A novel corpus of molecular to higher-order events that facilitates the understanding of the pathogenic mechanisms of idiopathic pulmonary fibrosis.

Idiopathic pulmonary fibrosis (IPF) is a severe and progressive chronic fibrosing interstitial lung disease with causes that have remained unclear to date. Development of effective treatments will require elucidation of the detailed pathogenetic mechanisms of IPF at both the molecular and cellular levels. With a biomedical corpus that includes IPF-related entities and events, text-mining systems can efficiently extract such mechanism-related information from huge amounts of literature on the disease. A novel corpus consisting of 150 abstracts with 9297 entities intended for training a text-mining system was constructed to clarify IPF-related pathogenetic mechanisms. For this corpus, entity information was annotated, as were relation and event information. To construct IPF-related networks, we also conducted entity normalization with IDs assigned to entities. Thereby, we extracted the same entities, which are expressed differently. Moreover, IPF-related events have been defined in this corpus, in contrast to existing corpora. This corpus will be useful to extract IPF-related information from scientific texts. Because many entities and events are related to lung diseases, this freely available corpus can also be used to extract information related to other lung diseases such as lung cancer and interstitial pneumonia caused by COVID-19.

Prevalence, Location, and Interference With Daily Life of Chronic Pain in Long-Term Survivors After Discharge From a Tertiary Emergency Center.

Background This study aimed to investigate the prevalence, location, and characteristics of new-onset chronic pain by using a new definition in long-term survivors after discharge from a tertiary emergency center. Materials and methods We conducted a single-center ambidirectional cohort study from January to May 2022. A survey of patients was conducted by postal mail two to 2.5 years after their discharge from a tertiary emergency center. We used the Brief Pain Inventory to investigate chronic pain parameters, and the painDETECT questionnaire to investigate neuropathic pain. Patient information during hospitalization was collected retrospectively from medical records. Results The survey was sent to 78 patients, 63 (81%) of whom responded and were included in the analysis. Nine of the 63 patients (14%) had new-onset chronic pain. Of these, six (67%) had chronic pain of moderate or severe intensity which interfered with daily life. The most frequent location of chronic pain was the foot/ankle (n=4, 44%). Neuropathic pain was present in four (44%) patients with new-onset chronic pain. Conclusion New-onset chronic pain may occur for up to two to 2.5 years after discharge from a tertiary emergency center, and this may interfere with daily life. Therefore, a follow-up system for chronic pain is warranted.

Effectiveness of Nurse-Led Interventions for the Prevention of Mental Health Issues in Patients Leaving Intensive Care: A Systematic Review.

This study aimed to evaluate the effectiveness of nurse-led interventions for the prevention of mental health disorders after intensive care unit discharge through a systematic review of the literature. The searches were conducted in the MEDLINE (via PubMed), CINAHL, PsycINFO, and Cochrane Library databases for studies pertaining to such interventions. Two independent reviewers analyzed the studies, extracted data, and assessed the quality of the evidence. Six eligible articles were identified, all of which were regarding post-traumatic stress disorder after intensive care unit discharge. Some of the interventions were conducted during the admission and some after the discharge. One study found that multimedia education during admission improved anxiety and depression one week after discharge. The remaining five studies concluded that nurse-led interventions did not prevent mental health disorders three months to one year after intensive care unit discharge. Our review revealed a paucity of research into the effectiveness of nurse-led interventions for the prevention of mental health disorders after intensive care unit discharge. The timing and the content of these interventions, and the adequate training of nurses, appear to be key factors. Therefore, multidisciplinary interventions are likely to be more effective than those led by nurses alone.

Unveiling the Biosynthetic Pathway of the Ribosomally Synthesized and Post-translationally Modified Peptide Ustiloxin B in Filamentous Fungi.

The biosynthetic machinery of the first fungal ribosomally synthesized and post-translationally modified peptide (RiPP) ustiloxin B was elucidated through a series of gene inactivation and heterologous expression studies. The results confirmed an essential requirement for novel oxidases possessing the DUF3328 motif for macrocyclization, and highly unique side-chain modifications by three oxidases (UstCF1F2) and a pyridoxal 5'-phosphate (PLP)-dependent enzyme (UstD). These findings provide new insight into the expression of the RiPP gene clusters found in various fungi.

