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Motivation Utilizing AI-driven approaches for drug–target interaction (DTI) prediction require large volumes of training data which are not available for the majority of target proteins. In this study, we investigate the use of deep transfer learning for the prediction of interactions between drug candidate compounds and understudied target proteins with scarce training data. The idea here is to first train a deep neural network classifier with a generalized source training dataset of large size and then to reuse this pre-trained neural network as an initial configuration for re-training/fine-tuning purposes with a small-sized specialized target training dataset. To explore this idea, we selected six protein families that have critical importance in biomedicine: kinases, G-protein-coupled receptors (GPCRs), ion channels, nuclear receptors, proteases, and transporters. In two independent experiments, the protein families of transporters and nuclear receptors were individually set as the target datasets, while the remaining five families were used as the source datasets. Several size-based target family training datasets were formed in a controlled manner to assess the benefit provided by the transfer learning approach. Results Here, we present a systematic evaluation of our approach by pre-training a feed-forward neural network with source training datasets and applying different modes of transfer learning from the pre-trained source network to a target dataset. The performance of deep transfer learning is evaluated and compared with that of training the same deep neural network from scratch. We found that when the training dataset contains fewer than 100 compounds, transfer learning outperforms the conventional strategy of training the system from scratch, suggesting that transfer learning is advantageous for predicting binders to under-studied targets. Availability and implementation The source code and datasets are available at https://github.com/cansyl/TransferLearning4DTI. Our web-based service containing the ready-to-use pre-trained models is accessible at https://tl4dti.kansil.org. [ABSTRACT FROM AUTHOR]

Motivation Experimental testing and manual curation are the most precise ways for assigning Gene Ontology (GO) terms describing protein functions. However, they are expensive, time-consuming and cannot cope with the exponential growth of data generated by high-throughput sequencing methods. Hence, researchers need reliable computational systems to help fill the gap with automatic function prediction. The results of the last Critical Assessment of Function Annotation challenge revealed that GO-terms prediction remains a very challenging task. Recent developments on deep learning are significantly breaking out the frontiers leading to new knowledge in protein research thanks to the integration of data from multiple sources. However, deep models hitherto developed for functional prediction are mainly focused on sequence data and have not achieved breakthrough performances yet. Results We propose DeeProtGO, a novel deep-learning model for predicting GO annotations by integrating protein knowledge. DeeProtGO was trained for solving 18 different prediction problems, defined by the three GO sub-ontologies, the type of proteins, and the taxonomic kingdom. Our experiments reported higher prediction quality when more protein knowledge is integrated. We also benchmarked DeeProtGO against state-of-the-art methods on public datasets, and showed it can effectively improve the prediction of GO annotations. Availability and implementation DeeProtGO and a case of use are available at https://github.com/gamerino/DeeProtGO. Supplementary information Supplementary data are available at Bioinformatics online. [ABSTRACT FROM AUTHOR]

• Columns in tandem better resolved NOM fractions by size exclusion chromatography. • Linear models with NOM fractions reliably predicted THMs-FP. • Spectroscopic benefited concurrent organic and inorganic precursor assessments. • Spectroscopic quantification of NOM fractions can track THMs-FP with ease. Chlorination is a common method for water disinfection; however, it leads to the formation of disinfection by-products (DBPs), which are undesirable toxic pollutants. To prevent their formation, it is crucial to understand the reactivity of natural organic matter (NOM), which is considered a dominant precursor of DBPs. We propose a novel size exclusion chromatography (SEC) approach to evaluate NOM reactivity and the formation potential of total trihalomethanes-formation potentials (tTHMs-FP) and four regulated species (i.e. CHCl 3, CHBrCl 2, CHBr 2 Cl, and CHBr 3). This method combines enhanced SEC separation with two analytical columns working in tandem and quantification of apparent molecular weight (AMW) NOM fractions using C content (organic carbon detector, OCD), 254-nm spectroscopic (diode-array detector, DAD) measurements, and spectral slopes at low (S 206–240) and high (S 350–380) wavelengths. Links between THMs-FP and NOM fractions from high performance size exclusion chromatography HPSEC-DAD-OCD were investigated using statistical modelling with multiple linear regressions for samples taken alongside conventional full-scale as well as full- and pilot-scale electrodialysis reversal and bench-scale ion exchange resins. The proposed models revealed promising correlations between the AMW NOM fractions and the THMs-FP. Methodological changes increased fractionated signal correlations relative to bulk regressions, especially in the proposed HPSEC-DAD-OCD method. Furthermore, spectroscopic models based on fractionated signals are presented, providing a promising approach to predict THMs-FP simultaneously considering the effect of the dominant THMs precursors, NOM and Br−. [Display omitted] [ABSTRACT FROM AUTHOR]

