Evaluation of off-targets predicted by sgRNA design tools.

The ease of programming CRISPR/Cas9 system for targeting a specific location within the genome has paved way for many clinical and industrial applications. However, its widespread use is still limited owing to its off-target effects. Though this off-target activity has been reported to be dependent on both sgRNA sequence and experimental conditions, a clear understanding of the factors imparting specificity to CRISPR/Cas9 system is important. A machine learning-based computational model has been developed for prediction of off-targets with more likelihood to be cleaved in vivo with an accuracy of 91.49%. The sequence features important for the prediction of positive off-targets were found to be accessibility, mismatches, GC-content and position-specific conservation of nucleotides. The instructions and code to generate the dataset and reproduce the analysis has been made available at http://web.iitd.ac.in/crispcut/off-targets/.

CRISPcut: A novel tool for designing optimal sgRNAs for CRISPR/Cas9 based experiments in human cells.

The ability to direct the CRISPR/Cas9 nuclease to a unique target site within a genome would have broad use in targeted genome engineering. However, CRISPR RNA is reported to bind to other genomic locations that differ from the intended target site by a few nucleotides, demonstrating significant off-target activity. We have developed the CRISPcut tool that screens the off-targets using various parameters and predicts the ideal genomic target for -guide RNAs in human cell lines. sgRNAs for four different types of Cas9 nucleases can be designed with an option for the user to work with different PAM sequences. Direct experimental measurement of genome-wide DNA accessibility is incorporated that effectively restricts the prediction of CRISPR targets to open chromatin. An option to predict target sites for paired CRISPR nickases is also provided. The tool has been validated using a dataset of experimentally used sgRNA and their identified off-targets. URL: http://web.iitd.ac.in/crispcut.

Bioinformatics and Molecular Insights to Anti-Metastasis Activity of Triethylene Glycol Derivatives.

The anti-metastatic and anti-angiogenic activities of triethylene glycol derivatives have been reported. In this study, we investigated their molecular mechanism(s) using bioinformatics and experimental tools. By molecular dynamics analysis, we found that (i) triethylene glycol dimethacrylate (TD-10) and tetraethylene glycol dimethacrylate (TD-11) can act as inhibitors of the catalytic domain of matrix metalloproteinases (MMP-2, MMP-7 and MMP-9) by binding to the S1' pocket of MMP-2 and MMP-9 and the catalytic Zn ion binding site of MMP-7, and that (ii) TD-11 can cause local disruption of the secondary structure of vascular endothelial growth factor A (VEGFA) dimer and exhibit stable interaction at the binding interface of VEGFA receptor R1 complex. Cell-culture-based in vitro experiments showed anti-metastatic phenotypes as seen in migration and invasion assays in cancer cells by both TD-10 and TD-11. Underlying biochemical evidence revealed downregulation of VEGF and MMPs at the protein level; MMP-9 was also downregulated at the transcriptional level. By molecular analyses, we demonstrate that TD-10 and TD-11 target stress chaperone mortalin at the transcription and translational level, yielding decreased expression of vimentin, fibronectin and hnRNP-K, and increase in extracellular matrix (ECM) proteins (collagen IV and E-cadherin) endorsing reversal of epithelial-mesenchymal transition (EMT) signaling.

Marine Carotenoid Fucoxanthin Possesses Anti-Metastasis Activity: Molecular Evidence.

Fucoxanthin is commonly found in marine organisms; however, to date, it has been one of the scarcely explored natural compounds. We investigated its activities in human cancer cell culture-based viability, migration, and molecular assays, and found that it possesses strong anticancer and anti-metastatic activities that work irrespective of the p53 status of cancer cells. In our experiments, fucoxanthin caused the transcriptional suppression of mortalin. Cell phenotype-driven molecular analyses on control and treated cells demonstrated that fucoxanthin caused a decrease in hallmark proteins associated with cell proliferation, survival, and the metastatic spread of cancer cells at doses that were relatively safe to the normal cells. The data suggested that the cancer therapy regimen may benefit from the recruitment of fucoxanthin; hence, it warrants further attention for basic mechanistic studies as well as drug development.

Identifying synthetic lethal targets using CRISPR/Cas9 system.