Expression of ustR and the Golgi protease KexB are required for ustiloxin B biosynthesis in Aspergillus oryzae.

Ustiloxin B, originally isolated from the fungus Ustilaginoidea virens, is a known inhibitor of microtubule assembly. Ustiloxin B is also produced by Aspergillus flavus and is synthesized through the ribosomal peptide synthesis pathway. In A. flavus, the gene cluster associated with ustiloxin B production contains 15 genes including those encoding a fungal C6-type transcription factor and ustiloxin B precursor. Although the koji mold Aspergillus oryzae, which is genetically close to A. flavus, has the corresponding gene cluster, it does not produce ustiloxin B, which may be explained by the fact that the gene encoding the transcription factor UstR is not expressed. Here, to investigate whether ustiloxin B can be produced by expressing ustR in A. oryzae, we constructed ustR expression (ustR (EX)) strains and analyzed ustiloxin B production. In the ustR (EX) strains, all genes in the cluster were up-regulated, in line with expression of ustR, and ustiloxin B produced. To elucidate whether the KexB protease is involved in the processing of the ustiloxin B precursor protein UstA, which has repeats of basic amino acid doublets resembling KexB target sites, we also constructed a ustR (EX) strain with the ∆kexB genotype. Although ustR was expressed in this strain, ustiloxin B was barely detectable. This finding strongly suggests that KexB is required for ustiloxin B production.

Class of cyclic ribosomal peptide synthetic genes in filamentous fungi.

Ustiloxins were found recently to be the first example of cyclic peptidyl secondary metabolites that are ribosomally synthesized in filamentous fungi. In this work, two function-unknown genes (ustYa/ustYb) in the gene cluster for ustiloxins from Aspergillus flavus were found experimentally to be involved in cyclization of the peptide. Their homologous genes are observed mainly in filamentous fungi and mushrooms. They have two "HXXHC" motifs that might form active sites. Computational genome analyses showed that these genes are frequently located near candidate genes for ribosomal peptide precursors, which have signal peptides at the N-termini and repeated sequences with core peptides for the cyclic portions, in the genomes of filamentous fungi, particularly Aspergilli, as observed in the ustiloxin gene cluster. Based on the combination of the ustYa/ustYb homologous genes and the nearby ribosomal peptide precursor candidate genes, 94 ribosomal peptide precursor candidates that were identified computationally from Aspergilli genome sequences were classified into more than 40 types including a wide variety of core peptide sequences. A set of the predicted ribosomal peptide biosynthetic genes was experimentally verified to synthesize a new cyclic peptide compound, designated as asperipin-2a, which comprises the amino acid sequence in the corresponding precursor gene, distinct from the ustiloxin precursors.

Creating a specialist protein resource network: a meeting report for the protein bioinformatics and community resources retreat.

During 11-12 August 2014, a Protein Bioinformatics and Community Resources Retreat was held at the Wellcome Trust Genome Campus in Hinxton, UK. This meeting brought together the principal investigators of several specialized protein resources (such as CAZy, TCDB and MEROPS) as well as those from protein databases from the large Bioinformatics centres (including UniProt and RefSeq). The retreat was divided into five sessions: (1) key challenges, (2) the databases represented, (3) best practices for maintenance and curation, (4) information flow to and from large data centers and (5) communication and funding. An important outcome of this meeting was the creation of a Specialist Protein Resource Network that we believe will improve coordination of the activities of its member resources. We invite further protein database resources to join the network and continue the dialogue.

Key challenges for the creation and maintenance of specialist protein resources.

