In silico and structure-based evaluation of deleterious mutations identified in human Chk1, Chk2, and Wee1 protein kinase

Checkpoint kinases Chk1, Chk2, Wee1 are playing a key role in DNA damage response and genomic integrity. Cancer-associated mutations identified in human Chk1, Chk2, and Wee1 were retrieved to understand the function associated with the mutation and also alterations in the folding pattern. Therefore, an attempt has been made to identify deleterious effect of variants using in silico and structure-based approach. Variants of uncertain significance for Chk1, Chk2, and Wee1 were retrieved from different databases and four prediction servers were employed to predict pathogenicity of mutations. Further, Interpro, I-Mutant 3.0, Consurf, TM-align, and have (y)our protein explained were used for comprehensive study of the deleterious effects of variants. The sequences of Chk1, Chk2, and Wee1 were analyzed using Clustal Omega, and the three-dimensional structures of the proteins were aligned using TM-align. The molecular dynamics simulations were performed to explore the differences in folding pattern between Chk1, Chk2, Wee1 wild-type, and mutant protein and also to evaluate the structural integrity. Thirty-six variants in Chk1, 250 Variants in Chk2, and 29 in Wee1 were categorized as pathogenic using in silico prediction tools. Furthermore, 25 mutations in Chk1, 189 in Chk2, and 14 in Wee1 were highly conserved, possessing deleterious effect and also influencing the protein structure and function. These identified mutations may provide underlying genetic intricacies to serve as potential targets for therapeutic inventions and clinical management.

Immunoinformatics-based multi-epitope containing fused polypeptide vaccine design against visceral leishmaniasis with high immunogenicity and TLR binding

Visceral leishmaniasis (VL) is the most lethal among all leishmaniasis diseases and remains categorized as a neglected tropical disease (NTD). This study aimed to develop a peptide-based multi-epitope vaccine construct against VL using immunoinformatics methodologies. To achieve this, four distinct proteins were screened to identify peptides consisting of 9-15 amino acids with high binding affinity to toll-like receptors (TLRs), strong antigenicity, low allergenicity, and minimal toxicity. The resulting multi-epitope vaccine construct was fused in a tandem arrangement with appropriate linker peptides and exhibited superior properties related to cytotoxic T lymphocytes (CTLs), helper T lymphocytes (HTLs), and B-cell epitopes. Subsequently, a three-dimensional (3D) model of the vaccine construct was generated, refined, and validated for structural stability and immune response capabilities. Molecular docking and simulations confirmed the vaccine construct's stability and binding affinities with TLRs, with TLR4 displaying the highest binding affinity, followed by TLR2 and TLR3. Additionally, simulations predicted robust cellular and humoral antibody-mediated immune responses elicited by the designed vaccine construct. Notably, this vaccine construct includes proteins from various pathways of Leishmania donovani (LD), which have not been previously utilized in VL vaccine design. Thus, this study opens new avenues for the development of vaccines against diverse protozoan diseases.

Applications of bioinformatics in epigenetics

Epigenetic modifications such as DNA methylation, post-translational chromatin modifications and non-coding RNA-mediated mechanisms are responsible for epigenetic inheritance. Change in gene expression due to these epigenetic modifications are responsible for new traits in different organisms leading to various diseases including cancer, diabetic kidney disease (DKD), diabetic nephropathy (DN) and renal fibrosis. Bioinformatics is an effective approach for epigenomic profiling. These epigenomic data can be analyzed by a large number of bioinformatics tools and software. Many databases are available online, which comprises huge amount of information regarding these modifications. Recent methodologies include many sequencing and analytical techniques to extrapolate different types of epigenetic data. This data can be used to design drugs against diseases linked to epigenetic modifications. This chapter briefly highlights different epigenetics databases (MethDB, REBASE, Pubmeth, MethPrimerDB, Histone Database, ChromDB, MeInfoText database, EpimiR, Methylome DB, and dbHiMo), and tools (compEpiTools, CpGProD, MethBlAST, EpiExplorer, and BiQ analyzer), which are being utilized to retrieve the data and mechanistically analysis of epigenetics modifications.

