Computational Biology Techniques Research Articles

Study transport of hesperidin based on the DPPC lipid model and the BSA transport model

Hesperidin (HE), a significant flavonoid polyphenolic compound present in citrus plants, exhibits diverse pharmacological effects. Considering the crucial involvement of biological membranes and transporter proteins in the transportation and biological processes of HE, it becomes essential to comprehend the potential mechanisms through which HE interacts with membranes and transporter proteins. In order to simulate the process of active molecule transport, a cell membrane model consisting of 1,2-dipalmitoyl-n-glycero-3-phosphatidylcholine (DPPC) and a transporter protein model of bovine serum albumin (BSA) were employed for investigation. The present study aimed to investigate the mechanism of action of hesperidin (HE) in DPPC and BSA using fluorescence quenching, Fourier transform infrared spectroscopy (FTIR), and differential scanning calorimetry (DSC). The localization and interaction of HE within liposomes were also elucidated. Furthermore, the binding of BSA and HE was analyzed through UV/Vis absorption spectroscopy, fluorescence spectroscopy, infrared spectroscopy, and computational biology techniques. Computational biology analysis revealed that the binding between HE and BSA primarily occurred via hydrogen bonding and hydrophobic interactions. This study aimed to investigate the role and mechanism of HE in the DPPC cell membrane model and the BSA transporter protein model, thereby offering novel insights into the action of HE in DPPC and BSA.

Identification of a novel Sorcin isoform with a different C-terminal but intact dimerization property

Sorcin (Sri), a member of penta EF-hand protein family plays a diverse role in maintaining calcium homeostasis, cell cycle and vesicular trafficking. Sri is highly conserved amongst mammals and consists of N-terminal glycine rich domain and C-terminal calcium binding domain that mediates its dimerization and interacts with different compounds. In the present study, with the help of combination of computational and molecular biology techniques, we have identified a novel isoform (Sri-N) in mouse which differs only in the C-terminal domain with that of Sri reported earlier. The novel isoform contains a new last exon that is different from the one present in the reported transcript (Sri). The presence of the novel isoform was further validated in different tissues by RT-PCR and DNA sequencing. The transcript was conceptually translated and subjected to in-silico analysis using different bioinformatics tools. The novel transcript variant encodes for a longer protein isoform without any change in the sub-cellular localization as predicted by PSORT-II online tool. Molecular modelling was performed to compare the structural changes in Sri-N and Sri isoforms. The structural characterization of the novel isoform using MD simulation depicted its overall stability under the physiological conditions. The molecular docking of proteins with various chemotherapeutic drugs revealed that their binding affinity is more for Sri-N as compared to that for the previously reported transcript Sri.

Unlocking the Potential of Field Effect Transistor (FET) Biosensors: A Perspective on Methodological Advances in Computational and Molecular Biology

AbstractField‐effect transistor (FET)‐based sensors are increasingly gaining relevance in diagnostic, healthcare, and environmental monitoring applications. A FET operates by transducing chemical interactions between a surface‐immobilized bioreceptor and the target analyte into a detectable electrical signal. FET biosensors can detect and monitor molecules (i.e., biomarkers, small molecules, viruses, bacterias) present in liquid samples, making a “liquid gate” configuration of FETs the most suitable approach. However, this FET architecture presents dimensional constraints that affect bioreceptors’ stability and immobilization in the liquid phase. To overcome these limitations, herein, a combination of computational and molecular biology techniques for improving bioreceptors' applicability in biosensing is proposed. This results in the optimized and problem‐tailored protein receptors for specific FET biosensors applications, thus enhancing their overall performance. The interplay between the computational and experimental approaches will represent a ground‐breaking solution for the development of next‐generation biosensors.

