Machine Learning, Molecular Docking, and Dynamics-Based Computational Identification of Potential Inhibitors against Lung Cancer.

Lung cancer is the most prevalent cause of cancer deaths worldwide. However, its treatment faces a significant hurdle due to the development of resistance. Phytomolecules are an important source of new chemical entities due to their rich chemical diversity. Therefore, a machine learning (ML) model was developed to computationally identify potential inhibitors using a curated data set of 649 phytomolecules with inhibitory activity against lung cancer cell lines. Four distinct ML approaches, including k-nearest neighbor, random forest, support vector machine, and extreme gradient boosting, were used in conjugation with MACCS and Morgan2 fingerprints to generate the models. It was observed that the random forest model developed by using the MACCS fingerprint shows the best performance. To further explore the chemical space and feature importance, k-means clustering, t-SNE analysis, and mean decrease in impurity had been calculated. Simultaneously, ∼400 000 natural products (NPs) retrieved from the COCONUT database were filtered for pharmacokinetic properties and taken for a multistep screening using docking against epidermal growth factor receptor (EGFR) mutant, a therapeutic drug target of lung cancer. Thereafter, the best-performing random forest model was used to predict the antilung cancer potential of the NPs having binding affinity better than the cocrystal ligand. This allowed the identification of 205 potential inhibitors, wherein the molecules with an indolocarbazole scaffold were enriched in top-scoring molecules. The top three indolocarbazole molecules with the lowest binding energy were further evaluated through 100 ns molecular dynamics (MD) simulations, which suggested that these molecules are strong binders. Also, structural similarity analysis against known drugs revealed that these NPs are similar to staurosporine, which demonstrates potent and selective activity against EGFR mutants. Thereby, the consensus analysis employing ML, molecular docking, and dynamics revealed that the molecules having an indolocarbazole scaffold are the most promising NPs that can act as potential inhibitors against lung cancer.

A systematic pipeline of protein structure selection for computer-aided drug discovery: A case study on T790M/L858R mutant EGFR structures.

Virtual screening (VS) is a routine method to evaluate chemical libraries for lead identification. Therefore, the selection of appropriate protein structures for VS is an essential prerequisite to identify true actives during docking. But the presence of several crystal structures of the same protein makes it difficult to select one or few structures rationally for screening. Therefore, a computational prioritization protocol has been developed for shortlisting crystal structures that identify true active molecules with better efficiency. As identification of small-molecule inhibitors is an important clinical requirement for the T790M/L858R (TMLR) EGFR mutant, it has been selected as a case study. The approach involves cross-docking of 21 co-crystal ligands with all the structures of the same protein to select structures that dock non-native ligands with lower RMSD. The cross docking performance was then correlated with ligand similarity and binding-site conformational similarity. Eventually, structures were shortlisted by integrating cross-docking performance, and ligand and binding-site similarity. Thereafter, binding pose metadynamics was employed to identify structures having stable co-crystal ligands in their respective binding pockets. Finally, different enrichment metrics like BEDROC, RIE, AUAC, and EF1% were evaluated leading to the identification of five TMLR structures (5HCX, 5CAN, 5CAP, 5CAS, and 5CAO). These structures docked a number of non-native ligands with low RMSD, contain structurally dissimilar ligands, have conformationally dissimilar binding sites, harbor stable co-crystal ligands, and also identify true actives early. The present approach can be implemented for shortlisting protein targets of any other important therapeutic kinases.

GlucoKinaseDB: A comprehensive, curated resource of glucokinase modulators for clinical and molecular research.

Glucokinase (GK), an isoform of hexokinase expressed predominantly in liver, pancreas and hypothalamus is crucial to blood glucose management. It is a critical component of the glucose-sensing mechanism of the pancreatic islet cells and glycogen regulation in hepatocytes. GK modulators such as allosteric GKAs (glucokinase activators) and GK-GKRP (glucokinase regulatory protein) disruptors have found potential applications as safer antihyperglycemics. Recent studies have also demonstrated the potential of GK modulators as antiparasitic agents. Researchers targeting GK often undertake the time-consuming task of independently collecting and compiling modulator information due to the lack of any dedicated single-platform resource. Towards this, in the present study we demonstrate the design and development of GlucoKinaseDB (GKDB), a comprehensive, curated, online resource of GK modulators. GKDB contains experimentally derived structural and bioactivity information of 1723 modulators along with their detailed molecular descriptors. The web-interface is user-friendly with features such as in-browser visualization, advanced search queries, cross-links to other databases and original reference etc. The bioactivity and descriptor data can be downloaded in bulk (for entire database) or for individual modulators. The 3D structures are also downloadable in multiple formats. GKDB employs a PHP-based web design with Bootstrap styling and a MySQL database backend. GKDB can be utilized for clinical and molecular research via development of pharmacophore hypotheses, QSAR/QSPR models, predictive machine learning models etc. GKDB is freely accessible online at https://glucokinasedb.in.