In Silico Tools to Thaw the Complexity of the Data: Revolutionizing Drug Research in Drug Metabolism, Pharmacokinetics and Toxicity Prediction

In silico tool is the flourishing pathway for Researchers and budding chemists to strain the analytical data in a snapshot. Traditionally, drug research has heavily relied on labor-intensive experiments, often limited by time, cost, and ethical constraints. In silico tools have paved the way for more efficient and cost-effective drug development processes. By employing advanced computational algorithms, these tools can screen large libraries of compounds, identifying potential toxicities and prioritizing safer drug candidates for further investigation. Integrating in silico tools into the drug research pipeline has significantly accelerated the drug discovery process, facilitating early-stage decision-making and reducing the reliance on resource-intensive experimentation. Moreover, these tools can potentially minimize the need for animal testing, promoting the principles of the 3Rs (reduction, refinement, and replacement) in animal research. This paper highlights the immense potential of in silico tools in revolutionizing drug research. By leveraging computational models to predict drug metabolism, pharmacokinetics, and toxicity. Researchers can make informed decisions and prioritize the most promising drug candidates for further investigation. The synchronicity of In silico tools in this article on trending topics is insightful and will play an increasingly integral role in expediting drug development.

Nutricosmetics: A new frontier in bioactive peptides' research toward skin aging

Food derived bioactive peptides are small protein fragments (2-20 amino acids long) that can exhibit health benefits, beyond basic nutrition. For example, food bioactive peptides can act as physiological modulators with hormone or drug-like activities including anti-inflammatory, antimicrobial, antioxidant, and the ability to inhibit enzymes related to chronic disease metabolism. Recently, bioactive peptides have been studied for their potential role as nutricosmetics. For example, bioactive peptides can impart skin-aging protection toward extrinsic (i.e., environmental and sun UV-ray damage) and intrinsic (i.e., natural cell or chronological aging) factors. Specifically, bioactive peptides have demonstrated antioxidant and antimicrobial activates toward reactive oxygen species (ROS) and pathogenic bacteria associated with skin diseases, respectively. The anti-inflammatory properties of bioactive peptides using in vivo models has also been reported, where peptides have shown to decreased the expression of IL-6, TNF-α, IL-1β, interferon-γ (INF-γ), and interleukin-17 (IL-17) in mice models. This chapter will discuss the main factors that trigger skin-aging processes, as well as provide examples of in vitro, in vivo, and in silico applications of bioactive peptides in relation to nutricosmetic applications.

Migration of styrene oligomers from food contact materials: in silico prediction of possible genotoxicity

Styrene oligomers (SO) are well-known side products formed during styrene polymerization. They consist mainly of dimers (SD) and trimers (ST) that have been shown to be still residual in polystyrene (PS) materials. In this study migration of SO from PS into sunflower oil at temperatures between 5 and 70 °C and contact times between 0.5 h and 10 days was investigated. In addition, the contents of SD and ST in the fatty foodstuffs créme fraiche and coffee cream, which are typically enwrapped in PS, were measured and the amounts detected (of up to 0.123 mg/kg food) were compared to literature data. From this comparison, it became evident, that the levels of SO migrating from PS packaging into real food call for a comprehensive risk assessment. As a first step towards this direction, possible genotoxicity has to be addressed. Due to technical and experimental limitations, however, the few existing in vitro tests available are unsuited to provide a clear picture. In order to reduce uncertainty of these in vitro tests, four different knowledge and statistics-based in silico tools were applied to such SO that are known to migrate into food. Except for SD4 all evaluated SD and ST showed no alert for genotoxicity. For SD4, either the predictions were inconclusive or the substance was assigned as being out of the chemical space (out of domain) of the respective in silico tool. Therefore, the absence of genotoxicity of SD4 requires additional experimental proof. Apart from SD4, in silico studies supported the limited in vitro data that indicated the absence of genotoxicity of SO. In conclusion, the overall migration of all SO together into food of up to 50 µg/kg does not raise any health concerns, given the currently available in silico and in vitro data.

Evaluating the impact of in silico predictors on clinical variant classification

PURPOSE

According to the American College of Medical Genetics and Genomics/Association of Medical Pathology (ACMG/AMP) guidelines, in silico evidence is applied at the supporting strength level for pathogenic (PP3) and benign (BP4) evidence. Although PP3 is commonly used, less is known about the effect of these criteria on variant classification outcomes.

