Vaccine design and development: Exploring the interface with computational biology and AI.

Computational biology involves applying computer science and informatics techniques in biology to understand complex biological data. It allows us to collect, connect, and analyze biological data at a large scale and build predictive models. In the twenty first century, computational resources along with Artificial Intelligence (AI) have been widely used in various fields of biological sciences such as biochemistry, structural biology, immunology, microbiology, and genomics to handle massive data for decision-making, including in applications such as drug design and vaccine development, one of the major areas of focus for human and animal welfare. The knowledge of available computational resources and AI-enabled tools in vaccine design and development can improve our ability to conduct cutting-edge research. Therefore, this review article aims to summarize important computational resources and AI-based tools. Further, the article discusses the various applications and limitations of AI tools in vaccine development.

The application of vaccines is one of the most promising treatments for numerous infectious diseases. However, the design and development of effective vaccines involve huge investments and resources, and only a handful of candidates successfully reach the market. Only relying on traditional methods is both time-consuming and expensive. Various computational tools and software have been developed to accelerate the vaccine design and development. Further, AI-enabled computational tools have revolutionized the field of vaccine design and development by creating predictive models and data-driven decision-making processes. Therefore, information and awareness of these AI-enabled computational resources will immensely facilitate the development of vaccines against emerging pathogens. In this review, we have meticulously summarized the available computational tools for each step of in-silico vaccine design and development, delving into the transformative applications of AI and ML in this domain, which would help to choose appropriate tools for each step during vaccine development, and also highlighting the limitations of these tools to facilitate the selection of appropriate tools for each step of vaccine design.

Comprehensive Investigation of Natural Ligands as Inhibitors of ß Secretase to Identify Alzheimer's Disease Therapeutics.

INTRODUCTION: Alzheimer's disease (AD) is an alarmingly prevalent worldwide neurological disorder that affects millions of people and has severe effects on cognitive functions. The amyloid hypothesis, which links AD to Aß (amyloid beta) plaque aggregation, is a well-acknowledged theory. The ß-secretase (BACE1) is the main cause of Aß production, which makes it a possible target for therapy. FDA-approved therapies for AD do exist, but none of them explicitly target BACE1, and their effectiveness is constrained and accompanied by adverse effects. MATERIALS AND METHODS: We determined the essential chemical components of medicinal herbs by conducting a thorough literature research for BACE1. Computational methods like molecular docking, ADMET (Absorption, distribution, metabolism, excretion, toxicity) screening, molecular dynamic simulations, and MMPBSA analysis were performed in order to identify the most promising ligands for ß-secretase. RESULTS: The results suggested that withasomniferol, tinosporide, and curcumin had better binding affinity with BACE1, suggesting their potential as therapeutic candidates against Alzheimer's disease. CONCLUSION: Herbal therapeutics have immense applications in the treatment of chronic diseases like Alzheimer's disease, and there is an urgent need to assess their efficacy as therapeutics.

Food-derived Peptides as Promising Neuroprotective Agents: Mechanism and Therapeutic Potential.

Many food-derived peptides have the potential to improve brain health and slow down neurodegeneration. Peptides are produced by the enzymatic hydrolysis of proteins from different food sources. These peptides have been shown to be involved in antioxidant and anti-inflammatory activity, neuro-transmission modulation, and gene expression regulation. Although few peptides directly affect chromatin remodeling and histone alterations, others indirectly affect the neuroprotection process by interfering with epigenetic changes. Fish-derived peptides have shown neuroprotective properties that reduce oxidative stress and improve motor dysfunction in Parkinson's disease models. Peptides from milk and eggs have been found to have anti-inflammatory properties that reduce inflammation and improve cognitive function in Alzheimer's disease models. These peptides are potential therapeutics for neurodegenerative diseases, but more study is required to assess their efficacy and the underlying neuroprotective benefits. Consequently, this review concentrated on each mechanism of action used by food-derived peptides that have neuroprotective advantages and applications in treating neurodegenerative diseases. This article highlights various pathways, such as inflammatory pathways, major oxidant pathways, apoptotic pathways, neurotransmitter modulation, and gene regulation through which food-derived peptides interact at the cellular level.

