Identification of potential driver mutations in glioblastoma using machine learning.

Glioblastoma is a fast and aggressively growing tumor in the brain and spinal cord. Mutation of amino acid residues in targets proteins, which are involved in glioblastoma, alters the structure and function and may lead to disease. In this study, we collected a set of 9386 disease-causing (drivers) mutations based on the recurrence in patient samples and experimentally annotated as pathogenic and 8728 as neutral (passenger) mutations. We observed that Arg is highly preferred at the mutant sites of drivers, whereas Met and Ile showed preferences in passengers. Inspecting neighboring residues at the mutant sites revealed that the motifs YP, CP and GRH, are preferred in drivers, whereas SI, IQ and TVI are dominant in neutral. In addition, we have computed other sequence-based features such as conservation scores, Position Specific Scoring Matrices (PSSM) and physicochemical properties, and developed a machine learning-based method, GBMDriver (GlioBlastoma Multiforme Drivers), for distinguishing between driver and passenger mutations. Our method showed an accuracy and AUC of 73.59% and 0.82, respectively, on 10-fold cross-validation and 81.99% and 0.87 in a blind set of 1809 mutants. The tool is available at https://web.iitm.ac.in/bioinfo2/GBMDriver/index.html. We envisage that the present method is helpful to prioritize driver mutations in glioblastoma and assist in identifying therapeutic targets.

Exploring Plausible Therapeutic Targets for Alzheimer's Disease using Multi-omics Approach, Machine Learning and Docking.

The progressive deterioration of neurons leads to Alzheimer's disease (AD), and developing a drug for this disorder is challenging. Substantial gene/transcriptome variability from multiple cell types leads to downstream pathophysiologic consequences that represent the heterogeneity of this disease. Identifying potential biomarkers for promising therapeutics is strenuous due to the fact that the transcriptome, epigenetic, or proteome changes detected in patients are not clear whether they are the cause or consequence of the disease, which eventually makes the drug discovery efforts intricate. The advancement in scRNA-sequencing technologies helps to identify cell type-specific biomarkers that may guide the selection of the pathways and related targets specific to different stages of the disease progression. This review is focussed on the analysis of multi-omics data from various perspectives (genomic and transcriptomic variants, and single-cell expression), which provide insights to identify plausible molecular targets to combat this complex disease. Further, we briefly outlined the developments in machine learning techniques to prioritize the risk-associated genes, predict probable mutations and identify promising drug candidates from natural products.

Tumor Heterogeneity and Molecular Characteristics of Glioblastoma Revealed by Single-Cell RNA-Seq Data Analysis.

Glioblastoma multiforme (GBM) is the most common infiltrating lethal tumor of the brain. Tumor heterogeneity and the precise characterization of GBM remain challenging, and the disease-specific and effective biomarkers are not available at present. To understand GBM heterogeneity and the disease prognosis mechanism, we carried out a single-cell transcriptome data analysis of 3389 cells from four primary IDH-WT (isocitrate dehydrogenase wild type) glioblastoma patients and compared the characteristic features of the tumor and periphery cells. We observed that the marker gene expression profiles of different cell types and the copy number variations (CNVs) are heterogeneous in the GBM samples. Further, we have identified 94 differentially expressed genes (DEGs) between tumor and periphery cells. We constructed a tissue-specific co-expression network and protein-protein interaction network for the DEGs and identified several hub genes, including CX3CR1, GAPDH, FN1, PDGFRA, HTRA1, ANXA2 THBS1, GFAP, PTN, TNC, and VIM. The DEGs were significantly enriched with proliferation and migration pathways related to glioblastoma. Additionally, we were able to identify the differentiation state of microglia and changes in the transcriptome in the presence of glioblastoma that might support tumor growth. This study provides insights into GBM heterogeneity and suggests novel potential disease-specific biomarkers which could help to identify the therapeutic targets in GBM.

Understanding disorder-to-order transitions in protein-RNA complexes using molecular dynamics simulations.