Lead optimization, pharmacophore development and scaffold design of protein kinase CK2 inhibitors as potential COVID-19 therapeutics.

Therapeutic agents being designed against COVID-19 have targeted either the virus directly or the host cellular machinery. A particularly attractive host target is the ubiquitous and constitutively active serine-threonine kinase, Protein kinase CK2 (CK2). CK2 enhances viral protein synthesis by inhibiting the sequestration of host translational machinery as stress granules and assists in viral egression via association with the N-protein at filopodial protrusions of the infected cell. CK2 inhibitors such as Silmitasertib have been proposed as possible therapeutic candidates in COVID-19 infections. The present study aims to optimize Silmitasertib, develop pharmacophore models and design unique scaffolds to modulate CK2. The lead optimization phase involved the generation of compounds structurally similar to Silmitasertib via bioisostere replacement followed by a multi-stage docking approach to identify drug-like candidates. Molecular dynamics (MD) simulations were performed for two promising candidates (ZINC-43206125 and PC-57664175) to estimate their binding stability and interaction. Top scoring candidates from the lead optimization phase were utilized to build ligand-based pharmacophore models. These models were then merged with structure-based pharmacophores (e-pharmacophores) to build a hybrid hypothesis. This hybrid hypothesis was validated against a decoy set and used to screen a diverse kinase inhibitors library to identify favored chemical features in the retrieved actives. These chemical features include; an anion, an aromatic ring and an H-bond acceptor. Based on the knowledge of these features; de-novo scaffold design was carried out which identified phenindiones, carboxylated steroids, macrocycles and peptides as novel scaffolds with the potential to modulate CK2.Communicated by Ramaswamy H. Sarma.

Reconstruction and Exploratory Analysis of mTORC1 Signaling Pathway and Its Applications to Various Diseases Using Network-Based Approach.

Mammalian target of rapamycin (mTOR) is a serine-threonine kinase member of the cellular phosphatidylinositol 3-kinase (PI3K) pathway, which is involved in multiple biological functions by transcriptional and translational control. mTOR is a downstream mediator in the PI3K/Akt signaling pathway and plays a critical role in cell survival. In cancer, this pathway can be activated by membrane receptors, including the HER (or ErbB) family of growth factor receptors, the insulin-like growth factor receptor, and the estrogen receptor. In the present work, we congregated an electronic network of mTORC1 built on an assembly of data using natural language processing, consisting of 470 edges (activations/interactions and/or inhibitions) and 206 nodes representing genes/proteins, using the Cytoscape 3.6.0 editor and its plugins for analysis. The experimental design included the extraction of gene expression data related to five distinct types of cancers, namely, pancreatic ductal adenocarcinoma, hepatic cirrhosis, cervical cancer, glioblastoma, and anaplastic thyroid cancer from Gene Expression Omnibus (NCBI GEO) followed by pre-processing and normalization of the data using R & Bioconductor. ExprEssence plugin was used for network condensation to identify differentially expressed genes across the gene expression samples. Gene Ontology (GO) analysis was performed to find out the over-represented GO terms in the network. In addition, pathway enrichment and functional module analysis of the protein-protein interaction (PPI) network were also conducted. Our results indicated NOTCH1, NOTCH3, FLCN, SOD1, SOD2, NF1, and TLR4 as upregulated proteins in different cancer types highlighting their role in cancer progression. The MCODE analysis identified gene clusters for each cancer type with MYC, PCNA, PARP1, IDH1, FGF10, PTEN, and CCND1 as hub genes with high connectivity. MYC for cervical cancer, IDH1 for hepatic cirrhosis, MGMT for glioblastoma and CCND1 for anaplastic thyroid cancer were identified as genes with prognostic importance using survival analysis.

Orthosteric and allosteric modulation of human kinases: A mechanistic view.

Human kinases represent a large family of enzymes with their primary function being the phosphorylation of various biomolecules. Kinases along with G-Protein Coupled Receptors (GPCRs) represent wo of the most common protein targest in drug discovery. Kinases are classified by the substrate they phosphorylate namely, protein kinases, carbohydrate kinases and lipid kinases. These different classes have unique mechanism of action but show considerable overlap in their structural assembly and sequence of chemical modifications. Compounds can modulate kinadse activity by interacting with the enzyme's ATP binding site (orthostatic site) or the allosteric site. These modulators have been classified as Types I, II, III and IV depending on their mode of binding. Inclusion of atypical kinases and pseuokinases in the targetable kinome along with the recent approval of kinase-based therapeutics provides an impetus to the ever-growing field of kinase modulation. This review attempts to summarize the identification, historical stance, catalytic structure and subsequent development of kinases as significvant drug targets with an emphasis on their catalytic machinery and modulation.

