Embedding electronic perpetual motion into single-atom catalysts for persistent Fenton-like reactions.

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 photocatalyst 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.

CAGI experiments: Modeling sequence variant impact on gene splicing using predictions from computational tools.

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.

Associating mutations causing cystinuria with disease severity with the aim of providing precision medicine.

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.

InteractoMIX: a suite of computational tools to exploit interactomes in biological and clinical research.

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).

Computational Analysis of Single Nucleotide Polymorphisms Associated with Altered Drug Responsiveness in Type 2 Diabetes.

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.

Discovering Novel Bacterial Small RNA by RNA-seq Analysis Toolkit ANNOgesic.

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.

Specifics of Metabolite-Protein Interactions and Their Computational Analysis and Prediction.

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.

Accurate Prediction of Protein Thermodynamic Stability Changes upon Residue Mutation using Free Energy Perturbation.

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 R2 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.

Type IV Collagen Variants in CKD: Performance of Computational Predictions for Identifying Pathogenic Variants.

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.

Enzyme Kinetics, Pharmacokinetics, and Inhibition of Aldehyde Oxidase.

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.

DeepGRP: engineering a software tool for predicting genomic repetitive elements using Recurrent Neural Networks with attention.

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.

Modelling the transmission of infectious diseases inside hospital bays: implications for COVID-19.

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.

Between computational predictions and high-throughput transcriptional profiling: in depth expression analysis of the OppB trans-membrane subunit of Escherichia coli OppABCDF oligopeptide transporter.

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 oppB 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.

In vitro and in silico assessment of the developability of a designed monoclonal antibody library.

Despite major advances in antibody discovery technologies, the successful development of monoclonal antibodies (mAbs) into effective therapeutic and diagnostic agents can often be impeded by developability liabilities, such as poor expression, low solubility, high viscosity and aggregation. Therefore, strategies to predict at the early phases of antibody development the risk of late-stage failure of antibody candidates are highly valuable. In this work, we employ the in silico solubility predictor CamSol to design a library of 17 variants of a humanized mAb predicted to span a broad range of solubility values, and we examine their developability potential with a battery of commonly used in vitro and in silico assays. Our results demonstrate the ability of CamSol to rationally enhance mAb developability, and provide a quantitative comparison of in vitro developability measurements with each other and with more resource-intensive solubility measurements, as well as with in silico predictors that offer a potentially faster and cheaper alternative. We observed a strong correlation between predicted and experimentally determined solubility values, as well as with measurements obtained using a panel of in vitro developability assays that probe non-specific interactions. These results indicate that computational methods have the potential to reduce or eliminate the need of carrying out laborious in vitro quality controls for large numbers of lead candidates. Overall, our study provides support to the emerging view that the implementation of in silico tools in antibody discovery campaigns can ensure rapid and early selection of antibodies with optimal developability potential.

Mapping of Protein-Protein Interactions: Web-Based Resources for Revealing Interactomes.

BACKGROUND: The significant number of protein-protein interactions (PPIs) discovered by harnessing concomitant advances in the fields of sequencing, crystallography, spectrometry and two-hybrid screening suggests astonishing prospects for remodelling drug discovery. The PPI space which includes up to 650 000 entities is a remarkable reservoir of potential therapeutic targets for every human disease. In order to allow modern drug discovery programs to leverage this, we should be able to discern complete PPI maps associated with a specific disorder and corresponding normal physiology. OBJECTIVE: Here, we will review community available computational programs for predicting PPIs and web-based resources for storing experimentally annotated interactions. METHODS: We compared the capacities of prediction tools: iLoops, Struck2Net, HOMCOS, COTH, PrePPI, InterPreTS and PRISM to predict recently discovered protein interactions. RESULTS: We described sequence-based and structure-based PPI prediction tools and addressed their peculiarities. Additionally, since the usefulness of prediction algorithms critically depends on the quality and quantity of the experimental data they are built on; we extensively discussed community resources for protein interactions. We focused on the active and recently updated primary and secondary PPI databases, repositories specialized to the subject or species, as well as databases that include both experimental and predicted PPIs. CONCLUSION: PPI complexes are the basis of important physiological processes and therefore, possible targets for cell-penetrating ligands. Reliable computational PPI predictions can speed up new target discoveries through prioritization of therapeutically relevant protein-protein complexes for experimental studies.

Computational Approaches to Develop Isoquinoline Based Antibiotics through DNA Gyrase Inhibition Mechanisms Unveiled through Antibacterial Evaluation and Molecular Docking.

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).

In Silico Vaccine Strain Prediction for Human Influenza Viruses.

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.

Bioinformatic Approach for Prediction of CsrA/RsmA-Regulating Small RNAs in Bacteria.

CsrA/RsmA is a RNA-binding protein that functions as a global regulator controlling important processes such as virulence, secondary metabolism, motility, and biofilm formation in diverse bacterial species. The activity of CsrA/RsmA is regulated by small RNAs that contain multiple binding sites for the protein. The expression of these noncoding RNAs effectively sequesters the protein and reduces free cellular levels of CsrA/RsmA. While multiple bacterial small RNAs that bind to and regulate CsrA/RsmA levels have been discovered, it is anticipated that there are several such small RNAs that remain undiscovered. To assist in the discovery of these small RNAs, we have developed a bioinformatics approach that combines sequence- and structure-based features to predict small RNA regulators of CsrA/RsmA. This approach analyzes structural motifs in the ensemble of low energy secondary structures of known small RNA regulators of CsrA/RsmA and trains a binary classifier on these features. The proposed machine learning approach leads to several testable predictions for small RNA regulators of CsrA/RsmA, thereby complementing and accelerating experimental efforts aimed at discovery of noncoding RNAs in the CsrA/RsmA pathway.

Drug repurposing for aging research using model organisms.

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.