As the volume of data relating to proteins increases, researchers rely more and more on the analysis of published data, thus increasing the importance of good access to these data that vary from the supplemental material of individual articles, all the way to major reference databases with professional staff and long-term funding. Specialist protein resources fill an important middle ground, providing interactive web interfaces to their databases for a focused topic or family of proteins, using specialized approaches that are not feasible in the major reference databases. Many are labors of love, run by a single lab with little or no dedicated funding and there are many challenges to building and maintaining them. This perspective arose from a meeting of several specialist protein resources and major reference databases held at the Wellcome Trust Genome Campus (Cambridge, UK) on August 11 and 12, 2014. During this meeting some common key challenges involved in creating and maintaining such resources were discussed, along with various approaches to address them. In laying out these challenges, we aim to inform users about how these issues impact our resources and illustrate ways in which our working together could enhance their accuracy, currency, and overall value.

EzCatDB: the enzyme reaction database, 2015 update.

The EzCatDB database (http://ezcatdb.cbrc.jp/EzCatDB/) has emphasized manual classification of enzyme reactions from the viewpoints of enzyme active-site structures and their catalytic mechanisms based on literature information, amino acid sequences of enzymes (UniProtKB) and the corresponding tertiary structures from the Protein Data Bank (PDB). Reaction types such as hydrolysis, transfer, addition, elimination, isomerization, hydride transfer and electron transfer have been included in the reaction classification, RLCP. This database includes information related to ligand molecules on the enzyme structures in the PDB data, classified in terms of cofactors, substrates, products and intermediates, which are also necessary to elucidate the catalytic mechanisms. Recently, the database system was updated. The 3D structures of active sites for each PDB entry can be viewed using Jmol or Rasmol software. Moreover, sequence search systems of two types were developed for the EzCatDB database: EzCat-BLAST and EzCat-FORTE. EzCat-BLAST is suitable for quick searches, adopting the BLAST algorithm, whereas EzCat-FORTE is more suitable for detecting remote homologues, adopting the algorithm for FORTE protein structure prediction software. Another system, EzMetAct, is also available to searching for major active-site structures in EzCatDB, for which PDB-formatted queries can be searched.

Ustiloxins, fungal cyclic peptides, are ribosomally synthesized in Ustilaginoidea virens.

MOTIVATION: Ustiloxins A and B are toxic cyclic tetrapeptides, Tyr-Val/Ala-Ile-Gly (Y-V/A-I-G), that were originally identified from Ustilaginoidea virens, a pathogenic fungus affecting rice plants. Contrary to our report that ustiloxin B is ribosomally synthesized in Aspergillus flavus, a recent report suggested that ustiloxins are synthesized by a non-ribosomal peptide synthetase in U.virens. Thus, we analyzed the U.virens genome, to identify the responsible gene cluster. RESULTS: The biosynthetic gene cluster was identified from the genome of U.virens based on homologies to the ribosomal peptide biosynthetic gene cluster for ustiloxin B identified from A.flavus. It contains a gene encoding precursor protein having five Tyr-Val-Ile-Gly and three Tyr-Ala-Ile-Gly motifs for ustiloxins A and B, respectively, strongly indicating that ustiloxins A and B from U.virens are ribosomally synthesized. AVAILABILITY AND IMPLEMENTATION: Accession codes of the U.virens and A.flavus gene clusters in NCBI are BR001221 and BR001206, respectively. Supplementary data are available at Bioinformatics online.

Characterization of the biosynthetic gene cluster for the ribosomally synthesized cyclic peptide ustiloxin B in Aspergillus flavus.

Ustiloxin B is a secondary metabolite known to be produced by Ustilaginoidea virens. In our previous paper, we observed the production of this compound by Aspergillus flavus, and identified two A. flavus genes responsible for ustiloxin B biosynthesis (Umemura et al., 2013). The compound is a cyclic tetrapeptide of Tyr-Ala-Ile-Gly, whose tyrosine is modified with a non-protein coding amino acid, norvaline. Although its chemical structure strongly suggested that ustiloxin B is biosynthesized by a non-ribosomal peptide synthetase, in the present study, we observed its synthesis through a ribosomal peptide synthetic (RiPS) pathway by precise sequence analyses after experimental validation of the cluster. The cluster possessed a gene (AFLA_094980), termed ustA, whose translated product, UstA, contains a 16-fold repeated peptide embedding a tetrapeptide, Tyr-Ala-Ile-Gly, that is converted into the cyclic moiety of ustiloxin B. This result strongly suggests that ustiloxin B is biosynthesized through a RiPS pathway and that UstA provides the precursor peptide of the compound. The present work is the first characterization of RiPS in Ascomycetes and the entire RiPS gene cluster in fungi. Based on the sequence analyses, we also proposed a biosynthetic mechanism involving the entire gene cluster. Our finding indicates the possibility that a number of unidentified RiPSs exist in Ascomycetes as the biosynthetic genes of secondary metabolites, and that the feature of a highly repeated peptide sequence in UstA will greatly contribute to the discovery of additional RiPS.