Emission of odours during the thermal drying in sludge handling processes is one of the main sources of odour problems in wastewater treatment plants. The objective of this work was to assess the use of the response surface methodology as a technique to optimize the chemical conditioning process of undigested sewage sludges, in order to improve the dewaterability, and to reduce the odour emissions during the thermal drying of the sludge. Synergistic effects between inorganic conditioners (iron chloride and calcium oxide) were observed in terms of sulphur emissions and odour reduction. The developed quadratic models indicated that optimizing the conditioners dosage is possible to increase a 70% the dewaterability, reducing a 50% and 54% the emission of odour and volatile sulphur compounds respectively. The optimization of the conditioning process was validated experimentally. [ABSTRACT FROM AUTHOR]

Genomic variation can impact normal biological function in complex ways and so understanding variant effects requires a broad range of data to be coherently assimilated. Whilst the volume of human variant data and relevant annotations has increased, the corresponding increase in the breadth of participating fields, standards and versioning mean that moving between genomic, coding, protein and structure positions is increasingly complex. In turn this makes investigating variants in diverse formats and assimilating annotations from different resources challenging. ProtVar addresses these issues to facilitate the contextualization and interpretation of human missense variation with unparalleled flexibility and ease of accessibility for use by the broadest range of researchers. By precalculating all possible variants in the human proteome it offers near instantaneous mapping between all relevant data types. It also combines data and analyses from a plethora of resources to bring together genomic, protein sequence and function annotations as well as structural insights and predictions to better understand the likely effect of missense variation in humans. It is offered as an intuitive web server https://www.ebi.ac.uk/protvar where data can be explored and downloaded, and can be accessed programmatically via an API., (© The Author(s) 2024. Published by Oxford University Press on behalf of Nucleic Acids Research.)

The 'canonical' protein sets distributed by UniProt are widely used for similarity searching, and functional and structural annotation. For many investigators, canonical sequences are the only version of a protein examined. However, higher eukaryotes often encode multiple isoforms of a protein from a single gene. For unreviewed (UniProtKB/TrEMBL) protein sequences, the longest sequence in a Gene-Centric group is chosen as canonical. This choice can create inconsistencies, selecting >95% identical orthologs with dramatically different lengths, which is biologically unlikely. We describe the ortho2tree pipeline, which examines Reference Proteome canonical and isoform sequences from sets of orthologous proteins, builds multiple alignments, constructs gap-distance trees, and identifies low-cost clades of isoforms with similar lengths. After examining 140 000 proteins from eight mammals in UniProtKB release 2022_05, ortho2tree proposed 7804 canonical changes for release 2023_01, while confirming 53 434 canonicals. Gap distributions for isoforms selected by ortho2tree are similar to those in bacterial and yeast alignments, organisms unaffected by isoform selection, suggesting ortho2tree canonicals more accurately reflect genuine biological variation. 82% of ortho2tree proposed-changes agreed with MANE; for confirmed canonicals, 92% agreed with MANE. Ortho2tree can improve canonical assignment among orthologous sequences that are >60% identical, a group that includes vertebrates and higher plants., (© The Author(s) 2024. Published by Oxford University Press on behalf of NAR Genomics and Bioinformatics.)

