Computational Biology and Chemistry

The rise of drug resistance in Plasmodium falciparum, rendering current treatments ineffective, has hindered efforts to eliminate malaria. To address this issue, the study employed a combination of Systems Biology approach and a structure-based pharmacophore method to identify a target against P. falciparum. Through text mining, 448 genes were extracted, and it was discovered that plasmepsins, found in the Plasmodium genus, play a crucial role in the parasite's survival. The metabolic pathways of these proteins were determined using the PlasmoDB genomic database and recreated using CellDesigner 4...

Hepatocellular carcinoma (HCC) persists to be one of the most devastating and deadliest malignancies globally. Recent research into the molecular signaling networks entailed in many malignancies has given some prominent insights that can be leveraged to create molecular therapeutics for combating HCC. Therefore, in the current communication, an in-silico drug repurposing approach has been employed to target the function of PTP4A3/PRL-3 protein in HCC using antidepressants: Fluoxetine hydrochloride, Citalopram, Amitriptyline, Imipramine, and Escitalopram oxalate as the desired ligands...

Identification of promoters, enhancers, and their interactions helps understand genetic regulation. This study proposes a graph-based semi-supervised learning model (GCN4EPI) for the enhancer-promoter classification problem. We adopt a graph convolutional network (GCN) architecture to integrate interaction information with sequence features. Nodes of the constructed graph hold word embeddings of DNA sequences while edges hold the Enhancer-Promoter Interaction (EPI) information. By means of semi-supervised learning, much less data (16%) and time are needed in model training...

Cancer is the most prevalent disease globally, which presents a significant challenge to the healthcare industry, with breast and lung cancer being predominant malignancies. This study used RNA-seq data from the TCGA database to identify potential biomarkers for lung and breast cancer. Tumor Necrosis Factor (TNFAIP8) and Sulfite Oxidase (SUOX) showed significant expression variation and were selected for further study using structure-based drug discovery (SBDD). Compounds derived from the Euphorbia ammak plant were selected for in-silico study with both TNFAIP8 and SUOX...

Metabolites represent the underlying information of biological systems. Revealing the links between metabolites and diseases can facilitate the development of targeted drugs. Traditional biological experiments can be used to validate the relationships of metabolite-disease, but these methods are time-consuming and labor-intensive. In contrast, the prevailing computational methods have improved efficiency but primarily rely on the metabolite-disease interactions, overlooking the impact of other biological components...

The local disruptions caused by the genes of one disease can influence the pathways associated with the other diseases resulting in comorbidity. For gene therapies, it is necessary to prioritize the key genes that regulate common biological mechanisms to tackle the issues caused by overlapping diseases. This work proposes a clustering-based computational approach for prioritising the comorbid genes within the overlapping disease modules by analyzing Protein-Protein Interaction networks. For this, a sub-network with gene interactions of the disease pair was extracted from the interactome...

Latest studies confirmed that abnormal function of histone deacetylase (HDAC) plays a pivotal role in formation of tumors and is a potential therapeutic target for treating breast cancer. In this research, in-silico drug discovery approaches via quantitative structure activity relationship (QSAR) and molecular docking simulations were adapted to 43 compounds of indazole derivatives with HDAC inhibition for anticancer activity against breast cancer. The QSAR models were built from multiple linear regression (MLR), and models predictability was cross-validated by leave-one-out (LOO) method...

Bioactive cobalt (II) macrocyclic complexes [Co(N4 O4 ML1 )Cl2 ]-[Co(N4 O4 ML3 )Cl2 ] have been synthesized by using the macrocyclic ligands [N4 O4 ML1 ], [N4 O4 ML2 ], and [N4 O4 ML3 ] that have an N4 O4 core. These three macrocyclic ligands were all isolated in pure form, together with their complexes. Microanalytical investigations, FT-IR NMR, Mass, magnetic moments, electronic, PXRD, TGA, and EPR spectrum studies were used to analyse their structures. For these complexes, an octahedral geometry is proposed for the metal ion...

Tuberculosis (TB) is one of the life-threatening infectious diseases with prehistoric origins and occurs in almost all habitable parts of the world. TB mainly affects the lungs, and its etiological agent is Mycobacterium tuberculosis (Mtb). In 2022, more than 10 million people were infected worldwide, and 1.3 million were children. The current study considered the in-silico and machine learning (ML) approaches to explore the potential anti-TB molecules from the SelleckChem database against Enoyl-Acyl Carrier Protein Reductase (InhA)...

As a promising alternative to conventional antibiotic drugs in the biomedical field, functional peptide has been widely used in disease treatment owing to its low toxicity, high absorption rate, and biological activity. Recently, several machine learning methods have been developed for functional peptide prediction. However, the main research heavily relies on statistical features and few consider multifunctional peptide identification. So, we propose SME-MFP, a novel predictor in the imbalanced multi-label functional peptide datasets...

