A web server for analysis, comparison and prediction of protein ligand binding sites

Computational epitope-based vaccine design with bioinformatics approach; a review

The significance of vaccine development has gained heightened importance in light of the COVID-19 pandemic. In such critical circumstances, global citizens anticipate researchers in this field to swiftly identify a vaccine candidate to combat the pandemic's root cause. It is widely recognized that the vaccine design process is traditionally both time-consuming and costly. However, a specialized subfield within bioinformatics, known as "multi-epitope vaccine design" or "reverse vaccinology," has significantly decreased the time and costs of the vaccine design process. The methodology reverses itself in this subfield and finds a potential vaccine candidate by analyzing the pathogen's genome. Leveraging the tools available in this domain, we strive to pinpoint the most suitable antigen for crafting a vaccine against our target. Once the optimal antigen is identified, the next step involves uncovering epitopes within this antigen. The immune system recognizes particular areas of an antigen as epitopes. By characterizing these crucial segments, we gain the opportunity to design a vaccine centered around these epitopes. Subsequently, after identifying and assembling the vital epitopes with the assistance of linkers and adjuvants, our vaccine candidate can be formulated. Finally, employing computational techniques, we can thoroughly evaluate the designed vaccine. This review article comprehensively covers the entire multi-epitope vaccine development process, starting from obtaining the pathogen's genome to identifying the relevant vaccine candidate and concluding with an evaluation. Furthermore, we will delve into the essential tools needed at each stage, comparing and introducing them.

Structural and Phylogenetic Analysis of CXCR4 Protein Reveals New Insights into Its Role in Emerging and Re-Emerging Diseases in Mammals

Chemokine receptor type 4 (CXCR4) is a G protein-coupled receptor that plays an essential role in immune system function and disease processes. Our study aims to conduct a comparative structural and phylogenetic analysis of the CXCR4 protein to gain insights into its role in emerging and re-emerging diseases that impact the health of mammals. In this study, we analyzed the evolution of CXCR4 genes across a wide range of mammalian species. The phylogenetic study showed species-specific evolutionary patterns. Our analysis revealed novel insights into the evolutionary history of CXCR4, including genetic changes that may have led to functional differences in the protein. This study revealed that the structural homologous human proteins and mammalian CXCR4 shared many characteristics. We also examined the three-dimensional structure of CXCR4 and its interactions with other molecules in the cell. Our findings provide new insights into the genomic landscape of CXCR4 in the context of emerging and re-emerging diseases, which could inform the development of more effective treatments or prevention strategies. Overall, our study sheds light on the vital role of CXCR4 in mammalian health and disease, highlighting its potential as a therapeutic target for various diseases impacting human and animal health. These findings provided insight into the study of human immunological disorders by indicating that Chemokines may have activities identical to or similar to those in humans and several mammalian species.

In Silico Structural and Functional Analyses of NLRP3 Inflammasomes to Provide Insights for Treating Neurodegenerative Diseases

Inflammasomes are cytoplasmic intracellular multiprotein complexes that control the innate immune system's activation of inflammation in response to derived chemicals. Recent advancements increased our molecular knowledge of activation of NLRP3 inflammasomes. Although several studies have been done to investigate the role of inflammasomes in innate immunity and other diseases, structural, functional, and evolutionary investigations are needed to further understand the clinical consequences of NLRP3 gene. The purpose of this study is to investigate the structural and functional impact of the NLRP3 protein by using a computational analysis to uncover putative protein sites involved in the stabilization of the protein-ligand complexes with inhibitors. This will allow for a deeper understanding of the molecular mechanism underlying these interactions. It was found that human NLRP3 gene coexpresses with PYCARD, NLRC4, CASP1, MAVS, and CTSB based on observed coexpression of homologs in other species. The NACHT, LRR, and PYD domain-containing protein 3 is a key player in innate immunity and inflammation as the sensor subunit of the NLRP3 inflammasome. The inflammasome polymeric complex, consisting of NLRP3, PYCARD, and CASP1, is formed in response to pathogens and other damage-associated signals (and possibly CASP4 and CASP5). Comprehensive structural and functional analyses of NLRP3 inflammasome components offer a fresh approach to the development of new treatments for a wide variety of human disorders.

