Neuroinflammation is a key hallmark of both neurodegenerative and neurospecific autoimmune diseases, including multiple sclerosis (MS), where immune dysregulation contributes to cellular stress, autophagy, and disease progression in Alzheimer’s disease (AD), Parkinson’s disease (PD), and MS. Emerging evidence suggests a shared mechanism behind MS, AD, and PD, driven by chronic interaction between the peripheral immune system and the central nervous system (CNS). While MS was traditionally viewed as a primary autoimmune condition, recent research indicated that all three disorders involve a breakdown of the blood–brain barrier (BBB). This structural failure enables peripheral immune cells and cytokines to enter the brain, causing sustained neuroinflammation and accelerating disease progression. Here, we propose an end-to-end framework for identification of the diagnostic and therapeutic cell-specific protein markers commonly regulated in mild–moderate AD (MMAD), early-stage PD (ESPD), and MS within peripheral blood mononuclear cells (PBMCs). PBMC markers were first identified based on shared differential protein expression, followed by filtering for BBB permeability. Subsequently, sorted cell markers were mapped to disease-specific neural cell types. Our analysis suggests that PBMC-derived cells, including astrocyte- and monocyte-like populations, share overlapping transcriptional signatures and functional similarity with macrophages and neuroglial cells, indicating potential transcriptional similarity or functional convergence. Furthermore, intra- and inter-cellular pathway analysis suggested both shared and disease-specific signaling mechanisms, with kinase–integrin interactions emerging as key regulatory factors. Selected potential seed markers, primarily kinases and immunoglobulins, were further analyzed through evolutionary sequence–structure space to identify druggable structural features. Next, protein moonlighting possibilities were tested to enhance the temporal functional trajectory of the markers for precise therapeutic impact. Hence, the framework provides a robust strategy to identify immune-based disease-specificcandidate diagnostic andpotential therapeutic targets.

Classifying drugs into symptomatic (SYM) and disease-modifying (DM) categories is essential for understanding their therapeutic effect and plays a key role in drug repurposing. While many computational approaches focus on drug–target prediction, they often ignore the nature of the drug’s action on disease progression. This study proposes a graph-based strategy to classify drugs as SYM or DM based on their effect on disease treatment. We construct a heterogeneous network comprising genes, diseases, and drugs, and apply a guided shortest path traversal framework for drug effect classification. During this traversal, certain genes appear frequently in the shortest metapaths linking diseases and drugs. These recurrent genes are identified based on their frequency of occurrence in known drug–disease paths. For a new drug–disease pair, if the traversal path contains recurrent genes marked for a specific treatment type, we classify the drug accordingly. Over and above classifying the drugs, the proposed method incorporates the metapath-based framework to improve interpretability. Experimental results show that our model achieves significantly better classification accuracy compared to advanced machine learning and deep learning methods. A case study on multiple sclerosis further supports the biological relevance of our approach.

Despite the abundance of large-scale molecular and drug-response data, current research on early-onset Parkinson’s disease (EOPD) markers often lacks mechanistic interpretations of drug-gene relationships, limiting our understanding of how drugs exert their therapeutic effects. While existing studies provide valuable EOPD markers, the mechanisms by which targeted drugs act remain poorly understood. We propose DTI-Prox, a novel workflow that identifies potentially overlooked EOPD markers and suggests relevant drug targets. DTI-Prox employs network proximity to measure how closely connected a drug and gene are within a biological network. Additionally, node similarity, which assesses the functional resemblance between network nodes, reveals meaningful drug-gene connections. DTI-Prox identifies 417 novel drug-target pairs and four previously unreported EOPD markers (PTK2B, APOA1, A2M, and BDNF), demonstrating significant pathway enrichment in neurodegenerative processes. Notably, shared pathway analysis shows that prioritized drugs such as Amantadine, Apomorphine, Atropine, Benztropine, Biperiden, Bromocriptine, Cabergoline, Carbidopa, and Citalopram, currently used for other conditions, interact with key EOPD-associated diagnostic markers, suggesting their potential for drug repurposing. The constructed functional network’s validity is reinforced by statistically significant drug-target pairs. The findings provide new insights into EOPD drug mechanisms and identify promising therapeutic candidates, potentially leading to more effective, personalized treatment approaches for EOPD patients.

