Molecular Computing and Bioinformatics

Scientometrics Evaluation of Published Scientific Papers on the Use of Proteomics Technologies in Mastitis Research in Ruminants

The objective of this study was the presentation of quantitative characteristics regarding the scientific content and bibliometric details of the relevant publications. In total, 156 papers were considered. Most papers presented original studies (n = 135), and fewer were reviews (n = 21). Most original articles (n = 101) referred to work involving cattle. Most original articles described work related to the diagnosis (n = 72) or pathogenesis (n = 62) of mastitis. Most original articles included field work (n = 75), whilst fewer included experimental (n = 31) or laboratory (n = 30) work. The tissue assessed most frequently in the studies was milk (n = 59). Milk was assessed more frequently in studies on the diagnosis (61.1% of relevant studies) or pathogenesis (30.6%) of the infection, but mammary tissue was assessed more frequently in studies on the treatment (31.0%). In total, 47 pathogens were included in the studies described; most were Gram-positive bacteria (n = 34). The three bacteria most frequently included in the studies were Staphylococcus aureus (n = 55 articles), Escherichia coli (n = 31) and Streptococcus uberis (n = 19). The proteomics technology employed more often in the respective studies was liquid chromatography-tandem mass spectrometry (LC-MS/MS), either on its own (n = 56) or in combination with other technologies (n = 40). The median year of publication of articles involving bioinformatics or LC-MS/MS and bioinformatics was the most recent: 2022. The 156 papers were published in 78 different journals, most frequently in the Journal of Proteomics (n = 16 papers) and the Journal of Dairy Science (n = 12). The median number of cited references in the papers was 48. In the papers, there were 1143 co-authors (mean: 7.3 ± 0.3 co-authors per paper, median: 7, min.-max.: 1-19) and 742 individual authors. Among them, 15 authors had published at least seven papers (max.: 10). Further, there were 218 individual authors who were the first or last authors in the papers. Most papers were submitted for open access (n = 79). The median number of citations received by the 156 papers was 12 (min.-max.: 0-339), and the median yearly number of citations was 2.0 (min.-max.: 0.0-29.5). The h-index of the papers was 33, and the m-index was 2. The increased number of cited references in papers and international collaboration in the respective study were the variables associated with most citations to published papers. This is the first ever scientometrics evaluation of proteomics studies, the results of which highlighted the characteristics of published papers on mastitis and proteomics. The use of proteomics in mastitis research has focused on the elucidation of pathogenesis and diagnosis of the infection; LC-MS/MS has been established as the most frequently used proteomics technology, although the use of bioinformatics has also emerged recently as a useful tool.

From genome to clinic: The power of translational bioinformatics in improving human health

Translational bioinformatics (TBI) has transformed healthcare by providing personalized medicine and tailored treatment options by integrating genomic data and clinical information. In recent years, TBI has bridged the gap between genome and clinical data because of significant advances in informatics like quantum computing and utilizing state-of-the-art technologies. This chapter discusses the power of translational bioinformatics in improving human health, from uncovering disease-causing genes and variations to establishing new therapeutic techniques. We discuss key application areas of bioinformatics in clinical genomics, such as data sources and methods used in translational bioinformatics, the impact of translational bioinformatics on human health, and how machine learning and artificial intelligence are being used to mine vast amounts of data for drug development and precision medicine. We also look at the problems, constraints, and ethical concerns connected with exploiting genomic data and the future of translational bioinformatics and its potential impact on medicine and human health. Ultimately, this chapter emphasizes the great potential of translational bioinformatics to alter healthcare and enhance patient outcomes.

In silico prospection of Lactobacillus acidophilus strains with potential probiotic activity

Lactic acid bacteria (LAB) are fermentative microorganisms and perform different roles in biotechnological processes, mainly in the food and pharmaceutical industries. Among the LAB, Lactobacillus acidophilus is a species that deserves to be highlighted for being used both in prophylaxis and in the treatment of pathologies. Most of the metabolites produced by this species are linked to the inhibition of pathogens. In this study, we utilized a pangenomic and metabolic annotation analysis using Roary and BlastKOALA, ML-based probiotic activity prediction with iProbiotic and whole-genome similarity using ANI to identify strains of L. acidophilus with potential probiotic activity. According to the results in BlastKOALA and iProbiotics, L. acidophilus NCTC 13721 had the greatest potential among the 64 strains tested, both in terms of its ability to be a Lactobacillus spp. probiotic, when in the amount of genes involved in the metabolism of organic acids and quorum sensing. In addition, DSM 20079 proved to be promising for prospecting new probiotic Lactobacillus from BlastKOALA analyses, as they presented similar results in the number of genes involved in the production of lactic acid, acetic acid, hydrogen peroxide, except for quorum sensing where the NCTC 13721 strain had 14 more genes. L. acidophilus NCTC 13721 and L. acidophilus La-5 strains showed greater ability to be Lactobacillus spp. probiotic capacity, showing 84.8% and 51.9% capacity in the iProbiotics tool, respectively. When analyzed in ANI, none of the evaluated strains showed genomic similarity with NCTC 13721. In contrast, the DSM 20079 strain showed genomic similarity with all evaluated strains except NCTC 13721. Furthermore, eight strains with characteristics with approximately 100% genomic similarity to La-5 were listed: S20_1, LA-5, FSI4, APC2845, LA-G80-111, DS1_1A, LA1, and BCRC 14065. Therefore, according to the findings in iProbiotics and BlastKoala, among the 64 strains evaluated, NCTC 13721 is the most promising strain to be used for future in vitro studies.

