Bioinformatics, Computational and Systems Biology

Scope & Topics International Journal of Computer Science, Engineering and Applications (IJCSEA) is an open access peer-reviewed journal that publishes articles which contribute new results in all areas of the computer science, Engineering and Applications. The journal is devoted to the publication of high quality papers on theoretical and practical aspects of computer science, Engineering and Applications.

With inadequate water purification and sewage disposal systems, cholera poses a significant health threat in developing nations. Vibrio cholerae (V. cholerae) is one of the main microscopic organisms associated with cholera illness. If cholera is not treated, renal failure, shock, hypokalemia, and pulmonary edema can occur, resulting in death in a matter of hours. The machinery of bacterial virulence factors is what causes this disease. Among the various V. cholerae strains, V. cholerae O1 is the most prevalent and pathogenic strain. The total genome succession of V. cholerae unravels the presence of different genes and uncharacterized proteins whose capabilities are not yet perceived. Therefore, it is essential to comprehend V. cholerae by analyzing the structure and annotating the function of uncharacterized proteins. The NCBI sequence of uncharacterized V. cholerae O1 EET91795.1 proteins was annotated for this study. The domain family, protein solubility, ligand binding sites, and other parameters were all determined using a variety of databases and computational tools. The protein's ligand-binding sites were found, and its three-dimensional structure was modeled. According to the analysis, the hotdog family protein may play metabolic roles like thioester hydrolysis in the metabolism of fatty acids and the breakdown of two products such as phenylacetic acid and the pollutant 4-chlorobenzoate. The structural prediction of this protein and detection of binding sites suggested a potential target to uncover promising inhibitors against the protein to treat infection caused by the target strain.

An ethnomedicinal survey was carried out in the villages of Muktarpur, Shyampur, Belgharia, Naodar, and Yusufpur situated in Boalia sub-district, Rajshahi district, Bangladesh. The objective of this survey was to find out about medicinal plants used by the folk medicinal practitioners (Kavirajes) of the five villages to treat various ailments. Informed consent was obtained from the Kavirajes prior to the survey. Interviews were conducted with the help of a semi-structured questionnaire and the guided field-walk method, where the Kavirajes took the interviewers to places from where they collected their medicinal plants, pointed out the plants, and described their uses. It was observed that the Kavirajes used a total of 48 plants distributed into 30 families. The Fabaceae family contributed 5 plants, followed by the Euphorbiaceae and Meliaceae families with 4 plants each, and the Combretaceae and Lamiaceae families with 3 plants each. Of the 48 medicinal plants used, 13 plants were cultivated for homestead use or commercial purposes. Leaves constituted the major plant part used (27.6%), followed by fruits (15.3%), and seeds (14.3%). Other plant parts administered for treatment included whole plant, roots, stems, barks, flowers, and tubers. Gastrointestinal disorders made up the major ailment treated by the Kavirajes, followed by skin disorders and respiratory tract disorders. The Kavirajes also treated ailments like reproductive disorders, hepatic disorders, cardiovascular disorders and hypertension, sexually transmitted diseases, cancer, helminthiasis, edema, diabetes, rheumatoid arthritis, epilepsy, cholera, tuberculosis, malaria, urinary tract disorders, nerve disorders, typhoid, eye disorders, leprosy, pain, hernia, goiter, anemia, cuts and wounds, and piles. Several of the above disorders are difficult to cure or incurable with modern allopathic medicines. These ailments include cancer, diabetes, rheumatoid arthritis, and epilepsy. That the Kavirajes have been treating these ailments for years points to considerable patient satisfaction in obtaining a cure. Thus the plants used by the Kavirajes present considerable potential for further scientific analysis and the discovery of better medicines.

Aberrant expression of long non-coding RNAs (lncRNAs), caused by alterations in DNA methylation, is a driving factor in several cancers. Interplay between lncRNAs’ aberrant methylation and expression in prostate cancer (PC) progression still remains largely elusive. Therefore, this study characterized the genome-wide epigenetic landscape and expression profiles of lncRNAs and their clinical impact by integrating multi-omics data implementing bioinformatics approaches. We identified 62 differentially methylated CpG-sites (DMCs) and 199 differentially expressed lncRNAs (DElncRNAs), where 32 DElncRNAs contain 32 corresponding DMCs within promoter regions. Significant negative correlation was observed between 8 DElncRNAs-DMCs pairs. 3 (cg23614229, cg23957912, and cg11052780) DMCs and 4 (CACNA1G-AS1, F11-AS1, NNT-AS1, and MSC-AS1) DElncRNAs were identified as high-risk factors