Synthetic lethality occurs when co-occurrence of two genetic events is unfavorable for the survival of the cell or organism. The conventional approach of high throughput screening of synthetic lethal targets using chemical compounds has been replaced by RNAi technology. CRISPR/Cas9, an RNA guided endonuclease system is the most recent technology for this work. Here, we have discussed the major considerations involved in designing a CRISPR/Cas9 based screening experiment for identification of synthetic lethal targets. It mainly includes CRISPR library to be used, cell types for conducting the experiment, the most appropriate screening strategy and ways of selecting the desired phenotypes from the complete cell population. The complete knockdown of genes can be achieved using CRISPR/Cas9 knockout libraries. For higher quality loss-of-function screens, haploid cells with defective homology-directed DNA repair mechanism could be used. Two widely used screening formats include arrayed and pooled screens followed by negative or positive selection of the cells with desired phenotype. However, pooled screening format with negative selection of cells serves the best. The advantages of using CRISPR/Cas9 system over the other RNAi approaches have also been discussed. Finally, some studies using CRISPR/Cas9 for genome-wide knockout screening in human cells and computational approaches for identification of synthetic lethal interactions have been discussed.

Function and structure-based screening of compounds, peptides and proteins to identify drug candidates.

Drug discovery in simple words is all about finding small molecular compounds that possess the potential to interact with specific bio-macromolecules, mainly proteins, thereby bringing a desired effect in the functioning of the target molecules. Virtual screening of large compound libraries using computational approaches has come up as a great alternative to cost and labor-intensive high-throughput screening carried out in laboratories. Virtual high-throughput screening enormously reduces the number of compounds for systematic analysis using biochemical assays before entering the clinical trials. Here, we first give a brief overview of the rationale behind virtual screening, types of virtual screening - structure-based, ligand-based and inverse virtual screening, and challenges that need to be addressed to improve the existing strategies. Subsequently, we describe the methodology adopted for virtual screening of small molecules, peptides and proteins. Finally, we use few case studies to provide a better insight to the application of computer-aided high-throughput screening.

Molecular dynamics-based identification of novel natural mortalin-p53 abrogators as anticancer agents.

INTRODUCTION: Cancer is one of the leading causes of mortality worldwide that requires attention in terms of extensive study and research. Eradication of mortalin-p53 interaction that leads to the inhibition of transcriptional activation or blocking of p53 from functioning as a suppressor and induction of nuclear translocation of p53 can prove to be one of the useful approaches for cancer management. RESULTS: In this study, we used structure-based approach to target the p53-binding domain of mortalin in order to prevent mortalin-p53 complex formation. We screened compounds from ZINC database against the modeled mortalin protein using Glide virtual screening. The top two compounds, DTOM (ZINC 28639308) and TTOM (ZINC 38143676) with Glide score of -12.27 and -12.16, respectively, were identified with the potential to abrogate mortalin-p53 interaction. Finally, molecular dynamics simulations were used to analyze the dynamic stability of the ligand-bound complex and it was observed that residues Tyr196, Asn198, Val264 and Thr267 were involved in intermolecular interactions in both the simulated ligand-bound complexes, and thus, these residues may have a paramount role in stabilizing the binding of the ligands with the protein. CONCLUSION: These detailed insights can further facilitate the development of potent inhibitors against mortalin-p53 complex.

A theoretical investigation of DNA dynamics and desolvation kinetics for zinc finger proteinZif268.