Intrinsically disordered regions (IDRs) in proteins are characterized by their flexibilities and low complexity regions, which lack unique 3 D structures in solution. IDRs play a significant role in signaling, regulation, and binding multiple partners, including DNA, RNA, and proteins. Although various experiments have shown the role of disordered regions in binding with RNA, a detailed computational analysis is required to understand their binding and recognition mechanism. In this work, we performed molecular dynamics simulations of 10 protein-RNA complexes to understand the binding governed by intrinsically disordered regions. The simulation results show that most of the disordered regions are important for RNA-binding and have a transition from disordered-to-ordered conformation upon binding, which often contribute significantly towards the binding affinity. Interestingly, most of the disordered residues are present at the interface or located as a linker between two regions having similar movements. The DOT regions are overlaped or flanked with experimentally reported functionally important residues in the recognition of protein-RNA complexes. This study provides additional insights for understanding the role and recognition mechanism of disordered regions in protein-RNA complexes.Communicated by Ramaswamy H. Sarma.

Deciphering the Role of Residues Involved in Disorder-To-Order Transition Regions in Archaeal tRNA Methyltransferase 5.

tRNA methyltransferase 5 (Trm5) enzyme is an S-adenosyl methionine (AdoMet)-dependent methyltransferase which methylates the G37 nucleotide at the N1 atom of the tRNA. The free form of Trm5 enzyme has three intrinsically disordered regions, which are highly flexible and lack stable three-dimensional structures. These regions gain ordered structures upon the complex formation with tRNA, also called disorder-to-order transition (DOT) regions. In this study, we performed molecular dynamics (MD) simulations of archaeal Trm5 in free and complex forms and observed that the DOT residues are highly flexible in free proteins and become stable in complex structures. The energetic contributions show that DOT residues are important for stabilising the complex. The DOT1 and DOT2 are mainly observed to be important for stabilising the complex, while DOT3 is present near the active site to coordinate the interactions between methyl-donating ligands and G37 nucleotides. In addition, mutational studies on the Trm5 complex showed that the wild type is more stable than the G37A tRNA mutant complex. The loss of productive interactions upon G37A mutation drives the AdoMet ligand away from the 37th nucleotide, and Arg145 in DOT3 plays a crucial role in stabilising the ligand, as well as the G37 nucleotide, in the wild-type complex. Further, the overall energetic contribution calculated using MMPBSA corroborates that the wild-type complex has a better affinity between Trm5 and tRNA. Overall, our study reveals that targeting DOT regions for binding could improve the inhibition of Trm5.

Tackling Covid-19 using disordered-to-order transition of residues in the spike protein upon angiotensin-converting enzyme 2 binding.

The 2019-novel coronavirus also known as severe acute respiratory syndrome-coronavirus 2 (SARS-CoV-2) is a common threat to animals and humans, and is responsible for the human SARS pandemic in 2019 to 2021. The infection of SARS-CoV-2 in humans involves a viral surface glycoprotein named as spike proteins, which bind to the human angiotensin-converting enzyme 2 (ACE2) proteins. Particularly, the receptor binding domains (RBDs) mediate the interaction and contain several disordered regions, which help in the binding. Investigations on the influence of disordered residues/regions in stability and binding of spike protein with ACE2 help to understand the disease pathogenesis, which has not yet been studied. In this study, we have used molecular-dynamics simulations to characterize the structural changes in disordered regions of the spike protein that result from ACE2 binding. We observed that the disordered regions undergo disorder-to-order transition (DOT) upon binding with ACE2, and the DOT residues are located at functionally important regions of RBD. Although the RBD is having rigid structure, DOT residues make conformational rearrangements for the spike protein to attach with ACE2. The binding is strengthened via hydrophilic and aromatic amino acids mainly present in the DOTs. The positively correlated motions of the DOT residues with its nearby residues also explain the binding profile of RBD with ACE2, and the residues are observed to be contributing more favorable binding energies for the spike-ACE2 complex formation. This study emphasizes that intrinsically disordered residues in the RBD of spike protein may provide insights into its etiology and be useful for drug and vaccine discovery.

COVID-19 outbreak: history, mechanism, transmission, structural studies and therapeutics.