METHODS

A total of 727 missense variants curated by Clinical Genome Resource expert groups were analyzed to determine how often PP3 and BP4 were applied and their impact on variant classification. The ACMG/AMP categorical system of variant classification was compared with a quantitative point-based system. The pathogenicity likelihood ratios of REVEL, VEST, FATHMM, and MPC were calibrated using a gold standard set of 237 pathogenic and benign variants (classified independent of the PP3/BP4 criteria).

RESULTS

The PP3 and BP4 criteria were applied by Variant Curation Expert Panels to 55% of missense variants. Application of those criteria changed the classification of 15% of missense variants for which either criterion was applied. The point-based system resolved borderline classifications. REVEL and VEST performed best at a strength level consistent with moderate evidence.

CONCLUSION

We show that in silico criteria are commonly applied and often affect the final variant classifications. When appropriate thresholds for in silico predictors are established, our results show that PP3 and BP4 can be used at a moderate strength.

Quantification and in silico analysis of taste dipeptides generated during dry-cured ham processing

Small peptides such as dipeptides contribute to a great extent to the characteristic taste of dry-cured hams. In this study, hydrophilic interaction liquid chromatography (HILIC) combined to tandem mass spectrometry was used to separate, identify, and quantify seven dipeptides in dry-cured hams sampled at different processing times (6, 12, 18, and 24 months). Results showed an increased concentration of dipeptides DA, DG, EE, ES, and EV with the length of processing, obtaining values up to 23 μg/g of dry-cured ham, which suggests an intense action of muscle enzymes dipeptidyl peptidases during the process. The dipeptide VG significantly decreased from 7 to 4 μg/g of dry-cured ham as the processing increased from 6 to 24 months, whereas the dipeptide PA showed low values between 380 and 550 ng/g of dry-cured ham at all the sampling times. Additionally, in silico analyses reported the sensory characteristics of the studied dipeptides, mostly giving bitter and umami taste, and predicted their allergenicity, toxicity, and physicochemical properties. These results could be useful for further studies related to the pleasant taste of dry-cured hams.

In Silico Methods for the Identification of Viral-Derived Small Interfering RNAs (vsiRNAs) and Their Application in Plant Genomics

The current era of high-throughput sequencing (HTS) technology has expedited the detection and diagnosis of viruses and viroids in the living system including plants. HTS data has become vital to study the etiology of the infection caused by both known as well as novel viral elements in planta, and their impact on overall crop health and productivity. Viral-derived small interfering RNAs are generated as a result of defence response by the host via RNAi machinery. They are immensely exploited for performing exhaustive viral investigations in plants using bioinformatics as well as experimental approaches.This chapter briefly presents the basics of virus-derived small interfering RNAs (vsiRNAs ) biology in plants and their applications in plant genomics and highlights in silico strategies exploited for virus/viroid detection. It gives a systematic pipeline for vsiRNAs identification using currently available bioinformatics tools and databases. This will surely work as a quick beginner's recipe for the in silico revelation of plant vsiRNAs as well as virus/viroid diagnosis using high-throughput sequencing data.

Feature importance of machine learning prediction models shows structurally active part and important physicochemical features in drug design

The objective of this study was to obtain the indicators of physicochemical parameters and structurally active sites to design new chemical entities with desirable pharmacokinetic profiles by investigating the process by which machine learning prediction models arrive at their decisions, which are called explainable artificial intelligence. First, we developed the prediction models for metabolic stability, CYP inhibition, and P-gp and BCRP substrate recognition using 265 physicochemical parameters for designing the molecular structures. Four important parameters, including the well-known indicator h_logD, are common in some in vitro studies; as such, these can be used to optimize compounds simultaneously to address multiple pharmacokinetic concerns. Next, we developed machine learning models that had been programmed to show structurally active sites. Many types of machine learning models were developed using the results of in vitro metabolic stability study of around 30000 in-house compounds. The metabolic sites of in-house compounds predicted using some prediction models matched experimentally identified metabolically active sites, with a ratio of number of metabolic sites (predicted/actual) of over 90%. These models can be applied to several screening projects. These two approaches can be employed for obtaining lead compounds with desirable pharmacokinetic profiles efficiently.