Introduction and Objectives: Mitochondrial pyruvate carrier (MPC) mediates the entry of pyruvate into mitochondria, determining whether pyruvate is incorporated into the Krebs cycle or metabolized in the cytosol. In heart failure (HF), a large amount of pyruvate is metabolized to lactate in the cytosol rather than being oxidized inside the mitochondria. Thus, MPC activity or expression might play a key role in the fate of pyruvate during HF. The purpose of this work was to study the levels of the two subunits of this carrier, named MPC1 and MPC2, in human hearts with HF of different etiologies., Methods: Protein and mRNA expression analyses were conducted in cardiac tissues from three donor groups: patients with HF with reduced ejection fraction (HFrEF) with ischemic cardiomyopathy (ICM) or idiopathic dilated cardiomyopathy (IDC), and donors without cardiac pathology (Control). MPC2 plasma levels were determined by ELISA., Results: Significant reductions in the levels of MPC1, MPC2, and Sirtuin 3 (SIRT3) were observed in ICM patients compared with the levels in the Control group. However, no statistically significant differences were revealed in the analysis of MPC1 and MPC2 gene expression among the groups. Interestingly, Pyruvate dehydrogenase complex (PDH) subunits expression were increased in the ICM patients. In the case of IDC patients, a significant decrease in MPC1 was observed only when compared with the Control group. Notably, plasma MPC2 levels were found to be elevated in both disease groups compared with that in the Control group., Conclusion: Decreases in MPC1 and/or MPC2 levels were detected in the cardiac tissues of HFrEF patients, with ischemic or idiopatic origen, indicating a potential reduction in mitochondrial pyruvate uptake in the heart, which could be linked to unfavorable clinical features., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (© 2024 Lopez-Vazquez, Fernandez-Caggiano, Barge-Caballero, Barge-Caballero, Couto-Mallon, Grille-Cancela, Blanco-Canosa, Paniagua-Martin, Enriquez-Vazquez, Vazquez-Rodriguez, Domenech and Crespo-Leiro.)

Rare genetic diseases affect millions, and identifying causal DNA variants is essential for patient care. Therefore, it is imperative to estimate the effect of each independent variant and improve their pathogenicity classification. Our study of 140 214 unrelated UK Biobank (UKB) participants found that each of them carries a median of 7 variants previously reported as pathogenic or likely pathogenic. We focused on 967 diagnostic-grade gene (DGG) variants for rare bleeding, thrombotic, and platelet disorders (BTPDs) observed in 12 367 UKB participants. By association analysis, for a subset of these variants, we estimated effect sizes for platelet count and volume, and odds ratios for bleeding and thrombosis. Variants causal of some autosomal recessive platelet disorders revealed phenotypic consequences in carriers. Loss-of-function variants in MPL, which cause chronic amegakaryocytic thrombocytopenia if biallelic, were unexpectedly associated with increased platelet counts in carriers. We also demonstrated that common variants identified by genome-wide association studies (GWAS) for platelet count or thrombosis risk may influence the penetrance of rare variants in BTPD DGGs on their associated hemostasis disorders. Network-propagation analysis applied to an interactome of 18 410 nodes and 571 917 edges showed that GWAS variants with large effect sizes are enriched in DGGs and their first-order interactors. Finally, we illustrate the modifying effect of polygenic scores for platelet count and thrombosis risk on disease severity in participants carrying rare variants in TUBB1 or PROC and PROS1, respectively. Our findings demonstrate the power of association analyses using large population datasets in improving pathogenicity classifications of rare variants., (© 2023 by The American Society of Hematology. Licensed under Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0), permitting only noncommercial, nonderivative use with attribution. All other rights reserved.)

The Universal Protein Resource (UniProt) is a comprehensive resource for protein sequence and annotation data. The UniProt website receives about 800,000 unique visitors per month and is the primary means to access UniProt. It provides 10 searchable datasets and four main tools. The key UniProt datasets are the UniProt Knowledgebase (UniProtKB), the UniProt Reference Clusters (UniRef), the UniProt Archive (UniParc), and protein sets for completely sequenced genomes (Proteomes). Other supporting datasets include information about proteins that is present in UniProtKB protein entries, such as literature citations, taxonomy, and subcellular locations, among others. This article focuses on how to use UniProt datasets. The first basic protocol describes navigation and searching mechanisms for the UniProt datasets, and two additional protocols build on the first protocol to describe advanced search and query building. © 2023 The Authors. Current Protocols published by Wiley Periodicals LLC. Basic Protocol 1: Searching UniProt datasets Basic Protocol 2: Advanced search and query building Basis Protocol 3: Adding parameters using advanced search., (© 2023 The Authors. Current Protocols published by Wiley Periodicals LLC.)