Spectroscopic studies on a natural biomarker for the identification of origin and quality of tea extracts for the development of a portable and field deployable prototype

Even in the era of smart technologies and IoT enabled devices, tea testing technique continues to be a person specific subjective task. In this study, we have employed optical spectroscopy-based detection technique for the quantitative validation of tea quality. In this regard, we have employed the external quantum yield of quercetin at 450 nm (λex = 360 nm), which is an enzymatic product generated by the activity of β-glucosidase on rutin, a naturally occurring metabolite responsible for tea-flavour (quality). We have found that a specific point in a graph representing Optical Density and external Quantum Yield as independent and dependent variables respectively of an aqueous tea extract objectively indicates a specific variety of the tea. A variety of tea samples from various geographical origin have been analysed with the developed technique and found to be useful for the tea quality assessment. The principal component analysis distinctly showed the tea samples originated from Nepal and Darjeeling having similar external quantum yield, while the tea samples from Assam region had a lower external quantum yield. Furthermore, we have employed experimental and computational biology techniques for the detection of adulteration and health benefit of the tea extracts. In order to assure the portability/field use, we have also developed a prototype which confirms the results obtained in the laboratory. We are of the opinion that the simple user interface and almost zero maintenance cost of the device will make it useful and attractive with minimally trained manpower at low resource setting.

Computational characteristics of the structure-activity relationship of inhibitors targeting Pks13-TE domain

Multiple studies have established the Pks13-TE domain as a promising target for anti-tuberculosis drug development. However, recent findings have revealed that the lead compound currently in the pipeline for Pks13-TE has significant cardiotoxicity issues. Given the pressing need for new chemical structures for Pks13-TE inhibitors, this study aims to provide a detailed understanding of the Pks13-TE domain binding site through the application of computational chemical biology techniques. Our results highlight the size and shape of the Pks13-TE domain binding pocket, key residues including Asp1644, Asn1640, Phe1670, and Tyr1674 within the pocket, and inhibitor pharmacophore characteristics such as aromatic ring sites, positively charged sites, and hydrogen bond donors. To our knowledge, these simulation results are novel and contribute to the discovery of next-generation Pks13-TE inhibitors without similar prior studies.

First person – Rebeka Popovic and Yizhou Yu

ABSTRACT First Person is a series of interviews with the first authors of a selection of papers published in Disease Models & Mechanisms, helping researchers promote themselves alongside their papers. Rebeka Popovic and Yizhou Yu are co-first authors on ‘ Upregulation of Tribbles decreases body weight and increases sleep duration’, published in DMM. Rebeka conducted the research described in this article while a PhD student in Dr Luis Miguel Martins’ lab at the University of Cambridge, Cambridge, UK. Her PhD investigated mechanisms of neurodegeneration, with a focus on endoplasmic reticulum stress and mitochondrial dysfunction, as well as inter-organ communication in disease. She is now a postdoc in the lab of Prof. Fiona Ducotterd at the Drug Discovery Institute at University College London, London, UK, exploring new therapeutic targets for the treatment of neurodegenerative diseases. Yizhou is a PhD student in the lab of Dr Luis Miguel Martins at the University of Cambridge, Cambridge, UK. His research involves combining computational biology and wet-lab techniques to understand the genetic and environmental causes of age-related diseases.

Single-cell computational machine learning approaches to immune-mediated inflammatory disease: New tools uncover novel fibroblast and macrophage interactions driving pathogenesis.