Unlocking the Potential of the CA2, CA7, and ITM2C Gene Signatures for the Early Detection of Colorectal Cancer: A Comprehensive Analysis of RNA-Seq Data by Utilizing Machine Learning Algorithms.

Colorectal cancer affects the colon or rectum and is a common global health issue, with 1.1 million new cases occurring yearly. The study aimed to identify gene signatures for the early detection of CRC using machine learning (ML) algorithms utilizing gene expression data. The TCGA-CRC and GSE50760 datasets were pre-processed and subjected to feature selection using the LASSO method in combination with five ML algorithms: Adaboost, Random Forest (RF), Logistic Regression (LR), Gaussian Naive Bayes (GNB), and Support Vector Machine (SVM). The important features were further analyzed for gene expression, correlation, and survival analyses. Validation of the external dataset GSE142279 was also performed. The RF model had the best classification accuracy for both datasets. A feature selection process resulted in the identification of 12 candidate genes, which were subsequently reduced to 3 (CA2, CA7, and ITM2C) through gene expression and correlation analyses. These three genes achieved 100% accuracy in an external dataset. The AUC values for these genes were 99.24%, 100%, and 99.5%, respectively. The survival analysis showed a significant logrank p-value of 0.044 for the final gene signatures. The analysis of tumor immunocyte infiltration showed a weak correlation with the expression of the gene signatures. CA2, CA7, and ITM2C can serve as gene signatures for the early detection of CRC and may provide valuable information for prognostic and therapeutic decision making. Further research is needed to fully understand the potential of these genes in the context of CRC.

Drug Repurposing Against Novel Therapeutic Targets in Plasmodium falciparum for Malaria: The computational perspective.

Malaria remains one of the most challenging tropical diseases. Since malaria cases are reportedly alarming in terms of infections and mortality, urgent attention is needed for addressing the issues of drug resistance in falciparum malaria. High throughput screening methods have paved the way for rapid identification of anti-malarial. Furthermore, drug repurposing helps in shortening the time required for drug safety approvals. Hence, the discovery of new antimalarials by drug repurposing is a promising approach for combating the disease. This article summarizes the recent computational approaches used for identifying novel antimalarials by using drug target interaction tools followed by pharmacokinetic studies.

Navigating Molecular Pathways: An Update on Drugs in Colorectal Cancer Treatment.

Colorectal cancer (CRC) is a multifaceted and heterogeneous ailment that affects the colon or rectum of the digestive system. It is the second most commonly occurring form of cancer and ranks third in terms of mortality rate. The progression of CRC does not occur due to a single mutational event; rather, it is the result of the sequential and cumulative accumulation of mutations in key driver genes of signaling pathways. The most significant signaling pathways, which have oncogenic potential due to their deregulation, include Wnt/ß-catenin, Notch, TGF-ß, EGFR/MAPK, and PI3K/AKT pathways. Numerous drug target therapies have been developed to treat CRC using small molecule inhibitors, antibodies, or peptides. Although drug-targeted therapy is effective in most cases, the development of resistance mechanisms in CRC has raised questions about their efficacy. To overcome this issue, a novel approach to drug repurposing has come to light, which utilizes already FDA-approved drugs to treat CRC. This approach has shown some promising experimental results, making it a crucial avenue of research in the treatment of CRC.

Prognostic model development for classification of colorectal adenocarcinoma by using machine learning model based on feature selection technique boruta.

Colorectal cancer (CRC) is the third most prevalent cancer type and accounts for nearly one million deaths worldwide. The CRC mRNA gene expression datasets from TCGA and GEO (GSE144259, GSE50760, and GSE87096) were analyzed to find the significant differentially expressed genes (DEGs). These significant genes were further processed for feature selection through boruta and the confirmed features of importance (genes) were subsequently used for ML-based prognostic classification model development. These genes were analyzed for survival and correlation analysis between final genes and infiltrated immunocytes. A total of 770 CRC samples were included having 78 normal and 692 tumor tissue samples. 170 significant DEGs were identified after DESeq2 analysis along with the topconfects R package. The 33 confirmed features of importance-based RF prognostic classification model have given accuracy, precision, recall, and f1-score of 100% with 0% standard deviation. The overall survival analysis had finalized GLP2R and VSTM2A genes that were significantly downregulated in tumor samples and had a strong correlation with immunocyte infiltration. The involvement of these genes in CRC prognosis was further confirmed on the basis of their biological function and literature analysis. The current findings indicate that GLP2R and VSTM2A may play a significant role in CRC progression and immune response suppression.

