Computational Biology Approaches Research Articles

Prognostic Model Construction of Disulfidptosis-Related Genes and Targeted Anticancer Drug Research in Pancreatic Cancer.

Pancreatic cancer stands as one of the most lethal malignancies, characterized by delayed diagnosis, high mortality rates, limited treatment efficacy, and poor prognosis. Disulfidptosis, a recently unveiled modality of cell demise induced by disulfide stress, has emerged as a critical player intricately associated with the onset and progression of various cancer types. It has emerged as a promising candidate biomarker for cancer diagnosis, prognosis assessment, and treatment strategies. In this study, we have effectively established a prognostic risk model for pancreatic cancer by incorporating multiple differentially expressed long non-coding RNAs (DElncRNAs) closely linked to disulfide-driven cell death. Our investigation delved into the nuanced relationship between the DElncRNA-based predictive model for disulfide-driven cell death and the therapeutic responses to anticancer agents. Our findings illuminate that the high-risk subgroup exhibits heightened susceptibility to the small molecule compound AZD1208, positioning it as a prospective therapeutic agent for pancreatic cancer. Finally, we have elucidated the underlying mechanistic potential of AZD1208 in ameliorating pancreatic cancer through its targeted inhibition of the peroxisome proliferator-activated receptor-γ (PPARG) protein, employing an array of comprehensive analytical methods, including molecular docking and molecular dynamics (MD) simulations. This study explores disulfidptosis-related genes, paving the way for the development of targeted therapies for pancreatic cancer and emphasizing their significance in the field of oncology. Furthermore, through computational biology approaches, the drug AZD1208 was identified as a potential treatment targeting the PPARG protein for pancreatic cancer. This discovery opens new avenues for exploring targets and screening drugs for pancreatic cancer.

Systems and computational analysis of gene expression datasets reveals GRB-2 suppression as an acute immunomodulatory response against enteric infections in endemic settings.

Enteric infections are a major cause of under-5 (age) mortality in low/middle-income countries. Although vaccines against these infections have already been licensed, unwavering efforts are required to boost suboptimalefficacy and effectiveness in regions that are highly endemic to enteric pathogens. The role of baseline immunological profiles in influencing vaccine-induced immune responses is increasingly becoming clearer for several vaccines. Hence, for the development of advanced and region-specific enteric vaccines, insights into differences in immune responses to perturbations in endemic and non-endemic settings become crucial. For this reason, we employed a two-tiered system and computational pipeline (i) to study the variations in differentially expressed genes (DEGs) associated with immune responses to enteric infections in endemic and non-endemic study groups, and (ii) to derive features (genes) of importance that keenly distinguish between these two groups using unsupervised machine learning algorithms on an aggregated gene expression dataset. The derived genes were further curated using topological analysis of the constructed STRING networks. The findings from these two tiers are validated using multilayer perceptron classifier and were further explored using correlation and regression analysis for the retrieval of associated gene regulatory modules. Our analysis reveals aggressive suppression of GRB-2, an adaptor molecule integral for TCR signaling, as a primary immunomodulatory response against S. typhi infection in endemic settings. Moreover, using retrieved correlation modules and multivariant regression models, we found a positive association between regulators of activated T cells and mediators of Hedgehog signaling in the endemic population, which indicates the initiation of an effector (involving differentiation and homing) rather than an inductive response upon infection. On further exploration, we found STAT3 to be instrumental in designating T-cell functions upon early responses to enteric infections in endemic settings. Overall, through a systems and computational biology approach, we characterized distinct molecular players involved in immune responses to enteric infections in endemic settings in the process, contributing to the mounting evidence of endemicity being a major determiner of pathogen/vaccine-induced immune responses. The gained insights will have important implications in the design and development of region/endemicity-specific vaccines.

Drug-target identification in COVID-19 disease mechanisms using computational systems biology approaches.