De novo design, synthesis and solution conformational study of two didehydroundecapeptides: effect of nature and number of amino acids interspersed between ΔPhe residues.

De novo design of peptides and proteins has recently surfaced as an approach for investigating protein structure and function. This approach vitally tests our knowledge of protein folding and function, while also laying the groundwork for the fabrication of proteins with properties not precedented in nature. The success relies heavily on the ability to design relatively short peptides that can espouse stable secondary structures. To this end, substitution with α,ß-didehydroamino acids, especially α,ß-didehydrophenylalanine (Δ(z)Phe), comes in use for spawning well-defined structural motifs. Introduction of ΔPhe induces ß-bends in small and 3(10)-helices in longer peptide sequences. The present work aims to investigate the effect of nature and the number of amino acids interspersed between two ΔPhe residues in two model undecapeptides, Ac-Gly-Ala-ΔPhe-Ile-Val-ΔPhe-Ile-Val-ΔPhe-Ala-Gly-NH(2) (I) and Boc-Val-ΔPhe-Phe-Ala-Phe-ΔPhe-Phe-Leu-Ala-ΔPhe-Gly-OMe (II). Peptide I was synthesized using solid-phase chemistry and characterized using circular dichroism spectroscopy. Peptide II was synthesized using solution-phase chemistry and characterized using circular dichroism and nuclear magnetic resonance spectroscopy. Peptide I was designed to examine the effect of incorporating ß-strand-favoring residues like valine and isoleucine as spacers between two ΔPhe residues on the final conformation of the resulting peptide. Circular dichroism studies on this peptide have shown the existence of a 3(10)-helical conformation. Peptide II possesses three amino acids as spacers between ΔPhe residues and has been reported to adopt a mixed 3(10)/α-helical conformation using circular dichroism and nuclear magnetic resonance spectroscopy studies.

Alpha,beta-dehydrophenylalanine containing cecropin-melittin hybrid peptides: conformation and activity.

Synthesis and conformational studies of a cecropin-melittin hybrid pentadecapeptide CA(1-7)MEL(2-9), and its three alpha, beta-dehydrophenylalanine (DeltaPhe) containing analogs in water-TFE mixtures are described. DeltaPhe is placed at strategic positions in order to preserve the amphipathicity of the molecule. The wild type CAMEL0 and its three analogs, containing one, two and three DeltaPhe residues namely CAMELDeltaPhe1, CAMELDeltaPhe2 and CAMELDeltaPhe3 respectively were synthesized in solid phase and their conformation determined by CD and NMR. CAMELDeltaPhe2 and CAMELDeltaPhe3 peptides exhibit the presence of 3(10)-helix and beta-turns in the former and only turns in the latter. CAMELDeltaPhe1 peptide was found to have a largely extended conformation. Antibacterial and hemolytic activities of the peptides were also evaluated. CAMELDeltaPhe2 peptide is maximally potent against both Staphylococcus aureus ATCC 259230 and Escherichia coli ATCC 11303. CAMELDeltaPhe1 with a single DeltaPhe at the center shows minimal hemolysis.

Stabilization of unusual structures in peptides using alpha,beta-dehydrophenylalanine: crystal and solution structures of Boc-Pro-DeltaPhe-Val-DeltaPhe-Ala-OMe and Boc-Pro-DeltaPhe-Gly-DeltaPhe-Ala-OMe.

The structures of two dehydropentapeptides, Boc-Pro-DeltaPhe-Val-DeltaPhe-Ala-OMe (I) and Boc-Pro-DeltaPhe-Gly-DeltaPhe-Ala-OMe (II) (Boc: t-butoxycarbonyl), have been determined by nuclear magnetic resonance (NMR), circular dichroism (CD), and X-ray crystallographic studies. The peptide I assumes a S-shaped flat beta-bend structure, characterized by two partially overlapping type II beta-bends and absence of a second 1 <-- 4 (N4--H . . . O1') intramolecular hydrogen bond. This is in contrast to the generally observed 3(10)-helical conformation in peptides with DeltaPhe at alternate positions. This report describes the novel conformation assumed by peptide I and compares it with that of the conserved tip of the V3 loop of the HIV-1 envelope glycoprotein gp120 (sequence, G:P319 to F:P324, PDB code 1ACY). The tip of the V3 loop also assumes a S-shaped conformation with Arg:P322, making an intramolecular side-chain-backbone interaction with the carbonyl oxygen of Gly:P319. Interestingly, in peptide I, C(gamma)HVal(3) makes a similar side-chain-backbone C--H . . . O hydrogen bond with the carbonyl oxygen of the Boc group. The observed overall similarity indicates the possible use of the peptide as a viral antagonist or synthetic antigen. Peptide II adopts a unique turn followed by a 3(10)-helix. Both peptides I and II are classical examples of stabilization of unusual structures in oligopeptides.