Exposure to nicotine during pregnancy through maternal smoking or nicotine replacement therapy is associated with adverse birth outcomes as well as several cognitive and neurobehavioral deficits. Several studies have shown that nicotine produces long-lasting effects on gene expression within many brain regions, including the ventral tegmental area (VTA), which is the origin of dopaminergic neurons and the dopamine reward pathway. Using a well-established rat model for perinatal nicotine exposure, we sought to investigate altered biological pathways using mRNA and miRNA expression profiles of dopaminergic (DA) and non-dopaminergic (non-DA) neurons in this highly-valuable area. Putative miRNA-gene target interactions were assessed as well as miRNA-pathway interactions. Our results indicate that extracellular matrix (ECM) receptor interactions were significantly altered in DA and non-DA neurons due to chronic nicotine exposure during pregnancy. They also show that the PI3K/AKT signaling pathway was enriched in DA neurons with multiple significant miRNA-gene targets, but the same changes were not seen in non-DA neurons. We speculate that nicotine exposure during pregnancy could differentially affect the gene expression of DA and non-DA neurons in the VTA. Maternal smoking during pregnancy is associated with adverse birth outcomes including cognitive and neurobehavioral deficits 1-3 . The results from several studies have suggested that the exposure to nicotine, the major addictive and psychoactive component of tobacco, from nicotine replacement therapy may carry risks to the offspring similar to maternal smoking during pregnancy. To investigate these underlying mechanisms, we used an established rat model of perinatal nicotine exposure using a moderate to heavy dose of nicotine 4,5 . To date, several studies have used this animal model to elucidate the genetic, biochemical, behavioral, and electrophysiological alterations that occur in response to nicotine exposure at different time points of development in different regions of the brain and by gender . Due to the complexity of these contrasts, we have specifically focused on the category of neuron type: dopaminergic (DA) and non-dopaminergic (non-DA) neurons in the VTA following perinatal nicotine exposure without separating for gender differences in order to investigate more universal alterations. Due to the addictive nature of nicotine, the brain regions of interest are part of the mesocorticolimbic pathway also known as the reward pathway. This pathway is a network of dopaminergic (DA) neurons connecting the ventral tegmental area (VTA) to the striatum, nucleus accumbens (NAc), and prefrontal cortex (PFC). In the VTA, there are three main neuron types: DA, GABA, and glutamate, which interact with each other to inhibit and activate the release of DA in other regions of the brain. Under normal behavior, DA neurons are inhibited by input from GABA neurons and activated by input from glutamate neurons. After perinatal nicotine exposure, this relationship is altered, as shown by nicotine-induced decreases in dopamine release in the NAc in adolescent rats 8 . Nicotine modulates nicotinic acetylcholine receptors (nAChRs), which are ligand-gated ion channels distributed through the central and peripheral nervous system. In the VTA, nicotine directly activates DA neurons through these receptors as well as indirectly via glutamate and GABA neurons . Following perinatal nicotine exposure, studies have found decreased expression of nAChR subunits in the VTA as well as a decrease in the number of DA neurons . These findings demonstrate specific changes that occur during development in response to chronic nicotine exposure during development. While previous studies investigating perinatal nicotine exposure have investigated biological pathways that are modulated by nicotine, none to date have explored the VTA .

Plotosus lineatus, also known as oriental catfish has been investigated for the characterization of proteinaceous toxins secreted from the venom glands in dorsal and pectoral spines. Particularly, a toxin (Toxin I) was characterized and reported to have more potential lethal, edema-forming and nociceptive activities and its primary structure was only elucidated. This toxin was highly homologous with other fish natterin-like proteins. Since, the unavailability of the 3D structure of natterin-like protein, this attempt was carried out and Homology modelling was performed using Phyre3D-PSSM folding server. In the present study, subcellular localization prediction suggested that it is a cytoplasmic protein. Quality analysis of the model indicated that it is a reliable model. Furthermore, it was discovered that this proteinaceous toxin (Toxin I) is involved in two biological processes, pathogenesis and inter-species interaction between the organisms and the biochemical function of the protein is hydrolase activity, acting on the glycosyl bonds.

The assessment of the allergenic potential of chemicals, crucial for ensuring public health safety, faces challenges in accuracy and raises ethical concerns due to reliance on animal testing. This paper presents a novel bioinformatic protocol designed to address the critical challenge of predicting immune responses to chemical sensitizers without the use of animal testing. The core innovation lies in the integration of advanced bioinformatics tools, including the Universal Immune System Simulator (UISS), which models detailed immune system dynamics. By leveraging data from structural predictions and docking simulations, our approach provides a more accurate and ethical method for chemical safety evaluations, especially in distinguishing between skin and respiratory sensitizers. Our approach integrates a comprehensive eight-step process, beginning with the meticulous collection of chemical and protein data from databases like PubChem and the Protein Data Bank. Following data acquisition, structural predictions are performed using cutting-edge tools such as AlphaFold to model proteins whose structures have not been previously elucidated. This structural information is then utilized in subsequent docking simulations, leveraging both ligand–protein and protein–protein interactions to predict how chemical compounds may trigger immune responses. The core novelty of our method lies in the application of UISS—an advanced agent-based modelling system that simulates detailed immune system dynamics. By inputting the results from earlier stages, including docking scores and potential epitope identifications, UISS meticulously forecasts the type and severity of immune responses, distinguishing between Th1-mediated skin and Th2-mediated respiratory allergic reactions. This ability to predict distinct immune pathways is a crucial advance over current methods, which often cannot differentiate between the sensitization mechanisms. To validate the accuracy and robustness of our approach, we applied the protocol to well-known sensitizers: 2,4-dinitrochlorobenzene for skin allergies and trimellitic anhydride for respiratory allergies. The results clearly demonstrate the protocol’s ability to differentiate between these distinct immune responses, underscoring its potential for replacing traditional animal-based testing methods. The results not only support the potential of our method to replace animal testing in chemical safety assessments but also highlight its role in enhancing the understanding of chemical-induced immune reactions. Through this innovative integration of computational biology and immunological modelling, our protocol offers a transformative approach to toxicological evaluations, increasing the reliability of safety assessments.