BACKGROUND: Transcription factors, regulating the expression inventory of a cell, interact with its respective DNA subjugated by a specific recognition pattern, which if well exploited may ensure targeted genome engineering. The mostly widely studied transcription factors are zinc finger proteins that bind to its target DNA via direct and indirect recognition levels at the interaction interface. Exploiting the binding specificity and affinity of the interaction between the zinc fingers and the respective DNA can help in generating engineered zinc fingers for therapeutic applications. Experimental evidences lucidly substantiate the effect of indirect interaction like DNA deformation and desolvation kinetics, in empowering ZFPs to accomplish partial sequence specificity functioning around structural properties of DNA. Exploring the structure-function relationships of the existing zinc finger-DNA complexes at the indirect recognition level can aid in predicting the probable zinc fingers that could bind to any target DNA. Deformation energy, which defines the energy required to bend DNA from its native shape to its shape when bound to the ZFP, is an effect of indirect recognition mechanism. Water is treated as a co-reactant for unfurling the affinity studies in ZFP-DNA binding equilibria that takes into account the unavoidable change in hydration that occurs when these two solvated surfaces come into contact. RESULTS: Aspects like desolvation and DNA deformation have been theoretically investigated based on simulations and free energy perturbation data revealing a consensus in correlating affinity and specificity as well as stability for ZFP-DNA interactions. Greater loss of water at the interaction interface of the DNA calls for binding with higher affinity, eventually distorting the DNA to a greater extent accounted by the change in major groove width and DNA tilt, stretch and rise. CONCLUSION: Most prediction algorithms for ZFPs do not account for water loss at the interface. The above findings may significantly affect these algorithms. Further the sequence dependent deformation in the DNA upon complexation with our prototype as well as preference of bases at the 2nd and 3rd position of the repeating triplet provide an absolutely new insight about the indirect interactions undergoing a change that have not been probed yet.

Computational identification of novel natural inhibitors of glucagon receptor for checking type II diabetes mellitus.

BACKGROUND: Interaction of the small peptide hormone glucagon with glucagon receptor (GCGR) stimulates the release of glucose from the hepatic cells during fasting; hence GCGR performs a significant function in glucose homeostasis. Inhibiting the interaction between glucagon and its receptor has been reported to control hepatic glucose overproduction and thus GCGR has evolved as an attractive therapeutic target for the treatment of type II diabetes mellitus. RESULTS: In the present study, a large library of natural compounds was screened against 7 transmembrane domain of GCGR to identify novel therapeutic molecules that can inhibit the binding of glucagon with GCGR. Molecular dynamics simulations were performed to study the dynamic behaviour of the docked complexes and the molecular interactions between the screened compounds and the ligand binding residues of GCGR were analysed in detail. The top scoring compounds were also compared with already documented GCGR inhibitors- MK-0893 and LY2409021 for their binding affinity and other ADME properties. Finally, we have reported two natural drug like compounds PIB and CAA which showed good binding affinity for GCGR and are potent inhibitor of its functional activity. CONCLUSION: This study contributes evidence for application of these compounds as prospective small ligand molecules against type II diabetes. Novel natural drug like inhibitors against the 7 transmembrane domain of GCGR have been identified which showed high binding affinity and potent inhibition of GCGR.

Structural insights into mode of actions of novel natural Mycobacterium protein tyrosine phosphatase B inhibitors.

BACKGROUND: Tuberculosis has become a major health problem being the second leading cause of death worldwide. Mycobacterium tuberculosis secretes a virulence factor, protein tyrosine phosphatase B (mPTPB) in the cytoplasm of host macrophage which suppresses its natural innate immune response and helps the pathogen survive and proliferate in the phagosome. The present study aims at indentifying potent inhibitors of mPTPB by using computational approaches of ligand based molecular modeling and docking studies. RESULTS: A 3D QSAR model was developed using a set of benzofuran salicylic acid based mPTPB inhibitors with experimentally known IC50 values. The model was generated using the statistical method of principle component regression analysis in combination with step wise forward variable selection algorithm. It was observed that steric and hydrophobic descriptors positively contribute towards the inhibitory activity of the ligands. The developed model had a robust internal as well as external predictive power as indicated by the q(2) value of 0.8920 and predicted r(2) value of 0.8006 respectively. Hence, the generated model was used to screen a large set of naturally occurring chemical compounds and predict their biological activity to identify more potent natural compounds targeting mPTPB. The two top potential hits (with pIC50 value of 1.459 and 1.677 respectively) had a similar interaction pattern as that of the most potent compound (pIC50 = 1.42) of the congeneric series. CONCLUSION: The contour plot provided a better understanding of the relationship between structural features of substituted benzofuran salicylic acid derivatives and their activities which would facilitate design of novel mPTPB inhibitors. The QSAR modeling was used to obtain an equation, correlating the important steric and hydrophobic descriptors with the pIC50 value. Thus, we report two natural compounds of inhibitory nature active against mPTPB enzyme of Mycobacterium tuberculosis. These inhibitors have the potential to evolve as lead molecules in the development of drugs for the treatment of tuberculosis.