The COVID-19 Disease Map project is a large-scale community effort uniting 277 scientists from 130 Institutions around the globe. We use high-quality, mechanistic content describing SARS-CoV-2-host interactions and develop interoperable bioinformatic pipelines for novel target identification and drug repurposing. Extensive community work allowed an impressive step forward in building interfaces between Systems Biology tools and platforms. Our framework can link biomolecules from omics data analysis and computational modelling to dysregulated pathways in a cell-, tissue- or patient-specific manner. Drug repurposing using text mining and AI-assisted analysis identified potential drugs, chemicals and microRNAs that could target the identified key factors. Results revealed drugs already tested for anti-COVID-19 efficacy, providing a mechanistic context for their mode of action, and drugs already in clinical trials for treating other diseases, never tested against COVID-19. The key advance is that the proposed framework is versatile and expandable, offering a significant upgrade in the arsenal for virus-host interactions and other complex pathologies.

Abstract IA14: Forecasting pancreatic carcinogenesis through spatial multi-omics

Abstract Combining genomics with mathematical modeling provides a forecast system that can yield computational predictions to anticipate cancer progression and therapeutic response. High-throughput profiling technologies can indicate the molecular and cellular pathways of malignancies, but not the effect of targeting those pathways with therapy. Precision interception requires relating therapies to the cellular phenotypes underlying pancreatic carcinogenesis. This talk presents a hybrid computational and experimental strategy to uncover interactions between neoplastic cells and the microenvironment during pancreatic carcinogenesis. As pancreatic cancer develops, it forms a complex microenvironment of multiple interacting cells. The microenvironment of advanced pancreatic cancer includes a heterogeneous and dense population of cells, such as macrophages and fibroblasts, that are associated with immunosuppression. New single-cell and spatial molecular profiling technologies enable unprecedented characterization of the cellular and molecular composition of the microenvironment. These technologies provide the potential to identify candidate therapeutics to intercept immunosuppression in pancreatic cancer. State-of-the-art mathematical approaches in computational biology are essential to uncover mechanistic insights for high-throughput data for these precision interception strategies. We have recently applied new machine learning techniques to integrate imaging and spatial transcriptomics to enable comprehensive characterization of the pancreatic precancer microenvironment. Developing targets for interception requires determining which of these cellular and molecular pathways drive subsequent development of cancer. Relating the transcriptional programs in premalignant lesions to carcinogenesis requires mapping their temporal changes across disease stages. To integrate the spatial molecular data from premalignancies and single-cell RNA-seq reference atlases, we developed a transfer learning approach. This integrated analysis identified a switch between fibroblast-induced inflammatory signaling and cellular proliferation in pancreatic cancer cells. We confirmed these transitions also occur in pancreatic cancer patient derived organoids co cultured with fibroblasts using transfer learning from the reference scRNA-seq atlases of pancreatic tumors. This approach demonstrates the potential of computational analysis of multi-omics data in human tissue with organoid models for bidirectional bench to bedside research. Citation Format: Elana J. Fertig. Forecasting pancreatic carcinogenesis through spatial multi-omics [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Pancreatic Cancer; 2023 Sep 27-30; Boston, Massachusetts. Philadelphia (PA): AACR; Cancer Res 2024;84(2 Suppl):Abstract nr IA14.

A Molecular Dynamics Investigation of Ligand-Mediated PPARγ Transrepression

Background and Hypothesis: PPARγ is a nuclear receptor expressed in most cells of the body and exerts metabolic function, including adipocyte differentiation and insulin sensitization. This mechanism is termed transactivation, where ligand binding leads to transcription of target genes. However, PPARγ also has a transrepression activity, where ligand binding inhibits other transcriptional pathways, leading to regulation of the immune system. While the molecular mechanisms of transactivation are well understood, there is little research on the mechanisms of transrepression. Previous studies have implied that these activity states act independently and can be selected for in small molecule design campaigns, but this has yet to be accomplished. Therefore, the aim of this project was to investigate if distinct molecular interactions exist between PPARγ transactivation versus transrepression using a combined computational and structural biology approach.
 Methods: Molecular dynamics (MD) simulations were performed using the CHARMM molecular modeling software to produce 300 ns simulations of PPARγ-ligand complexes. Simulations were analyzed to identify potential protein-ligand interactions, and distances were measured between atoms of interest and compared.
 Results: Although PPARγ’s binding site is large, structural analyses showed that most ligands remained near their initial starting position within the binding pocket throughout an MD simulation. Starting coordinates were taken from previously solved crystal structures or via manual docking based on molecular similarities. Four residues were identified to consistently interact with most of the ligands: Arg280, Ser342, Glu259, and Cys285. These residues differ from previously reported interactions associated with PPARγ transactivation.
 Conclusion: Our results suggest that these residues may play a role in PPARγ transrepression, but these conclusions are preliminary and should be confirmed through further investigation. Future research would include performing replicate simulations and mutational analyses to further explore the involvement of these residues in PPARγ transrepression. Additional PPARγ ligands should also be explored.