Recent advances in single-cell sequencing technologies call for greater computational scalability and sensitivity to analytically decompose diseased tissues and expose meaningful biological relevance in individual cells with high resolution. And while fibroblasts, one of the most abundant cell types in tissues, were long thought to display relative homogeneity, recent analytical and technical advances in single-cell sequencing have exposed wide variation and sub-phenotypes of fibroblasts of potential and apparent clinical significance to inflammatory diseases. Alongside anticipated improvements in single cell spatial sequencing resolution, new computational biology techniques have formed the technical backbone when exploring fibroblast heterogeneity. More robust models are required, however. This review will summarize the key advancements in computational techniques that are being deployed to categorize fibroblast heterogeneity and their interaction with the myeloid compartments in specific biological and clinical contexts. First, typical machine-learning-aided methods such as dimensionality reduction, clustering, and trajectory inference, have exposed the role of fibroblast subpopulations in inflammatory disease pathologies. Second, these techniques, coupled with single-cell predicted computational methods have raised novel interactomes between fibroblasts and macrophages of potential clinical significance to many immune-mediated inflammatory diseases such as rheumatoid arthritis, ulcerative colitis, lupus, systemic sclerosis, and others. Third, recently developed scalable integrative methods have the potential to map cross-cell-type spatial interactions at the single-cell level while cross-tissue analysis with these models reveals shared biological mechanisms between disease contexts. Finally, these advanced computational omics approaches have the potential to be leveraged toward therapeutic strategies that target fibroblast-macrophage interactions in a wide variety of inflammatory diseases.

The Risk of Barrett’s Esophagus Transforming to Esophageal Cancer

Barrett’s esophagus (BE) refers to the mucosal transition from normal squamous mucosa to a genetically unstable specialized columnar-type intestinal metaplasia in the esophagus. Recent sophisticated studies using orthogonal techniques of molecular and computational biology strongly support that BE is the predominant pathway to esophageal adenocarcinoma (EAC). In this treatise, we review the risk of BE transforming to EAC and the latest developments in risk assessment of BE progression to EAC. The progression rate of BE to EAC can vary from one study to another depending on the region of origin, definition of BE, and variations in data capture. Overall, the published studies suggest that the risk of BE transforming to EAC is less than 0.5% per year and that to high-grade dysplasia (HGD) or EAC is less than 1% per year. Clinical tools to predict risk such as the Progression in Barrett’s (PIB) have been developed but will need further validation. p53 is the biomarker for risk stratification and has stood the test of time but is still not being widely used due to incomplete correlation between the mutational status and protein expression. Molecular studies using genomics and proteomics approaches are being evaluated but need to demonstrate good clinical accuracy before being translated into practice.

Computational biology insights into genotype-clinical phenotype-protein phenotype relationships between novel SLC26A2 variants identified in inherited skeletal dysplasias

BackgroundPathogenic variants in the transmembrane sulfate transporter protein SLC26A2 are associated with different phenotypes of inherited chondrodysplasias. As limited data is published from India, in this study we sought to elucidate the molecular basis of inherited chondrodysplasias in an Indian cohort. MethodsMolecular screening of 32 fetuses with antenatally diagnosed lethal skeletal dysplasia was performed by next generation sequencing and Sanger sequencing. The genotype-protein phenotype characterization was done using computational biology techniques like homology modelling, stability and pathogenicity predictions. ResultsWe identified five rare autosomal recessive SLC26A2 [NM_000112.4] variants, including three homozygous c.796dupA(p.Thr266Asnfs*12), c.1724delA(p.Lys575Serfs*10), and c.1375_1377dup(p.Val459dup) and two heterozygous variants (c.532C > T(p.Arg178*)) and (c.1382C > T(p.Ala461Val)) in compound heterozygous form in a total of four foetuses. Genotype-protein phenotype annotations highlighted that the clinically severe achondrogenesis 1B causative c.796dupA(p.Thr266Asnfs*12) and c.1724delA(p.Lys575Serfs*10)variants impact SLC26A2 protein structure by deletion of the protein core and transmembrane STAS domains, respectively. In clinically moderate atelosteogenesis type 2 phenotype, the c.1382C > T(p.Ala461Val) variant is predicted to distort alpha helix conformation and alter the bonding properties and free energy dynamics of transmembrane domains and the c.532C > T(p.Arg178*) variant results in loss of both core transmembrane and STAS domains of the SLC26A2 protein. The c.1375_1377dup(p.Val459dup) variant identified in clinically milder atelosteogenesis type II-diastrophic dysplasia spectrum lethal phenotype is predicted to decrease the Qualitative Model Energy Analysis (QMean), which affects major geometrical aspects of the SLC26A2 protein structure. ConclusionWe expand the spectrum of SLC26A2 related lethal chondrodysplasia and report three novel variants correlating clinical severity and protein phenotype within the lethal spectrum of this rare dysplasia. We demonstrate the relevance of structural characterization to aid novel variant reclassification to provide better prenatal management and reproductive options to families with lethal antenatal skeletal disorder.