Allergic asthma is the most common type of asthma. It is characterized by T H 2 cell-driven inflammation in which interleukin-13 (IL-13) plays a pivotal role. Cytoplasmic RNAs (Y-RNAs), a variety of non-coding RNAs that are dysregulated in many cancer types, are also differentially expressed in patients with allergic asthma. Their function in the development of the disease is still unknown. We investigated the potential role of RNY3 RNA (hY3) in the T H 2 cell inflammatory response using the Jurkat cell line as a model. hY3 expression levels were modulated to mimic the upregulation effect in allergic disease. We evaluated the effect of hY3 over cell stimulation and the expression of the T H 2 cytokine IL13. Total RNA was isolated and retrotranscribed, and RNA levels were assessed by qPCR. In Jurkat cells, hY3 levels increased upon stimulation with phorbol 12-myristate 13-acetate (PMA) and ionomycin. When transfecting with high levels of hY3 mimic molecules, cell proliferation rate decreased while IL13 mRNA levels increased upon stimulation compared to stimulated control cells. Our results show the effect of increased hY3 levels on cell proliferation and the levels of IL13 mRNA in Jurkat cells. Also, we showed that hY3 could act over other cells via exosomes. This study opens up new ways to study the potential regulatory function of hY3 over IL-13 production and its implications for asthma development., (© 2022. The Author(s).)

The Orthology Benchmark Service (https://orthology.benchmarkservice.org) is the gold standard for orthology inference evaluation, supported and maintained by the Quest for Orthologs consortium. It is an essential resource to compare existing and new methods of orthology inference (the bedrock for many comparative genomics and phylogenetic analysis) over a standard dataset and through common procedures. The Quest for Orthologs Consortium is dedicated to maintaining the resource up to date, through regular updates of the Reference Proteomes and increasingly accessible data through the OpenEBench platform. For this update, we have added a new benchmark based on curated orthology assertion from the Vertebrate Gene Nomenclature Committee, and provided an example meta-analysis of the public predictions present on the platform., (© The Author(s) 2022. Published by Oxford University Press on behalf of Nucleic Acids Research.)

Finding epistatic interactions among loci when expressing a phenotype is a widely employed strategy to understand the genetic architecture of complex traits in GWAS. The abundance of methods dedicated to the same purpose, however, makes it increasingly difficult for scientists to decide which method is more suitable for their studies. This work compares the different epistasis detection methods published during the last decade in terms of runtime, detection power and type I error rate, with a special emphasis on high-order interactions. Results show that in terms of detection power, the only methods that perform well across all experiments are the exhaustive methods, although their computational cost may be prohibitive in large-scale studies. Regarding non-exhaustive methods, not one could consistently find epistasis interactions when marginal effects are absent. If marginal effects are present, there are methods that perform well for high-order interactions, such as BADTrees, FDHE-IW, SingleMI or SNPHarvester. As for false-positive control, only SNPHarvester, FDHE-IW and DCHE show good results. The study concludes that there is no single epistasis detection method to recommend in all scenarios. Authors should prioritize exhaustive methods when sufficient computational resources are available considering the data set size, and resort to non-exhaustive methods when the analysis time is prohibitive.

To control gene transcription, DNA-binding transcription factors recognise specific sequence motifs in gene regulatory regions. A complete and reliable GO annotation of all DNA-binding transcription factors is key to investigating the delicate balance of gene regulation in response to environmental and developmental stimuli. The need for such information is demonstrated by the many lists of transcription factors that have been produced over the past decade. The COST Action Gene Regulation Ensemble Effort for the Knowledge Commons (GREEKC) Consortium brought together experts in the field of transcription with the aim of providing high quality and interoperable gene regulatory data. The Gene Ontology (GO) Consortium provides strict definitions for gene product function, including factors that regulate transcription. The collaboration between the GREEKC and GO Consortia has enabled the application of those definitions to produce a new curated catalogue of over 1400 human DNA-binding transcription factors, that can be accessed at https://www.ebi.ac.uk/QuickGO/targetset/dbTF. This catalogue has facilitated an improvement in the GO annotation of human DNA-binding transcription factors and led to the GO annotation of almost sixty thousand DNA-binding transcription factors in over a hundred species. Thus, this work will aid researchers investigating the regulation of transcription in both biomedical and basic science., (Copyright © 2021. Published by Elsevier B.V.)