Prediction of detailed enzyme functions and identification of specificity determining residues by random forests.

Determining enzyme functions is essential for a thorough understanding of cellular processes. Although many prediction methods have been developed, it remains a significant challenge to predict enzyme functions at the fourth-digit level of the Enzyme Commission numbers. Functional specificity of enzymes often changes drastically by mutations of a small number of residues and therefore, information about these critical residues can potentially help discriminate detailed functions. However, because these residues must be identified by mutagenesis experiments, the available information is limited, and the lack of experimentally verified specificity determining residues (SDRs) has hindered the development of detailed function prediction methods and computational identification of SDRs. Here we present a novel method for predicting enzyme functions by random forests, EFPrf, along with a set of putative SDRs, the random forests derived SDRs (rf-SDRs). EFPrf consists of a set of binary predictors for enzymes in each CATH superfamily and the rf-SDRs are the residue positions corresponding to the most highly contributing attributes obtained from each predictor. EFPrf showed a precision of 0.98 and a recall of 0.89 in a cross-validated benchmark assessment. The rf-SDRs included many residues, whose importance for specificity had been validated experimentally. The analysis of the rf-SDRs revealed both a general tendency that functionally diverged superfamilies tend to include more active site residues in their rf-SDRs than in less diverged superfamilies, and superfamily-specific conservation patterns of each functional residue. EFPrf and the rf-SDRs will be an effective tool for annotating enzyme functions and for understanding how enzyme functions have diverged within each superfamily.

MIDDAS-M: motif-independent de novo detection of secondary metabolite gene clusters through the integration of genome sequencing and transcriptome data.

Many bioactive natural products are produced as "secondary metabolites" by plants, bacteria, and fungi. During the middle of the 20th century, several secondary metabolites from fungi revolutionized the pharmaceutical industry, for example, penicillin, lovastatin, and cyclosporine. They are generally biosynthesized by enzymes encoded by clusters of coordinately regulated genes, and several motif-based methods have been developed to detect secondary metabolite biosynthetic (SMB) gene clusters using the sequence information of typical SMB core genes such as polyketide synthases (PKS) and non-ribosomal peptide synthetases (NRPS). However, no detection method exists for SMB gene clusters that are functional and do not include core SMB genes at present. To advance the exploration of SMB gene clusters, especially those without known core genes, we developed MIDDAS-M, a motif-independent de novodetection algorithm for SMB gene clusters. We integrated virtual gene cluster generation in an annotated genome sequence with highly sensitive scoring of the cooperative transcriptional regulation of cluster member genes. MIDDAS-M accurately predicted 38 SMB gene clusters that have been experimentally confirmed and/or predicted by other motif-based methods in 3 fungal strains. MIDDAS-M further identified a new SMB gene cluster for ustiloxin B, which was experimentally validated. Sequence analysis of the cluster genes indicated a novel mechanism for peptide biosynthesis independent of NRPS. Because it is fully computational and independent of empirical knowledge about SMB core genes, MIDDAS-M allows a large-scale, comprehensive analysis of SMB gene clusters, including those with novel biosynthetic mechanisms that do not contain any functionally characterized genes.

Discriminative structural approaches for enzyme active-site prediction.