Retinoic Acid Receptor Is a Novel Therapeutic Target for Postoperative Cognitive Dysfunction

Postoperative cognitive dysfunction (POCD) is a clinical syndrome characterizing by cognitive impairments in the elderly after surgery. There is limited effective treatment available or clear pathological mechanisms known for this syndrome. In this study, a Connectivity Map (CMap) bioinformatics model of POCD was established by using differently expressed landmark genes in the serum samples of POCD and non-POCD patients from the only human transcriptome study. The predictability and reliability of this model were further supported by the positive CMap scores of known POCD inducers and the negative CMap scores of anti-POCD drug candidates. Most retinoic acid receptor (RAR) agonists were negatively associated with POCD in this CMap model, suggesting that RAR might be a novel target for POCD. Most importantly, acitretin, a clinically used RAR agonist, significantly inhibited surgery-induced cognitive impairments and prevented the reduction in RARα and RARα-target genes in the hippocampal regions of aged mice. The study denotes a reliable CMap bioinformatics model of POCD for future use and establishes that RAR is a novel therapeutic target for treating this clinical syndrome.

Throwing and manipulating and cheating with a DNA nano-dice

Artificial molecular machines have captured the imagination of researchers, given their clear potential to mimic and influence human life. Key to behavior simulation is to reproduce the specific properties of physical or abstract systems. Dice throwing, as a stochastic model, is commonly used for result judgment or plan decision in real life. In this perspective we utilize DNA cube framework for the design of a dice device at the nanoscale to reproduce probabilistic events in different situations: equal probability, high probability, and low probability. We first discuss the randomness of DNA cube, or dice, adsorbing on graphene oxide, or table, and then explore a series of events that change the probability through the way in which the energy released from entropy-driven strand displacement reactions or changes in intermolecular forces. As such, the DNA nano-dice system provides guideline and possibilities for the design, engineering, and quantification of behavioral probability simulation, a currently emerging area of molecular simulation research.

Ubiquitin D promotes the progression of rheumatoid arthritis via activation of the p38 MAPK pathway

Ubiquitin D (UBD), a member of the ubiquitin‑like modifier family, has been reported to be highly expressed in various types of cancer and its overexpression is positively associated with tumor progression. However, the role and mechanism of UBD in rheumatoid arthritis (RA) remain elusive. In the present study, the gene expression profiles of GSE55457 were downloaded from the Gene Expression Omnibus database to assess differentially expressed genes and perform functional enrichment analyses. UBD was overexpressed by lentivirus transfection. The protein level of UBD, p‑p38 and p38 in RA‑fibroblast‑like synoviocytes (FLSs) were examined by western blotting. Cell Counting Kit‑8 and flow cytometry assays were used to detect the functional changes of RA‑FLSs transfected with UBD and MAPK inhibitor SB202190. The concentrations of inflammatory factors (IL‑2, IL‑6, IL‑10 and TNF‑α) were evaluated using ELISA kits. The results revealed that UBD was overexpressed in RA tissues compared with in the healthy control tissues. Functionally, UBD significantly accelerated the viability and proliferation of RA‑FLSs, whereas it inhibited their apoptosis. Furthermore, UBD significantly promoted the secretion of inflammatory factors (IL‑2, IL‑6, IL‑10 and TNF‑α). Mechanistically, elevated UBD activated phospohorylated‑p38 in RA‑FLSs. By contrast, UBD overexpression and treatment with the p38 MAPK inhibitor SB202190 not only partially relieved the UBD‑dependent effects on cell viability and proliferation, but also reversed its inhibitory effects on cell apoptosis. Furthermore, SB202190 partially inhibited the effects of UBD overexpression on the enhanced secretion of inflammatory factors. The present study indicated that UBD may mediate the activation of p38 MAPK, thereby facilitating the proliferation of RA‑FLSs and ultimately promoting the progression of RA. Therefore, UBD may be considered a potential therapeutic target and a promising prognostic biomarker for RA.