Cajanus cajan (L.) Millsp. (Fabaceae, local name: orhor, English name: pigeon pea) is widely cultivated in Bangladesh for its edible seeds. The leaves are also consumed during times of food scarcity. In the folk medicinal system of Bangladesh, the leaves and stems of the plant are predominantly used for treatment of jaundice and diabetes. The objective of the present study was to evaluate the antihyperglycemic and cytotoxic potential of methanol extract of leaves and roots of the plant. Antihyperglycemic activity was studied by oral glucose tolerance tests in glucose-loaded Swiss albino mice. The methanol extract of leaves showed dosedependent and significant reductions of serum glucose levels in mice. At doses of 200 and 400 mg leaf extract/kg body weight, the reductions in glucose levels were comparable to that of the standard antihyperglycemic drug, glibenclamide, administered at a dose of 10 mg/kg body weight. In contrast, the methanol extract of roots showed a dose-dependent increase in serum glucose levels, which was statistically significant at the highest dose of 400 mg extract/kg body weight. The cytotoxic potential of methanol extracts of leaves and roots were measured with the brine shrimp lethality bioassay. Both leaf and root extracts showed a high degree of toxicity to brine shrimp with LC 50 values, respectively, of 0.0264 and 1.849 mg/ml. The results obtained with antihyperglycemic tests with leaves of the plant validate its folk medicinal use for diabetes. The brine shrimp lethality assay results suggest that the plant can be a promising source of anticancer compounds.

Traditional medicine even in the present century plays an important role in delivering necessary health-care to the people of particularly the developing countries. Traditional medicinal systems usually rely on medicinal plants for treatment, which plants are utilized by traditional medicinal practitioners for treatment of a diverse variety of diseases, against some of which allopathic medicine has no known cure. Folk medicine is the most widely used form of traditional medicine in Bangladesh. Folk medicinal ...

This systematic review and meta-analysis evaluated the performance of machine learning models in predicting prostate cancer progression using lifestyle factors as predictive biomarkers to improve prognostic accuracy. Various models were analyzed, including Support Vector Machine (SVM), Logistic Regression (LR), Random Forest (RF), Multi-Layer Perceptron (MLP), and Convolutional Neural Networks (CNN). These models were applied to identify diagnostic and prognostic biomarkers and to enhance the forecasting of prostate cancer progression. The meta-analysis demonstrated high predictive effectiveness across models, with mean performance metrics of 0.901 AUC (Area Under the Curve), 0.914 F1 Score, 0.889 accuracy, and 0.914 sensitivity. Among the models, the Multi-Layer Perceptron (MLP) emerged as the most effective, achieving 97% accuracy and an AUC of 95.8%. These findings underscore the potential of machine learning to integrate lifestyle factors as predictive biomarkers, advancing precision oncology in prostate cancer care.

Bangladesh has a number of traditional medicinal systems among which folk medicinal practitioners constitute the majority and who can be found practicing in virtually every village, town and city of the country. Bangladesh also has over a hundred different tribes scattered throughout the country, who have their own tribal medicinal practitioners. Some of these tribes have maintained their unique cultural traditions, while others are in various stages of assimilation with the mainstream Bengali-speaking population. The common feature between folk medicinal and tribal medicinal practitioners is that both groups rely on medicinal plants as the major ingredient in their medicinal formulations. The Santals are one of the largest tribes in north-western districts of Bangladesh. While some Santal communities reside in their separate villages apart from the villages of the mainstream population, other Santal communities can be found living among the mainstream population. It was of interest to determine whether such close proximity of living between Santal and non-Santal people have led to mutual exchanges of medicinal information regarding choice of medicinal plants for treatment of common ailments. An ethnomedicinal survey was therefore carried out among a Santal practitioner of Dabidubi village in Dinajpur district, and a mainstream folk medicinal practitioner of Goreya Bazaar village in Thakurgaon district, the two villages being in close proximity. It is to be noted that both villages were inhabited by a heterogeneous population consisting of both people from the Santal tribe as well as mainstream Bengali-speaking people. Altogether 76 formulations were obtained from the two healers, the Santal healer providing 44 formulations and the mainstream folk healer providing 32 formulations. Both healers used medicinal plants as the chief ingredient in their formulations. The Santal healer used a total of 50 plant species in his formulations, while the non-Santal healer used a total of 44 plant species in his formulations. Surprisingly, although many ailments treated by the two healers were in common, the formulation used by the Santal healer differed totally from the non-Santal mainstream healer, even though all plant species used by the two healers could be found in both the villages of their residences. Only one plant species, namely Aegle marmelos, was used in common by both healers as a stomachic and for the treatment of dysentery, but even then the formulations were different. A second plant species, Syzygium cumini, used in common by both healers, were used for treatment of different ailments. The same applied to two other plant species, Nigella sativa and Piper nigrum, which though commonly used by the healers were used in different types of formulations for treatment of different diseases. The results not only suggest that the Santals have their own unique repertoire of medicinal plants but also suggests that despite centuries of living in close proximity to each other, tribal medicinal practitioners and mainstream folk medicinal practitioners have evolved their medicinal plant selections separately and which are clearly distinguishable from each other. The results further highlight the study of traditional healers among all communities and villages of Bangladesh to obtain a comprehensive account of the various medicinal plants used and their purpose of use.

The interleukin (IL)-1 family is a major proinflammatory cytokine family, ranging from the well-studied IL-1s to the most recently discovered IL-33. As a new focus, IL-33 has attracted extensive research for its crucial immunoregulatory roles, leading to the development of notable monoclonal antibodies as clinical candidates. Efforts to develop small molecules disrupting IL-33/ST2 interaction remain highly desired but encounter challenges due to the shallow and featureless interfaces. The information from relative cytokines has shown that traditional binding site identification methods still struggle in mapping cryptic sites, necessitating dynamic approaches to uncover druggable pockets on IL-33. Here, we employed mixed-solvent molecular dynamics (MixMD) simulations with diverse-property probes to map the hotspots of IL-33 and identify potential binding sites. The protocol was first validated using the known binding sites of two IL-1 family members and then applied to the structure of IL-33. Our simulations revealed several binding sites and proposed sidechain rearrangements essential for the binding of a known inhibitor, aligning well with experimental NMR findings. Further microsecond-time scale simulations of this IL-33-protein complex unveiled distinct binding modes with varying occurrences. These results could facilitate future efforts in developing ligands to target challenging flexible pockets of IL-33 and IL-1 family cytokines in general.