SARS-CoV-2 (severe acute respiratory syndrome coronavirus 2) is a novel virus of the family Coronaviridae. The virus causes the infectious disease COVID-19. The biology of coronaviruses has been studied for many years. However, bioinformatics tools designed explicitly for SARS-CoV-2 have only recently been developed as a rapid reaction to the need for fast detection, understanding and treatment of COVID-19. To control the ongoing COVID-19 pandemic, it is of utmost importance to get insight into the evolution and pathogenesis of the virus. In this review, we cover bioinformatics workflows and tools for the routine detection of SARS-CoV-2 infection, the reliable analysis of sequencing data, the tracking of the COVID-19 pandemic and evaluation of containment measures, the study of coronavirus evolution, the discovery of potential drug targets and development of therapeutic strategies. For each tool, we briefly describe its use case and how it advances research specifically for SARS-CoV-2. All tools are free to use and available online, either through web applications or public code repositories. Contact:evbc@unj-jena.de., (© The Author(s) 2020. Published by Oxford University Press.)

An obesogenic environment during pregnancy has been shown to increase the risk of dysregulation on adipogenesis and insulin resistance in the offspring. Being essential for the growing fetus, glucose supply is guaranteed by a number of modifications in the mother's metabolism, and thus, glucose control during pregnancy especially among obese or diabetic women is paramount to prevent adverse consequences in their children. Besides the election of low-glycemic-index carbohydrates, the rate of carbohydrate digestion could be relevant to keep a good glucose control. In the present study, we compared the effects of two high-fat diets with similar glycemic load but different rates of carbohydrate digestion given to pregnant insulin-resistant rats. After birth, all animals were fed a standard diet until age 14 weeks. We analyzed offspring body composition, plasma and adipocyte lipidomics, lipid metabolism in adipose tissue and insulin sensitivity. Those animals whose mothers were fed the rapid-digesting carbohydrate diet exhibited an excessive adipogenesis. Thus, these animals showed a marked lipidemia, increased lipid synthesis in the adipose tissue and reduced glucose transporter amount in the adipose. On the contrary, those animals whose mothers were fed the slow-digesting carbohydrate diet showed a profile in the measured parameters closer to that of the offspring of healthy mothers. These results support the hypothesis that not only glycemic index but the rate of carbohydrate digestion during gestation may be critical to regulate the programming of adipogenesis in the offspring., (Copyright © 2018 The Authors. Published by Elsevier Inc. All rights reserved.)

The Consortium for Top-Down Proteomics (CTDP) proposes a standardized notation, ProForma, for writing the sequence of fully characterized proteoforms. ProForma provides a means to communicate any proteoform by writing the amino acid sequence using standard one-letter notation and specifying modifications or unidentified mass shifts within brackets following certain amino acids. The notation is unambiguous, human-readable, and can easily be parsed and written by bioinformatic tools. This system uses seven rules and supports a wide range of possible use cases, ensuring compatibility and reproducibility of proteoform annotations. Standardizing proteoform sequences will simplify storage, comparison, and reanalysis of proteomic studies, and the Consortium welcomes input and contributions from the research community on the continued design and maintenance of this standard.

It is becoming more evident that computational methods are needed for the identification and the mapping of pathways in new genomes. We introduce an automatic annotation system (ARBA4Path Association Rule-Based Annotator for Pathways) that utilizes rule mining techniques to predict metabolic pathways across wide range of prokaryotes. It was demonstrated that specific combinations of protein domains (recorded in our rules) strongly determine pathways in which proteins are involved and thus provide information that let us very accurately assign pathway membership (with precision of 0.999 and recall of 0.966) to proteins of a given prokaryotic taxon. Our system can be used to enhance the quality of automatically generated annotations as well as annotating proteins with unknown function. The prediction models are represented in the form of human-readable rules, and they can be used effectively to add absent pathway information to many proteins in UniProtKB/TrEMBL database.

The Universal Protein Resource (UniProt) is a freely available comprehensive resource for protein sequence and annotation data. UniProt is a collaboration between the European Bioinformatics Institute (EMBL-EBI), the Swiss Institute of Bioinformatics (SIB), and the Protein Information Resource (PIR). Across the three institutes more than 100 people are involved through different tasks such as expert curation, software development, and support.This chapter introduces the functionality and data provided by UniProt. It describes example use cases for which you might come to UniProt and the methods to help you achieve your goals.

Neurodegenerative diseases are characterized by a progressive deterioration of brain function, with a consequent significant decline in the quality of life of patients and their families. Due to the concurrent increase in life expectancy, the incidence of these diseases has been increasing over the last years and thus there is a growing interest in finding potential risk factors. This review focuses on the correlation between peripheral inflammatory diseases and neurodegeneration, in particular on the relationship between gastrointestinal disorders and Parkinson's disease, especially through the so called gut-brain axis.