Aptamer-Based Recognition of Breast Tumor Cells: A New Era for Breast Cancer Diagnosis.

Breast cancer is one of the leading causes of death among women worldwide and can be classified into four major distinct molecular subtypes based on the expression of specific receptors. Despite significant advances, the lack of biomarkers for detailed diagnosis and prognosis remains a major challenge in the field of oncology. This study aimed to identify short single-stranded oligonucleotides known as aptamers to improve breast cancer diagnosis. The Cell-SELEX technique was used to select aptamers specific to the MDA-MB-231 tumor cell line. After selection, five aptamers demonstrated specific recognition for tumor breast cell lines and no binding to non-tumor breast cells. Validation of aptamer specificity revealed recognition of primary and metastatic tumors of all subtypes. In particular, AptaB4 and AptaB5 showed greater recognition of primary tumors and metastatic tissue, respectively. Finally, a computational biology approach was used to identify potential aptamer targets, which indicated that CSKP could interact with AptaB4. These results suggest that aptamers are promising in breast cancer diagnosis and treatment due to their specificity and selectivity.

Molecular docking, molecular dynamics simulation, and MM/PBSA analysis of ginger phytocompounds as a potential inhibitor of AcrB for treating multidrug-resistant Klebsiella pneumoniae infections

The emergence of Multidrug resistance (MDR) in human pathogens has defected the existing antibiotics and compelled us to understand more about the basic science behind alternate anti-infective drug discovery. Soon, proteome analysis identified AcrB efflux pump protein as a promising drug target using plant-driven phytocompounds used in traditional medicine systems with lesser side effects. Thus, the present study aims to explore the novel, less toxic, and natural inhibitors of Klebsiella pneumoniae AcrB pump protein from 69 Zingiber officinale phyto-molecules available in the SpiceRx database through computational-biology approaches. AcrB protein’s homology-modelling was carried out to get a 3D structure. The multistep-docking (HTVS, SP, and XP) were employed to eliminate less-suitable compounds in each step based on the docking score. The chosen hit-compounds underwent induced-fit docking (IFD). Based on the XP GScore, the top three compounds, epicatechin (−10.78), 6-gingerol (−9.71), and quercetin (−9.09) kcal/mol, were selected for further calculation of binding free energy (MM/GBSA). Furthermore, the short-listed compounds were assessed for their drug-like properties based on in silico ADMET properties and Pa, Pi values. In addition, the molecular dynamics simulation (MDS) studies for 250 ns elucidated the binding mechanism of epicatechin, 6-gingerol, and quercetin to AcrB. From the dynamic binding free energy calculations using MM/PBSA, 6-gingerol exhibited a strong binding affinity towards AcrB. Further, the 6-gingerol complex’s energy fluctuation was observed from the free energy landscape. In conclusion, 6-gingerol has a promising inhibiting potential against the AcrB efflux pump and thus necessitates further validation through in vitro and in vivo experiments. Communicated by Ramaswamy H. Sarma

Mechanistic Approach of Effective Combination of Antibiotics Against Clinical Bacterial Strains Having New Delhi Metallo-Beta-Lactamase Variants and Functional Gain Laboratory Mutant.