Revisiting transplant immunology through the lens of single-cell technologies.

Solid organ transplantation (SOT) is the standard of care for end-stage organ disease. The most frequent complication of SOT involves allograft rejection, which may occur via T cell– and/or antibody-mediated mechanisms. Diagnosis of rejection in the clinical setting requires an invasive biopsy as there are currently no reliable biomarkers to detect rejection episodes. Likewise, it is virtually impossible to identify patients who exhibit operational tolerance and may be candidates for reduced or complete withdrawal of immunosuppression. Emerging single-cell technologies, including cytometry by time-of-flight (CyTOF), imaging mass cytometry, and single-cell RNA sequencing, represent a new opportunity for deep characterization of pathogenic immune populations involved in both allograft rejection and tolerance in clinical samples. These techniques enable examination of both individual cellular phenotypes and cell-to-cell interactions, ultimately providing new insights into the complex pathophysiology of allograft rejection. However, working with these large, highly dimensional datasets requires expertise in advanced data processing and analysis using computational biology techniques. Machine learning algorithms represent an optimal strategy to analyze and create predictive models using these complex datasets and will likely be essential for future clinical application of patient level results based on single-cell data. Herein, we review the existing literature on single-cell techniques in the context of SOT.Supplementary InformationThe online version contains supplementary material available at 10.1007/s00281-022-00958-0.

Review article: current and emerging therapies for acute alcohol-associated hepatitis.

Alcohol-associated hepatitis is an acute manifestation of alcohol-associated liver disease (ALD) and is associated with 30%-40% mortality at 28 days. Abstinence and corticosteroids are the mainstays of treatment, but the latter only improves short-term mortality, so new and improved therapies remain an unmet need. The aim was to review the pathophysiology of alcohol-associated hepatitis and how various targets can be used by current and emerging therapies as treatment. A thorough literature review was conducted on acute alcohol-associated hepatitis, current therapies and therapies under investigation. With the increasing prevalence of alcohol use disorder and ALD, the burden of alcohol-associated hepatitis is also expected to rise. The current understanding of alcohol-associated hepatitis pathophysiology has led to clinical trials of several therapies involving IL-1 antagonism, modification of the gut microbiome and liver regeneration. Corticosteroid therapy for alcohol-associated hepatitis is restricted in its applicability and has limited efficacy. Developing multidisciplinary, patient-centred care models based on digital health technologies, in combination with continued discovery of novel therapies using multiomics data and computational biology techniques will be necessary to tackle the increasing burden of alcohol-associated hepatitis.

A Pyroptosis-Related Gene Signature Predicts Prognosis and Immune Microenvironment for Breast Cancer Based on Computational Biology Techniques.

Breast cancer (BC) is a malignant tumor with high morbidity and mortality, which seriously threatens women’s health worldwide. Pyroptosis is closely correlated with immune landscape and the tumorigenesis and development of various cancers. However, studies about pyroptosis and immune microenvironment in BC are limited. Therefore, our study aimed to investigate the potential prognostic value of pyroptosis-related genes (PRGs) and their relationship to immune microenvironment in BC. First, we identified 38 differentially expressed PRGs between BC and normal tissues. Further on, the least absolute shrinkage and selection operator (LASSO) Cox regression and computational biology techniques were applied to construct a four-gene signature based on PRGs and patients in The Cancer Genome Atlas (TCGA) cohort were classified into high- and low-risk groups. Patients in the high-risk group showed significantly lower survival possibilities compared with the low-risk group, which was also verified in an external cohort. Furthermore, the risk model was characterized as an independent factor for predicting the overall survival (OS) of BC patients. What is more important, functional enrichment analyses demonstrated the robust correlation between risk score and immune infiltration, thereby we summarized genetic mutation variation of PRGs, evaluated the relationship between PRGs, different risk group and immune infiltration, tumor mutation burden (TMB), microsatellite instability (MSI), and immune checkpoint blockers (ICB), which indicated that the low-risk group was enriched in higher TMB, more abundant immune cells, and subsequently had a brighter prognosis. Except for that, the lower expression of PRGs such as GZMB, IL18, IRF1, and GZMA represented better survival, which verified the association between pyroptosis and immune landscape. In conclusion, we performed a comprehensive bioinformatics analysis and established a four-PRG signature consisting of GZMB, IL18, IRF1, and GZMA, which could robustly predict the prognosis of BC patients.