Computational resources in the management of antibiotic resistance: Speeding up drug discovery

This article reviews more than 50 computational resources developed in past two decades for forecasting of antibiotic resistance (AR)-associated mutations, genes and genomes. More than 30 databases have been developed for AR-associated information, but only a fraction of them are updated regularly. A large number of methods have been developed to find AR genes, mutations and genomes, with most of them based on similarity-search tools such as BLAST and HMMER. In addition, methods have been developed to predict the inhibition potential of antibiotics against a bacterial strain from the whole-genome data of bacteria. This review also discuss computational resources that can be used to manage the treatment of AR-associated diseases.

Predicting drug metabolism and pharmacokinetics features of in-house compounds by a hybrid machine-learning model

We constructed machine learning-based pharmacokinetic prediction models with very high performance. The models were trained on 26138 and 16613 compounds involved in metabolic stability and cytochrome P450 inhibition, respectively. Because the compound features largely differed between the publicly available and in-house compounds, the models learned on the public data could not predict the in-house compounds, suggesting that outside of a certain applicability domain (AD), the prediction results are unreliable and can mislead the design of novel compounds. To exclude the uncertain prediction results, we constructed another machine learning model that determines whether the newly designed compound is inside or outside the AD. The AD was evaluated multi-dimensionally with some explanatory variables: The structural similarities and the probability obtained from the pharmacokinetic prediction model. The accuracy of predicting metabolic stability was 79.9% on the test set, increasing significantly to 93.6% after excluding the low-reliability compounds. The model properly classified the reliability of the compounds. After learning on the in-house compounds, the reliability model classified almost all (90%) of the public compounds as low reliability, indicating that the AD was properly determined by the model.

Green toxicological investigation for biofuel candidates

To avoid potential risks of biofuels on the environment and human, ecotoxicity investigation should be integrated into the early design stage for promising biofuel candidates. In the present study, a green toxicology testing strategy combining experimental bioassays with in silico tools was established to investigate the potential ecotoxicity of biofuel candidates. Experimental results obtained from the acute immobilisation test, the fish embryo acute toxicity test and the in vitro micronucleus assay (Chinese hamster lung fibroblast cell line V79) were compared with model prediction results by ECOSAR and OECD QSAR Toolbox. Both our experimental and model prediction results showed that 1-Octanol (1-Oct) and Di-n-butyl ether (DNBE) were the most toxic to Daphnia magna and zebrafish among all the biofuel candidates we investigated, while Methyl ethyl ketone (MEK), Dimethoxymethane (DMM) and Diethoxymethane (DEM) were the least toxic. Moreover, both in vitro micronucleus assay and OECD QSAR Toolbox evaluation suggested that the metabolites present higher genotoxicity than biofuel candidates themselves. Overall, our results proved that this green toxicology testing strategy is a useful tool for assessing ecotoxicity of biofuel candidates.

Turning Nature's own processes into design strategies for living bone implant biomanufacturing: a decade of Developmental Engineering

A decade after the term developmental engineering (DE) was coined to indicate the use of developmental processes as blueprints for the design and development of engineered living implants, a myriad of proof-of-concept studies demonstrate the potential of this approach in small animal models. This review provides an overview of DE work, focusing on applications in bone regeneration. Enabling technologies allow to quantify the distance between in vitro processes and their developmental counterpart, as well as to design strategies to reduce that distance. By embedding Nature's robust mechanisms of action in engineered constructs, predictive large animal data and subsequent positive clinical outcomes can be gradually achieved. To this end, the development of next generation biofabrication technologies should provide the necessary scale and precision for robust living bone implant biomanufacturing.

Pentabromobenzyl-RP versus triazole-HILIC columns for separation of the polar basic analytes famotidine and famotidone: LC method development combined with in silico tools to follow the potential consequences of famotidine gastric instability