Dengue is a global arbovirus disease primarily carried by Aedes aegypti and Aedes albopictus mosquitoes. It has four serotypes (DENV1, DENV2, DENV3, and DENV4) and is classified into distinct genotypes. The epidemic is complicated by immunological interactions and viral lineage turnover. Neurological problems are commonly associated with DENV2 and DENV3, with DENV2 displaying the most severe symptoms. Direct viral invasion, host-mediated immune system reactions, or host-mediated metabolic alterations can all result in dengue-related neurological issues. The three dengue vaccinations and the significance of meta-analyses for genetic data will also be covered. Finally, establish a connection with the microRNAs associated with dengue fever, creating new opportunities for the creation of dengue treatment regimens involving microRNAs.

The present study attempts to focus on the pattern of codon usage bias (CUB) in mitochondrial genes among four species of Calypogeia viz. C. arguta, C. integristipula, C. neogaea and C. suecica along with the factors influencing their CUB as no work was reported yet. The four taxa exhibit noticeable difference in their morphological features, habitat preference and growth forms. To understand the patterns of codon usage in Calypogeia mitochondrial genomes, we used bioinformatic tools to analyze the mitochondrial protein-coding sequences of four Calypogeia. In our current study, the mean nucleobase T was the highest while G was the lowest in all four species. The overall GC content was higher than AT content i.e. genes were AT rich. The CUB was low in mitochondrial genes of C. arguta, C. integristipula, C. neogaea and C. suecica, respectively as revealed from low synonymous codon usage order (SCUO) values. Heat map study revealed that most of the GC and AT ending codons showed positi...

Background: With widespread therapeutic advancement, targeted anticancer therapeutics are taking over traditional treatment protocols. Nevertheless, the resistance to targeted therapeutics has halted the enthusiastic treatment response. An example of targeted therapy is Lapatinib. Lapatinib is a tyrosine kinase inhibitor used in HER2-positive breast cancer. It is widely used in HER2-positive metastatic breast cancer in combination with other drugs. Methods: This study's main objective was to provide a plausible mechanistic insight into lapatinib’s resistance in two HER2 positive breast cancer cell lines, SKBR3 and BT-474. We performed gene set analysis to identify the differentially expressed genes (DEG) in response to treatment with lapatinib from the gene expression profiles obtained from GSE38376 and GSE16179. The DEG was then analyzed by Ingenuity Pathway Analysis (IPA). Results: The IPA analysis showed that the increased expression of Hypoxia-inducible factor-1 alpha (HIF-1...

Aim: Aromatherapy products, hydrosol beverages and distillates containing essential oils are widely used for cardiovascular conditions. Investigation of the possible activity of their major constituents with the cardiovascular-related receptors may lead to developing new therapeutics. It also may prevent unwanted side effects and drug-herb interactions. Materials and Methods: A list of 243 volatile molecules (mainly monoterpene and sesquiterpene) was prepared from a literature survey in Scopus and PubMed (2000-2019) on hydrosols and essential oils which are used for cardiovascular diseases (CVD) and its risk factors (diabetes mellitus and hyperlipidemia). The PDB files of the receptors (229 native PDB files) included alpha-glucosidase, angiotensin-converting enzymes, beta-2 adrenergic receptor, glucocorticoid, HMG-CoA reductase, insulin, mineralocorticoid, potassium channel receptors and peroxisome proliferator-activated receptoralpha, were downloaded from Protein Data Bank. An in silico study using AutoDock 4.2 and Vina in parallel mode was performed to investigate possible interaction of the molecules with the receptors. Drug likeliness of the most active molecules was investigated using DruLiTo software. Results: Spathulenol, bisabolol oxide A, bisabolone oxide, bergapten, bergamotene, dill apiole, pcymene, methyl jasmonate, pinocarveol, intermedeol, α-muurolol, S-camphor, ficusin, selinen-4-ol, iso-dihydrocarveol acetate, 3-thujanone, linanool oxide and cadinol isomers made a better interaction with some of the named receptors. All of the named molecules had an acceptable dug likeliness except for α-bergamotene. In addition, all of the named molecules had the ability to pass the bloodbrain barrier and it is possible to produce unwanted side effects. Conclusion: Some ingredients of essential oils might be active on cardiovascular-related receptors.

Advances in next-generation sequencing technologies have accelerated the momentum of metagenomic studies, which is increasing yearly. The metagenomics field is one of the versatile applications in microbiology, where any interaction in the environment involving microorganisms can be the topic of study. Due to this versatility, the number of applications of this omics technology reached its horizons. Agriculture is a crucial sector involving crop plants and microorganisms interacting together. Hence, studying these interactions through the lenses of metagenomics would completely disclose a new meaning to crop health and development. The rhizosphere is an essential reservoir of the microbial community for agricultural soil. Hence, we focus on the R&D of metagenomic studies on the rhizosphere of crops such as rice, wheat, legumes, chickpea, and sorghum. These recent developments are impossible without the continuous advancement seen in the next-generation sequencing platforms; thus, a ...

