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In our quest to leverage the capabilities of the emerging single-atom catalysts (SACs) for wastewater purification, we confronted fundamental challenges related to electron scarcity and instability. Through meticulous theoretical calculations, we identified optimal placements for nitrogen vacancies (Nv) and iron (Fe) single-atom sites, uncovering a dual-site approach that significantly amplified visible-light absorption and charge transfer dynamics. Informed by these computational insights, we cleverly integrated Nv into the catalyst design to boost electron density around iron atoms, yielding a potent and flexible photoactivator for benign peracetic acid. This exceptional catalyst exhibited remarkable stability and effectively degraded various organic contaminants over 20 cycles with self-cleaning properties. Specifically, the Nv sites captured electrons, enabling their swift transfer to adjacent Fe sites under visible light irradiation. This mechanism accelerated the reduction of the formed "peracetic acid-catalyst" intermediate. Theoretical calculations were used to elucidate the synergistic interplay of dual mechanisms, illuminating increased adsorption and activation of reactive molecules. Furthermore, electron reduction pathways on the conduction band were elaborately explored, unveiling the production of reactive species that enhanced photocatalytic processes. A six-flux model and associated parameters were also applied to precisely optimize the photocatalytic process, providing invaluable insights for future photocata- lyst design. Overall, this study offers a molecule-level insight into the rational design of robust SACs in a photo-Fenton-like system, with promising implications for wastewater treatment and other high-value applications. [ABSTRACT FROM AUTHOR]

Triple-oxygen isotope (δ 18O and Δ ′ 17 O) analysis of sulfate is becoming a common tool to assess several biotic and abiotic sulfur-cycle processes, both today and in the geologic past. Multi-step sulfur redox reactions often involve intermediate sulfoxyanions such as sulfite, sulfoxylate, and thiosulfate, which may rapidly exchange oxygen atoms with surrounding water. Process-based reconstructions therefore require knowledge of equilibrium oxygen-isotope fractionation factors (18 α and 17 α) between water and each individual sulfoxyanion. Despite this importance, there currently exist only limited experimental 18 α data and no 17 α estimates due to the difficulty of isolating and analyzing short-lived intermediate species. To address this, we theoretically estimate 18 α and 17 α for a suite of sulfoxyanions—including several sulfate, sulfite, sulfoxylate, and thiosulfate species—using quantum computational chemistry. We determine fractionation factors for sulfoxyanion "water droplets" using the B3LYP/6-31G+(d,p) method; we additionally calculate higher-order method (CCSD/aug-cc-pVTZ and MP2/aug-cc-pVTZ) scaling factors, and we qualitatively estimate the importance of anharmonic zero-point energy (ZPE) corrections using a suite of gaseous sulfoxy compounds. Methodological scaling factors greatly impact 18 α predictions, whereas ZPE corrections are likely small (i.e., ⩽ 1 ‰) at Earth-surface temperatures; existing experimental data best agree with 18 α predictions when including redox state-specific CCSD/aug-cc-pVTZ scaling factors. Theoretical pH- and temperature-specific bulk-solution (i.e., abundance-weighted average of all species) 18 α values yield root-mean-square errors for sulfate/water, sulfite/water, and thiosulfate/water equilibrium of 4.5‰ (n = 18 experimental conditions), 3.7‰ (n = 27), and 2.2‰ (n = 3), respectively. However, sulfate- and sulfite-system agreement improves considerably when comparing experimental results only to SO 3 (OH)−/H 2 O (RMSE = 1.6‰) and SO 2 (OH)−/H 2 O (RMSE = 2.2‰) predictions, rather than bulk solutions. This is particularly true for the sulfite system at high and low pH, when SO 2 (OH)− is not the dominant species. We discuss potential experimental and theoretical biases that may lead to this apparent improvement. By combining 18 α and 17 α predictions, we additionally estimate that sulfate, sulfite, sulfoxylate, and thiosulfate species can exhibit Δ ′ 17 O values as much as 0.199‰, 0.205‰, 0.101‰, and 0.186‰ more negative than equilibrated water at Earth-surface temperatures (reference line slope = 0.5305). This theoretical framework provides a foundation to interpret experimental and observational triple-oxygen isotope results of several sulfur-cycle processes including pyrite oxidation, microbial metabolisms (e.g., sulfate reduction, thiosulfate disproportionation), and hydrothermal anhydrite precipitation. We highlight this with several examples. [ABSTRACT FROM AUTHOR]