Blockage of the Monoamine Oxidase by a Natural Compound to Overcome Parkinson’s Disease via Computational Biology

The dopamine (DA) metabolism changes are significant in Parkinson’s disease (PD). Levels of monoamine oxidases (MAOs) play a critical role in DA metabolism and oxidative damage. Increased levels of the MAO-B enzyme in the elderly raise oxidative damage and enhance neurodegenerative processes. Inhibiting MAO-B as an attractive target would be the best method for treating and understanding Parkinson’s disease. This study aimed to recognize a suitable inhibitor for the MAO-B enzyme using computational biology and compared it with Safinamide as a positive control. We used various computational biology techniques such as binding free energy, virtual screening, molecular dynamics (MD), and docking considerations to achieve the goal. To obtain a potent inhibitor, 41,852 compounds were taken from the Zinc database. After preparing compounds and the MAO-B enzyme, screening was performed using AutoDock Vina software. After screening, a potent natural inhibitor (ZINC00261335) was picked, and then, subsequent MD simulations for both ZINC00261335 and Safinamide were conducted via GROMACS software. The stability of the MAO-B_ZINC00261335 complex was excellent during the simulation, and the results of MM-PBSA analysis explicated that ZINC00261335 with ([Formula: see text]118.353[Formula: see text]kJ[Formula: see text]mol[Formula: see text]) is more potent than Safinamide ([Formula: see text]89.305[Formula: see text]kJ[Formula: see text]mol[Formula: see text]). Ultimately, the ADME study (lipophilicity, drug similarity and pharmacokinetic parameters) for ZINC00261335 was revealed, which is acceptable for human use. This study indicates that ZINC00261335 is a suitable MAO-B inhibitor and a great candidate for more laboratory studies.

Genome Instability and Long Noncoding RNA Reveal Biomarkers for Immunotherapy and Prognosis and Novel Competing Endogenous RNA Mechanism in Colon Adenocarcinoma.

Background: Long noncoding RNAs (lncRNAs) crucially modulate DNA damage responses/repair in cancer cells. However, the underlying regulatory role of genome integrity and its clinical value in colon adenocarcinoma (COAD) remains unclear. This study links genome instability to lncRNA using computational biology techniques, in attempt to propose novel biomarkers of immunotherapy outcome, and investigated a potential competing endogenous RNA (ceRNA) as a molecular regulatory mechanism.Methods: TCGA-COAD patients were divided into genome unstable (GU)-like and genome stable (GS)-like clusters via hierarchical clustering to predict immunotherapy outcomes. Multivariate Cox model was established to predict the overall survival rate in COAD patients. Additionally, SVM and LASSO algorithms were applied to obtain hub lncRNAs. A novel genome instability-related ceRNA network was predicted with the Starbase 2.0 database. To better understand how these genes fundamentally interact during tumor progression and development, the mutation analysis and single-gene analysis for each gene was performed.Results: In contrast to those in the GS-like cluster, GU-like-cluster patients demonstrated a higher tumor mutational burden (TMB)/microsatellite instability (MSI), DNA polymerase epsilon (POLE) mutation rate, and immune checkpoint expression, all indicate a greater predictive power for response rate for immunotherapy. The novel prognostic signature demonstrated an outstanding predictive performance (AUC > 0.70). The genes in the genome insatiability-related ceRNA network (including four axes: AL161772.1-has-miR-671-5p (hsa-miR-181d-5p, has-miR-106a-5p)-NINL, AL161772.1-has-miR-106a-5p-TNFSF11, AC124067.4-hsa-miR-92b-3p (hsa-miR-589-5p)-PHYHIPL, and BOLA3-AS1-has-miR-130b-3p-SALL4) were identified as critical regulators of tumor microenvironment infiltration, cancer stemness, and drug resistance. qPCR was performed to validate the expression patterns of these genes. Furthermore, the MSI-high proportion was greater in patients with mutated type than in those with the wild type according to all four target genes, indicating that these four genes modulate genomic integrity and could serve as novel immunotherapy biomarkers.Conclusion: We demonstrated that genome instability-related lncRNA is a novel biomarker for immunotherapy outcomes and prognosis. A novel ceRNA network that modulates genomic integrity, including four lncRNA-miRNA-mRNA axes, was proposed.