Mechanistic insights into mode of action of potent natural antagonists of BACE-1 for checking Alzheimer's plaque pathology.

Alzheimer's is a neurodegenerative disorder resulting in memory loss and decline in cognitive abilities. Accumulation of extracellular beta amyloidal plaques is one of the major pathology associated with this disease. ß-Secretase or BACE-1 performs the initial and rate limiting step of amyloidic pathway in which 37-43 amino acid long peptides are generated which aggregate to form plaques. Inhibition of this enzyme offers a viable prospect to check the growth of these plaques. Numerous efforts have been made in recent years for the generation of BACE-1 inhibitors but many of them failed during the preclinical or clinical trials due to drug related or drug induced toxicity. In the present work, we have used computational methods to screen a large dataset of natural compounds to search for small molecules having BACE-1 inhibitory activity with low toxicity to normal cells. Molecular dynamics simulations were performed to analyze molecular interactions between the screened compounds and the active residues of the enzyme. Herein, we report two natural compounds of inhibitory nature active against ß-secretase enzyme of amyloidic pathway and are potent lead molecules against Alzheimer's disease.

Embelin inhibits TNF-α converting enzyme and cancer cell metastasis: molecular dynamics and experimental evidence.

BACKGROUND: Embelin, a quinone derivative, is found in the fruits of Embelia ribes Burm (Myrsinaceae). It has been shown to have a variety of therapeutic potentials including anthelmintic, anti-tumor, anti-diabetic, anti-bacterial and anti-inflammation. Inflammation is an immunological response to external harmful stimuli and is regulated by an endogenous pyrogen and pleiotropic pro-inflammatory cytokine, tumor necrosis factor alpha (TNF-α). TNF-α production has been implicated in a variety of other human pathologies including neurodegeneration and cancer. Several studies have shown that the anti-inflammatory activity of embelin is mediated by reduction in TNF-α. The latter is synthesized as a membrane anchored protein (pro-TNF-α); the soluble component of pro-TNF-α is then released into the extracellular space by the action of a protease called TNF-α converting enzyme (TACE). TACE, hence, has been proposed as a therapeutic target for inflammation and cancer. METHODS: We used molecular docking and experimental approaches to investigate the docking potential and molecular effects of embelin to TACE and human cancer cell characteristics, respectively. RESULTS: We demonstrate that embelin is a potential inhibitor of TACE. Furthermore, in vitro studies revealed that it inhibits malignant properties of cancer cells through inactivation of metastatic signaling molecules including MMPs, VEGF and hnRNP-K in breast cancer cells. CONCLUSION: Based on the molecular dynamics and experimental data, embelin is proposed as a natural anti-inflammatory and anticancer drug.

TCR-ESM: Employing protein language embeddings to predict TCR-peptide-MHC binding.

Cognate target identification for T-cell receptors (TCRs) is a significant barrier in T-cell therapy development, which may be overcome by accurately predicting TCR interaction with peptide-bound major histocompatibility complex (pMHC). In this study, we have employed peptide embeddings learned from a large protein language model- Evolutionary Scale Modeling (ESM), to predict TCR-pMHC binding. The TCR-ESM model presented outperforms existing predictors. The complementarity-determining region 3 (CDR3) of the hypervariable TCR is located at the center of the paratope and plays a crucial role in peptide recognition. TCR-ESM trained on paired TCR data with both CDR3α and CDR3ß chain information performs significantly better than those trained on data with only CDR3ß, suggesting that both TCR chains contribute to specificity, the relative importance however depends on the specific peptide-MHC targeted. The study illuminates the importance of MHC information in TCR-peptide binding which remained inconclusive so far and was thought dependent on the dataset characteristics. TCR-ESM outperforms existing approaches on external datasets, suggesting generalizability. Overall, the potential of deep learning for predicting TCR-pMHC interactions and improving the understanding of factors driving TCR specificity are highlighted. The prediction model is available at http://tcresm.dhanjal-lab.iiitd.edu.in/ as an online tool.

Corrigendum to 'Cancer chemotherapy and beyond: Current status, drug candidates, associated risks and progress in targeted therapeutics' [Genes & Diseases 10 (2023) 1367-1401].

[This corrects the article DOI: 10.1016/j.gendis.2022.02.007.].