The Chakma community is the largest tribal group in the Chittagong Hill Tracts region in the southeastern part of Bangladesh. It is a thriving community with their well-established cultural rituals and traditional medicinal practices. The community is spread over a large area comprising of at least four districts. An ethnomedicinal survey was carried out among the tribal medicinal practitioners of the community residing in Rangapanir chara area, which consists of five villages in Panchari Upazila, Khagrachaari district. Three tribal medicinal practitioners were interviewed during the course of this survey. It was observed that the practitioners among themselves used a total of 62 plants distributed into 33 families. The various plants or plant parts were administered for a number of diseases in the form of juice, paste and pills or administered topically. The diseases treated and claimed to be cured by the practitioners included jaundice, helminthiasis, fever, pain, wounds and sores, respiratory tract disorders, gastrointestinal disorders, eye disorders, leucorrhea, menstrual disorders, chicken pox, high blood pressure, rheumatism, insomnia, bone fracture, scorpion or poisonous insect bite, tumor in stomach, sexual disorders, kidney or gall bladder stone, cholera, burning sensations, infections of genital organs, kala azar, hydrocele, diabetes, obesity, skin diseases, burns, and urinary problems. It appeared from the number of plants used for treatment that rheumatism is a major disease affecting the Chakma community that was surveyed. Altogether, 13 plants were used for treatment of rheumatism or rheumatic pain. Rheumatism is a disease which cannot be cured with allopathic medicine. As such, the plants used by the practitioners for treatment of rheumatism merit further scientific studies toward a possible cure for this disease.

Cofactor-independent phosphoglycerate mutase (iPGM) is an indispensable enzyme for most of the nematodes including filarial nematodes. Therefore could be targeted as a vital drug target in Wuchereria bancrofti (Wb) for the treatment of lymphatic filariasis. It catalyzes the inter-conversion between 2-phosphoglycerate and 3-phosphoglycerate in the glycolytic and gluconeogenic pathways. Since, the crystal structure of Wb-iPGM is not available; we have developed the tertiary structure of Wb-iPGM and investigated its structural stability by molecular dynamics simulation (MD) study. Wb-iPGM is linked with cofactors 3-phosphoglycerate (3PGA) and divalent Mn 2? ions. Here, we probe the stability and dynamics of the Wb-iPGM, Wb-iPGM?3PGA, Wb-iPGM?3PGA?Mn 2? and Wb-iPGM?Mn 2? complexes to investigate the role of Mn 2? ions in terms of protein stability and functionality. We investigated Wb-iPGM and their cofactors to get insight into their role in protein. Notably, we provide the first time the molecular organization and the role of cofactors in Wb-iPGM proteins using MD and free energy landscape. Interestingly, we had observed domain motions between a and b domains of Wb-iPGM when they were in bound and free form with 3PGA molecules using DynDom. Domain motion study revealed opening and partial closing mechanisms of these domains in the presence and absence of Mn 2? ions in Wb-iPGM and 3PGA complexes. This study provides insight into the molecular mechanisms of Mn 2? for the secondary structure establishment and their significant role in the catalytic site.

In recent years, significant progress has been made in the field of protein function prediction with the development of various machine-learning approaches. However, most existing methods formulate the task as a multi-classification problem, i.e. assigning predefined labels to proteins. In this work, we propose a novel approach, Prot2Text, which predicts a protein's function in a free text style, moving beyond the conventional binary or categorical classifications. By combining Graph Neural Networks(GNNs) and Large Language Models(LLMs), in an encoder-decoder framework, our model effectively integrates diverse data types including protein sequence, structure, and textual annotation and description. This multimodal approach allows for a holistic representation of proteins' functions, enabling the generation of detailed and accurate functional descriptions. To evaluate our model, we extracted a multimodal protein dataset from SwissProt, and demonstrate empirically the effectiveness of Prot2Text. These results highlight the transformative impact of multimodal models, specifically the fusion of GNNs and LLMs, empowering researchers with powerful tools for more accurate function prediction of existing as well as first-to-see proteins.

Tuberculosis is one of the oldest diseases with a death rate of 1.5 million per year. Tuberculosis spreads from one person to another through Mycobacterium tuberculosis. This bacteria belongs to the family Mycobacteriaceae, genus Mycobacterium, member of the tuberculosis complex. Mycobacterium tuberculosis is an acid-fast, aerobic, rod-shaped bacteria, ranging from 2 to 4 Â lm in length and 0.2 to 0.5 Â lm in width. Tuberculosis spreads through infected people via sneezing, coughing, etc., with humans acting as the host for the bacteria. The genome of Mycobacterium tuberculosis H37Rv encodes 3906 proteins, of which 1055 are hypothetical proteins (HPs), wherein the functions of the proteins are unknown. The sequences of 1055 HPs of Mycobacterium tuberculosis were analyzed and the functions of 578 HPs were subsequently predicted with a high level of confidence. Several enzymes, transporters and binding proteins of 1055 HPs in M. tuberculosis were analyzed and potential targets were discovered which contribute to the overall survival of the bacteria. The analysis will be of relevance in understanding the mechanism of the bacteria and will prove to be beneficial in the discovery of new drugs.

The aim of this study was to use IEDB software to predict the suitable MERS-CoV epitope vaccine against the most known world population alleles through four selecting proteins such as S glycoprotein and envelope protein and their modification sequences after the pandemic spread of MERS-CoV in 2012. IEDB services is one of the computational methods; the output of this study showed that S glycoprotein, envelope (E) protein, and S and E protein modified sequences of MERS-CoV might be considered as a protective immunogenic with high conservancy because they can elect both neutralizing antibodies and T-cell responses when reacting with B-cell, T-helper cell, and cytotoxic T lymphocyte. NetCTL, NetChop, and MHC-NP were used to confirm our results. Population coverage analysis showed that the putative helper T-cell epitopes and CTL epitopes could cover most of the world population in more than 60 geographical regions. According to AllerHunter results, all those selected different protein showed non-allergen; this finding makes this computational vaccine study more desirable for vaccine synthesis.