Molecular dynamics analysis of N-acetyl-D-glucosamine against specific SARS-CoV-2's pathogenicity factors.

The causative agent of the pandemic identified as SARS-CoV-2 leads to a severe respiratory illness similar to SARS and MERS with fever, cough, and shortness of breath symptoms and severe cases that can often be fatal. In our study, we report our findings based on molecular docking analysis which could be the new effective way for controlling the SARS-CoV-2 virus and additionally, another manipulative possibilities involving the mimicking of immune system as occurred during the bacterial cell recognition system. For this purpose, we performed molecular docking using computational biology techniques on several SARS-CoV-2 proteins that are responsible for its pathogenicity against N-acetyl-D-glucosamine. A similar molecular dynamics analysis has been carried out on both SARS-CoV-2 and anti-Staphylococcus aureus neutralizing antibodies to establish the potential of N-acetyl-D-glucosamine which likely induces the immune response against the virus. The results of molecular dynamic analysis have confirmed that SARS-CoV-2 spike receptor-binding domain (PDB: 6M0J), RNA-binding domain of nucleocapsid phosphoprotein (PDB: 6WKP), refusion SARS-CoV-2 S ectodomain trimer (PDB: 6X79), and main protease 3clpro at room temperature (PDB: 7JVZ) could bind with N-acetyl-D-glucosamine that these proteins play an important role in SARS-CoV-2’s infection and evade the immune system. Moreover, our molecular docking analysis has supported a strong protein-ligand interaction of N-acetyl-D-glucosamine with these selected proteins. Furthermore, computational analysis against the D614G mutant of the virus has shown that N-acetyl-D-glucosamine affinity and its binding potential were not affected by the mutations occurring in the virus’ receptor binding domain. The analysis on the affinity of N-acetyl-D-glucosamine towards human antibodies has shown that it could potentially bind to both SARS-CoV-2 proteins and antibodies based on our predictive modelling work. Our results confirmed that N-acetyl-D-glucosamine holds the potential to inhibit several SARS-CoV-2 proteins as well as induce an immune response against the virus in the host.

Edible and Herbal Plants for the Prevention and Management of COVID-19.

Background: The outbreak of the pandemic coronavirus disease 2019 (COVID-19) has now become a global pandemic spreading throughout the world. Unfortunately, due to the high infectiousness of the novel β-coronavirus, it is very likely to become an ordinary epidemic. The development of dietary supplements and functional foods might provide a strategy for the prevention and management of COVID-19. Scope and Approach: A great diversity of potential edible and medicinal plants and/or natural compounds showed potential benefits in managing SARS, which may also combat COVID-19. Moreover, many plants and compounds have currently been proposed to be protective against COVID-19. This information is based on data-driven approaches and computational chemical biology techniques. In this study, we review promising candidates of edible and medicinal plants for the prevention and management of COVID-19. We primarily focus on analyzing their underlying mechanisms. We aim to identify dietary supplements and functional foods that assist in managing this epidemic. Key findings and Conclusion: We infer that acetoside, glyasperin, isorhamnetin, and several flavonoid compounds may prevent and/or be effective in managing COVID-19 by targeting the viral infection, reducing the host cytokine storm, regulating the immune response, and providing organ protection. These bioactive dietary components (used either alone or in combination) might assist in the development of dietary supplements or functional foods for managing COVID-19.