Computer-Aided Drug Design Boosts RAS Inhibitor Discovery

The Rat Sarcoma (RAS) family (NRAS, HRAS, and KRAS) is endowed with GTPase activity to regulate various signaling pathways in ubiquitous animal cells. As proto-oncogenes, RAS mutations can maintain activation, leading to the growth and proliferation of abnormal cells and the development of a variety of human cancers. For the fight against tumors, the discovery of RAS-targeted drugs is of high significance. On the one hand, the structural properties of the RAS protein make it difficult to find inhibitors specifically targeted to it. On the other hand, targeting other molecules in the RAS signaling pathway often leads to severe tissue toxicities due to the lack of disease specificity. However, computer-aided drug design (CADD) can help solve the above problems. As an interdisciplinary approach that combines computational biology with medicinal chemistry, CADD has brought a variety of advances and numerous benefits to drug design, such as the rapid identification of new targets and discovery of new drugs. Based on an overview of RAS features and the history of inhibitor discovery, this review provides insight into the application of mainstream CADD methods to RAS drug design.

Membrane Permeabilization and Antimicrobial Activity of Recombinant Defensin-d2 and Actifensin against Multidrug-Resistant Pseudomonas aeruginosa and Candida albicans

Antimicrobial resistance requires urgent efforts towards the discovery of active antimicrobials, and the development of strategies to sustainably produce them. Defensin and defensin-like antimicrobial peptides (AMPs) are increasingly gaining pharmacological interest because of their potency against pathogens. In this study, we expressed two AMPs: defensin-d2 derived from spinach, and defensin-like actifensin from Actinomyces ruminicola. Recombinant pTXB1 plasmids carrying the target genes encoding defensin-d2 and actifensin were generated by the MEGAWHOP cloning strategy. Each AMP was first expressed as a fusion protein in Escherichia coli, purified by affinity chromatography, and was thereafter assayed for antimicrobial activity against multidrug-resistant (MDR) pathogens. Approximately 985 µg/mL and 2895 µg/mL of recombinant defensin-d2 and actifensin, respectively, were recovered with high purity. An analysis by MALDI-TOF MS showed distinct peaks corresponding to molecular weights of approximately 4.1 kDa for actifensin and 5.8 kDa for defensin-d2. An in vitro antimicrobial assay showed that MDR Pseudomonas aeruginosa and Candida albicans were inhibited at minimum concentrations of 7.5 µg/mL and 23 µg/mL for recombinant defensin-d2 and actifensin, respectively. The inhibitory kinetics of the peptides revealed cidal activity within 4 h of the contact time. Furthermore, both peptides exhibited an antagonistic interaction, which could be attributed to their affinities for similar ligands, as deduced by peptide–ligand profiling. Moreover, both peptides inhibited biofilm formation, and they exhibited no resistance potential and low hemolytic activity. The peptides also possess the ability to permeate and disrupt the cell membranes of MDR P. aeruginosa and C. albicans. Therefore, recombinant actifensin and defensin-d2 exhibit broad-spectrum antimicrobial activity and have the potential to be used as therapy against MDR pathogens.

Prediction of FMN Binding Sites in Electron Transport Chains Based on 2-D CNN and PSSM Profiles

Flavin mono-nucleotides (FMNs) are cofactors that hold responsibility for carrying and transferring electrons in the electron transport chain stage of cellular respiration. Without being facilitated by FMNs, energy production is stagnant due to the interruption in most of the cellular processes. Investigation on FMN's functions, therefore, can gain holistic understanding about human diseases and molecular information on drug targets. We proposed a deep learning model using a two-dimensional convolutional neural network and position specific scoring matrices that could identify FMN interacting residues with the sensitivity of 83.7 percent, specificity of 99.2 percent, accuracy of 98.2 percent, and Matthews correlation coefficients of 0.85 for an independent dataset containing 141 FMN binding sites and 1,920 non-FMN binding sites. The proposed method outperformed other previous studies using similar evaluation metrics. Our positive outcome can also promote the utilization of deep learning in dealing with various problems in bioinformatics and computational biology.