PURPOSE: The coronavirus outbreak emerged as a severe pandemic, claiming more than 0.8 million lives across the world and raised a major global health concern. We survey the history and mechanism of coronaviruses, and the structural characteristics of the spike protein and its key residues responsible for human transmissions. METHODS: We have carried out a systematic review to summarize the origin, transmission and etiology of COVID-19. The structural analysis of the spike protein and its disordered residues explains the mechanism of the viral transmission. A meta-data analysis of the therapeutic compounds targeting the SARS-CoV-2 is also included. RESULTS: Coronaviruses can cross the species barrier and infect humans with unexpected consequences for public health. The transmission rate of SARS-CoV-2 infection is higher compared to that of the closely related SARS-CoV infections. In SARS-CoV-2 infection, intrinsically disordered regions are observed at the interface of the spike protein and ACE2 receptor, providing a shape complementarity to the complex. The key residues of the spike protein have stronger binding affinity with ACE2. These can be probable reasons for the higher transmission rate of SARS-CoV-2. In addition, we have also discussed the therapeutic compounds and the vaccines to target SARS-CoV-2, which can help researchers to develop effective drugs/vaccines for COVID-19. The overall history and mechanism of entry of SARS-CoV-2 along with structural study of spike-ACE2 complex provide insights to understand disease pathogenesis and development of vaccines and drugs.

VEPAD - Predicting the effect of variants associated with Alzheimer's disease using machine learning.

INTRODUCTION: Alzheimer's disease (AD) is a complex and heterogeneous disease that affects neuronal cells over time and it is prevalent among all neurodegenerative diseases. Next Generation Sequencing (NGS) techniques are widely used for developing high-throughput screening methods to identify biomarkers and variants, which help early diagnosis and treatments. OBJECTIVE: The primary purpose of this study is to develop a classification model using machine learning for predicting the deleterious effect of variants with respect to AD. METHODS: We have constructed a set of 20,401 deleterious and 37,452 control variants from Genome-Wide Association Study (GWAS) and Genotype-Tissue Expression (GTEx) portals, respectively. Recursive feature elimination using cross-validation (RFECV) followed by a forward feature selection method was utilized to select the important features and a random forest classifier was used for distinguishing between deleterious and neutral variants. RESULTS: Our method showed an accuracy of 81.21% on 10-fold cross-validation and 70.63% on a test set of 5785 variants. The same test set was used to compare the performance of CADD and FATHMM and their accuracies are in the range of 54%-62%. CONCLUSION: Our model is freely available as the Variant Effect Predictor for Alzheimer's Disease (VEPAD) at http://web.iitm.ac.in/bioinfo2/vepad/. VEPAD can be used to predict the effect of new variants associated with AD.

Role of disordered regions in transferring tyrosine to its cognate tRNA.

Aminoacyl tRNA synthetase (AARS) plays an important role in transferring each amino acid to its cognate tRNA. Specifically, tyrosyl tRNA synthetase (TyrRS) is involved in various functions including protection from DNA damage due to oxidative stress, protein synthesis and cell signaling and can be an attractive target for controlling the pathogens by early inhibition of translation. TyrRS has two disordered regions, which lack a stable 3D structure in solution, and are involved in tRNA synthetase catalysis and stability. One of the disordered regions undergoes disorder-to-order transition (DOT) upon complex formation with tRNA whereas the other remains disordered (DR). In this work, we have explored the importance of these disordered regions using molecular dynamics simulations of both free and RNA-complexed states. We observed that the DOT and DR regions of the first subunit acts as a flap and interact with the acceptor arm of the tRNA. The DOT-DR flap closes when tyrosine (TyrRSTyr) is present at the active site of the complex and opens in the presence of tyrosine monophosphate (TyrRSYMP). The DOT and DR regions of the second subunit interact with the anticodon stem as well as D-loop of the tRNA, which might be involved in stabilizing the complex. The anticodon loop of the tRNA binds to the structured region present in the C-terminal of the protein, which is observed to be flexible during simulations. Detailed energy calculations also show that TyrRSTyr complex has stronger binding energy between tRNA and protein compared to TyrRSYMP; on the contrary, the anticodon is strongly bound in TyrRSYMP. The results obtained in the present study provide additional insights for understanding catalysis and the involvement of disordered regions in Tyr transfer to cognate tRNA.