Herbal Therapeutics for Alzheimer's Disease: Ancient Indian Medicine System from the Modern Viewpoint.

Alzheimer's is a chronic neurodegenerative disease where amyloid-beta (Aß) plaques and neurofibrillary tangles are formed inside the brain. It is also characterized by progressive memory loss, depression, neuroinflammation, and derangement of other neurotransmitters. Due to its complex etiopathology, current drugs have failed to completely cure the disease. Natural compounds have been investigated as an alternative therapy for their ability to treat Alzheimer's disease (AD). Traditional herbs and formulations which are used in the Indian ayurvedic system are rich sources of antioxidant, anti-amyloidogenic, neuroprotective, and anti-inflammatory compounds. They promote quality of life by improving cognitive memory and rejuvenating brain functioning through neurogenesis. A rich knowledge base of traditional herbal plants (Turmeric, Gingko, Ashwagandha, Shankhpushpi, Giloy, Gotu kola, Garlic, Tulsi, Ginger, and Cinnamon) combined with modern science could suggest new functional leads for Alzheimer's drug discovery. In this article Ayurveda, the ancient Indian herbal medicine system based on multiple clinical and experimental, evidence have been reviewed for treating AD and improving brain functioning. This article presents a modern perspective on the herbs available in the ancient Indian medicine system as well as their possible mechanisms of action for AD treatment. The main objective of this research is to provide a systematic review of herbal drugs that are easily accessible and effective for the treatment of AD.

Screening of potential antiplasmodial agents targeting cysteine protease-Falcipain 2: a computational pipeline.

The spread of antimalarial drug resistance is a substantial challenge in achieving global malaria elimination. Consequently, the identification of novel therapeutic candidates is a global health priority. Malaria parasite necessitates hemoglobin degradation for its survival, which is mediated by Falcipain 2 (FP2), a promising antimalarial target. In particular, FP2 is a key enzyme in the erythrocytic stage of the parasite's life cycle. Here, we report the screening of approved drugs listed in DrugBank using a computational pipeline that includes drug-likeness, toxicity assessments, oral toxicity evaluation, oral bioavailability, docking analysis, maximum common substructure (MCS) and molecular dynamics (MD) Simulations analysis to identify capable FP2 inhibitors, which are hence potential antiplasmodial agents. A total of 45 drugs were identified, which have positive drug-likeness, no toxic features and good bioavailability. Among these, six drugs showed good binding affinity towards FP2 compared to E64, an epoxide known to inhibit FP2. Notably, two of them, Cefalotin and Cefoxitin, shared the highest MCS with E64, which suggests that they possess similar biological activity as E64. In an investigation using MD for 100 ns, Cefalotin and Cefoxitin showed adequate protein compactness as well as satisfactory complex stability. Overall, these computational approach findings can be applied for designing and developing specific inhibitors or new antimalarial agents for the treatment of malaria infections.Communicated by Ramaswamy H. Sarma.

Fungal assisted bio-treatment of environmental pollutants with comprehensive emphasis on noxious heavy metals: Recent updates.

In the present time of speedy developments and industrialization, heavy metals are being uncovered in aquatic environment and soil via refining, electroplating, processing, mining, metallurgical activities, dyeing and other several metallic and metal based industrial and synthetic activities. Heavy metals like lead (Pb), mercury (Hg), cadmium (Cd), arsenic (As), Zinc (Zn), Cobalt (Co), Iron (Fe), and many other are considered as seriously noxious and toxic for the aquatic environment, human, and other aquatic lives and have damaging influences. Such heavy metals, which are very tough to be degraded, can be managed by reducing their potential through various processes like removal, precipitation, oxidation-reduction, bio-sorption, recovery, bioaccumulation, bio-mineralization etc. Microbes are known as talented bio-agents for the heavy metals detoxification process and fungi are one of the cherished bio-sources that show noteworthy aptitude of heavy metal sorption and metal tolerance. Thus, the main objective of the authors was to come with a comprehensive review having methodological insights on the novel and recent results in the field of mycoremediation of heavy metals. This review significantly assesses the potential talent of fungi in heavy metal detoxification and thus, in environmental restoration. Many reported works, methodologies and mechanistic sights have been evaluated to explore the fungal-assisted heavy metal remediation. Herein, a compact and effectual discussion on the recent mycoremediation studies of organic pollutants like dyes, petroleum, pesticides, insecticides, herbicides, and pharmaceutical wastes have also been presented.