Antimicrobial resistance has emerged as a serious issue for physicians and health-care workers treating infections that could lead to the next pandemic. One of the key resistance mechanisms is beta-lactamases. Although several beta-lactamase inhibitors in combination with antibiotics have been created and are being utilized in clinical settings, resistance to these formulations has also been evolving in the bacterial population due to their distinct targets. In this study we used effective combination of antibiotic as an approach to inhibit multidrug resistance bacteria. We used four combinations and checked its efficacy against NDM (New Delhi Metallo-beta-lactamase) variants and functional gain laboratory mutant by employing FICI, enzyme kinetics, fluorescence and computational biology approaches (Docking and Molecular Dynamics Simulation). FICI values of all the combinations were either less than 0.5 or equal to 0.5. Binding features acquired by spectroscopic techniques showed important interaction and complex formation between drugs and enzymes with decreased ksv and kq values. In steady-state kinetics, a reduction in hydrolytic efficiency of enzymes was shown by cooperative binding behaviour when they were treated with different drugs. We have also tested functional gain laboratory mutant developed in our lab, keeping in view that if in future upcoming variants of this kind be emerged then these mutants could also be subsided by combinational therapy. This study identifies three other combinations better than fluoroquinolones effective against NDM variants and laboratory mutant.

Prognostic significance and expression pattern of glucose related genes in breast cancer: A comprehensive computational biology approach

Breast cancer (BC) is the most common type of malignancy globally and the main reason why women die from tumours. The Warburg effect, a characteristic of tumor, describes how most solid tumour cells acclimatize to their diverse surroundings by relying heavily on aerobic glycolysis for production of energy. In addition to producing key metabolic intermediates that are crucial for the production of macromolecules, which enable cancer cell division, invasiveness, and drug resistance, the transformed energy metabolism also supplies tumor cells with ATP for cellular energy. Here, we evaluated the expression profile, prognostic significance, and clinical relevance of glucose-related genes in BC using a bioinformatic approach. To clarify the significance of glucose-related genes in the development of breast tumours, we also performed a functional enrichment investigation of deregulated genes using the STRING and KEGG portal. The study depicted that of the 61 genes examined, 8 genes had a fold change </=± 1.5, that is, ADH1C, ADH4, ALDH1A3, ALDOC, FBP1, PCK1, PFKFB1, PFKFB3. Among the highly deregulated genes, ADH1C showed a fold change of −6.669. These deregulated genes were associated with poor prognosis. The study signifies that glucose related genes are highly dysregulated in breast cancer. Deregulation of glucose related genes is linked with a poor prognosis in BC individuals. Thus, targeting glucose related genes will provide an effective treatment approach for BC individuals.

AKR1C1 and hormone metabolism in lipedema pathogenesis: a computational biology approach.

Lipedema is an autosomal dominant genetic disease that mainly affects women. It is characterized by excess deposition of subcutaneous adipose tissue, pain, and anxiety. The genetic and environmental etiology of lipedema is still largely unknown. Although considered a rare disease, this pathology has been suggested to be underdiagnosed or misdiagnosed as obesity or lymphedema. Steroid hormones seem to be involved in the pathogenesis of lipedema. Indeed, aldo-keto reductase family 1 member C1 (AKR1C1), a gene coding for a protein involved in steroid hormones metabolism, was the first proposed to be correlated with lipedema. In this study, we employed a molecular dynamics approach to assess the pathogenicity of AKR1C1 genetic variants found in patients with lipedema. Moreover, we combined information theory and structural bioinformatics to identify AKR1C1 polymorphisms from the gnomAD database that could predispose to the development of lipedema. Three genetic variants in AKR1C1 found in patients with lipedema were disruptive to the protein's function. Furthermore, eight AKR1C1 variants found in the general population could predispose to the development of lipedema. The results of this study provide evidence that AKR1C1 may be a key gene in lipedema pathogenesis, and that common polymorphisms could predispose to lipedema development.

Thrombospondin 2 is a key determinant of fibrogenesis in non-alcoholic fatty liver disease.

