Structure-Based Survey of the Human Proteome for Opportunities in Proximity Pharmacology

Proximity pharmacology (ProxPharm) is a novel paradigm in drug discovery where a small molecule brings two proteins in close proximity to elicit a signal, generally from one protein onto another. The potential of ProxPharm compounds as a new therapeutic modality is firmly established by proteolysis targeting chimeras (PROTACs) that bring an E3 ubiquitin ligase in proximity to a target protein to induce ubiquitination and subsequent degradation of the target protein. The concept can be expanded to induce other post-translational modifications via the recruitment of different types of protein-modifying enzymes. To survey the human proteome for opportunities in proximity pharmacology, we systematically mapped non-catalytic drug binding pockets on the structure of protein-modifying enzymes available from the Protein Databank. In addition to binding sites exploited by previously reported ProxPharm compounds, we identified putative ligandable non-catalytic pockets in 188 kinases, 42 phosphatases, 26 deubiquitinases, 9 methyltransferases, 7 acetyltransferases, 7 glycosyltransferases, 4 deacetylases, 3 demethylases and 2 glycosidases, including cavities occupied by chemical matter that may serve as starting points for future ProxPharm compounds. This systematic survey confirms that proximity pharmacology is a versatile modality with largely unexplored and promising potential, and reveals novel opportunities to pharmacologically rewire molecular circuitries.

GC-MS analysis and Molecular docking of Quercetin compounds of Phytolacca octandra with the target protein of infection causing Staphylococcus aureus

Phytolacca octandra is a perennial usually about 1m high herb, dense and erect in full sun. As only few reports were available on the studies about the bioactive compounds and various activities in the Phytolacca octandra, the present study focuses on the bio active compounds attributed to antibacterial activity in the plant extracts by Gas Chromatography – Mass Spectrometry (GCMS) and molecular docking methods. Antibacterial activity of Phytolacca octandra showed maximum inhibitory zones of 21mm, 18mm, 19mm, 19mm and 20mm against respective organisms for 25mg/ml of acetone extracts. The outcome of Phytolacca octandra extracts that was exposed to GC-MS analysis, showed the presence of 20 more compounds. The most identified compounds to have anti-oxidant activity are Dodecane, Octadecane and Octacosane. The other major compounds present in extract are Cyclohexen-oxopropyl, 1,2-Benzenedicarboxylic acid.The overall docking energies of the target protein, rhamnolipids biosynthesis 3-oxoacyl-[acyl-carrier-protein] reductase with quercetin with the number of hydrogen bonds were presented in the study; The docking report revealed –8.01Kcal/Mol binding energies and 8 hydrogen bonding between the Phytolacca octandra compound, quercetin and the target binding protein, rhlG of infection causing pathogen Staphylococcus aureus.

miR-18a-3p and Its Target Protein HuR May Regulate Myogenic Differentiation in Immune-Mediated Necrotizing Myopathy

Immune-mediated necrotizing myopathy (IMNM) is characterized by manifestation of myonecrosis and regeneration of muscle fibers; however, the underlying pathogenesis remains unclear. This study aimed to investigate the role and mechanism of miR-18a-3p and its target RNA-binding protein HuR in IMNM. HuR and miR-18a-3p levels were detected in the skeletal muscles of 18 patients with IMNM using quantitative reverse-transcription real-time polymerase chain reaction (qRT-PCR) and western blotting analysis. Human myoblasts were transfected with small interfering RNA targeting HuR and miR-18a-3p mimic or inhibitor. Myogenic differentiation markers, myogenin and myosin heavy chain, were analyzed by qRT-PCR, western blotting analysis, and immunofluorescence staining. The results showed that miR-18a-3p was upregulated (p=0.0002), whereas HuR was downregulated (p=0.002) in the skeletal muscles of patients with IMNM. The expression of miR-18a-3p in patients with IMNM was negatively correlated with those of HuR (r = -0.512, p = 0.029). We also found that disease activity was positively correlated with HuR expression (r = 0.576, p = 0.012) but muscle activity was negatively correlated with miR-18a-3p expression (r = -0.550, p = 0.017). Besides, bioinformatics analysis and dual-luciferase reporter assays suggested that miR-18a-3p could directly target HuR. Cellular experiments showed that overexpression of miR-18a-3p inhibited myogenic differentiation by targeting HuR, whereas inhibition of miR-18a-3p led to opposite results. Therefore, miR-18a-3p and its target protein HuR may be responsible for modulating the myogenic process in IMNM and can thus be therapeutic targets for the same.