Peptide design using alpha,beta-dehydro amino acids: from beta-turns to helical hairpins.

Incorporation of alpha,beta-dehydrophenylalanine (DeltaPhe) residue in peptides induces folded conformations: beta-turns in short peptides and 3(10)-helices in larger ones. A few exceptions-namely, alpha-helix or flat beta-bend ribbon structures-have also been reported in a few cases. The most favorable conformation of DeltaPhe residues are (phi,psi) approximately (-60 degrees, -30 degrees ), (-60 degrees, 150 degrees ), (80 degrees, 0 degrees ) or their enantiomers. DeltaPhe is an achiral and planar residue. These features have been exploited in designing DeltaPhe zippers and helix-turn-helix motifs. DeltaPhe can be incorporated in both right and left-handed helices. In fact, consecutive occurrence of three or more DeltaPhe amino acids induce left-handed screw sense in peptides containing L-amino acids. Weak interactions involving the DeltaPhe residue play an important role in molecular association. The C--H.O==C hydrogen bond between the DeltaPhe side-chain and backbone carboxyl moiety, pi-pi stacking interactions between DeltaPhe side chains belonging to enantiomeric helices have shown to stabilize folding. The unusual capability of a DeltaPhe ring to form the hub of multicentered interactions namely, a donor in aromatic C--H.pi and C--H.O==C and an acceptor in a CH(3).pi interaction suggests its exploitation in introducing long-range interactions in the folding of supersecondary structures.

Role of a two-residue spacer in an alpha,beta-Didehydrophenylalanine containing hexapeptide: crystal and solution structure of Boc-val-deltaPhe-Leu-Ala-deltaPhe-Ala-OMe.

The peptide Boc-Val1-deltaPhe2-Leu3-Ala4-deltaPhe5-Ala6-OMe has been examined for the structural consequence of placing a two-residue segment between the deltaPhe residues. The peptide is stabilized by four consecutive beta-turns. The overall conformation of the molecule is a right-handed 3(10)-helix, with average (phi, psi) values (-67.7 degrees, -22.7 degrees), unwound at the C-terminus. The 1H NMR results also suggest that the peptide maintains its 3(10)-helical structure in solution as observed in the crystal state. The crystal structure is stabilized through head-to-tail hydrogen bonds and a repertoire of aromatic interactions laterally directed between adjacent helices, which are antiparallel to each other. The aromatic ring of deltaPhe5 forms the hub of multicentred interactions, namely as a donor in aromatic C-H...pi and aromatic C-H...O=C interactions and as an acceptor in a CH3...pi interaction. The present structure uniquely illustrates the unusual capability of a deltaPhe ring to host such concerted interactions and suggests its exploitation in introducing long-range interactions in the folding of supersecondary structures.

Dehydrophenylalanine zippers: strong helix-helix clamping through a network of weak interactions.

A decapeptide Boc-L-Ala-(Delta Delta Phe)(4)-L-Ala-(Delta Delta Phe)3-Gly-OMe (Peptide I) was synthesized to study the preferred screw sense of consecutive alpha,beta-dehydrophenylalanine (Delta Delta Phe) residues. Crystallographic and CD studies suggest that, despite the presence of two L-Ala residues in the sequence, the decapeptide does not have a preferred screw sense. The peptide crystallizes with two conformers per asymmetric unit, one of them a slightly distorted right-handed 3(10)-helix (X) and the other a left-handed 3(10)-helix (Y) with X and Y being antiparallel to each other. An unanticipated and interesting observation is that in the solid state, the two shape-complement molecules self-assemble and interact with an extensive network of C-H...O hydrogen bonds and pi-pi interactions, directed laterally to the helix axis with amazing regularity. Here, we present an atomic resolution picture of the weak interaction mediated mutual recognition of two secondary structural elements and its possible implication in understanding the specific folding of the hydrophobic core of globular proteins and exploitation in future work on de novo design.