Hepatic overexpression of the thrombospondin 2 gene (THBS2) and elevated levels of circulating thrombospondin 2 (TSP2) have been observed in patients with chronic liver disease. This study aimed to identify the specific cells expressing THBS2/TSP2 in non-alcoholic fatty liver disease (NAFLD) and investigate the underlying mechanism behind THBS2/TSP2 upregulation. Comprehensive NAFLD liver gene datasets, including single-cell RNA sequencing (scRNA-seq), in-house NAFLD liver tissue, and LX-2 cells derived from human hepatic stellate cells (HSCs), were analysed using a combination of computational biology, genetic, immunological, and pharmacological approaches. Analysis of the genetic dataset revealed the presence of 1433 variable genes in patients with advanced fibrosis NAFLD, with THBS2 ranked among the top 2 genes. Quantitative polymerase chain reaction (qPCR) examination of NAFLD livers showed a significant correlation between THBS2 expression and fibrosis stage (r = .349, p < .001). In support of this, scRNA-seq data and insitu hybridization demonstrated that the THBS2 gene was highly expressed in HSCs of NAFLD patients with advanced fibrosis. Pathway analysis of the gene dataset revealed THBS2 expression to be associated with the transforming growth factor beta (TGFβ) pathway and collagen gene activation. Moreover, the activation of LX-2 cells with TGFβ increased THBS2/TSP2 and collagen expression independently of the TGFβ-SMAD2/3 pathway. THBS2 gene knockdown significantly decreased collagen expression in LX-2 cells. THBS2/TSP2 is highly expressed in HSCs and plays a role in regulating fibrogenesis in NAFLD patients. THBS2/TSP2 may therefore represent a potential target for anti-fibrotic therapy in NAFLD.

Computational study of the motor neuron protein KIF5A to identify nsSNPs, bioactive compounds, and its key regulators.

Introduction: Kinesin family member 5A (KIF5A) is a motor neuron protein expressed in neurons and involved in anterograde transportation of organelles, proteins, and RNA. Variations in the KIF5A gene that interfere with axonal transport have emerged as a distinguishing feature in several neurodegenerative disorders, including hereditary spastic paraplegia (HSP10), Charcot-Marie-Tooth disease type 2 (CMT2), and Amyotrophic Lateral Sclerosis (ALS). Methods: In this study, we implemented a computational structural and systems biology approach to uncover the role of KIF5A in ALS. Using the computational structural biology method, we explored the role of non-synonymous Single Nucleotide Polymorphism (nsSNPs) in KIF5A. Further, to identify the potential inhibitory molecule against the highly destabilizing structure variant, we docked 24 plant-derived phytochemicals involved in ALS. Results: We found KIF5AS291F variant showed the most structure destabilizing behavior and the phytocompound "epigallocatechin gallate" showed the highest binding affinity (-9.0Kcal/mol) as compared to wild KIF5A (-8.4Kcal/mol). Further, with the systems biology approach, we constructed the KIF5A protein-protein interaction (PPI) network to identify the associated Kinesin Families (KIFs) proteins, modules, and their function. We also constructed a transcriptional and post-transcriptional regulatory network of KIF5A. With the network topological parameters of PPIN (Degree, Bottleneck, Closeness, and MNC) using CytoHubba and computational knock-out experiment using Network Analyzer, we found KIF1A, 5B, and 5C were the significant proteins. The functional modules were highly enriched with microtubule motor activity, chemical synaptic transmission in neurons, GTP binding, and GABA receptor activity. In regulatory network analysis, we found KIF5A post-transcriptionally down-regulated by miR-107 which is further transcriptionally up-regulated by four TFs (HIF1A, PPARA, SREBF1, and TP53) and down-regulated by three TFs (ZEB1, ZEB2, and LIN28A). Discussion: We concluded our study by finding a crucial variant of KIF5A and its potential therapeutic target (epigallocatechin gallate) and KIF5A associated significant genes with important regulators which could decrypt the novel therapeutics in ALS and other neurodegenerative diseases.

Lupenone, a wonder chemical obtained from Euphorbia segetalis to boost affinity for the transcriptional factor escalating drought-tolerance in Solanum Lycopersicum: A cutting-edge computational biology approach.