Therapeutic potential of methylxanthine drug on alpha synuclein protein in Parkinson's disease

Parkinson's disease (PD) is the second most common neurodegenerative disorder after Alzheimer. It exists in sporadic (90 to 95%) and familial (5 to 10%) form. Its pathogenesis is due to oxidative stress, glutamate excitotoxicity, protein aggregation, neuroinflammation and neurodegeneration. There is currently no cure for this disease. The protein- protein interaction and gene ontology/functional enrichment analysis have been performed to find out the prominent interactor protein and shared common biological pathways, especially PD pathway. Further in silico docking analysis was performed on target protein to investigate the prominent drug molecule for PD. Through computational molecular virtual screening of small molecules from selected twelve natural compounds, and among these compounds methylxanthine was shown to be prominent inhibitor to SNCA protein that ultimately prevent PD. The interaction of methylxanthine compound with the target protein SNCA suggested that, it interacted with prominent binding site with good docking score and might be involved in blocking the binding of neuroinducing substances like: 1-methyl-4-phenyl-1,2,3,6-tetrahydropyridine (MPTP) to SNCA protein. Thus methylxanthine compounds can be explored as promising drugs for the prevention of Parkinson's disease.

Molecular Docking and Validation of Methicillin Resistant Staphylococcus aureus Targets against Geninthiocin

Antibiotic resistance has been a serious public health concern in recent years. Methicillin resistant “Staphylococcus aureus” (MRSA) is a superbug that causes life threatening infections of Humanity which is difficult to treat. Geninthiocin is a macrocyclic thiopeptide with a 35-membered core moiety, which was isolated from marine streptomyces sp. ICN19, which has proven potent activity against MRSA. Five target proteins PDB ID: 4YMX, 3ZDS, 3QLB, 4IEN and 1DXL were identified from MRSA for their presumptive action for Geninthiocin. In this study, we used molecular docking and molecular dynamic simulation, in order to validate Geninthiocin’s potential target protein. Target proteins were subjected to ligand-protein docking studies. Based on their docking scores and Hydrogen bonding interactions, two possible proteins 4YMX and 3ZDS were further subjected to simulation strategies to validate the protein-drug interaction. Out of which, homogentisate1,2 dioxygenase turned out to be a possible drug target for Geninthiocin. The compound Geninthiocin could be developed as a potential inhibitor against the target protein homogentisate1,2-dioxygenase for exhibiting an effective antimicrobial activity.

AttentionSiteDTI: Attention Based Model for Predicting Drug-Target Interaction Using 3D Structure of Protein Binding Sites

Investigating drug-target interactions plays a critical role in drug design and discovery. The vast chemical and proteomic space, along with the cost associated with invirto experiments motivate the use of computational methods to narrow down the search space for novel interaction of drug target pairs. Among all computational methods, deep learning algorithms have gained increased attention due to their power in automatically learning and extracting feature representations, and therefore identifying, processing and extrapolating complex hidden interactions between drugs and targets. In this study, we introduce and implement a new graph-based prediction model called AttentionSiteDTI. Our proposed model utilize the binding sites (pockets) of the proteins as the input for the target protein, and it uses a self-attention mechanism to make the model learn which binding sites of the protein interact with a given ligand. This, indeed, complements the black-box nature of deep learning-based methods and enables interpretability, while achieving state of the art results in drug target interaction prediction task on three datasets. The AttentionSite DTI achieves AUC of 0.97 (for seen proteins), 0.94 (for unseen proteins) in the customized BindingDB dataset, 0.971 in the DUD-E dataset, and 0.991 in the human dataset. In general, the prediction results on these datasets show the superiority of our AttentionSiteDTI compared to previous graph-based models, and our ablation studies proves the effectiveness of our proposed model in prediction of drug-target interactions. In addition, through multidisciplinary collaboration in this work, we further experimentally evaluate the practical potential of our proposed approach. To achieve this, we first computationally predict binding interaction of some candidate compounds with a target protein, and then experimentally validate the binding interactions for these pairs in the laboratory. The high agreement between the computationally-predicted and experimentally observed (measured) drug-target interactions illustrates the potential of our AttentionSiteDTI as effective pre-screening tool in drug repurposing applications.

Predicting the Toxicity of Druggable Proteins to Human Tissues

Assessing in vivo tissue toxicity of therapeutic targets remains a major challenge in drug development and drug safety research. We developed TissueTox, an algorithm that learns from multi-omic features of a target protein and predicts toxicity in human body systems and tissues. Predicted TissueTox scores accurately differentiate drugs that failed clinical trials from those that succeeded, and, importantly, can be used to identify the tissues where toxic events occurred.