Abstract Background Repetitive elements contribute a large part of eukaryotic genomes. For example, about 40 to 50% of human, mouse and rat genomes are repetitive. So identifying and classifying repeats is an important step in genome annotation. This annotation step is traditionally performed using alignment based methods, either in a de novo approach or by aligning the genome sequence to a species specific set of repetitive sequences. Recently, Li (Bioinformatics 35:4408–4410, 2019) developed a novel software tool dna-brnn to annotate repetitive sequences using a recurrent neural network trained on sample annotations of repetitive elements. Results We have developed the methods of dna-brnn further and engineered a new software tool DeepGRP. This combines the basic concepts of Li (Bioinformatics 35:4408–4410, 2019) with current techniques developed for neural machine translation, the attention mechanism, for the task of nucleotide-level annotation of repetitive elements. An evaluation on the human genome shows a 20% improvement of the Matthews correlation coefficient for the predictions delivered by DeepGRP, when compared to dna-brnn. DeepGRP predicts two additional classes of repeats (compared to dna-brnn) and is able to transfer repeat annotations, using RepeatMasker-based training data to a different species (mouse). Additionally, we could show that DeepGRP predicts repeats annotated in the Dfam database, but not annotated by RepeatMasker. DeepGRP is highly scalable due to its implementation in the TensorFlow framework. For example, the GPU-accelerated version of DeepGRP is approx. 1.8 times faster than dna-brnn, approx. 8.6 times faster than RepeatMasker and over 100 times faster than HMMER searching for models of the Dfam database. Conclusions By incorporating methods from neural machine translation, DeepGRP achieves a consistent improvement of the quality of the predictions compared to dna-brnn. Improved running times are obtained by employing TensorFlow as implementation framework and the use of GPUs. By incorporating two additional classes of repeats, DeepGRP provides more complete annotations, which were evaluated against three state-of-the-art tools for repeat annotation.

Rationale & Objective: Pathogenic variants in type IV collagen have been reported to account for a significant proportion of chronic kidney disease. Accordingly, genetic testing is increasingly used to diagnose kidney diseases, but testing also may reveal rare missense variants that are of uncertain clinical significance. To aid in interpretation, computational prediction (called in silico) programs may be used to predict whether a variant is clinically important. We evaluate the performance of in silico programs for COL4A3/A4/A5 variants. Study Design, Setting, & Participants: Rare missense variants in COL4A3/A4/A5 were identified in disease cohorts, including a local focal segmental glomerulosclerosis (FSGS) cohort and publicly available disease databases, in which they are categorized as pathogenic or benign based on clinical criteria. Tests Compared & Outcomes: All rare missense variants identified in the 4 disease cohorts were subjected to in silico predictions using 12 different programs. Comparisons between the predictions were compared with: (1) variant classification (pathogenic or benign) in the cohorts and (2) functional characterization in a randomly selected smaller number (17) of pathogenic or uncertain significance variants obtained from the local FSGS cohort. Results: In silico predictions correctly classified 75% to 97% of pathogenic and 57% to 100% of benign COL4A3/A4/A5 variants in public disease databases. The congruency of in silico predictions was similar for variants categorized as pathogenic and benign, with the exception of benign COL4A5 variants, in which disease effects were overestimated. By contrast, in silico predictions and functional characterization classified all 9 pathogenic COL4A3/A4/A5 variants correctly that were obtained from a local FSGS cohort. However, these programs also overestimated the effects of genomic variants of uncertain significance when compared with functional characterization. Each of the 12 in silico programs used yielded similar results. Limitations: Overestimation of in silico program sensitivity given that they may have been used in the categorization of variants labeled as pathogenic in disease repositories. Conclusions: Our results suggest that in silico predictions are sensitive but not specific to assign COL4A3/A4/A5 variant pathogenicity, with misclassification of benign variants and variants of uncertain significance. Thus, we do not recommend in silico programs but instead recommend pursuing more objective levels of evidence suggested by medical genetics guidelines.

Healthcare associated transmission of viral infections is a major problem that has significant economic costs and can lead to loss of life. Infections with the highly contagious SARS-CoV-2 virus have been shown to have a high prevalence in hospitals around the world. The spread of this virus might be impacted by the density of patients inside hospital bays. To investigate this aspect, in this study we consider a mathematical modelling and computational approach to describe the spread of SARS-CoV-2 among hospitalised patients. We focus on 4-bed bays and 6-bed bays, which are commonly used to accommodate various non-COVID-19 patients in many hospitals across the United Kingdom (UK). We investigate the spread of SARS-CoV-2 infections among patients in non-COVID bays, in the context of various scenarios: placing the initially-exposed individual in different beds, varying the recovery and incubation periods, having symptomatic vs. asymptomatic patients, removing infected individuals from these hospital bays once they are known to be infected, and the role of periodic testing of hospitalised patients. Our results show that 4-bed bays reduce the spread of SARS-CoV-2 compared to 6-bed bays. Moreover, we show that the position of a new (not infected) patient in specific beds in a 6-bed bay might also slow the spread of the disease. Finally, we propose that regular SARS-CoV-2 testing of hospitalised patients would allow appropriate placement of infected patients in specific (COVID-only) hospital bays.