Identification of Potential Antimalarial Drug Candidates Targeting Falcipain-2 Protein of Malaria Parasite-A Computational Strategy.

Falcipain-2 (FP-2) is one of the main haemoglobinase of P. falciparum which is an important molecular target for the treatment of malaria. In this study, we have screened alkaloids to identify potential inhibitors against FP-2 since alkaloids possess great potential as anti-malarial agents. A total of 340 alkaloids were considered for the study using a series of computational pipelines. Initially, pharmacokinetics and toxicity risk assessment parameters were applied to screen compounds. Subsequently, molecular docking algorithms were utilised to understand the binding efficiency of alkaloids against FP-2. Further, oral toxicity prediction was done using the pkCSM tool, and 3D pharmacophore features were analysed using the PharmaGist server. Finally, MD simulation was performed for Artemisinin and the top 3 drug candidates (Noscapine, Reticuline, Aclidinium) based on docking scores to understand the functional impact of the complexes, followed by a binding site interaction residues study. Overall analysis suggests that Noscapine conceded good pharmacokinetics and oral bioavailability properties. Also, it showed better binding efficiency with FP-2 when compared to Artemisinin. Interestingly, structure alignment analysis with artemisinin revealed that Noscapine, Reticuline, and Aclidinium might possess similar biological action. Molecular dynamics and free energy calculations revealed that Noscapine could be a potent antimalarial agent targeting FP-2 that can be used for the treatment of malaria and need to be studied experimentally in the future.

Correlating multi-functional role of cold shock domain proteins with intrinsically disordered regions.

Cold shock proteins (CSPs) are an ancient and conserved family of proteins. They are renowned for their role in response to low-temperature stress in bacteria and nucleic acid binding activities. In prokaryotes, cold and non-cold inducible CSPs are involved in various cellular and metabolic processes such as growth and development, osmotic oxidation, starvation, stress tolerance, and host cell invasion. In prokaryotes, cold shock condition reduces cell transcription and translation efficiency. Eukaryotic cold shock domain (CSD) proteins are evolved form of prokaryotic CSPs where CSD is flanked by N- and C-terminal domains. Eukaryotic CSPs are multi-functional proteins. CSPs also act as nucleic acid chaperons by preventing the formation of secondary structures in mRNA at low temperatures. In human, CSD proteins play a crucial role in the progression of breast cancer, colon cancer, lung cancer, and Alzheimer's disease. A well-defined three-dimensional structure of intrinsically disordered regions of CSPs family members is still undetermined. In this article, intrinsic disorder regions of CSPs have been explored systematically to understand the pleiotropic role of the cold shock family of proteins.

Aß peptides stabilize GPCRs in inactive form and trigger inverse agonism in Alzheimer's disease.

Several G-protein coupled receptors (GPCR) are upregulated in Alzheimer's Disease (AD), which ought to facilitate neurotransmission, and improve cognition. Yet, despite this upregulation, associated physiological effects are not observed in AD patients. This paradox solicits urgent attention to find a suitable justification for disturbed neurotransmission in AD. This article focuses on the role of Aß granules and their possible interaction with GPCRs that modulate neurotransmission and AD progression.

Screening natural inhibitors against upregulated G-protein coupled receptors as potential therapeutics of Alzheimer's disease.