Understanding the cellular heterogeneity is a break-through in both the field of biology and medicine. Cells harboring from the same genome show functional disparity in various microenvironment. Here, cell-specific regulatory circuits help to reveal the mode of regulation of the biomarkers and the cause of abnormalities regarding the disease progression. Therefore, each signal during the regulation process is important and crucial to determine the heterogeneity of disease. Low resolution cell isolation techniques used previously, averaging the signals of each cell, are not feasible to reconstruct the gene regulatory network. Recently, advancement of single-cell RNA sequencing techniques enabled to capture the transcriptomic aspects of each cell. Though single-cell gene expression studies open new pathway to unveil the biological complexities, but not sufficient to understand the cellular state under a disease condition. The local biological networks will further escalate the perception of the cellular heterogeneity more clearly. In this study, we established the local networks of the cell types responsible for one of the most aggressive cancers known as glioblastoma multiform. We identified the transcription factors those are responsible to regulate the mode of the cell-specific hub biomarkers and finally leads to disease progression. Moreover, the identified crucial transcription factors from the network are RELA, NFKB-family, STAT3, SP1, FOS and JUN. In the future, these transcription factors can be considered as a successful therapeutic target during designing precision medicine strategies.

Prostate cancer is the second leading cause of cancer-related death in men. Metastasis shows poor survival even though the recovery rate is high. In spite of numerous studies regarding prostate carcinoma, multiple questions are still unanswered. In this regards, gene regulatory network can uncover the mechanisms behind cancer progression, and metastasis. Under a feed forward loop, transcription factors (TFs) can be a good druggable candidate. We have proposed a computational model to study the uncertainty of TFs and suggest the appropriate cellular conditions for drug targeting. We have selected feed-forward loops depending on the shared list of the functional annotations among TFs, genes and miRNAs. From the potential feed forward loop cores, six TFs were identified as druggable targets, which include AR, CEBPB, CREB1, ETS1, NFKB1 and RELA. However, TFs are known for their Protein Moonlighting properties, which provide unrelated multi-functionalities within the same or different subcellular localizations. Following that, we have identified such functions that are suitable for drug targeting. On the other hand, we have tried to identify membraneless organelles for providing more specificity to the proposed time and space theory. The study has provided certain possibilities on TF-based therapeutics. The controlled dynamic nature of the TF may have enhanced the chances where TFs can be considered as one of the prime drug targets. Finally, the combination of membranless phase separation and protein moonlighting has provided possible druggable period within the biological clock.

Parkinson’s disease is a common neurodegenerative disease. The differential expression of alpha-synuclein within Lewy Bodies leads to this disease. Some missense mutations of alpha-synuclein may resultant in functional aberrations. In this study, our objective is to verify the functional adaptation due to early and late-onset mutation which can trigger or control the rate of alpha-synuclein aggregation. In this regard, we have proposed a computational model to study the difference and similarities among the Wild type alpha-synuclein and mutants i.e., A30P, A53T, G51D, E46K, and H50Q. Evolutionary sequence space analysis is also performed in this experiment. Subsequently, a comparative study has been performed between structural information and sequence space outcomes. The study shows the structural variability among the selected subtypes. This information assists inter pathway modeling due to mutational aberrations. Based on the structural variability, we have identified the protein–protein interaction partners for each protein that helps to increase the robustness of the inter-pathway connectivity. Finally, few pathways have been identified from 12 semantic networks based on their association with mitochondrial dysfunction and dopaminergic pathways.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), a causative agent of the coronavirus disease (COVID-19), is a part of the $beta $-Coronaviridae family. The virus contains five major protein classes viz., four structural proteins [nucleocapsid (N), membrane (M), envelop (E) and spike glycoprotein (S)] and replicase polyproteins (R), synthesized as two polyproteins (ORF1a and ORF1ab). Due to the severity of the pandemic, most of the SARS-CoV-2-related research are focused on finding therapeutic solutions. However, studies on the sequences and structure space throughout the evolutionary time frame of viral proteins are limited. Besides, the structural malleability of viral proteins can be directly or indirectly associated with the dysfunctionality of the host cell proteins. This dysfunctionality may lead to comorbidities during the infection and may continue at the post-infection stage. In this regard, we conduct the evolutionary sequence-structure analysis of the viral proteins to evaluate their malleability. Subsequently, intrinsic disorder propensities of these viral proteins have been studied to confirm that the short intrinsically disordered regions play an important role in enhancing the likelihood of the host proteins interacting with the viral proteins. These interactions may result in molecular dysfunctionality, finally leading to different diseases. Based on the host cell proteins, the diseases are divided in two distinct classes: (i) proteins, directly associated with the set of diseases while showing similar activities, and (ii) cytokine storm-mediated pro-inflammation (e.g. acute respiratory distress syndrome, malignancies) and neuroinflammation (e.g. neurodegenerative and neuropsychiatric diseases). Finally, the study unveils that males and postmenopausal females can be more vulnerable to SARS-CoV-2 infection due to the androgen-mediated protein transmembrane serine protease 2.