Structural mechanism of DNA-mediated Nanog-Sox2 cooperative interaction.

The efficiency of stem cell transcriptional regulation always depends on the cooperative association and expression of transcription factors (TFs). Among these, Oct4, Sox2, and Nanog play major roles. Their cooperativity is facilitated via direct protein-protein interactions or DNA-mediated interactions, yet the mechanism is not clear. Most biochemical studies have examined Oct4/Sox2 cooperativity, whereas few studies have evaluated how Nanog competes in the connection between these TFs. In this study, using computational models and molecular dynamics simulations, we built a framework representing the DNA-mediated cooperative interaction between Nanog and Sox2 and analyzed the plausible interaction factors experienced by Nanog because of Sox2, its cooperative binding partner. Comparison of a wild-type and mutant Nanog/Sox2 model with the Nanog crystal structure revealed the regulatory structural mechanism between Nanog/Sox2-DNA-mediated cooperative bindings. Along with the transactivation domains interaction, the DNA-mediated allosteric interactions are also necessary for Nanog cooperative binding. DNA-mediated Nanog-Sox2 cooperativity influences the protein conformational changes and a stronger interaction profile was observed for Nanog-Mut (L103E) in comparison with the Nanog-WT complex.

Evaluation of Sox2 binding affinities for distinct DNA patterns using steered molecular dynamics simulation.

Transcription factors (TFs) are gene expression regulators that bind to DNA in a sequence-specific manner and determine the functional characteristics of the gene. It is worthwhile to study the unique characteristics of such specific TF-binding pattern in DNA. Sox2 recognizes a 6- to 7-base pair consensus DNA sequence; the central four bases of the binding site are highly conserved, whereas the two to three flanking bases are variable. Here, we attempted to analyze the binding affinity and specificity of the Sox2 protein for distinct DNA sequence patterns via steered molecular dynamics, in which a pulling force is employed to dissociate Sox2 from Sox2-DNA during simulation to study the behavior of a complex under nonequilibrium conditions. The simulation results revealed that the first two stacking bases of the binding pattern have an exclusive impact on the binding affinity, with the corresponding mutant complexes showing greater binding and longer dissociation time than the experimental complexes do. In contrast, mutation of the conserved bases tends to reduce the affinity, and mutation of the complete conserved region disrupts the binding. It might pave the way to identify the most likely binding pattern recognized by Sox2 based on the affinity of each configuration. The α2-helix of Sox2 was found to be the key player in the Sox2-DNA association. The characterization of Sox2's binding patterns for the target genes in the genome helps in understanding of its regulatory functions.

Structural insights into the Middle East respiratory syndrome coronavirus 4a protein and its dsRNA binding mechanism.

Middle East respiratory syndrome coronavirus (MERS-CoV) has evolved to navigate through the sophisticated network of a host's immune system. The immune evasion mechanism including type 1 interferon and protein kinase R-mediated antiviral stress responses has been recently attributed to the involvement of MERS-CoV protein 4a (p4a) that masks the viral dsRNA. However, the structural mechanism of how p4a recognizes and establishes contacts with dsRNA is not well explained. In this study, we report a dynamic mechanism deployed by p4a to engage the viral dsRNA and make it unavailable to the host immune system. Multiple variants of p4a-dsRNA were created and investigated through extensive molecular dynamics procedures to highlight crucial interfacial residues that may be used as potential pharmacophores for future drug development. The structural analysis revealed that p4a exhibits a typical αßßßα fold structure, as found in other dsRNA-binding proteins. The α1 helix and the ß1-ß2 loop play a crucial role in recognizing and establishing contacts with the minor grooves of dsRNA. Further, mutational and binding free energy analyses suggested that in addition to K63 and K67, two other residues, K27 and W45, might also be crucial for p4a-dsRNA stability.

Proteins Recognizing DNA: Structural Uniqueness and Versatility of DNA-Binding Domains in Stem Cell Transcription Factors.