Interactomes: Experimental and In Silico Approaches.

Any part of the Central Dogma of Molecular Biology (DNA replication, transcription, and translation) is based on an intricate protein-protein interaction. On this chapter, we will navigate over the techniques that enable us to construct or fulfill the gaps on an interactome study, directly using assessment of the biochemical and/or molecular machinery that allow two proteins to interact with each other; or rely on computational biology techniques to gather information on PPI from public available databases and evaluate this interaction.

Binding of raloxifene to human complement fragment 5a (hC5a): a perspective on cytokine storm and COVID19

Human C5a (hC5a), one of the pro-inflammatory glycoproteins of the complement system is known to undergo production hyperdrive in response to stress and infection. hC5a has been associated with the pathogenesis of many chronic and acute diseases, due to its proven ability in triggering the ‘cytokine storm’, by binding to its cognate receptor C5aR, expressed in myriad of tissues. Given the pleiotropic downstream function of hC5a, it is logical to consider the hC5a or its precursors as potential drug targets, and thus, we have been rationally pursuing the idea of neutralizing the harmful effect of excessive hC5a, by implementing the repurposing strategies for FDA-approved drugs. Indeed, the proof of principle biophysical studies published recently is encouraging, which strongly supports the potential of this strategy. Considering BSA-carprofen as a reference model system, the current study further explores the inherent conformational plasticity of hC5a and its effect in accommodating more than one drug molecule cooperatively at multiple sites. The data generated by recruiting a battery of experimental and computational biology techniques strongly suggest that hC5a can sequentially accommodate more than one raloxifene molecule with an estimated Ki ∼ 0.5 µM and Ki ∼ 3.58 µM on its surface at non-analogous sites. The study hints at exploration of polypharmacology approach, as a new avenue for discovering synergistic drug molecule pairs, or drug molecules with ‘broad-range’ binding affinity for targeting the different ‘hot spots’ on hC5a, as an alternative combination therapy for possible management of the ‘cytokine storm’-related inflammatory diseases, like COVID19. Communicated by Ramaswamy H. Sarma

Online laboratory exercise on computational biology: Phylogenetic analyses and protein modeling based on SARS-CoV-2 data during COVID-19 remote instruction.

Suspended on-site instruction during the COVID-19 created an exceptional challenge for teaching hands-on laboratory classes. We designed an online laboratory activity using computational biology techniques to overcome this issue. This set of online exercises introduces bioinformatics skills into existing curricula in the form of guided tutorials based on molecular data on SARS-CoV-2 virus.

Molecular Docking Study of Expansin Proteins in Fibers of Medicinal Plants Calotropis Procera

Calotropis procera is used in several traditional medicines to treat a variety of diseases. Calotropis is also used as a homeopathic medicine. The bast fiber can substitute cotton wool for surgical or stuffing purposes. This fiber can be used as an excellent model system to study the genes involved in fiber elongation. Expansins are typically 250-275 amino acids long, pH-dependent cell wall-loosening proteins required for cell wall expansion in many developmental processes. Four Expansin A proteins are present in fast growing Calotropis procera fibers. Expansins proteins disrupt the cellulose-hemicellulose network transiently, allowing slippage of cell wall polymers. But the molecular mechanism by which expansin loosens the cellulosic network is not yet established. To understand of the role of protein expansins in the xyloglucan-cellulose, different computational biology techniques are used. Molecular docking analysis of protein expansins with ligand xylose present in xyloglucan shows that arginine and serine are responsible for protein-ligand interactions.