The competence of hydrophilic interaction (HILIC) and reversed phase liquid chromatography (RPLC) modes, employing two new stationary phases: triazole- and pentabromobenzyl-bonded silica (PBr), respectively, was inspected for separation of two polar basic analytes: famotidine (FAM) and its acidic degradant famotidone (FON). Comparison of the chromatographic efficiency, greenness, and economy aspects showed that the RPLC is superior to the HILIC. Hence, the RPLC method was adopted and validated adhering to the FDA guidelines showing excellent linearity for FAM (1.0-20.0 μg/mL) with a detection limit of 0.14 μg/mL. The method was applied to study the behavior of FAM in simulated gastric juice (SGJ), where it exhibited rapid degradation yielding FON. This degradation pathway is a probable major reason for the poor bioavailability of FAM. The kinetic study of the gastric degradation of FAM in SGJ demonstrated pseudo-first order reaction with a rate constant of 8.1 × 10^-3 min^-1. Moreover, FAM degradation has been proven to be pH-dependent and catalyzed by the gastric juice components. Hence, in situ buffered dosage form is recommended to overcome or decrease this problem. Molecular docking study shows that FON is missing a crucial stabilizing interaction with the key amino acid Asp98 causing a reduced activity at hH₂R receptor relative to FAM. Moreover, ADMET properties prediction revealed some differences in the toxicity, pharmacokinetics, metabolism, and solubility profiles of FAM and FON.

Purification and characterization of anti-tubercular and anticancer protein from Staphylococcus hominis strain MANF2: In silico structural and functional insight of peptide

The present context was investigated to purify and characterize anti-tubercular as well as anticancer protein from fermented food associated Staphylococcus hominis strain MANF2. Initially, the anti-tubercular potency of strain MANF2 was assessed against Mycobacterium tuberculosis H37Rv using luciferase reporter phase assay which revealed pronounced relative light unit (RLU) reduction of 92.5 ± 1.2%. The anticancer property of strain MANF2 was demonstrated against lung cancer (A549) and colon cancer (HT-29) cell lines using MTT assay which showed reduced viabilities. Anti-tubercular activities of the purified protein were observed to be increased significantly (P < 0.05) ranging from 34.6 ± 0.3 to 71.4 ± 0.4% of RLU reduction. Likewise, the purified protein showed significantly (P < 0.05) reduced viabilities of A549 and HT-29 cancer cells with IC₅₀ values of 46.6 and 48.9 µg/mL, respectively. The nominal mass of the purified protein was found to be 7712.3 Da as obtained from MALDI-TOF MS/MS spectrum. The protein showed the sequence homology with 1–336 amino acids of Glyceraldehyde-3-phosphate dehydrogenase from Staphylococcus sp., thus, categorizing as a new class of Glyceraldehyde-3-phosphate dehydrogenase-like protein. The amino acid sequence of the most abundant peptide (m/z = 1922.12) in the purified protein was obtained as ‘KAIGLVIPEIDGKLDGGAQRV’ and it was identified as peptide NMANF2. In silico tools predicted significant stereo-chemical, physiochemical, and functional characteristics of peptide NMANF2. In a nutshell, protein purified from strain MANF2 can certainly be used as an ideal therapeutic agent against tuberculosis and cancer (lung and colon).

In silico approaches and tools for the prediction of drug metabolism and fate: A review

The fate of administered drugs is largely influenced by their metabolism. For example, endogenous enzyme-catalyzed conversion of drugs may result in therapeutic inactivation or activation or may transform the drugs into toxic chemical compounds. This highlights the importance of drug metabolism in drug discovery and development, and accounts for the wide variety of experimental technologies that provide insights into the fate of drugs. In view of the high cost of traditional drug development, a number of computational approaches have been developed for predicting the metabolic fate of drug candidates, allowing for screening of large numbers of chemical compounds and then identifying a small number of promising candidates. In this review, we introduce in silico approaches and tools that have been developed to predict drug metabolism and fate, and assess their potential to facilitate the virtual discovery of promising drug candidates. We also provide a brief description of various recent models for predicting different aspects of enzyme-drug reactions and provide a list of recent in silico tools used for drug metabolism prediction.