Resisting resistant Mycobacterium tuberculosis naturally: mechanistic insights into the inhibition of the parasite's sole signal peptidase Leader peptidase B.

Tuberculosis (TB) is the second highest cause of mortality after HIV/AIDS and is one of the leading public health problems worldwide. The growing resistance to anti-TB drugs and the recalcitrant nature of tenacious infections present arduous challenges for the treatment of TB. Thus, the need to develop therapeutics against novel drug targets to help overcome multi-drug resistant TB is inevitable. Leader peptidase B (LepB), the sole signal peptidase of Mycobacterium tuberculosis (MTb), is one such potential drug target. The present work aims at identifying potential inhibitors of LepB, so as to repress the formation of the functional proteins essential for the growth and pathogenesis of MTb. In this study, we screened a large dataset of natural compounds against LepB using a high throughput approach. The screening was directed toward a binding pocket consisting of residues, some of which are critical for the catalytic activity of the enzyme, while others are part of the conserved domains of the signal peptidases. We also carried out molecular dynamics simulations of the two top-scoring compounds in order to study their molecular interactions with the active site functional residues of LepB and also to assess their dynamic behavior. We report herein two prospective non-covalent type inhibitory drugs of natural origin which are active against tuberculosis. These lead molecules possess improved binding properties, have low toxicity and are specific against MTb.

Effect of Withaferin-A, Withanone, and Caffeic Acid Phenethyl Ester on DNA Methyltransferases: Potential in Epigenetic Cancer Therapy.

BACKGROUND: DNA methyltransferases (DNMTs) have been reported to be potential drug targets in various cancers. The major hurdle in inhibiting DNMTs is the lack of knowledge about different DNMTs and their role in the hypermethylation of gene promoters in cancer cells. Lack of information on specificity, stability, and higher toxicity of previously reported DNMT inhibitors is the major reason for inadequate epigenetic cancer therapy. DNMT1 and DNMT3A are the two DNMTs that are majorly overexpressed in cancers. OBJECTIVE: In this study, we have presented computational and experimental analyses of the potential of some natural compounds, withaferin A (Wi-A), withanone (Wi-N), and caffeic acid phenethyl ester (CAPE), as DNMT inhibitors, in comparison to sinefungin (SFG), a known dual inhibitor of DNMT1 and DNMT3A. METHODS: We used classical simulation methods, such as molecular docking and molecular dynamics simulations, to investigate the binding potential and properties of the test compounds with DNMT1 and DNMT3A. Cell culture-based assays were used to investigate the inactivation of DNMTs and the resulting hypomethylation of the p16INK4A promoter, a key tumour suppressor that is inactivated by hypermethylation in cancer cells, resulting in upregulation of its expression. RESULTS: Among the three test compounds (Wi-A, Wi-N, and CAPE), Wi-A showed the highest binding affinity to both DNMT1 and DNMT3A; CAPE showed the highest affinity to DNMT3A, and Wi-N showed a moderate affinity interaction with both. The binding energies of Wi-A and CAPE were further compared with SFG. Expression analysis of DNMTs showed no difference between control and treated cells. Cell viability and p16INK4A expression analysis showed a dose-dependent decrease in viability, an increase in p16INK4A, and a stronger effect of Wi-A compared to Wi-N and CAPE. CONCLUSION: The study demonstrated the differential binding ability of Wi-A, Wi-N, and CAPE to DNMT1 and DNMT3A, which was associated with their inactivation, leading to hypomethylation and desilencing of the p16INK4A tumour suppressor in cancer cells. The test compounds, particularly Wi-A, have the potential for cancer therapy.

Cancer chemotherapy and beyond: Current status, drug candidates, associated risks and progress in targeted therapeutics.