Proteins in the form of transcription factors (TFs) bind to specific DNA sites that regulate cell growth, differentiation, and cell development. The interactions between proteins and DNA are important toward maintaining and expressing genetic information. Without knowing TFs structures and DNA-binding properties, it is difficult to completely understand the mechanisms by which genetic information is transferred between DNA and proteins. The increasing availability of structural data on protein-DNA complexes and recognition mechanisms provides deeper insights into the nature of protein-DNA interactions and therefore, allows their manipulation. TFs utilize different mechanisms to recognize their cognate DNA (direct and indirect readouts). In this review, we focus on these recognition mechanisms as well as on the analysis of the DNA-binding domains of stem cell TFs, discussing the relative role of various amino acids toward facilitating such interactions. Unveiling such mechanisms will improve our understanding of the molecular pathways through which TFs are involved in repressing and activating gene expression.

Toll-like receptor 2 antagonists identified through virtual screening and experimental validation.

Toll-like receptor 2 (TLR2) antagonists are key therapeutic targets because they inhibit several inflammatory diseases caused by surplus TLR2 activation. In this study, we identified two novel nonpeptide TLR2 antagonists, C11 and C13, through pharmacophore-based virtual screening. At 10 µm, the level of interleukin (IL)-8 inhibition by C13 and C11 in human embryonic kidney TLR2 overexpressing cells was comparable to the commercially available TLR2 inhibitor CU-CPT22. In addition, C11 and C13 acted in mouse macrophage-like RAW 264.7 cells as TLR2-specific inhibitors and did not suppress the tumor necrosis factor-α induction by TLR3 and TLR4 activators. Moreover, the two identified compounds bound directly to the human recombinant TLR2 ectodomain, during surface plasmon resonance analysis, and did not affect cell viability in a 3-(4,5-dimethylthiazol-2-yl)-5(3-carboxymethonyphenol)-2-(4-sulfophenyl)-2H-tetrazolium assay. In total, two virtually screened molecules, C11 and C13, were experimentally proven to be effective as TLR2 antagonists, and thus will provide new insights into the structure of TLR2 antagonists, and pave the way for the development of TLR2-targeted drug molecules.

A structural insight into the negative effects of opioids in analgesia by modulating the TLR4 signaling: An in silico approach.

Opioids are considered the gold standard therapy for pain. However, TLR-dependent negative effects in analgesia have highlighted the complexities in the pharmacodynamics of opioids. While successive studies have reported that morphine and Morphine-3-glucuronide (M3G) activate the TLR4 pathway, the structural details of this mechanism are lacking. Here, we have utilized various computational tools to reveal the structural dynamics of the opioid-bound TLR4/MD2 complex, and have proposed a potential TLR4 activation mechanism. Our results support previous findings, and include the novel insight that the stable binding of morphine and naloxone, but not M3G, in the MD2 cavity, is TLR4 dependent. Morphine interacts with MD2 near its Phe126 loop to induce the active conformation (MD2C); however, this binding is likely reversible, and the complex gains stability upon interaction with TLR4. M3G also induces the MD2C state, with both the Phe126 loop and the H1 loop being involved in MD2-M3G complex stability. Remarkably, naloxone, which requires TLR4 interaction for complex stability, switches the conformation of the gating loop to the inactive state (MD2°). Cumulatively, our findings suggest that ligand binding and receptor clustering occur successively in opioid-induced TLR4 signaling, and that MD2 plasticity and pocket hydrophobicity are crucial for the recognition and accommodation of ligands.

Toll-like receptors: promising therapeutic targets for inflammatory diseases.

The health of living organisms is constantly challenged by bacterial and viral threats. The recognition of pathogenic microorganisms by diverse receptors triggers a variety of host defense mechanisms, leading to their eradication. Toll-like receptors (TLRs), which are type I transmembrane proteins, recognize specific signatures of the invading microbes and activate a cascade of downstream signals inducing the secretion of inflammatory cytokines, chemokines, and type I interferons. The TLR response not only counteracts the pathogens but also initiates and shapes the adaptive immune response. Under normal conditions, inflammation is downregulated after the removal of the pathogen and cellular debris. However, a dysfunctional TLR-mediated response maintains a chronic inflammatory state and leads to local and systemic deleterious effects in host cells and tissues. Such inappropriate TLR response has been attributed to the development and progression of multiple diseases such as cancer, autoimmune, and inflammatory diseases. In this review, we discuss the emerging role of TLRs in the pathogenesis of inflammatory diseases and how targeting of TLRs offers a promising therapeutic strategy for the prevention and treatment of various inflammatory diseases. Additionally, we highlight a number of TLR-targeting agents that are in the developmental stage or in clinical trials.