Background: Repetitive elements contribute a large part of eukaryotic genomes. For example, about 40 to 50% of human, mouse and rat genomes are repetitive. So identifying and classifying repeats is an important step in genome annotation. This annotation step is traditionally performed using alignment based methods, either in a de novo approach or by aligning the genome sequence to a species specific set of repetitive sequences. Recently, Li (Bioinformatics 35:4408–4410, 2019) developed a novel software tool dna-brnn to annotate repetitive sequences using a recurrent neural network trained on sample annotations of repetitive elements. Results: We have developed the methods of dna-brnn further and engineered a new software tool DeepGRP. This combines the basic concepts of Li (Bioinformatics 35:4408–4410, 2019) with current techniques developed for neural machine translation, the attention mechanism, for the task of nucleotide-level annotation of repetitive elements. An evaluation on the human genome shows a 20% improvement of the Matthews correlation coefficient for the predictions delivered by DeepGRP, when compared to dna-brnn. DeepGRP predicts two additional classes of repeats (compared to dna-brnn) and is able to transfer repeat annotations, using RepeatMasker-based training data to a different species (mouse). Additionally, we could show that DeepGRP predicts repeats annotated in the Dfam database, but not annotated by RepeatMasker. DeepGRP is highly scalable due to its implementation in the TensorFlow framework. For example, the GPU-accelerated version of DeepGRP is approx. 1.8 times faster than dna-brnn, approx. 8.6 times faster than RepeatMasker and over 100 times faster than HMMER searching for models of the Dfam database. Conclusions: By incorporating methods from neural machine translation, DeepGRP achieves a consistent improvement of the quality of the predictions compared to dna-brnn. Improved running times are obtained by employing TensorFlow as implementation framework and the use of GPUs. By incorporating two additional classes of repeats, DeepGRP provides more complete annotations, which were evaluated against three state-of-the-art tools for repeat annotation. [ABSTRACT FROM AUTHOR]

This comprehensive review explores the integration of machine learning (ML) in managing diabetic cataracts. It discusses the potential application of ML to identify novel methodologies for early detection, diagnosis, and therapeutic interventions. The review also addresses clinical translation challenges, including pharmacokinetics properties and ethical considerations. The approach toward cataractogenesis, therefore, has to be from a holistic viewpoint, bringing oxidative stress and metabolic disturbances to the top of importance. It outlines the important requirements, including continued research, diversified datasets, and uses interdisciplinary collaborations in making improvements in ML models and thereafter bridging the gap between computational promise and clinical implication, with the aim to help in the maximization of patient care in the management of diabetic cataract. A literature search through databases like PubMed and Scopus focusing on understanding of current innovations, challenges, and future directions in employing ML in diabetic cataract management was undertaken. This review has explored both recent and foundational studies in order to explain the development and gaps of current research with an aim to enhance outcomes of patient care by promoting future investigation. Key findings revealed a wide application of ML in ophthalmology including treatment identification, cataract detection and grading, and improving the surgical outcomes. However, this is accompanied by some obstacles, including risk of bias, concerns regarding artificial intelligence application as a diagnostic tool, and legal regulations. ML promises extraordinary developments in the treatment of diabetic cataracts through betterment in diagnosis, treatment, and patient care. With this, it is full of clinical translation and ethical challenges, yet there is recognition in general that continuous model refinement and interdisciplinary collaboration, along with the expansion of the two identified key elements in enhancing patient outcomes, are essential for this to continue., Competing Interests: Conflicts of interest: In compliance with the ICMJE uniform disclosure form, all authors declare the following: Payment/services info: All authors have declared that no financial support was received from any organization for the submitted work. Financial relationships: All authors have declared that they have no financial relationships at present or within the previous three years with any organizations that might have an interest in the submitted work. Other relationships: All authors have declared that there are no other relationships or activities that could appear to have influenced the submitted work., (Copyright © 2024, Mohammad et al.)

Bacterial oligopeptide transporters encoded by arrays of opp genes are implicated in a wide variety of physiological functions including nutrient acquisition, cell-to-cell communication, host-pathogen interaction. Combining the five opp genes in one oppABCDF operon of Escherichia coli assumes unified principle of their transcriptional regulation, which should provide a comparable amounts of translated products. This, however, contradicts the experimentally detected disproportion in the abundance of periplasmic OppA and the trans-membrane subunits OppB and OppC. As a first step towards understanding differential regulation of intraoperonic genes we examined genomic region proximal to opp B for its competence to initiate RNA synthesis using in silico promoter predictions, data of high-throughput RNA sequencing and targeted transcription assay. A number of transcription start sites (TSSs), whose potency depends on the presence of cationic oligopeptide protamine in cultivation medium, was found at the end of oppA and in the early coding part of oppB. We also show that full-size OppB conjugated with EGFP is produced under the control of its own genomic regulatory region and may be detected in analytical quantities of bacterial cell culture. [ABSTRACT FROM AUTHOR]