Computer Aided Drug Design Methodologies with Natural Products in the Drug Research Against Alzheimer's Disease

Natural products are compounds isolated from plants that provide a variety of lead structures for the development of new drugs by the pharmaceutical industry. The interest in these substances increases because of their beneficial effects on human health. Alzheimer's disease (AD) affects occur in about 80% of individuals aged 65 years. AD, the most common cause of dementia in elderly people, is characterized by progressive neurodegenerative alterations, as decrease of cholinergic impulse, increased toxic effects caused by reactive oxygen species and the inflammatory process that the amyloid plaque participates. In silico studies is relevant in the process of drug discovery; through technological advances in the areas of structural characterization of molecules, computational science and molecular biology have contributed to the planning of new drugs used against neurodegenerative diseases. Considering the social impairment caused by an increased incidence of disease and that there is no chemotherapy treatment effective against AD; several compounds are studied. In the researches for effective neuroprotectants as potential treatments for Alzheimer's disease, natural products have been extensively studied in various AD models. This study aims to carry out a literature review with articles that address the in silico studies of natural products aimed at potential drugs against Alzheimer's disease (AD) in the period from 2015 to 2021.

AOPM: Application of Antioxidant Protein Classification Model in Predicting the Composition of Antioxidant Drugs

Antioxidant proteins can not only balance the oxidative stress in the body, but are also an important component of antioxidant drugs. Accurate identification of antioxidant proteins is essential to help humans fight diseases and develop new drugs. In this paper, we developed a friendly method AOPM to identify antioxidant proteins. 188D and the Composition of k-spaced Amino Acid Pairs were adopted as the feature extraction method. In addition, the Max-Relevance-Max-Distance algorithm (MRMD) and random forest were the feature selection and classifier, respectively. We used 5-folds cross-validation and independent test dataset to evaluate our model. On the test dataset, AOPM presented a higher performance compared with the state-of-the-art methods. The sensitivity, specificity, accuracy, Matthew’s Correlation Coefficient and an Area Under the Curve reached 87.3, 94.2, 92.0%, 0.815 and 0.972, respectively. In addition, AOPM still has excellent performance in predicting the catalytic enzymes of antioxidant drugs. This work proved the feasibility of virtual drug screening based on sequence information and provided new ideas and solutions for drug development.

An Ensemble Learning-Based Method for Inferring Drug-Target Interactions Combining Protein Sequences and Drug Fingerprints

Identifying the interactions of the drug-target is central to the cognate areas including drug discovery and drug reposition. Although the high-throughput biotechnologies have made tremendous progress, the indispensable clinical trials remain to be expensive, laborious, and intricate. Therefore, a convenient and reliable computer-aided method has become the focus on inferring drug-target interactions (DTIs). In this research, we propose a novel computational model integrating a pyramid histogram of oriented gradients (PHOG), Position-Specific Scoring Matrix (PSSM), and rotation forest (RF) classifier for identifying DTIs. Specifically, protein primary sequences are first converted into PSSMs to describe the potential biological evolution information. After that, PHOG is employed to mine the highly representative features of PSSM from multiple pyramid levels, and the complete describers of drug-target pairs are generated by combining the molecular substructure fingerprints and PHOG features. Finally, we feed the complete describers into the RF classifier for effective prediction. The experiments of 5-fold Cross-Validations (CV) yield mean accuracies of 88.96%, 86.37%, 82.88%, and 76.92% on four golden standard data sets (enzyme, ion channel, G protein-coupled receptors (GPCRs), and nuclear receptor, respectively). Moreover, the paper also conducts the state-of-art light gradient boosting machine (LGBM) and support vector machine (SVM) to further verify the performance of the proposed model. The experimental outcomes substantiate that the established model is feasible and reliable to predict DTIs. There is an excellent prospect that our model is capable of predicting DTIs as an efficient tool on a large scale.

A parallel biological computing algorithm to solve the vertex coloring problem with polynomial time complexity

The vertex coloring problem is a well-known combinatorial problem that requires each vertex to be assigned a corresponding color so that the colors on adjacent vertices are different, and the total number of colors used is minimized. It is a famous NP-hard problem in graph theory. As of now, there is no effective algorithm to solve it. As a kind of intelligent computing algorithm, DNA computing has the advantages of high parallelism and high storage density, so it is widely used in solving classical combinatorial optimization problems. In this paper, we propose a new DNA algorithm that uses DNA molecular operations to solve the vertex coloring problem. For a simple n-vertex graph and k different kinds of color, we appropriately use DNA strands to indicate edges and vertices. Through basic biochemical reaction operations, the solution to the problem is obtained in the O (kn2) time complexity. Our proposed DNA algorithm is a new attempt and application for solving Nondeterministic Polynomial (NP) problem, and it provides clear evidence for the ability of DNA calculations to perform such difficult computational problems in the future.

Recent Advances in Predicting Protein S-Nitrosylation Sites

Protein S-nitrosylation (SNO) is a process of covalent modification of nitric oxide (NO) and its derivatives and cysteine residues. SNO plays an essential role in reversible posttranslational modifications of proteins. The accurate prediction of SNO sites is crucial in revealing a certain biological mechanism of NO regulation and related drug development. Identification of the sites of SNO in proteins is currently a very hot topic. In this review, we briefly summarize recent advances in computationally identifying SNO sites. The challenges and future perspectives for identifying SNO sites are also discussed. We anticipate that this review will provide insights into research on SNO site prediction.