Structural Mechanism behind Distinct Efficiency of Oct4/Sox2 Proteins in Differentially Spaced DNA Complexes.

The octamer-binding transcription factor 4 (Oct4) and sex-determining region Y (SRY)-box 2 (Sox2) proteins induce various transcriptional regulators to maintain cellular pluripotency. Most Oct4/Sox2 complexes have either 0 base pairs (Oct4/Sox2(0bp)) or 3 base pairs (Oct4/Sox2(3bp)) separation between their DNA-binding sites. Results from previous biochemical studies have shown that the complexes separated by 0 base pairs are associated with a higher pluripotency rate than those separated by 3 base pairs. Here, we performed molecular dynamics (MD) simulations and calculations to determine the binding free energy and per-residue free energy for the Oct4/Sox2(0bp) and Oct4/Sox2(3bp) complexes to identify structural differences that contribute to differences in induction rate. Our MD simulation results showed substantial differences in Oct4/Sox2 domain movements, as well as secondary-structure changes in the Oct4 linker region, suggesting a potential reason underlying the distinct efficiencies of these complexes during reprogramming. Moreover, we identified key residues and hydrogen bonds that potentially facilitate protein-protein and protein-DNA interactions, in agreement with previous experimental findings. Consequently, our results confess that differential spacing of the Oct4/Sox2 DNA binding sites can determine the magnitude of transcription of the targeted genes during reprogramming.

A Combined Molecular Docking/Dynamics Approach to Probe the Binding Mode of Cancer Drugs with Cytochrome P450 3A4.

Cytarabine, daunorubicin, doxorubicin and vincristine are clinically used for combinatorial therapies of cancers in different combinations. However, the knowledge about the interaction of these drugs with the metabolizing enzyme cytochrome P450 is limited. Therefore, we utilized computational methods to predict and assess the drug-binding modes. In this study, we performed docking, MD simulations and free energy landscape analysis to understand the drug-enzyme interactions, protein domain motions and the most populated free energy minimum conformations of the docked protein-drug complexes, respectively. The outcome of docking and MD simulations predicted the productive, as well as the non-productive binding modes of the selected drugs. Based on these interaction studies, we observed that S119, R212 and R372 are the major drug-binding residues in CYP3A4. The molecular mechanics Poisson-Boltzmann surface area analysis revealed the dominance of hydrophobic forces in the CYP3A4-drug association. Further analyses predicted the residues that may contain favorable drug-specific interactions. The probable binding modes of the cancer drugs from this study may extend the knowledge of the protein-drug interaction and pave the way to design analogs with reduced toxicity. In addition, they also provide valuable insights into the metabolism of the cancer drugs.

Multiple roles of toll-like receptor 4 in colorectal cancer.

Toll-like receptor (TLR) signaling has been implicated in the inflammatory responses in intestinal epithelial cells (IECs). Such inflammatory signals mediate complex interactions between commensal bacteria and TLRs and are required for IEC proliferation, immune response, repair, and homeostasis. The upregulation of certain TLRs in colorectal cancer (CRC) tissues suggests that TLRs may play an essential role in the prognosis of chronic and inflammatory diseases that ultimately culminate in CRC. Here, we provide a comprehensive review of the literature on the involvement of the TLR pathway in the initiation, progression, and metastasis of CRC, as well as inherited genetic variation and epigenetic regulation. The differential expression of TLRs in epithelial cells has also been discussed. In particular, we emphasize the physiological role of TLR4 in CRC development and pathogenesis, and propose novel and promising approaches for CRC therapeutics with the aid of TLR ligands.