Identification of Dual Natural Inhibitors for Chronic Myeloid Leukemia by Virtual Screening, Molecular Dynamics Simulation and ADMET Analysis

Synthesis and Characterization of Folic Acid-Conjugated Terbium Complexes as Luminescent Probes for Targeting Folate Receptor-Expressing Cells

Several conjugates between folic acid and a series of kinetically stable lanthanide complexes have been synthesized, using amide coupling and azide-alkyne cycloaddition methodologies to link the metal-binding domain to folate through a variety of spacer groups. While all these complexes exhibit affinity for the folate receptor, it is clear that the point of attachment to folate is essential, with linkage through the γ-carboxylic acid giving rise to significantly enhanced receptor affinity. All the conjugates studied show affinities consistent with displacing biological circulating folate derivatives, 5-methyltetrahydrofolate, from folate receptors. All the complexes exhibit luminescence with a short-lived component arising from ligand fluorescence overlaid on a much longer lived terbium-centered component. These can be separated using time-gating methods. From the results obtained, the most promising approach to achieve sensitized luminescence in these systems requires incorporating a sensitizing chromophore close to the lanthanide.

Artificial intelligence in fusion protein three-dimensional structure prediction: Review and perspective

Recent advancements in artificial intelligence (AI) have accelerated the prediction of unknown protein structures. However, accurately predicting the three-dimensional (3D) structures of fusion proteins remains a difficult task because the current AI-based protein structure predictions are focused on the WT proteins rather than on the newly fused proteins in nature. Following the central dogma of biology, fusion proteins are translated from fusion transcripts, which are made by transcribing the fusion genes between two different loci through the chromosomal rearrangements in cancer. Accurately predicting the 3D structures of fusion proteins is important for understanding the functional roles and mechanisms of action of new chimeric proteins. However, predicting their 3D structure using a template-based model is challenging because known template structures are often unavailable in databases. Deep learning (DL) models that utilize multi-level protein information have revolutionized the prediction of protein 3D structures. In this review paper, we highlighted the latest advancements and ongoing challenges in predicting the 3D structure of fusion proteins using DL models. We aim to explore both the advantages and challenges of employing AlphaFold2, RoseTTAFold, tr-Rosetta and D-I-TASSER for modelling the 3D structures. HIGHLIGHTS: This review provides the overall pipeline and landscape of the prediction of the 3D structure of fusion protein. This review provides the factors that should be considered in predicting the 3D structures of fusion proteins using AI approaches in each step. This review highlights the latest advancements and ongoing challenges in predicting the 3D structure of fusion proteins using deep learning models. This review explores the advantages and challenges of employing AlphaFold2, RoseTTAFold, tr-Rosetta, and D-I-TASSER to model 3D structures.

Structure-Based Virtual Screening of Helicobacter pylori SecA Inhibitors

The human bacterial pathogen Helicobacter pylori causes a range of gastric diseases. The killing rate of Helicobacter pylori is declining year by year because of high antibiotics resistance. It is urgent to develop new target and novel anti- Helicobacter pylori drugs. As an "energy pump" for bacterial cells, SecA is essential for bacterial growth and drives bacterial protein transmembrane transport, moreover SecA is absent in mammals, all of which nominate SecA as an attractive antimicrobial target. Here, we provided a structure-based virtual screening method to screen the 3D-diversity natural-product-like screening library against SecA for novel anti- Helicobacter pylori inhibitors with novel scaffolds. In this study, homology modeling was used to construct the three-dimensional structure of Helicobacter pylori SecA. Two rounds of molecular docking were then used to find new small-molecule inhibitors of SecA, identifying six lead candidates that maintained key interactions with the binding pocket. After that, molecular dynamics simulations were used to explore more accurate ligand-receptor binding modes in states close to natural conditions. Encouragingly, all six compounds were relatively stable during the simulation. Apart from that the binding free energy calculation based on MM/PBSA demonstrated favorable results of < -13.642 kcal/mol. Finally, ADME-T analysis indicated that these compounds were also sufficiently druggable. All six compounds can be well combined with the crystal structure, which further facilitate the development of SecA inhibitors and lead compounds against Helicobacter pylori.

Fisetin 8-C-glucoside as entry inhibitor in SARS CoV-2 infection: molecular modelling study

Coronaviruses are RNA viruses that infect varied species including humans. TMPRSS2 is gateway for SARS CoV-2 entry into the host cell. It causes proteolytic activation of spike protein and discharge of the peptide into host cell. The TMPRSS2 inhibition could be one of the approaches to stop the viral entry, therefore, interaction pattern and binding energies for Fisetin and TMPRSS2 have been explored in the present study. TMPRSS2 peptide was used for homology modelling and then for further study. Molecular docking score and MMGBSA Binding energy of Fisetin was better than Nafamostat, a known inhibitor of TMPRSS2. Post docking MM-GBSA free energy for Fisetin and Nafamostat was -42.78 and -21.11 kcal/mol, respectively. Fisetin forms H bond with Val 25, His 41, Lys 42, Lys 45, Glu 44, Ser186. Nafamostat formed H bonds with Lys 85, Asp 90, Asp 203. RMSD plots of TMPRSS2, TMPRSS2-Fisetin and TMPRSS2-Nafamostat complex showed stable profile with very small fluctuation during entire simulation of 150 ns. Significant decrease in TMPRSS2-Fisetin and TMPRSS2-Nafamostat complex fluctuation occurred around His 41, Glu 44, Gly 136, Ser 186 in RMSF study. During simulation Fisetin interaction was observed with residues Val 25, His 41, Glu 44, Lys 45, Lys 87, Gly 136, Gln 183, Ser 186 likewise interaction of Nafamostat with Lys 85, Asp 90, Asn 163, Asp 203 and Ser 205. Post simulation MM-GBSA free energy was found to be -51.87 ± 4.3 and -48.23 ± 4.39 kcal/mol for TMPRSS2 with Fisetin and Nafamostat, respectively.Communicated by Ramaswamy H. Sarma.