Abstract Background Cystinuria is an inherited disease that results in the formation of cystine stones in the kidney, which can have serious health complications. Two genes (SLC7A9 and SLC3A1) that form an amino acid transporter are known to be responsible for the disease. Variants that cause the disease disrupt amino acid transport across the cell membrane, leading to the build-up of relatively insoluble cystine, resulting in formation of stones. Assessing the effects of each mutation is critical in order to provide tailored treatment options for patients. We used various computational methods to assess the effects of cystinuria associated mutations, utilising information on protein function, evolutionary conservation and natural population variation of the two genes. We also analysed the ability of some methods to predict the phenotypes of individuals with cystinuria, based on their genotypes, and compared this to clinical data. Results Using a literature search, we collated a set of 94 SLC3A1 and 58 SLC7A9 point mutations known to be associated with cystinuria. There are differences in sequence location, evolutionary conservation, allele frequency, and predicted effect on protein function between these mutations and other genetic variants of the same genes that occur in a large population. Structural analysis considered how these mutations might lead to cystinuria. For SLC7A9, many mutations swap hydrophobic amino acids for charged amino acids or vice versa, while others affect known functional sites. For SLC3A1, functional information is currently insufficient to make confident predictions but mutations often result in the loss of hydrogen bonds and largely appear to affect protein stability. Finally, we showed that computational predictions of mutation severity were significantly correlated with the disease phenotypes of patients from a clinical study, despite different methods disagreeing for some of their predictions. Conclusions The results of this study are promising and highlight the areas of research which must now be pursued to better understand how mutations in SLC3A1 and SLC7A9 cause cystinuria. The application of our approach to a larger data set is essential, but we have shown that computational methods could play an important role in designing more effective personalised treatment options for patients with cystinuria.

Improving predictions of phenotypic consequences for genomic variants is part of ongoing efforts in the scientific community to gain meaningful insights into genomic function. Within the framework of the critical assessment of genome interpretation experiments, we participated in the Vex‐seq challenge, which required predicting the change in the percent spliced in measure (ΔΨ) for 58 exons caused by more than 1,000 genomic variants. Experimentally determined through the Vex‐seq assay, the Ψ quantifies the fraction of reads that include an exon of interest. Predicting the change in Ψ associated with specific genomic variants implies determining the sequence changes relevant for splicing regulators, such as splicing enhancers and silencers. Here we took advantage of two computational tools, SplicePort and SPANR, that incorporate relevant sequence features in their models of splice sites and exon‐inclusion level, respectively. Specifically, we used the SplicePort and SPANR outputs to build mathematical models of the experimental data obtained for the variants in the training set, which we then used to predict the ΔΨ associated with the mutations in the test set. We show that the sequence changes captured by these computational tools provide a reasonable foundation for modeling the impact on splicing associated with genomic variants. [ABSTRACT FROM AUTHOR]

Urban areas are projected to expand at a rapid pace. In the context of supporting sustainable design of cities and buildings, computational fluid dynamics (CFD) can be used to provide detailed information on the urban flow field. However, the complexity and natural variability of atmospheric boundary layer flows can limit the predictive performance of CFD. In this paper, we present a validation study for a Bayesian inference method that estimates the inflow boundary conditions for Reynolds-averaged Navier-Stokes (RANS) simulations of urban flow by assimilating data from urban sensor measurements. The method employs the ensemble Kalman filter to iteratively estimate the probability density functions of the incoming wind and improve the subsequent RANS prediction. The measurements used in this study were obtained during a full-scale experimental campaign on Stanfords campus. Six sonic anemometers were deployed at roof and pedestrian level; a subset of the sensors was used for data assimilation while the remaining ones were used for validation. The accuracy of the proposed inference method is compared to the conventional approach that defines the boundary conditions based on weather station data. The hit rates increased by a factor of two when using the inference method, and the predicted mean values were ∼ 20% more likely to be within the 95% confidence interval of the experimental data. An analysis of the impact of the number of sensors and their location indicates that the assimilation approach can consistently improve the predictions, as long as the inlet flow properties are identifiable from the sensor measurements. Image 1 [ABSTRACT FROM AUTHOR]

ANNOgesic is an RNA-seq analysis pipeline that can detect sRNAs and many other genomic features in bacteria and archaea. In addition to listing sRNA candidates, ANNOgesic also generates various formats of data files for visual examination and downstream experimental design. Based on validations from previous studies, the sRNA predictions are accurate and reliable. In this chapter, we outline the sRNA detection algorithm, important parameters used, step-by-step execution, and data interpretation with a B. pertussis study as an example. Following those procedures, novel sRNA can be revealed by ANNOgesic., (© 2024. The Author(s), under exclusive license to Springer Science+Business Media, LLC, part of Springer Nature.)