The 16S rRNA gene is a widely used target for phylogenetic analysis
of prokaryote communities. This analysis starts with the sequencing
of the 16S rRNA gene of a microbial sample, and includes several steps
such as paired-end merging (when the sequencing technique produces
paired-end reads), chimera removal, clustering, and sequence database
search. The end-product is the phylogeny of the prokaryote taxa in
the sample and an estimation of their abundance.
The problem is that there are multiple tools available to carry out this
analysis, and it is unclear which is the most e?ective. Namely, there
are three analysis pipelines in wide use by the community: mothur,
QIIME and USEARCH. These use di?erent paired-end merging al-
gorithms, di?erent clustering algorithms, and di?erent sequence ref-
erence databases (Silva, Greengenes, and RDP respectively). Addi-
tionally, there are a number of other paired-end mergers available
and again, it is unclear which performs better in the context of this
analysis.
In this study, we start by evaluating each of the seven publicly avail-
able paired-end merging algorithms: BBmerge, FastqJoin (QIIME's
merger), FLASH, mothur's merger, PANDAseq, PEAR and USE-
ARCH's merger. Then, we assess the e?ectiveness of each the three
analysis pipelines in conjunction with each of the three reference
databases, and each of the most promising paired-end mergers.
To do this evaluation, we use two sequencing datasets from mock
communities, one publicly available and the other produced in-house.
We evaluated the paired-end mergers by using BLAST against the
known references to compare the number of mismatches before and
after merging, and thereby calculate their precision and recall. We
evaluated the analysis pipelines by implementing the UniFrac metric
(a community standard) in order to measure the similarity between
the predicted phylogeny and the real one. We implemented both a
qualitative and a quantitative variant of UniFrac.
We found that the best mergers were PEAR, FastqJoin and FLASH
in terms of balance between precision and recall, whereas mothur was
the best in terms of recall, and USEARCH the most correct in terms
of the quality scores of the merged sequences. Regarding the analysis
pipelines, in terms of qualitative UniFrac, QIIME with Silva as the
reference and mothur's merger was the best on the ?rst dataset, and
mothur with either Greengenes or RDP and its own merger was the
best in the second dataset. In terms of quantative unifrac, mothur
with Greengenes and its own merger was the best for the ?rst dataset,
and USEARCH with SILVA and mothur's merger was the best on the
second dataset.
We concluded that having a high recall in the merging step is more
important than having a high precision for the downstream phyloge-
netic analysis, as mothur's merger was either the best or tied for the
best in all settings.

Background: Cancer is a debilitating disease that is on the increase in both developed and developing countries. The plant extract of A. muricata have been known to have a variety of anticancer effects, including anti-angiogenic potential. An in silico study is needed as a preliminary study to understand the mechanism underline this process. Objective: The aim of this study was to investigate the potential of the bioactive compounds of A. muricata in regulating angiogenesis process, primarily by the regulation of hypoxia inducible factor (HIF)-1α expression by in silico study. Methods: This study was performed by in silico analysis including the bioactive compounds preparation, biological activity prediction, protein target and pathway analysis, 3D protein modelling, protein-ligand and protein-protein docking, and the visualization of docking results. Results: There are 3 bioactive compounds of A. muricata with the ability to inhibit HIF-1α expression, including kaempferol, genistein, and glycitein. The inhibition of HIF-1α expression was associated with phosphoinositide 3-kinases (PI3K)/Akt signaling pathway, which involved tyrosine kinase receptor activity on the cell membrane. Based on the silico analysis in this study, we shown that kaempferol, genistein, and glycitein inhibit HIF-1α expression through the disruption of interleukin (IL)-6R and toll-like receptor (TLR)-4 and their respective ligands interaction. Conclusion: The findings of this study show that A. muricata bioactive compounds could inhibit HIF-1α expression through disruption of the tyrosine kinase receptor binding with its ligand.

All rights reserved. No part of this publication may be reproduced, stored in a retrieval system, or transmitted in any form or by any means-electronic, mechanical, photocopy, recording, or any other except for brief quotations in printed reviews-without the prior permission of the publisher. Designations used by companies to distinguish their products are often claimed as trademarks or registered trademarks. In all instances in which Morgan & Claypool is aware of a claim, the product names appear in initial capital or all capital letters. Readers, however, should contact the appropriate companies for more complete information regarding trademarks and registration. Contents Preface xi Chapter Introduction to Protein Complex Prediction 1 1.1 From Protein Interactions to Protein Complexes 6 1.2 Databases for Protein Complexes 11 1.3 Organization of the Rest of the Book 13

Background The transforming growth factor beta-1 (TGF β-1) cytokine exerts both pro-tumor and anti-tumor effects in carcinogenesis. An increasing body of literature suggests that TGF β-1 signaling outcome is partially dependent on the regulatory targets of downstream receptor-regulated Smad (R-Smad) proteins Smad2 and Smad3. However, the lack of Smad-specific antibodies for ChIP-seq hinders convenient identification of Smad-specific binding sites. Results In this study, we use localization and affinity purification (LAP) tags to identify Smad-specific binding sites in a cancer cell line. Using ChIP-seq data obtained from LAP-tagged Smad proteins, we develop a convolutional neural network with long-short term memory (CNN-LSTM) as a deep learning approach to classify a pool of Smad-bound sites as being Smad2- or Smad3-bound. Our data showed that this approach is able to accurately classify Smad2- versus Smad3-bound sites. We use our model to dissect the role of each R-Smad in the prog...

Segundo-Ortin & Calvo (2023) have made a valuable effort in directing the discussion about plant sentience toward a strict scientific path. However, scientific endeavors must reconcile with common sense beliefs. Although nowadays people tend to accept the idea of animal sentience, this is not easily extended to plants. One reason is the difference in time scale in which phenomena occur in plants and animals; but this still does not preclude the possibility , in principle, of plant sentience in a form difficult for us to imagine.