Cancer is an abnormal state of cells where they undergo uncontrolled proliferation and produce aggressive malignancies that causes millions of deaths every year. With the new understanding of the molecular mechanism(s) of disease progression, our knowledge about the disease is snowballing, leading to the evolution of many new therapeutic regimes and their successive trials. In the past few decades, various combinations of therapies have been proposed and are presently employed in the treatment of diverse cancers. Targeted drug therapy, immunotherapy, and personalized medicines are now largely being employed, which were not common a few years back. The field of cancer discoveries and therapeutics are evolving fast as cancer type-specific biomarkers are progressively being identified and several types of cancers are nowadays undergoing systematic therapies, extending patients' disease-free survival thereafter. Although growing evidence shows that a systematic and targeted approach could be the future of cancer medicine, chemotherapy remains a largely opted therapeutic option despite its known side effects on the patient's physical and psychological health. Chemotherapeutic agents/pharmaceuticals served a great purpose over the past few decades and have remained the frontline choice for advanced-stage malignancies where surgery and/or radiation therapy cannot be prescribed due to specific reasons. The present report succinctly reviews the existing and contemporary advancements in chemotherapy and assesses the status of the enrolled drugs/pharmaceuticals; it also comprehensively discusses the emerging role of specific/targeted therapeutic strategies that are presently being employed to achieve better clinical success/survival rate in cancer patients.

Protein Structure Readouts of Cancer Drivers for Precision Medicine.

Cancer is fundamentally a disease of perturbed genes. Although many mutations can be marked in the genome of cancer or a transformed cell, the initiation and progression are driven by only a few mutational events, viz., driver mutations that progressively govern and execute the functional impacts. The driver mutations are thus believed to dictate and dysregulate the subsequent cellular proliferative function/decisions, thereby producing a cancerous state. Therefore, identifying the driver events from the genomic alterations in a patient's cancer cell gained enormous attention recently for designing better targeting therapies and paving the way for precision cancer medicine. With rolling advancements in high-throughput omic technologies, analysis of genetic variations and gene expression profiles for cancer patients has become a routine clinical practice. However, it is anticipated that protein structural alterations resulting from such driver mutations can provide more direct and clinically relevant evidence of disease states than genetic signatures alone. This review comprehensively discusses various aspects and approaches that have been developed for the prediction of cancer drivers using genetic signatures and protein structures and their potential application in developing precision cancer therapies.

Computational Approaches for Designing Highly Specific and Efficient sgRNAs.

The easily programmable CRISPR/Cas9 system has found applications in biomedical research as well as microbial and crop applications, due to its ability to create site-specific edits. This powerful and flexible system has also been modified to enable inducible gene regulation, epigenome modifications and high-throughput screens. Designing efficient and specific guides for the nuclease is a key step and also a major challenge in effective application. This chapter describes rules for sgRNA design and important features to consider while touching upon bioinformatics advances in predicting efficient guides. Computational tools that suggest improved guides, depending on application, or predict off-targets have also been mentioned and compared.

Withanone and Withaferin-A are predicted to interact with transmembrane protease serine 2 (TMPRSS2) and block entry of SARS-CoV-2 into cells.

Coronavirus disease 2019 (COVID-19) initiated in December 2019 in Wuhan, China and became pandemic causing high fatality and disrupted normal life calling world almost to a halt. Causative agent is a novel coronavirus called Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2/2019-nCoV). While new line of drug/vaccine development has been initiated world-wide, in the current scenario of high infected numbers, severity of the disease and high morbidity, repurposing of the existing drugs is heavily explored. Here, we used a homology-based structural model of transmembrane protease serine 2 (TMPRSS2), a cell surface receptor, required for entry of virus to the target host cell. Using the strengths of molecular docking and molecular dynamics simulations, we examined the binding potential of Withaferin-A (Wi-A), Withanone (Wi-N) and caffeic acid phenethyl ester to TPMRSS2 in comparison to its known inhibitor, Camostat mesylate. We found that both Wi-A and Wi-N could bind and stably interact at the catalytic site of TMPRSS2. Wi-N showed stronger interactions with TMPRSS2 catalytic residues than Wi-A and was also able to induce changes in its allosteric site. Furthermore, we investigated the effect of Wi-N on TMPRSS2 expression in MCF7 cells and found remarkable downregulation of TMPRSS2 mRNA in treated cells predicting dual action of Wi-N to block SARS-CoV-2 entry into the host cells. Since the natural compounds are easily available/affordable, they may even offer a timely therapeutic/preventive value for the management of SARS-CoV-2 pandemic. We also report that Wi-A/Wi-N content varies in different parts of Ashwagandha and warrants careful attention for their use.Communicated by Ramaswamy H. Sarma.