Vaccines preventing seasonal influenza infections save many lives every year; however, due to rapid viral evolution, they have to be updated frequently to remain effective. To identify appropriate vaccine strains, the World Health Organization (WHO) operates a global program that continually generates and interprets surveillance data. Over the past decade, sophisticated computational techniques, drawing from multiple theoretical disciplines, have been developed that predict viral lineages rising to predominance, assess their suitability as vaccine strains, link genetic to antigenic alterations, as well as integrate and visualize genetic, epidemiological, structural, and antigenic data. These could form the basis of an objective and reproducible vaccine strain-selection procedure utilizing the complex, large-scale data types from surveillance. To this end, computational techniques should already be incorporated into the vaccine-selection process in an independent, parallel track, and their performance continuously evaluated. [ABSTRACT FROM AUTHOR]

Toxicity and severe adverse effects are the primary cause of drug-candidate failures at the late stages of preclinical and clinical trials. Since most xenobiotics undergo biotransformations, their interaction with human organism reveals the effects produced by parent compounds and all metabolites. To increase the chances of successful drug development, estimation of the entire toxicity for drug substance and its metabolites is necessary for filtering out the potentially toxic compounds. We proposed the computational approach to the integral evaluation of xenobiotics' toxicity based on the structural formula of the drug-like compound. In the framework of this study, the consensus QSAR model was developed based on the analysis of over 3000 compounds with information about their rat acute toxicity for intravenous route of administration. Four different numerical methods, estimating the integral toxicity, were proposed, and their comparative performance was studied using the external evaluation set consisting of 37 structures of drugs and 200 their metabolites. It was shown that, on the average, the best correspondence between the predicted and published data is obtained using the method that takes into account the estimated characteristics for both the parent compound and its most toxic metabolite. [ABSTRACT FROM AUTHOR]

Many increasingly prevalent diseases share a common risk factor: age. However, little is known about pharmaceutical interventions against aging, despite many genes and pathways shown to be important in the aging process and numerous studies demonstrating that genetic interventions can lead to a healthier aging phenotype. An important challenge is to assess the potential to repurpose existing drugs for initial testing on model organisms, where such experiments are possible. To this end, we present a new approach to rank drug-like compounds with known mammalian targets according to their likelihood to modulate aging in the invertebrates Caenorhabditis elegans and Drosophila. Our approach combines information on genetic effects on aging, orthology relationships and sequence conservation, 3D protein structures, drug binding and bioavailability. Overall, we rank 743 different drug-like compounds for their likelihood to modulate aging. We provide various lines of evidence for the successful enrichment of our ranking for compounds modulating aging, despite sparse public data suitable for validation. The top ranked compounds are thus prime candidates for in vivo testing of their effects on lifespan in C. elegans or Drosophila. As such, these compounds are promising as research tools and ultimately a step towards identifying drugs for a healthier human aging. [ABSTRACT FROM AUTHOR]

Background: Cystinuria is an inherited disease that results in the formation of cystine stones in the kidney, which can have serious health complications. Two genes (SLC7A9 and SLC3A1) that form an amino acid transporter are known to be responsible for the disease. Variants that cause the disease disrupt amino acid transport across the cell membrane, leading to the build-up of relatively insoluble cystine, resulting in formation of stones. Assessing the effects of each mutation is critical in order to provide tailored treatment options for patients. We used various computational methods to assess the effects of cystinuria associated mutations, utilising information on protein function, evolutionary conservation and natural population variation of the two genes. We also analysed the ability of some methods to predict the phenotypes of individuals with cystinuria, based on their genotypes, and compared this to clinical data. Results: Using a literature search, we collated a set of 94 SLC3A1 and 58 SLC7A9 point mutations known to be associated with cystinuria. There are differences in sequence location, evolutionary conservation, allele frequency, and predicted effect on protein function between these mutations and other genetic variants of the same genes that occur in a large population. Structural analysis considered how these mutations might lead to cystinuria. For SLC7A9, many mutations swap hydrophobic amino acids for charged amino acids or vice versa, while others affect known functional sites. For SLC3A1, functional information is currently insufficient to make confident predictions but mutations often result in the loss of hydrogen bonds and largely appear to affect protein stability. Finally, we showed that computational predictions of mutation severity were significantly correlated with the disease phenotypes of patients from a clinical study, despite different methods disagreeing for some of their predictions. Conclusions: The results of this study are promising and highlight the areas of research which must now be pursued to better understand how mutations in SLC3A1 and SLC7A9 cause cystinuria. The application of our approach to a larger data set is essential, but we have shown that computational methods could play an important role in designing more effective personalised treatment options for patients with cystinuria. [ABSTRACT FROM AUTHOR]