The interaction of the bioflavonoids with five SARS-CoV-2 proteins targets: An in silico study

Flavonoids have been shown to have antioxidant, anti-inflammatory, anti-proliferative, antibacterial and antiviral efficacy. Therefore, in this study, we choose 85 flavonoid compounds and screened them to determine their in-silico interaction with protein targets crucial for SARS-CoV-2 infection. The five important targets chosen were the main protease (Mpro), Spike receptor binding domain (Spike-RBD), RNA - dependent RNA polymerase (RdRp or Nsp12), non-structural protein 15 (Nsp15) of SARS-CoV-2 and the host angiotensin converting enzyme-2 (ACE-2) spike-RBD binding domain. The compounds were initially docked at the selected sites and further evaluated for binding free energy, using the molecular mechanics/generalized Born surface area (MMGBSA) method. The three compounds with the best binding scores were subjected to molecular dynamics (MD) simulations. The compound, tribuloside, had a high average binding free energy of -86.99 and -88.98 kcal/mol for Mpro and Nsp12, respectively. The compound, legalon, had an average binding free energy of -59.02 kcal/mol at the ACE2 spike-RBD binding site. The compound, isosilybin, had an average free binding energy of -63.06 kcal/mol for the Spike-RBD protein. Overall, our results suggest that tribuloside, legalon and isosilybin should be evaluated in future studies to determine their efficacy to inhibit SARS-CoV-2 infectivity.

Identification of repurposed protein kinase B binders from FDA-approved drug library: a hybrid-structure activity relationship and systems modeling based approach

Food and Drug Administration (FDA)-approved drugs may be repurposed against those diseases, for which their therapeutic action has not been described. The present study deals with repurposing FDA-approved drugs for selective targeting of protein kinase B (PKB/Akt) for anti-cancer activity, through a two-tier (Cell and Target) model hybridization protocol implemented with support vector machine-based learning method. The hybridization was done as per rules of reaction kinetics. The hybridization process was facilitated as a standalone application for free access at https://github.com/undwivedi/Akt-Selective.git. The selectivity of the ligands for PKB/Akt binding was also evaluated on the basis of mitophagy system model for anti-apoptotic activity. Screening of the FDA-approved drug library, using the developed H- SAR model, led to identification of four compounds (Cas nos. 94749-08-3, 57808-66-9, 62-13-5, 76-43-7), bearing the selectivity for PKB/Akt. Since, the identified compounds have already crossed the barriers of absorption, distribution, metabolism, excretion, toxicity in clinical trials, therefore are safe to be considered for repurposing individually or in combination with other drugs.Communicated by Ramaswamy H. Sarma.

In silico frameworks for systematic pre-clinical screening of potential anti-leukemia therapeutics

INTRODUCTION

Leukemia is a collection of highly heterogeneous cancers that arise from neoplastic transformation and clonal expansion of immature hematopoietic cells. Post-treatment recurrence is high, especially among elderly patients, thus necessitating more effective treatment modalities. Development of novel anti-leukemic compounds relies heavily on traditional in vitro screens which require extensive resources and time. Therefore, integration of in silico screens prior to experimental validation can improve the efficiency of pre-clinical drug development. Areas covered: This article reviews different methods and frameworks used to computationally screen for anti-leukemic agents. In particular, three approaches are discussed including molecular docking, transcriptomic integration, and network analysis. Expert opinion: Today's data deluge presents novel opportunities to develop computational tools and pipelines to screen for likely therapeutic candidates in the treatment of leukemia. Formal integration of these methodologies can accelerate and improve the efficiency of modern day anti-leukemic drug discovery and ease the economic and healthcare burden associated with it.

Exploring dual inhibitors for STAT1 and STAT5 receptors utilizing virtual screening and dynamics simulation validation

Signal transducer and activator of transcription (STAT) proteins are latent cytoplasmic transcription factors that transduce signals from cytokines and growth factors to the nucleus and thereby regulate the expression of a variety of target genes. Although mutations of STATs have not been reported in human tumors but the activity of several members of the family, such as STAT1 and STAT5, is deregulated in a variety of human carcinoma. STAT1 and STAT5 share a structural similarity with a highly conserved SH2 domain which is responsible for the activation of STAT proteins on interaction with phosphotyrosine motifs for specific STAT-receptor contacts and STAT dimerization. The purpose of this study is to identify domain-specific dual inhibitors for both STAT1 and STAT5 proteins from a database of natural products and natural product-like compounds comprising of over 90,000 compounds. Virtual screening-based molecular docking was performed in order to find novel natural dual inhibitors. Further, the study was supported by the 50-ns molecular dynamics simulation for receptor-ligand complexes (STAT1-STOCK-1N-69677 and STAT5-STOCK-1N-69677). Analysis of molecular interactions in the SH2 domains of both STAT1 and STAT5 proteins with the ligand revealed few conserved amino acid residues which are responsible to stabilize the ligands within the binding pocket through bonded and non-bonded interactions. This study suggested that compound STOCK-1N-69677 might putatively act as a dual inhibitor of STAT1 and STAT5 receptors, through its binding to the SH2 domain.