The novel coronavirus or COVID-19 has first been found in Wuhan, China, and became pandemic. Angiotensin-converting enzyme 2 (ACE2) plays a key role in the host cells as a receptor of Spike-I Glycoprotein of COVID-19 which causes final infection. ACE2 is highly expressed in the bladder, ileum, kidney and liver, comparing with ACE2 expression in the lung-specific pulmonary alveolar type II cells. In this study, the single-cell RNAseq data of the five tissues from different humans are curated and cell types with high expressions of ACE2 are identified. Subsequently, the protein-protein interaction networks have been established. From the network, potential biomarkers which can form functional hubs, are selected based on k-means network clustering. It is observed that angiotensin PPAR family proteins show important roles in the functional hubs. To understand the functions of the potential markers, corresponding pathways have been researched thoroughly through the pathway semantic networks. Subsequently, the pathways have been ranked according to their influence and dependency in the network using PageRank algorithm. The outcomes show some important facts in terms of infection. Firstly, renin-angiotensin system and PPAR signaling pathway can play a vital role for enhancing the infection after its intrusion through ACE2. Next, pathway networks consist of few basic metabolic and influential pathways, e.g. insulin resistance. This information corroborate the fact that diabetic patients are more vulnerable to COVID-19 infection. Interestingly, the key regulators of the aforementioned pathways are angiontensin and PPAR family proteins. Hence, angiotensin and PPAR family proteins can be considered as possible therapeutic targets. Contact: sagnik.sen2008@gmail.com, umaulik@cse.jdvu.ac.in Supplementary information: Supplementary data are available online.

POU domain class 2 homebox 1 or POU2F1 is broadly known as an important transcription factor. Due to its association with different types of malignancies, POU2F1 became one of the key factors in pancancer analysis. However, in spite of considering this protein as a potential drug target, none of the drug targeting POU2F1 has been designed as of yet due to the extreme structural flexibility of this protein. In this article, we have proposed a three-level comprehensive framework for understanding the structural conservation and co-variation of POU2F1. First, a gene regulatory network based on the normal and pathological functions of POU2F1 has been created for better understanding the strong association between POU2F1 deregulation and cancers. After that, based on the evolutionary sequence space analysis, the comparative sequence dynamics of the protein members of POU domain family has been studied mostly between non-human and human species. Subsequently, the reciprocity effect of the residual co-variation has been identified through direct coupling analysis. Along with that, the structure of POU2F1 has been analyzed depending on quality assessment and normal mode-based structure network. Comparing the sequence and structure space information, the most significant set of residues viz., 3, 9, 13, 17, 20, 21, 28, 35, and 36 have been identified as structural facet for function. This study demonstrates that the structural malleability of POU2F1 serves as one of the prime reason behind its functional multiplicity in terms of protein moonlighting. Communicated by Ramaswamy H. Sarma.

Genome-wide analysis of miRNA molecules can reveal important information for understanding the biology of cancer. Typically, miRNAs are used as features in statistical learning methods in order to train learning models to predict cancer. This motivates us to propose a method that integrates clustering and classification techniques for diverse cancer types with survival analysis via regression to identify miRNAs that can potentially play a crucial role in the prediction of different types of tumors. Our method has two parts. The first part is a feature selection procedure, called the stochastic covariance evolutionary strategy with forward selection (SCES-FS), which is developed by integrating stochastic neighbor embedding (SNE), the covariance matrix adaptation evolutionary strategy (CMA-ES), and classifiers, with the primary objective of selecting biomarkers. SNE is used to reorder the features by performing an implicit clustering with highly correlated neighboring features. A subset of features is selected heuristically to perform multi-class classification for diverse cancer types. In the second part of our method, the most important features identified in the first part are used to perform survival analysis via Cox regression, primarily to examine the effectiveness of the selected features. For this purpose, we have analyzed next generation sequencing data from The Cancer Genome Atlas in form of miRNA expression of 1,707 samples of 10 different cancer types and 333 normal samples. The SCES-FS method is compared with well-known feature selection methods and it is found to perform better in multi-class classification for the 17 selected miRNAs, achieving an accuracy of 96%. Moreover, the biological significance of the selected miRNAs is demonstrated with the help of network analysis, expression analysis using hierarchical clustering, KEGG pathway analysis, GO enrichment analysis, and protein-protein interaction analysis. Overall, the results indicate that the 17 selected miRNAs are associated with many key cancer regulators, such as MYC, VEGFA, AKT1, CDKN1A, RHOA, and PTEN, through their targets. Therefore the selected miRNAs can be regarded as putative biomarkers for 10 types of cancer.