Computational approaches have been helpful in high throughput screening of drug libraries and designing ligands against receptors. Alzheimer's disease is a complex neurological disorder, which causes dementia. In this disease neurons are damaged due to formation of Amyloid-beta plaques and neurofibrillary tangles, which along with some other factors contributes to disease development and progression. The objective of this study was to predict tertiary structures of five G-protein coulped neurotransmitter receptors; CHRM5, CYSLTR2, DRD5, GALR1 and HTR2C, that are upregulated in Alzheimer's disease, and to screen potential inhibitors for against these receptors. In this study, Comparative modelling, molecular docking, MMGBSA analysis, ADMET screening and molecular dynamics simulation were performed. Tertiary structures of the five GPCRs were predicted and further subjected to molecular docking against natural compounds. Pharmacokinetic studies of natural compounds were also conducted for assessing drug-likeness properties. Molecular dynamics simulations were performed to investigate the structural stability and binding affinities of each complex. Finally, the results suggested that ZINC04098704, ZINC31170017, ZINC05998597, ZINC67911229, and ZINC67910690 had better binding affinity with CHRM5, CYSLTR2, DRD5, GALR1, and HTR2C (5-HT2C) proteins, respectively.Communicated by Ramaswamy H. Sarma.

Transcriptome profiling by combined machine learning and statistical R analysis identifies TMEM236 as a potential novel diagnostic biomarker for colorectal cancer.

Colorectal cancer (CRC) is a common cause of cancer-related deaths worldwide. The CRC mRNA gene expression dataset containing 644 CRC tumor and 51 normal samples from the cancer genome atlas (TCGA) was pre-processed to identify the significant differentially expressed genes (DEGs). Feature selection techniques Least absolute shrinkage and selection operator (LASSO) and Relief were used along with class balancing for obtaining features (genes) of high importance. The classification of the CRC dataset was done by ML algorithms namely, random forest (RF), K-nearest neighbour (KNN), and artificial neural networks (ANN). The significant DEGs were 2933, having 1832 upregulated and 1101 downregulated genes. The CRC gene expression dataset had 23,186 features. LASSO had performed better than Relief for classifying tumor and normal samples through ML algorithms namely RF, KNN, and ANN with an accuracy of 100%, while Relief had given 79.5%, 85.05%, and 100% respectively. Common features between LASSO and DEGs were 38, from them only 5 common genes namely, VSTM2A, NR5A2, TMEM236, GDLN, and ETFDH had shown statistically significant survival analysis. Functional review and analysis of the selected genes helped in downsizing the 5 genes to 2, which are VSTM2A and TMEM236. Differential expression of TMEM236 was statistically significant and was markedly reduced in the dataset which solicits appreciation for assessment as a novel biomarker for CRC diagnosis.

Perspectives on the Role of APOE4 as a Therapeutic Target for Alzheimer's Disease.

Alzheimer's disease (AD) is a neurodegenerative disease that is coupled with chronic cognitive dysfunction. AD cases are mostly late onset, and genetic risk factors like the Apolipoprotein E (APOE) play a key role in this process. APOE ɛ2, APOE ɛ3, and APOE ɛ4 are three key alleles in the human APOE gene. For late onset, APOE ɛ4 has the most potent risk factor while APOE ɛ2 plays a defensive role. Several studies suggests that APOE ɛ4 causes AD via different processes like neurofibrillary tangle formation by amyloid-ß accumulation, exacerbated neuroinflammation, cerebrovascular disease, and synaptic loss. But the pathway is still unclear that which actions of APOE ɛ4 lead to AD development. Since APOE was found to contribute to many AD pathways, targeting APOE ɛ4 can lead to a hopeful plan of action in development of new drugs to target AD. In this review, we focus on recent studies and perspectives, focusing on APOE ɛ4 as a key molecule in therapeutic strategies.

Casuarictin: A new herbal drug molecule for Alzheimer's disease as inhibitor of presenilin stabilization factor like protein.

Alzheimer's disease is a progressive neurodegenerative disorder. In this disease neurodegeneration occurs due to deposition of aggregated amyloid-beta plaques and neurofibrillary tangles (hyperphosphorylated tau proteins). Present study focuses on interaction of different phytochemicals with presenilin stabilization factor like protein (PSFL). PSFL protein is known to stabilize Presenilin, which is mainly involved in intramembrane hydrolysis of selected type- I membrane proteins, including amyloid-beta precursor protein, and produces amyloid-beta protein. Amyloid-beta are small peptides comprising of 36-43 amino acids, which play a significant role in senile plaques formation in the brains of Alzheimer patients. Virtual screening and docking of phytochemicals with PSFL protein was done to find the potential inhibitor. Based on binding affinity, docked energy and molecular dynamics simulations, three phytochemicals namely Saponin, Casuarictin, and Enoxolone, were identified as potential inhibitors for the target protein.