Drought is the single greatest abiotic factor influencing crop yield worldwide. Plants remain in one area for extended periods, making them vulnerable to natural and man-made influences. Understanding plant drought responses will help us develop strategies for breeding drought-resistant crops. Large proteome analysis revealed that leaf and root tissue proteins respond to drought differently depending on the plant's genotype. Commonly known as tomatoes, Solanum Lycopersicum is a globally important vegetable crop. However, drought stress is one of the most significant obstacles to tomato production, making the development of cultivars adapted to dry conditions an essential goal of agricultural biotechnology. Breeders have put quite a lot of time and effort into the tomato to increase its productivity, adaptability, and resistance to biotic and abiotic challenges. However, conventional tomato breeding has only improved drought resistance due to the complexity of drought traits. The resilience of tomatoes under drought stress has been the subject of extensive study. Using contemporary sequencing approaches like genomics, transcriptomics, proteomics, and metabolomics has dramatically aided in discovering drought-responsive genes. One of the most prominent families of plant transcription factors, WRKY genes, plays a crucial role in plant growth and development in response to natural and abiotic stimuli. To develop plants that can withstand both biotic and abiotic stress, understanding the relationships between WRKY-proteins (transcription factors) and other proteins and ligands in plant cells is essential. This is despite the fact that tomatoes have a long history of domestication. This research aims to utilize Lupenone, a hormone produced in plant roots in response to stress, to increase drought resistance in plants. Lupenone exhibits a strong affinity for the WRKY protein at -9.64 kcal/mol. Molecular docking and modeling studies show that these polyphenols have a significant role in making Solanum Lycopersicum drought-resistant and improving the quality of its fruit. As a result of climate change, droughts are occurring more frequently and persisting for more extended periods, making it necessary to breed crops resistant to drought. While considerable variability for tolerance exists in wild cousins, little is known about the processes and essential genes influencing drought tolerance in cultivated tomato species.

Cytogenetic Infractions of Latex Extract of the Floristic Dumbcane (Dieffenbachia amoena) on Mitotic Chromosomes of Onion (Allium cepa)

Background: Diffenbachia amoena commonly called dumb cane is a houseplant found in homes, offices, banks and landscape premises as ornamental plants. This family of plants has shown high levels of acute and chronic toxicities with high cytogenic, mutagenic, carcinogenic, genotoxic potentials. Several studies had implicated some phytochemicals contained in the whitish latex sap including saponins, glycosides, tannins and oxalates to be responsible for the toxicity of this houseplant to plant cells and proteins. Methods: Cytological investigation protocol was used to determine cytogenetic infractions such as chromosome stickiness, lagging chromosomes, bridged chromosomes, deletions and chromosomal aberrations. The phytochemicals contained in the latex were determined using high performance liquid chromatography while computational biology approach was used to determine the latex phytochemicals interactions with the onion plant proteins using SIB stitch of expasy.org. Result: Cytogenetic studies reveals that Dumb cane causes significant effect and infractions in the cytogenetics of onion cells ranging from chromatid-type breakage-fusion-bridge, chromosome stickiness, lagging chromosomes, bridged chromosomes, deletions and chromosomal aberrations. The latex sap from the stem contains oxalates, saponins, glycosides, tannins, alkaloids to varying degree which impairs photosynthetic and biochemical processes in the plant system. Phytochemicals-proteins interactions revealed that oxalates impairs and inhibits the formation and functionality of alanine glycoxylatetransferase (GRHPR), Chromobox homolog 5 chromosome (CBX5), alpha ketaglutarate dehydrogenase (AGXT) and glyoxylate reductase (OGDH) genes. Hence, there is need for enlightenment of the public on the dangers and toxicity of this houseplant in rural community households, urban cities, offices, recreational parks, business centers where the use of this deadly plant as ornamental is still very predominant.