1-Naphthyl acetate: A chromogenic substrate for the detection of erythrocyte acetylcholinesterase activity

Erythrocyte acetylcholinesterase (AChE) is a preferred biomarker for the detection of organophosphorus poisoning. Acetylthiocholine (ATCh) is the most popular substrate for the detection of AChE activity. However, oximolysis is a prominent feature with ATCh. In this context, we have searched alternative substrates for AChE using in silico tools for screening of a better substrate. The in silico approach was performed to understand the fitness and the Total Interaction Energy (TIE) of substrates for AChE. The alternative substrates for AChE were screened in terms of high Goldscore and favorable TIE in comparison to acetylcholine (ACh)-AChE complex and other relevant esterases. Among the screened substrates, 1-Naphthyl acetate (1-NA) exhibited the most favorable interaction with AChE in terms of highest TIE and corresponding high Goldscore. The Molecular Dynamic (MD) simulation of the 1-NA-AChE complex showed a stable complex formation over a period of 5 ns. The results obtained in the in silico studies were validated in vitro using pure erythrocyte AChE and hemolysate. We observed 1-NA to be a better alternative substrate for AChE than ATCh in terms of lower Km value. Its specificity appeared at least similar to ATCh. Therefore, we propose that 1-NA can be an attractive chromogenic substrate for the measurement of AChE activity, and it possess the potential to detect organophosphorus pesticide (OP) poisoning.

In Silico Tools for the Prediction of Protein Import into Secondary Plastids

The in silico identification of proteins targeting to secondary plastids is a difficult task. Such plastids are complex in structure and can be surrounded by up to four membranes, which have to be crossed during import. Nucleus-encoded plastidial preproteins in organisms with secondary plastids contain specific N-terminal targeting signals, the so-called bipartite targeting signal (BTS) sequences consisting of a classical signal peptide followed by a transit peptide-like sequence, mediating this intricate process. As these signal sequences differ significantly from transit peptides of plastid preproteins in plants and other organisms with primary plastids, existing in silico tools for primary plastid targeting prediction are not directly suitable to detect nucleus-encoded proteins destined for the import into secondary plastids. In this chapter I describe the current state-of-the-art methods to reliably predict proteins that might be imported into secondary plastids of red- and green-algal origin using either the "classical" approach, which involves a combination of bits of information produced by existing in silico tools, or, if available, via consulting specifically developed algorithms.

Analyzing the Effect of V66M Mutation in BDNF in Causing Mood Disorders: A Computational Approach

Mental disorders or mood disorders are prevalent globally irrespective of region, race, and ethnic groups. Of the types of mood disorders, major depressive disorder (MDD) and bipolar disorder (BPD) are the most prevalent forms of psychiatric condition. A number of preclinical studies emphasize the essential role of brain-derived neurotrophic factor (BDNF) in the pathophysiology of mood disorders. Additionally, BDNF is the most common growth factor in the central nervous system along with their essential role during the neural development and the synaptic elasticity. A malfunctioning of this protein is associated with many types of mood disorders. The variant methionine replaces valine at 66th position is strongly related to BPD, and an individual with a homozygous condition of this allele is at a greater risk of developing MDD. There are very sparse reports suggesting the structural changes of the protein occurring upon the mutation. Consequently, in this study, we applied a computational pipeline to understand the effects caused by the mutation on the protein's structure and function. With the use of in silico tools and computational macroscopic methods, we identified a decrease in the alpha-helix nature, and an overall increase in the random coils that could have probably resulted in deformation of the protein.

Evaluation of damaging effects of splicing mutations: validation of an in vitro method for diagnostic laboratories

BACKGROUND

Pre-mRNA splicing defects may have an important impact on clinical phenotype in several diseases, but often their pathogenic role is difficult to demonstrate. The aim of this study was to validate an in vitro method to assess the effects of putative splicing variants.

MATERIALS AND METHODS

We studied three novel variants in vitro using a novel minigene approach and compared results with in silico and ex vivo strategies from patient samples.

RESULTS

For the c.1146C>T variant in the LMNA gene, in vitro and ex vivo studies were concordant with the prediction obtained by in silico tools, confirming the loss of 13 bp at the end of exon 6. In the second case (c.1140+1G>A, SCN5A gene), in vitro experiments identified the insertion of 94 intronic bp in exon 9 as well as exon 9 skipping, but these results were not correctly predicted by ex vivo data and in silico tools. In the third case (c.1608+1C>T, LMNA gene) in vitro and ex vivo studies suggested the recognition of an exonic cryptic site leading to the loss of 29 bp in exon 9, not predicted by in silico analysis.

CONCLUSION

Our results revealed how in silico tools are often unreliable requiring "wet" RNA analysis. Since ex vivo studies are not always feasible, the use of an in vitro construct represents an efficient and useful method for the evaluation of damaging effects of unknown splicing variants, especially in diagnostic laboratories.