Addressing data imbalance problems in ligand-binding site prediction using a variational autoencoder and a convolutional neural network

Since 2015, a fast growing number of deep learning-based methods have been proposed for protein-ligand binding site prediction and many have achieved promising performance. These methods, however, neglect the imbalanced nature of binding site prediction problems. Traditional data-based approaches for handling data imbalance employ linear interpolation of minority class samples. Such approaches may not be fully exploited by deep neural networks on downstream tasks. We present a novel technique for balancing input classes by developing a deep neural network-based variational autoencoder (VAE) that aims to learn important attributes of the minority classes concerning nonlinear combinations. After learning, the trained VAE was used to generate new minority class samples that were later added to the original data to create a balanced dataset. Finally, a convolutional neural network was used for classification, for which we assumed that the nonlinearity could be fully integrated. As a case study, we applied our method to the identification of FAD- and FMN-binding sites of electron transport proteins. Compared with the best classifiers that use traditional machine learning algorithms, our models obtained a great improvement on sensitivity while maintaining similar or higher levels of accuracy and specificity. We also demonstrate that our method is better than other data imbalance handling techniques, such as SMOTE, ADASYN, and class weight adjustment. Additionally, our models also outperform existing predictors in predicting the same binding types. Our method is general and can be applied to other data types for prediction problems with moderate-to-heavy data imbalances.

Ligand Binding Site Comparison - LiBiSCo - a web-based tool for analyzing interactions between proteins and ligands to explore amino acid specificity within active sites

Interaction between protein and ligands are ubiquitous in a biological cell, and understanding these interactions at the atom level in protein-ligand complexes is crucial for structural bioinformatics and drug discovery. Here, we present a web-based protein-ligand interaction application named Ligand Binding Site Comparison (LiBiSCo) for comparing the amino acid residues interacting with atoms of a ligand molecule between different protein-ligand complexes available in the Protein Data Bank (PDB) database. The comparison is performed at the ligand atom level irrespectively of having binding site similarity or not between the protein structures of interest. The input used in LiBiSCo is one or several PDB IDs of protein-ligand complex(es) and the tool returns a list of identified interactions at ligand atom level including both bonded and non-bonded interactions. A sequence profile for the interaction for each ligand atoms is provided as a WebLogo. The LiBiSco is useful in understanding ligand binding specificity and structural promiscuity among families that are structurally unrelated. The LiBiSCo tool can be accessed through https://albiorix.bioenv.gu.se/LiBiSCo/HomePage.py.

In Silico Structural, Functional, and Phylogenetic Analysis of Cytochrome (CYPD) Protein Family

Cytochrome (CYP) enzymes catalyze the metabolic reactions of endogenous and exogenous compounds. The superfamily of enzymes is found across many organisms, regardless of type, except for plants. Information was gathered about CYP2D enzymes through protein sequences of humans and other organisms. The secondary structure was predicted using the SOPMA. The structural and functional study of human CYP2D was conducted using ProtParam, SOPMA, Predotar 1.03, SignalP, TMHMM 2.0, and ExPASy. Most animals shared five central motifs according to motif analysis results. The tertiary structure of human CYP2D, as well as other animal species, was predicted by Phyre2. Human CYP2D proteins are heavily conserved across organisms, according to the findings. This indicates that they are descended from a single ancestor. They calculate the ratio of alpha-helices to extended strands to beta sheets to random coils. Most of the enzymes are alpha-helix, but small amounts of the random coil were also found. The data were obtained to provide us with a better understanding of mammalian proteins' functions and evolutionary relationships.

Comparative analysis of the mitochondrial proteins reveals complex structural and functional relationships in Fasciola species

