagonists (α-conotoxins (α-CTxs) MII, ImI, LvIA, and PeIA) that demonstrate a luminescence response correlating to the increase or decrease of dopamine release. A survey of cell growth and treatment conditions, including nerve growth factor, nicotine, ethanol, and temperature, led to optimal assay requirements to achieve maximal signal intensity and consistent response to ligand treatment. It was determined that PC12 cells treated with a combination of nerve growth factor and nicotine, and incubated at 37 °C, provided favorable results for a reduction in luminescence signal upon treatment of cells with α-CTxs. The PC12 assay is intended for use as a fast, efficient, and economic qualitative method to assess the bioactivity of molecules that act on nAChRs, in which testing of ligand-nAChR binding hypotheses and computational predictions can be validated. As a screening method for nAChR bioactivity, lead compounds can be assessed for their likelihood of exhibiting desired bioactivity prior to being subjected to more complex quantitative methods, such as electrophysiology or live animal studies.

A Computational Bipartite Graph-Based Drug Repurposing Method

We present a bipartite graph-based approach to calculate drug pairwise similarity for identifying potential new indications of approved drugs. Both chemical and molecular features were used in drug similarity calculation. In this paper, we first extracted drug chemical structures and drug-target interactions. Second, we computed chemical structure similarity and drug- target profile similarity. Further, we constructed a bipartite graph model with known relationships between drugs and their target proteins. Finally, we weighted summing drug structure similarity with target profile similarity to derive drug pairwise similarity, so that we can predict potential indication of a drug from its similar drugs. In addition, we summarized some alternative strategies and variations follow-up to each section in the overall analysis.

Snails In Silico: A Review of Computational Studies on the Conopeptides

Marine cone snails are carnivorous gastropods that use peptide toxins called conopeptides both as a defense mechanism and as a means to immobilize and kill their prey. These peptide toxins exhibit a large chemical diversity that enables exquisite specificity and potency for target receptor proteins. This diversity arises in terms of variations both in amino acid sequence and length, and in posttranslational modifications, particularly the formation of multiple disulfide linkages. Most of the functionally characterized conopeptides target ion channels of animal nervous systems, which has led to research on their therapeutic applications. Many facets of the underlying molecular mechanisms responsible for the specificity and virulence of conopeptides, however, remain poorly understood. In this review, we will explore the chemical diversity of conopeptides from a computational perspective. First, we discuss current approaches used for classifying conopeptides. Next, we review different computational strategies that have been applied to understanding and predicting their structure and function, from machine learning techniques for predictive classification to docking studies and molecular dynamics simulations for molecular-level understanding. We then review recent novel computational approaches for rapid high-throughput screening and chemical design of conopeptides for particular applications. We close with an assessment of the state of the field, emphasizing important questions for future lines of inquiry.