Advancement of single-cell sequencing technology has made it possible to describe high throughput and low-cost genome-wide sequencing. There are several methods that utilized the single-cell sequencing techniques to determine the cell types that construct a complex tissue. Clustering followed by dimensionality reduction are used to determine cell type. Moreover, statistical analysis is performed to identify the uniqueness of each cell type. In this study, traditional hierarchical clustering is performed to identify the cell types present in peripheral blood cells. This classification reveals the morphologically and phonetically different cells present in peripheral blood cells of a healthy donor. Each cell type contains a specific marker that defines the individual character of the cell type. Furthermore, these markers play an important role in biological pathways. The association of markers with pathway helps in understanding the cell-to-cell heterogeneity. During the study, cell markers of each cell types are further considered to analysis the associated pathways. The analysis shows how the change in gene expression contributes to pathway shift due to several biological causes. This information provides new insight in the understanding of biology process from a single-cell perspective.

A dual band antenna operating at 2.45 and 5.8 GHz ISM bands is proposed for wearable applications. The antenna is simple in design where a coplanar waveguide (CPW) fed structure along with a slot in the patch are responsible for generation of dual band property. As the wearable antenna works in close proximity of the human body so, it is susceptible to various performance degradations. In order to restore the antenna performance, a fully flexible artificial magnetic conductor (AMC) based ground plane is placed beneath the antenna which is found to enhance the front to back ratio, gain and efficiency of the standalone antenna. Additionally, it reduces the specific absorption rate (SAR) of the antenna which is a merit from the wearable point of view.

Background: Malignant diseases have become a threat for health care system. A panoply of biological processes is involved as the cause of these diseases. In order to unveil the mechanistic details of these diseased states, we analyzed protein families relevant to these diseases. Results: Our present study pivots around four apparently unrelated cancer types among which two are commonly occurring viz. Prostate Cancer, Breast Cancer and two relatively less frequent viz. Acute Lymphoblastic Leukemia and Lymphoma. Eight protein families were found to have implications for these cancer types. Our results strikingly reveal that some of the proteins with implications in the cancerous cellular states were showing the structural organization disparate from the signature of the family it constitutes. The sequences were further mapped onto respective structures and compared with the entropic profile. The structures reveal that entropic scores were able to reveal the inherent structural bias of these proteins with quantitative precision, otherwise unseen from other analysis. Subsequently, the betweenness centrality scoring of each residue from the structure network models was resorted to explore the changes in dependencies on residue owing to structural disorder. Conclusion: These observations help to obtain the mechanistic changes resulting from the structural orchestration of protein structures. Finally, the hydropathy indexes were obtained to validate the sequence space observations using Shannon entropy and in-turn establishing the compatibility.

Kidney Renal Clear Cell Carcinoma (KIRC) is the common kidney cancer and ranks 8th among all other popular cancer types. Mostly adult humans are affected by this malignant disease. However, enough information have not been gathered for this special case of renal carcinoma. The experimentally validated data are also in a non uniformed form which makes it more difficult to decode the relation among miRNAs, genes and TFs. To address this obstacle in this current study, miRNAs, genes and TFs those associated with KIRC and played a significant role are considered as individual elements and a framework is proposed in order to find the connection among each entity. From this proposed framework some genes are identified those are not only targeted by miRNAs or TFs separately but also gene regulation has an impact due to the passive effect of TFs. From this network it is clear that gene regulation due to hsa-miR-146a-5p is indirectly controlled by those TFs also. For the further validation of these informative miRNAs and genes KEGG pathway and GO enrichment analysis are performed. In future study, these framework should be granted more attention in order to understand better prognosis about malignant diseases.

Over the past two decades, various research works have been going on Multiple Sequence Alignment (MSA) and it becomes an important domain in bioinformatics. This is an NPhard problem. For this purpose, various traditional, heuristics and metaheuristic methods have been applied. Among these methods, metaheuristics show an effective output to overcome the bottleneck of MSA problem. Different metaheuristic methods and software have been developed to overcome the speed and accuracy problem of MSA, while the number of sequences increases. In this article, we have surveyed widely used metaheuristic methods and alignment tools applied for solving MSA problem. However, after reviewing we can conclude that the time complexity is still a big challenge for MSA problem.

Metaheuristics are widely perceived optimization methods that provide optimal solutions to an expansive range of computational problems. This paper provides a survey of the quantum-inspired metaheuristics, which is a successful alternative to the classical approach for solving optimization problems and combines the principles of quantum computing and metaheuristic. The idea of employing the concept of quantum mechanics in classical computers for better working of the metaheuristic methods has been a flourishing area of research since the last few decades. This paper aims to provide details of current state-of-the-art quantum-inspired metaheuristics by explaining their working principles and the applications of it in order to do further research in this field.