A new multi-epitope vaccine candidate based on S and M proteins is effective in inducing humoral and cellular immune responses against SARS-CoV-2 variants: an in silico design approach

Available COVID-19 vaccines are primarily based on SARS-CoV-2 spike protein (S). Due to the emergence of new SARS-CoV-2 variants, other virus proteins with more conservancy, such as Membrane (M) protein, are desired for vaccine development. The reverse vaccinology approach was employed to design a multi-epitope SARS-CoV-2 vaccine candidate based on S and M proteins. Cytotoxic T-lymphocyte (CTL), helper T-lymphocyte (HTL), linear B-lymphocyte (LBL) and conformational B-lymphocyte (CBL) of S and M proteins were predicted and screened to choose the best epitopes. A multi-epitope vaccine candidate was constructed using selected CTL, HTL and LBL epitopes. The efficiency of the construct in binding to some immune receptors and an RBD-potent neutralizing monoclonal antibody (bebtelovimab) was predicted, and its immunogenicity was simulated. Finally, in silico cloning of the constructed gene was performed. The potency of our construct as a SARS-CoV-2 vaccine was validated using several bioinformatics tools. The simulation results showed that the construct can induce both cellular and humoral immune responses by producing appropriate cytokines, and it can even create an excellent immune memory response. Furthermore, the designed construct interacts with innate immune receptors such as TLR2 and TLR4 and the terminal variable domain of bebtelovimab with high affinity. We developed a multi-epitope construct based on the S and M proteins of the SARS-CoV-2 virus with high immunogenicity potential using the most up-to-date immunoinformatics and computational biology approaches. The actual efficiency of this multi-epitope vaccine should be further evaluated via in vitro and in vivo studies. Communicated by Ramaswamy H. Sarma

A computational biology approach to identify potential protein biomarkers and drug targets for sporadic amyotrophic lateral sclerosis

Amyotrophic lateral sclerosis (ALS) is a neurodegenerative disease caused by the loss of upper and lower motor neurons. The sporadic ALS (sALS) is a multigenic disorder and the complex mechanisms underlying its onset are still not fully delineated. Despite the recent scientific advancements, certain aspects of ALS pathogenic targets need to be yet clarified. The aim of the presented study is to identify potential genetic biomarkers and drug targets for sALS, by analysing gene expression profiles, presented in the publicly available GSE68605 dataset, of motor neurons cells obtained from sALS patients. We used different computational approaches including differential expression analysis, protein network mapping, candidate protein biomarker (CPB) identification, elucidation of the role of functional modules, and molecular docking analysis. The resultant top ten up- and downregulated genes were further used to construct protein-protein interaction network (PPIN). The PPIN analysis resulted in identifying four CPBs (namely RIOK2, AKT1, CTNNB1, and TNF) that commonly overlapped with one another in network parameters (degree, bottleneck and maximum neighbourhood component). The RIOK2 protein emerged as a potential mediator of top five functional modules that are associated with RNA binding, lipoprotein particle receptor binding in pre-ribosome, and interferon, cytokine-mediated signaling pathway. Furthermore, molecular docking analysis revealed that cyclosporine exhibited the highest binding affinity (−8.6 kJ/mol) with RIOK2, and surpassed the FDA-approved ALS drugs, such as riluzole and edaravone. This suggested that cyclosporine may serve as a promising candidate for targeting RIOK2 downregulation observed in sALS patients. In order to validate our computational results, it is suggested that in vitro and in vivo studies may be conducted in future to provide a more detailed understanding of ALS diagnosis, prognosis, and therapeutic intervention.

Computational designing of the ligands of Protein L affinity chromatography based on molecular docking and molecular dynamics simulations

Protein L is a multidomain protein from Peptostreptococcus magnus with binding affinity to kappa light chain of human immunoglobulin (Ig) which is used for the purification of antibody fragments by affinity chromatography. The advances in protein engineering and computational biology approaches lead to the development of engineered affinity ligands with improved properties including binding affinity. In this study, molecular dynamics simulations (MDs) and Osprey software were used to design single B domains of the Protein L with higher affinity to antibody fragments. The modified B domains were then polymerized to ligand with six B domains by homology modeling methods. The results showed that single B domain mutants of MB1 (Thr865Trp) and MB2 (Thr847Met-Thr865Trp) had higher binding affinity to Fab compared to the wild single B domain. Also, MDs and molecular docking results showed that the polymerized Proteins L including the wild and mutated six B domains (6B0, 6B1, and 6B2) were stable during MDs and the two mutants of 6B1 and 6B2 showed higher binding affinity to Fab relative to the wild type. Communicated by Ramaswamy H. Sarma