Machine learning method using position-specific mutation based classification outperforms one hot coding for disease severity prediction in haemophilia 'A'.

Haemophilia is an X-linked genetic disorder in which A and B types are the most common that occur due to absence or lack of protein factors VIII and IX, respectively. Severity of the disease depends on mutation. Available Machine Learning (ML) methods that predict the mutational severity by using traditional encoding approaches, generally have high time complexity and compromised accuracy. In this study, Haemophilia 'A' patient mutation dataset containing 7784 mutations was processed by the proposed Position-Specific Mutation (PSM) and One-Hot Encoding (OHE) technique to predict the disease severity. The dataset processed by PSM and OHE methods was analyzed and trained for classification of mutation severity level using various ML algorithms. Surprisingly, PSM outperformed OHE, both in terms of time efficiency and accuracy, with training and prediction time improvement in the range of approximately 91 to 98% and 80 to 99% respectively. The severity prediction accuracy also improved by using PSM with different ML algorithms.

Structural insight of two 4-Coumarate CoA ligase (4CL) isoforms in Leucaena suggests targeted genetic manipulations could lead to better lignin extractability from the pulp.

4-Coumarate: coenzyme A ligase (4CL) is a key enzyme involved in the early steps of the monolignol biosynthetic pathway. It is hypothesized to modulate S and G monolignol content in the plant. Lignin removal is imperative to the paper industry and higher S/G ratio governs better extractability of lignin and economics of the pulping process. This background prompted us to predict 3D structure of two isoforms of 4CL in Leucaena leucocephala and evaluate their substrate preferences. The 3D structure of Ll4CL1 and Ll4CL2 protein were created by homology modeling and further refined by loop refinement. Molecular docking studies suggested differential substrate preferences of both the isoforms. Ll4CL1 preferred sinapic acid (- 4.91 kcal/mole), ferulic acid (- 4.84 kcal/mole), hydroxyferulic acid (- 4.72 kcal/mole), and caffeic acid (- 4.71 kcal/mole), in their decreasing order. Similarly, Ll4CL2 preferred caffeic acid (- 6.56 kcal/mole, 4 H bonds), hydroxyferulic acid (- 6.56 kcal/mole, 3 H bonds), and ferulic acid (- 6.32 kcal/mole) and sinapic acid (- 5.00 kcal/mole) in their decreasing order. Further, active site residues were identified in both the isoforms and in silico mutation and docking analysis was performed. Our analysis suggested that ASP228, TYR262, and PRO326 for Ll4CL1 and SER165, LYS247 and PRO315 for Ll4CL2 were important for their functional activity. Based on differential substrate preferences of the two isoforms, as a first step towards genetically modified Leuaena having the desired phenotype, it can be proposed that over-expression of Ll4CL1 gene and/or down-regulation of Ll4CL2 gene could yield higher S/G ratio leading to better extractability of lignin.

Identification of drought stress-responsive genes in rice (Oryza sativa) by meta-analysis of microarray data.

Meta-analysis provides a systematic access to the previously studied microarray datasets that can recognize several commonsignatures of stresses. Three different datasets of abiotic stresses on rice were used for meta-analysis. These microarray datasets were normalized to regulate data for technical variation, as opposed to biological differences between the samples. A t-test was performed to recognize the differentially-expressed genes (DEGs) between stressed and normal samples. Gene ontology enrichment analysis revealed the functional distribution of DEGs in different stressed conditions. Further analysis was carried out using software RICE NET DB and divided into three different categories: biological process (homoiothermy and protein amino acid phosphorylation), cellular component (nucleus and membrane), and molecular function (zinc ion binding ad DNA binding). The study revealed that 5686 genes were constantly expressed differentially in Oryza sativa (2089 upregulated and 3597 downregulated). The lowest P value (P = 0.003756) among upregulated DEGs was observed for naringenin, 2-oxoglutrate 3-dioxygenase protein. The lowest P value (P = 0.002866816) among the downregulated DEGs was also recorded for retrotransposon protein. The network constructed from 48 genes revealed 10 hub genes that are connected with topological genes. These hub genes are stress responsive genes that may also be regarded as the marker genes for drought stress response. Our study reported a new set of hub genes (reference genes) that have potentially significant role in development of stress tolerant rice.