BACKGROUND: Tripterygium wilfordii Hook. f. (TW) shows anticancer activity, and no study has comprehensively investigated the effects of TW in treating cholangiocarcinoma (CHOL). This study was designed to identify the therapeutic role and the mechanism of TW against CHOL to obtain anti-CHOL candidate components and targets. METHODS: Ingredients of TW were collected from the Traditional Chinese Medicine System Pharmacology Database and literature. Limma package and weighted gene co-expression network analysis were used to identify the genes related to CHOL...

The exploration encompassed the synthesis and characterization of two innovative complexes, namely FePHNS and CuPHNS, employing a diverse array of analytical techniques such as elemental analysis, infrared and ultraviolet-visible spectroscopy, mass spectrometry, molar conductivity measurements, magnetic susceptibility assessments, and thermal analysis (TGA). In the spectral domain, infrared spectroscopy substantiated the tridentate ONS coordination of the PHNS ligand to the central metal atom. Thermal analysis offered valuable insights into the distribution and content of water molecules within the complexes...

Cancer is a global public health problem characterized by deviations in the mechanisms that control cell proliferation, resulting in mutations and variations in the structure of DNA. The mechanisms of action of chemotherapeutic drugs are related to their interactions and binding with DNA; consequently, the development of antineoplastic agents that target DNA has extensively focused on use of acridine, a heterocyclic molecule that binds to deoxyribonucleic acid via intercalation, a process that modifies DNA and makes replication impossible...

High throughput RNA sequencing brings new perspective to the elucidation of molecular mechanisms of diseases. Normalization is the first and most important step for RNA-Seq data, and it can differ based on the purpose of the analysis. Within-sample normalization methods (eg. TPM) are preferred when genes in a sample are compared with each other, and between-sample normalization methods (eg. deseq2, TMM, Voom) are used when the samples in a dataset are compared. Normalization approaches rescale the data, and, therefore, they affect the results of the analysis...

Studying gene regulatory networks associated with cancer provides valuable insights for therapeutic purposes, given that cancer is fundamentally a genetic disease. However, as the number of genes in the system increases, the complexity arising from the interconnections between network components grows exponentially. In this study, using Boolean logic to adjust the existing relationships between network components has facilitated simplifying the modeling process, enabling the generation of attractors that represent cell phenotypes based on breast cancer RNA-seq data...

AI-enhanced bioinformatics and cheminformatics pivots on generating increasingly descriptive and generalized molecular representation. Accurate prediction of molecular properties needs a comprehensive description of molecular geometry. We design a novel Graph Isomorphic Network (GIN) based model integrating a three-level network structure with a dual-level pre-training approach that aligns the characteristics of molecules. In our Spatial Molecular Pre-training (SMPT) Model, the network can learn implicit geometric information in layers from lower to higher according to the dimension...

Channel-regulated peptides (CRPs) derived from animal venom hold great promise as potential drug candidates for numerous diseases associated with channel proteins. However, discovering and identifying CRPs using traditional bio-experimental methods is a time-consuming and laborious process. While there were a few computational studies on CRPs, they were limited to specific channel proteins, relied heavily on complex feature engineering, and lacked the incorporation of multi-source information. To address these problems, we proposed a novel deep learning model, called DeepCRPs, based on graph neural networks for systematically mining CRPs from animal venom...

Cytokines are small protein molecules that exhibit potent immunoregulatory properties, which are known as the essential components of the tumor immune microenvironment (TIME). While some cytokines are known to be universally upregulated in TIME, the unique cytokine expression patterns have not been fully resolved in specific types of cancers. To address this challenge, we develop a TIME single-cell RNA sequencing (scRNA-seq) dataset, which is designed to study cytokine expression patterns for precise cancer classification...

Transcription profiling is a key process that can reveal those biological mechanisms driving the response to various exposure conditions or gene perturbations. In this work, we investigate the prediction of differentially expressed genes (DEGs) when exposed to conditions in space from a set of diverse engineered features. To do this, we collected DEGs and non-differentially expressed genes (NDEGs) of Mus musculus-based experiments on the GeneLab database. We engineered a diverse set of features from factors reported in the literature to affect gene expression...

The conventional computational approaches to investigating a disease confront inherent constraints as they often need to improve in delving beyond protein functional associations and grasping their deeper contextual significance within the disease framework. Such context-specificity can be explored using clinical data by evaluating the change in interaction between the biological entities in different conditions by investigating the differential co-expression relationships. We believe that the integration and analysis of differential co-expression and the functional relationships, primarily focusing on the source nodes, will open novel insights about disease progression as the source proteins could trigger signaling cascades, mostly because they are transcription factors, cell surface receptors, or enzymes that respond instantly to a particular stimulus...