State-of-the-art strategies to prioritize Mycobacterium tuberculosis drug targets for drug discovery using a subtractive genomics approach

Tuberculosis remains one of the causes of death from a single infectious bacterium. The inappropriate use of antibiotics and patients’ non-compliance among other factors drive the emergence of drug-resistant tuberculosis. Multidrug-resistant and extensively drug-resistant strains of tuberculosis pose significant challenges to current treatment regimens, as their reduced efficacy against these strains limits successful patient outcomes. Furthermore, the limited effectiveness and associated toxicity of second-line drugs further compound the issue. Moreover, the scarcity of novel pharmacological targets and the subsequent decline in the number of anti-TB compounds in the drug development pipeline has further hindered the emergence of new therapies. As a result, researchers need to develop innovative approaches to identify potential new anti-TB drugs. The evolution of technology and the breakthrough in omics data allow the use of computational biology approaches, for example, metabolomic analysis to uncover pharmacological targets for structured-based drug design. The role of metabolism in pathogen development, growth, survival, and infection has been established. Therefore, this review focuses on the M. tb metabolic network as a hub for novel target identification and highlights a step-by-step subtractive genomics approach for target prioritization.

Rational peptide design for targeting cancer cell invasion.

Cell invasion is an important process in cancer progression and recurrence. Invasion and implantation of cancer cells from their original place to other tissues, by disabling vital organs, challenges the treatment of cancer patients. Given the importance of the matter, many molecular treatments have been developed to inhibit cancer cell invasion. Because of their low production cost and ease of production, peptides are valuable therapeutic molecules for inhibiting cancer cell invasion. In recent years, advances in the field of computational biology have facilitated the design of anti-cancer peptides. In our investigation, using computational biology approaches such as evolutionary analysis, residue scanning, protein-peptide interaction analysis, molecular dynamics, and free energy analysis, our team designed a peptide library with about 100 000 candidates based on A6 (acetyl-KPSSPPEE-amino) sequence which is an anti-invasion peptide. During computational studies, two of the designed peptides that give the highest scores and showed the greatest sequence similarity to A6 were entered into the experimental analysis workflow for further analysis. In experimental analysis steps, the anti-metastatic potency and other therapeutic effects of designed peptides were evaluated using MTT assay, RT-qPCR, zymography analysis, and invasion assay. Our study disclosed that the IK1 (acetyl-RPSFPPEE-amino) peptide, like A6, has great potency to inhibit the invasion of cancer cells.

Attenuation of Aging-Related Oxidative Stress Pathways by Phytonutrients: A Computational Systems Biology Analysis.

Aging results from gradual accumulation of damage to the cellular functions caused by biochemical processes such as oxidative stress, inflammation-driven prolonged cellular senescence state, immune system malfunction, psychological stress, and epigenetic changes due to exposure to environmental toxins. Plant-derived bioactive molecules have been shown to ameliorate the damage from oxidative stress. This research seeks to uncover the mechanisms of action of how phytochemicals from fruit/berry/vegetable (FBV) juice powder mitigate oxidative stress. The study uses a computational systems biology approach to (1) identify biomolecular pathways of oxidative stress; (2) identify phytochemicals from FBV juice powder and their specific action on oxidative stress mechanisms; and (3) quantitatively estimate the effects of FBV juice powder bioactive compounds on oxidative stress. The compounds in FBV affected two oxidative stress molecular pathways: (1) reactive oxygen species (ROS) production and (2) antioxidant enzyme production. Six bioactive compounds including cyanidin, delphinidin, ellagic acid, kaempherol, malvidin, and rutin in FBV significantly lowered production of ROS and increased the production of antioxidant enzymes such as catalase, heme oxygenase-1, superoxide dismutase, and glutathione peroxidase. FBV juice powder provides a combination of bioactive compounds that attenuate aging by affecting multiple pathways of oxidative stress.