Type 2 diabetes (T2D) is one of the most frequent mortality causes in western countries, with rapidly increasing prevalence. Anti-diabetic drugs are the first therapeutic approach, although many patients develop drug resistance. Most drug responsiveness variability can be explained by genetic causes. Inter-individual variability is principally due to single nucleotide polymorphisms, and differential drug responsiveness has been correlated to alteration in genes involved in drug metabolism (CYP2C9) or insulin signaling (IRS1, ABCC8, KCNJ11 and PPARG). However, most genome-wide association studies did not provide clues about the contribution of DNA variations to impaired drug responsiveness. Thus, characterizing T2D drug responsiveness variants is needed to guide clinicians toward tailored therapeutic approaches. Here, we extensively investigated polymorphisms associated with altered drug response in T2D, predicting their effects in silico. Combining different computational approaches, we focused on the expression pattern of genes correlated to drug resistance and inferred evolutionary conservation of polymorphic residues, computationally predicting the biochemical properties of polymorphic proteins. Using RNA-Sequencing followed by targeted validation, we identified and experimentally confirmed that two nucleotide variations in the CAPN10 gene-currently annotated as intronic-fall within two new transcripts in this locus. Additionally, we found that a Single Nucleotide Polymorphism (SNP), currently reported as intergenic, maps to the intron of a new transcript, harboring CAPN10 and GPR35 genes, which undergoes non-sense mediated decay. Finally, we analyzed variants that fall into non-coding regulatory regions of yet underestimated functional significance, predicting that some of them can potentially affect gene expression and/or post-transcriptional regulation of mRNAs affecting the splicing. [ABSTRACT FROM AUTHOR]

Virtually all the biological processes that occur inside or outside cells are mediated by protein-protein interactions (PPIs). Hence, the charting and description of the PPI network, initially in organisms, the interactome, but more recently in specific tissues, is essential to fully understand cellular processes both in health and disease. The study of PPIs is also at the heart of renewed efforts in the medical and biotechnological arena in the quest of new therapeutic targets and drugs. Here, we present a mini review of 11 computational tools and resources tools developed by us to address different aspects of PPIs: from interactome level to their atomic 3D structural details. We provided details on each specific resource, aims and purpose and compare with equivalent tools in the literature. All the tools are presented in a centralized, one-stop, web site: InteractoMIX (http://interactomix.com). [ABSTRACT FROM AUTHOR]

Carbohydrazides of N-substituted β-amino acids exhibit a variety of different biological activities including antibacterial, antiviral, fungicidal, antihelminthic, anticancer, antiinflammatory, etc. New potentially biologically active N-(4-iodophenyl)-β-alanine derivatives, N-(4-iodophenyl)- N-carboxyethyl-β-alanine derivatives, and their cyclization products were designed and synthesized. To determine the most propitious directions for further investigation of the obtained compounds, we tried to appraise their biological activity in silico using the ChemSpider and chemical structure lookup service (CSLS), chemical similarity assessment (Integrity and SuperPred), and machine learning methods [prediction of activity spectra for substances (PASS)]. No useful hints on potential biological activity of the obtained novel compounds were delivered by ChemSpider, CSLS, Integrity or SuperPred. In contrast, PASS predicted some biological activities that could be verified experimentally. Neither antibacterial nor antifungal activity was predicted for the compounds under study despite these actions being known for compounds from this chemical class. Evaluation of antibacterial ( Escherichia coli B-906, Staphylococcus aureus 209-P, and Mycobacterium luteum B-91) and antifungal ( Candida tenuis VKM Y-70 and Aspergillus niger F-1119) activities in vitro did not reveal any significant antimicrobial action, which corresponds to the computational prediction. Advantages and shortcomings of chemical similarity and machine learning techniques in computational assessment of biological activities are discussed. Based on the obtained results, we conclude that academic organic chemistry studies could provide a significant impact on drug discovery due to the novelty and diversity of the designed and synthesized compounds; however, practical utilization of this potential is narrowed by the limited facilities for assaying biological activities. [ABSTRACT FROM AUTHOR]

Ocean currents have the potential to supply electricity from a renewable source to coastal regions. The assessment of the potential energy that could be generated is the first step towards developing this resource. Data from the Hybrid Coordinate Ocean Model (HYCOM) and high-frequency radar measurements have been used to assess an area extending from 34.85° N to 35.15°N, and from 74.85°W to 74.5°W near the North Carolina shore. The assessment shows the area to exhibit a power density of at least 500 W/m 2 in over 50% of the days and 1000 W/m 2 or higher one third of the studied period. The results also show the direction of the ocean velocity to be very uniform in the northeast direction, which would facilitate a future exploitation of the resource. In addition, statistical analysis applying Weibull, Rayleigh, and Gaussian distributions is also presented. It is shown that the use of a Weibull probability distribution facilitates the analysis of ocean velocity conditions and is also able to predict the power density with a high degree of accuracy. [ABSTRACT FROM AUTHOR]

Computational approaches to the characterization and prediction of compound-protein interactions have a long research history and are well established, driven primarily by the needs of drug development. While, in principle, many of the computational methods developed in the context of drug development can also be applied directly to the investigation of metabolite-protein interactions, the interactions of metabolites with proteins (enzymes) are characterized by a number of particularities that result from their natural evolutionary origin and their biological and biochemical roles, as well as from a different problem setting when investigating them. In this review, these special aspects will be highlighted and recent research on them and developed computational approaches presented, along with available resources. They concern, among others, binding promiscuity, allostery, the role of posttranslational modifications, molecular steering and crowding effects, and metabolic conversion rate predictions. Recent breakthroughs in the field of protein structure prediction and newly developed machine learning techniques are being discussed as a tremendous opportunity for developing a more detailed molecular understanding of metabolism., (© 2023. The Author(s), under exclusive license to Springer Science+Business Media, LLC, part of Springer Nature.)