Mitochondria is a cellular source of energy, appears to play an essential role in dealing with cellular stress induced by environmental stimuli. The genetic diversity of mitochondrial genes involved in oxidative phosphorylation affecting the production of cellular energy and regional adaptation to various ecological (climatic) pressures affecting amino acid sequences (variants of protein). However, little is known about the combined effect of protein changes on cell-level metabolic alterations in simultaneous exposure to various environmental conditions, including mitochondrial dysfunction and oxidative stress induction. The present study was designed to address this issue by analyzing the mitochondrial proteins in Fasciola species including Cytochrome oxidase (COX1, COX2, COX3, and CYTB) and NADH dehydrogenase (ND1, ND2, ND3, ND4, ND5, and ND6). Mitochondrial proteins were used for detailed computational investigation, using available standard bioinformatics tools to exploit structural and functional relationships. These proteins in Fasciola hepatica, Fasciola gigentica, and Fasciola jacksoni were functionally annotated using public databases. The results showed that the protein of COX1 of F. hepatica, F. gigantica, and F. jacksoni consist of 510, 513, and 517 amino acids, respectively. The alignment of proteins showed that these proteins are conserved in the same regions at ten positions in COX and CYTB proteins while at twelve locations in NADH. Three-dimensional structure of COX, CYTB, and NADH proteins were compared and showed differences in additional conserved and binding sites in COX and CYTB proteins as compared to NADH in three species of Fasciola. These results based on the amino acid diversity pattern were used to identify sites in the enzyme and the variations in mitochondrial proteins among Fasciola species. Our study provides valuable information for future experimental studies, including identification of therapeutic, diagnostic, and immunoprophylactic interests with novel mitochondrial proteins.

NAGbinder: An approach for identifying N-acetylglucosamine interacting residues of a protein from its primary sequence

N-acetylglucosamine (NAG) belongs to the eight essential saccharides that are required to maintain the optimal health and precise functioning of systems ranging from bacteria to human. In the present study, we have developed a method, NAGbinder, which predicts the NAG-interacting residues in a protein from its primary sequence information. We extracted 231 NAG-interacting nonredundant protein chains from Protein Data Bank, where no two sequences share more than 40% sequence identity. All prediction models were trained, validated, and evaluated on these 231 protein chains. At first, prediction models were developed on balanced data consisting of 1,335 NAG-interacting and noninteracting residues, using various window size. The model developed by implementing Random Forest using binary profiles as the main principle for identifying NAG-interacting residue with window size 9, performed best among other models. It achieved highest Matthews Correlation Coefficient (MCC) of 0.31 and 0.25, and Area Under Receiver Operating Curve (AUROC) of 0.73 and 0.70 on training and validation data set, respectively. We also developed prediction models on realistic data set (1,335 NAG-interacting and 47,198 noninteracting residues) using the same principle, where the model achieved MCC of 0.26 and 0.27, and AUROC of 0.70 and 0.71, on training and validation data set, respectively. The success of our method can be appraised by the fact that, if a sequence of 1,000 amino acids is analyzed with our approach, 10 residues will be predicted as NAG-interacting, out of which five are correct. Best models were incorporated in the standalone version and in the webserver available at https://webs.iiitd.edu.in/raghava/nagbinder/.

Molecular docking of novel 5-O-benzoylpinostrobin derivatives as wild type and L858R/T790M/V948R mutant EGFR inhibitor

Background Previous studies have shown that 5-O-benzoylpinostrobin derivatives is a potential anti-breast cancer, with the highest potential being the HER2 inhibitors, is a protein's member of the epidermal growth factor receptor (EGFR) family. Overexpression of EGFR itself is known to be one of the causes of other cancer, including non-small cell lung cancer (NSCLC). Thus, it is possible that 5-O-benzoylpinostrobin derivatives can also inhibit the overexpression of EGFR in NSCLC. In the case of NSCLC, mutations of EGFR are often found in several amino acids, such as L858R, T790M, and V948R. This study aimed to determine the potential of 5-O-benzoylpinostrobin derivatives as an inhibitor of wild type and L858R/T790M/V948R-mutant EGFR. Methods Docking was performed using AutoDock Vina 1.1.2 on both wild type and L858R/T790M/V948R-mutant EGFR. Parameters observed, consisted of free energy of binding (ΔG) and amino acid interactions of each ligand. Results Docking results showed that all 5-O-benzoylpinostrobin derivatives showed a lower ΔG for both wild type and L858R/T790M/V948R-mutant EGFR, with the lowest ΔG shown by 4-methyl-5-O-benzoylpinostrobin and 4-trifluoromethyl-5-O-benzoylpinostrobin. Both the ligands have the similarity of interacting amino acids compared to reference ligands between 76.47 and 88.24%. Specifically, the ΔG of all test ligands was lower in mutant EGFR than in the wild type, which indicates the potential of the ligand as EGFR inhibitors where a mutation to EGFR occurs. Conclusions These results confirm that 5-O-benzoylpinostrobin derivatives have the potential to inhibit EGFR in both wild type and L858R/T790M/V948R-mutant.