Despite the availability of drugs to treat Leishmaniasis, various other factors including drug resistance and adverse side effects encourage the researchers to search for new strategies and alternatives for treating Leishmaniasis. Repurposing and devising combination therapy with the existing small molecules would serve as an alternative strategy to address the issue, especially the drug resistance. Hence, here we report LeishInDB, a web-accessible resource of small molecule inhibitors having a varying degree of activity towards Leishmania sp. The database includes searchable information of > 7000 small molecules collected from > 600 literature. The comprehensive information of inhibitors mainly include the activity details (IC50, EC50, Ki, binding energy etc., if any); information on species and form of Leishmania the inhibitor is active against; and the details about their protein target (actively linked to TriTrypDB). In addition, chemical properties including the log P-value, number of rotatable bonds, number of hydrogen bond donors and acceptors, molecular weight, 2D/3D structural information etc., were also included. Toxicity prediction for each molecule was performed using admetSAR and their corresponding results were available to perform the filtered search. In addition, facility to perform substructure search, facility to perform the dynamic search on various fields, and facility to download all the structure of molecules that match the search criteria were also included. We believe that the scope of LeishInDB allows the researchers to utilize the available information for repurposing the inhibitors as well as for the investigation of new therapeutics.

Food sustainability, e.g., fruit and vegetables, is a major agricultural problem that requires monitoring. Rhizosphere microbiomes’ abundance and functionality are essential in promoting tomato plants’ growth and health. We selected farms in South Africa’s North West Province and present the metagenomes of their tomato rhizospheres and associated functional potentials.

Detecting novel functional modules in molecular networks is an important step in biological research. In the absence of gold standard functional modules, functional annotations are often used to verify whether detected modules/communities have biological meaning. However, as we show, the uneven distribution of functional annotations means that such evaluation methods favor communities of well-studied proteins. We propose a novel framework for the evaluation of communities as functional modules. Our proposed framework, CommWalker, takes communities as inputs and evaluates them in their local network environment by performing short random walks. We test CommWalker's ability to overcome annotation bias using input communities from four community detection methods on two protein interaction networks. We find that modules accepted by CommWalker are similarly co-expressed as those accepted by current methods. Crucially, CommWalker performs well not only in well-annotated regions, but ...

Metabolic associated fatty liver disease (MAFLD) is associated with obesity and metabolic dysfunctions, but its molecular mechanisms remain elusive and effective treatments are lacking. Metformin is a potential therapeutic agent for MAFLD due to its effects on insulin resistance and fat metabolism dysregulation. This study used bioinformatics analysis to identify differentially expressed genes (DEGs) in MAFLD and explore the potential targets of metformin treatment. We selected a MAFLD dataset from the Gene Expression Omnibus (GEO) database and screened 425 DEGs, including 278 up-regulated and 147 down-regulated genes. We also identified 183 genes related to metformin treatment using the Genclip3 database and found 13 common genes with the DEGs. The Genome Ontology (GO) enrichment analysis of these genes revealed biological processes such as suppression of inflammation and promotion of glycolipid metabolism, while the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways involved diabetic complications and inflammatory signaling. The protein-protein interaction (PPI) analysis identified IL1B, IL6, IL10 and CCL2 as the core proteins. Based on the PPI network, we selected the top 9 genes as hub genes, including IL6, IL1B, IL10, CCL2, FOXO1, PIGS2, IGFBP1, GCK, and MYC. The study suggests that metformin may target multiple pathways for the treatment of MAFLD, mainly involving anti-inflammation, regulation of glycolipid metabolism, and anti-hepatic fibrosis.

The purpose of this research paper is to show the results of a study on Quantitative Structure-Activity Relationships (QSAR), mathematical and computer models for determination of the relation between the structure of the compounds and the induced by them measurable biological effect. The key points of the research paper are: 1) Binding Mechanisms, 2) Docking Problem, 3) Models of partial agonism, 4) Parameters characterizing the partial agonism. The theoretical models and results presented for each of the key points are supported by experimental data obtained by studies of mu- and delta- opioid ligands for mu- and delta- opioid receptors [1]. The new results from this research are: (a) Applying the Castillo-Katz scheme for ligand-receptor interactions; (b) The properties of the ligands, obtained {\it in vitro} experiments, have been confirmed through molecular docking of mu- and delta- opioid receptors with mu- and delta- opioid ligands [2,3]; (c) A generalization of the Theoretica...

In Bioinformatics and other areas the model selection is a process of choosing a model from set of candidate models of different classes which will provide the best balance between goodness of fitting of the data and complexity of the model. There are many criteria for evaluation of mathematical models for data fitting. The main objectives of this study are: (1) to fitting artificial experimental data with different models with increasing complexity; (2) to test whether two known criteria as Akaike’s information criterion (AIC) and Bayesian information criterion (BIC) can correctly identify the model, used to generate the artificial data and (3) to assess and compare empirically the performance of AIC and BIC.

The deposition of metagenomic data in large scales bears great potential to understand universal mechanisms and environmental factors of microbial community assembly. Notable efforts are the Human Microbiome Project, the Earth Microbiome Project, IMNGS and Qiita/QiimeDB [1-4]. We have thus entered a new era, in which it is in principle possible to discover commonalities between microbial communities from entirely different ecosystems. E.g., samples from below the ocean floor were surprisingly similar to non-marine communities due to methanogens [5]. However, with the current data deluge from Next Generation Sequencing projects it is becoming increasingly difficult to perform manual exhaustive searches to find the most similar microbial communities. Suitable search algorithms for microbiomes are required and solutions for automated microbiome search are beginning to appear, e.g., Meta-Storms [6] and Visibiome [7].