Abstract: Over the past two decades, nanotechnology has emerged as a key player in various disciplines of science and technology. Some of the most exciting applications are in the field of biomedicine – for theranostics (for combined diagnostic and therapeutic purposes) as well as for exploration of biological systems. A detailed understanding of the molecular interactions between nanoparticles and biological nano-machinery – macromolecules, membranes, and intracellular organelles – is crucial for obtaining adequate information on mechanisms of action of nanomaterials as well as a perspective on the long term effects of these materials and their possible toxicological outcomes. This review focuses on the use of structure-based computational molecular modeling as a tool to understand and to predict the interactions between nanomaterials and nano-biosystems. We review major approaches and provide examples of computational analysis of the structural principles behind such interactions. A rationale on how nanoparticles of different sizes, shape, structure and chemical properties can affect the organization and functions of nano-machinery of cells is also presented. [Copyright &y& Elsevier]

Abstract: This paper describes the numerical simulation results of two experimental studies [Teeling-Smith RG, Nurick GN. The deformation and tearing of thin circular plates subjected to impulsive loads. Int J Impact Eng 1991;11:77–91; Nurick GN, Bryant MW. Fragmentation damage as a result of an explosion. In: Gupta NK, editor. Plasticity and impact mechanics, December 1996, New Delhi, India, p. 484–498] in which the deformation and post-failure response of a plate subjected to blast loading were investigated. In Ref. Teeling-Smith and Nurick, uniform blast loading was considered, while in Ref. Nurick and Bryant, localised blast loading was investigated. The finite element code ABAQUS was used to simulate the structural response of the respective blast structures, while the hydro-dynamic code AUTODYN was used to characterise the localised blast pressure, time, and spatial history. The simulations showed satisfactory correlation with the experiments for energy input, large inelastic deformations and post-failure motion. [Copyright &y& Elsevier]

We present here a computational analysis showing that sigma70 house-keeping promoters are located within zones with high densities of promoter-like signals in Escherichia coli, and we introduce strategies that allow for the correct computer prediction of sigma70 promoters. Based on 599 experimentally verified promoters of E. coli K-12, we generated and evaluated more than 200 weight matrices optimizing different criteria to obtain the best recognition matrices. The alignments generating the best statistical models did not fully correspond with the canonical sigma70 model. However, matrices that correspond to such a canonical model performed better as tools for prediction. We tested the predictive capacity of these matrices on 250 bp long regions upstream of gene starts, where 90% of the known promoters occur. The computational matrix models generated an average of 38 promoter-like signals within each 250 bp region. In more than 50% of the cases, the true promoter does not have the best score within the region. We observed, in fact, that real promoters occur mostly within regions with high densities of overlapping putative promoters. We evaluated several strategies to identify promoters. The best one uses an intrinsic score of the −10 and −35 hexamers that form the promoter as well as an extrinsic score that uses the distribution of promoters from the start of the gene. We were able to identify 86% true promoters correctly, generating an average of 4.7 putative promoters per region as output, of which 3.7, on average, exist in clusters, as a series of overlapping potentially competing RNA polymerase-binding sites. As far as we know, this is the highest predictive capability reported so far. This high signal density is found mainly within regions upstream of genes, contrasting with coding regions and regions located between convergently transcribed genes. These results are consistent with experimental evidence that show the existence of multiple overlapping promoter sites that become functional under particular conditions. This density is probably the consequence of a rich number of vestiges of promoters in evolution. We suggest that transcriptional regulators as well as other functional promoters play an important role in keeping these latent signals suppressed. [Copyright &y& Elsevier]

[Display omitted] • Compared performance of MM/GBSA and FEP+ in predicting stability of protein variants. • Validated on a dataset of 328 experimentally characterized single mutants.. • FEP+ has R 2 = 0.65 and MUE = 0.95 kcal/mol and outperforms MM/GBSA. • Correct prediction of mutant charge state is essential for reliable FEP+ predictions • Proposed computational workflow: scan with MM/GBSA, and accurately predict with FEP+. This work describes the application of a physics-based computational approach to predict the relative thermodynamic stability of protein variants, and evaluates the quantitative accuracy of those predictions compared to experimental data obtained from a diverse set of protein systems assayed at variable pH conditions. Physical stability is a key determinant of the clinical and commercial success of biological therapeutics, vaccines, diagnostics, enzymes and other protein-based products. Although experimental techniques for measuring the impact of amino acid residue mutation on the stability of proteins exist, they tend to be time consuming and costly, hence the need for accurate prediction methods. In contrast to many of the commonly available computational methods for stability prediction, the Free Energy Perturbation approach applied in this paper explicitly accounts for solvent effects and samples conformational dynamics using a rigorous molecular dynamics simulation process. On the entire validation dataset, consisting of 328 single point mutations spread across 14 distinct protein structures, our results show good overall correlation with experiment with an R 2 of 0.65 and a low mean unsigned error of 0.95 kcal/mol. Application of the FEP approach in conjunction with experimental assessment techniques offers opportunities to lower the time and expense of product development and reduce the risk of costly late-stage failures. [ABSTRACT FROM AUTHOR]

Aldehyde oxidase (AO) has emerged as an important drug metabolizing enzyme over the last decade. Several compounds have failed in the clinic because the clearance or toxicity was underestimated by preclinical species. Human AO is much more active than rodent AO, and dogs do not have functional AO. Metabolic products from AO-catalyzed oxidation are generally nonreactive and often they have much lower solubility. AO metabolism is not limited to oxidation as AO can also catalyze reduction of oxygen and nitrite. Reduction of oxygen leads to the reactive oxygen species (ROS) superoxide radical anion and hydrogen peroxide. Reduction of nitrite leads to the formation of nitric oxide with potential pharmacological implications. AO is also reported to catalyze the reductive metabolism of nitro-compounds, N-oxides, sulfoxides, isoxazoles, isothiazoles, nitrite, and hydroxamic acids. These reductive transformations may cause toxicity due to the formation of reactive metabolites. Moreover, the inhibition kinetics are complex, and multiple probe substrates should be used when assessing the potential for DDIs. Finally, AO appears to be amenable to computational predictions of both regioselectivity and rates of reaction, which holds promise for virtual screening., (© 2021. Springer Science+Business Media, LLC, part of Springer Nature.)

Healthcare associated transmission of viral infections is a major problem that has significant economic costs and can lead to loss of life. Infections with the highly contagious SARS-CoV-2 virus have been shown to have a high prevalence in hospitals around the world. The spread of this virus might be impacted by the density of patients inside hospital bays. To investigate this aspect, in this study we consider a mathematical modelling and computational approach to describe the spread of SARS-CoV-2 among hospitalised patients. We focus on 4-bed bays and 6-bed bays, which are commonly used to accommodate various non-COVID-19 patients in many hospitals across the United Kingdom (UK). We investigate the spread of SARS-CoV-2 infections among patients in non-COVID bays, in the context of various scenarios: placing the initially-exposed individual in different beds, varying the recovery and incubation periods, having symptomatic vs. asymptomatic patients, removing infected individuals from these hospital bays once they are known to be infected, and the role of periodic testing of hospitalised patients. Our results show that 4-bed bays reduce the spread of SARS-CoV-2 compared to 6-bed bays. Moreover, we show that the position of a new (not infected) patient in specific beds in a 6-bed bay might also slow the spread of the disease. Finally, we propose that regular SARS-CoV-2 testing of hospitalised patients would allow appropriate placement of infected patients in specific (COVID-only) hospital bays.

Developing a new antibacterial drug by using (Z/E)‐4‐(4‐substituted‐benzylidene)‐2‐isoquinoline‐1,3(2H,4H)‐diones (5a–h) via DNA gyrase inhibition mechanism is the main aim of this study. DNA gyrase inhibition assay was executed to confirm the DNA gyrase inhibition potentials of 5a–h. DNA gyrase inhibitory potentials were further validated through molecular docking. Docking study was also intended to get more insight into the binding mode of 5a–h into the active site of DNA gyrase A. Agar well diffusion method antimicrobial activity on Gram−ve bacteria Escherichia coli (MTCC 443), Pseudomonas aeruginosa (MTCC 424), and Gram+ve bacteria (Staphylococcus aureus (MTCC 96) and Streptococcus pyogenes (MTCC 442) was evaluated. Excellent DNA gyrase inhibition was exhibited by the compound 5c, IC50 0.55±0.12 μM; 5d, IC50 0.65±0.075 μg/mL; 5e, IC50 0.45±0.035 μM; 5f, IC50 0.58±0.025 μM; 5h, IC50 0.25±0.015 μM while Clorobiocin (standard) showed IC50 0.5±0.05 μM. Apart from all the in vitro studies, a plausible mechanism of DNA gyrase inhibition was also proposed through the in silico validations that are including molecular docking, predicted SAR, functional group availability, pharmacokinetic, and ADMET properties. These predictions are well supported to confirm the druggability possibility of the most potent compounds among (Z/E)‐4‐(4‐substituted‐benzylidene)‐2‐isoquinoline‐1,3(2H,4H) ‐diones (5a–h). [ABSTRACT FROM AUTHOR]