Motivation: Multi-scale modeling of biological systems requires integration of various information about genes and proteins that are connected together in networks. Spatial, temporal and functional information is available; however, it is still a challenge to retrieve and explore this knowledge in an integrated, quick and user-friendly manner. Results: We present GEMMER (GEnome-wide tool for Multi-scale Modeling data Extraction and Representation), a web-based data-integration tool that facilitates high quality visualization of physical, regulatory and genetic interactions between proteins/genes in Saccharomyces cerevisiae. GEMMER creates network visualizations that integrate information on function, temporal expression, localization and abundance from various existing databases. GEMMER supports modeling efforts by effortlessly gathering this information and providing convenient export options for images and their underlying data.

Motivation Multi-scale modeling of biological systems requires integration of various information about genes and proteins that are connected together in networks. Spatial, temporal and functional information is available; however, it is still a challenge to retrieve and explore this knowledge in an integrated, quick and user-friendly manner. Results We present GEMMER (GEnome-wide tool for Multi-scale Modeling data Extraction and Representation), a web-based data-integration tool that facilitates high quality visualization of physical, regulatory and genetic interactions between proteins/genes in Saccharomyces cerevisiae. GEMMER creates network visualizations that integrate information on function, temporal expression, localization and abundance from various existing databases. GEMMER supports modeling efforts by effortlessly gathering this information and providing convenient export options for images and their underlying data. Availability and implementation GEMMER is freely avail...

This paper discusses the digitization of indigenous knowledge on forest foods and medicine for the effective management of Ghana’s forest resources. The paper is based on a survey conducted in nine communities in Ghana where primary data were obtained from 606 respondents using in-depth face-to-face interviews. The aim of the study was to assess what knowledge local communities had about products of the forest especially indigenous forest foods and medicine. The findings reveal that local communities have an in-depth knowledge of indigenous forest foods and medicines. They are conversant with what foods and medicines are available in the forests, how they are consumed and when they mature. The study reveals that consumption of indigenous forest foods is on the decline, while the use of traditional medicine is on the ascendancy. The study recommends the promulgation of laws and legal instruments to protect communities from bio-piracy.

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LBM adalah salah satu metode pendekatan computational Fluid Dynamics (CFD) yang sangat baik untuk menggambar kan system fisik dalam bentuk cell mikro, dimana pada setiap cellnya melakukan interaksi seperti streming dan tumbukan, hasil yang di dapatkan dari metode ini , di ambil kemudian di baca menggunakan library openGL untuk menvisualkan simulasinya, untuk dimulasi 2 fase menggunakan metode shan dan chen yang menambahkan nilai untuk gaya eksternal seperti gravitasi, penerapan pararel computing menggunakan GPU di butuhkan untuk mengurangi beban komputasinya serta pengurangan waktu hingga mendekati kondisi nyata, namun perbedaan arsitekur dan tata cara penulisan program membuat implementasi simulasi cairan 2D dua fase membuthkan modifikasi formula untuk mendapatkan nilai setiap cellnya agar hasil simulasi mendekati hasil nyata.

In oral glucose tolerance tests, methanol extract of boiled seeds of Cicer arietinum (MECA) significantly and dose-dependently reduced blood glucose levels in glucose-loaded mice by 27.7, 31.9, 36.2, and 42.2%, respectively, at doses of 50, 100, 200 and 400 mg per kg in comparison to a standard antihyperglycemic drug, glibenclamide, which reduced blood glucose levels by 48.2% at a dose of 10 mg per kg. In acetic acid induced writhing tests in mice, MECA at the afore-mentioned doses reduced the number of writhings, respectively, by 21.4, 35.7, 39.3, and 46.4% versus the 42.9 and 53.6% reductions observed with a standard analgesic drug, aspirin, at doses of 200 and 400 mg per kg, respectively. MECA thus demonstrated considerable antihyperglycemic and analgesic properties and may be used to alleviate high blood glucose levels and provide relief from pain.

Proteins are essential components of all living organisms and participate in almost every biological process. However, most proteins do not function as a single entity; instead, they often interact with other proteins to form large macromolecules, i.e. protein complexes, that are involved in different cellular functions. Identifying protein complexes allows assigning functions to proteins of yet unknown roles by using the known function of their interacting partners, following the principle of guilt-by-association (Tian et al. 2008). Moreover, due to the protein structures, proteins are often involved in more than one complex in different subcellular compartments and biological processes. Therefore, studying protein complexes is important to understand the functional principles of the cell system, from signaling to metabolism (Pawson and Nash 2000; Maslov and Sneppen 2002; Reyes-Turcu et al. 2009; Sweetlove and Fernie 2018), and provide a better understanding hierarchy of intra-and inter-cellular activities (Bauer and Kuster 2003).

Objective: The aim of the study is to perform a computational study consisting of molecular docking for polyphenols subjected to in silico studies to identify a new lead for antimicrobial activity which has been reported yet or not been used yet. Methods: The Schrödinger Maestro 11.3 performed molecular docking of the enzyme FabH (β-ketoacyl-acyl carrier protein synthase III) (PDB ID: 5BNR) with polyphenol. The targeted compounds were docked against FabH enzyme and also evaluated for MM-GBSA and ADMET analysis. Results: The top hits shows remarkable results and good binding interactions with a pocket of the enzyme. The best binding score are as-8.6 (kcal/mol) of Geniestein,-8.579 (kcal/mol) of 4-naphthoquinone,-7.651(kcal/mol) of Pelargonidin. All the targeted compounds were found in the given limits of ADMET parameters. They also showed good free-binding energy. Conclusion: The computational study reveals that the targeted polyphenols show good binding interactions and are also com...