BACKGROUND: Predicting enzyme active-sites in proteins is an important issue not only for protein sciences but also for a variety of practical applications such as drug design. Because enzyme reaction mechanisms are based on the local structures of enzyme active-sites, various template-based methods that compare local structures in proteins have been developed to date. In comparing such local sites, a simple measurement, RMSD, has been used so far. RESULTS: This paper introduces new machine learning algorithms that refine the similarity/deviation for comparison of local structures. The similarity/deviation is applied to two types of applications, single template analysis and multiple template analysis. In the single template analysis, a single template is used as a query to search proteins for active sites, whereas a protein structure is examined as a query to discover the possible active-sites using a set of templates in the multiple template analysis. CONCLUSIONS: This paper experimentally illustrates that the machine learning algorithms effectively improve the similarity/deviation measurements for both the analyses.

SAHG, a comprehensive database of predicted structures of all human proteins.

Most proteins from higher organisms are known to be multi-domain proteins and contain substantial numbers of intrinsically disordered (ID) regions. To analyse such protein sequences, those from human for instance, we developed a special protein-structure-prediction pipeline and accumulated the products in the Structure Atlas of Human Genome (SAHG) database at http://bird.cbrc.jp/sahg. With the pipeline, human proteins were examined by local alignment methods (BLAST, PSI-BLAST and Smith-Waterman profile-profile alignment), global-local alignment methods (FORTE) and prediction tools for ID regions (POODLE-S) and homology modeling (MODELLER). Conformational changes of protein models upon ligand-binding were predicted by simultaneous modeling using templates of apo and holo forms. When there were no suitable templates for holo forms and the apo models were accurate, we prepared holo models using prediction methods for ligand-binding (eF-seek) and conformational change (the elastic network model and the linear response theory). Models are displayed as animated images. As of July 2010, SAHG contains 42,581 protein-domain models in approximately 24,900 unique human protein sequences from the RefSeq database. Annotation of models with functional information and links to other databases such as EzCatDB, InterPro or HPRD are also provided to facilitate understanding the protein structure-function relationships.

Metric learning for enzyme active-site search.

MOTIVATION: Finding functionally analogous enzymes based on the local structures of active sites is an important problem. Conventional methods use templates of local structures to search for analogous sites, but their performance depends on the selection of atoms for inclusion in the templates. RESULTS: The automatic selection of atoms so that site matches can be discriminated from mismatches. The algorithm provides not only good predictions, but also some insights into which atoms are important for the prediction. Our experimental results suggest that the metric learning automatically provides more effective templates than those whose atoms are selected manually. AVAILABILITY: Online software is available at http://www.net-machine.net/∼kato/lpmetric1/

Relationships between functional subclasses and information contained in active-site and ligand-binding residues in diverse superfamilies.

To investigate the relationships between functional subclasses and sequence and structural information contained in the active-site and ligand-binding residues (LBRs), we performed a detailed analysis of seven diverse enzyme superfamilies: aldolase class I, TIM-barrel glycosidases, alpha/beta-hydrolases, P-loop containing nucleotide triphosphate hydrolases, collagenase, Zn peptidases, and glutamine phosphoribosylpyrophosphate, subunit 1, domain 1. These homologous superfamilies, as defined in CATH, were selected from the enzyme catalytic-mechanism database. We defined active-site and LBRs based solely on the literature information and complex structures in the Protein Data Bank. From a structure-based multiple sequence alignment for each CATH homologous superfamily, we extracted subsequences consisting of the aligned positions that were used as an active-site or a ligand-binding site by at least one sequence. Using both the subsequences and full-length alignments, we performed cluster analysis with three sequence distance measures. We showed that the cluster analysis using the subsequences was able to detect functional subclasses more accurately than the clustering using the full-length alignments. The subsequences determined by only the literature information and complex structures, thus, had sufficient information to detect the functional subclasses. Detailed examination of the clustering results provided new insights into the mechanism of functional diversification for these superfamilies.

Mechanism of translation based on intersubunit complementarities of ribosomal RNAs and tRNAs.

A universal rule is found about nucleotide sequence complementarities between the regions 2653-2666 in the GTPase-binding site of 23S rRNA and 1064-1077 of 16S rRNA as well as between the region 1103-1107 of 16S rRNA and GUUCG (or GUUCA) of tRNAs. This rule holds for all species in the living kingdoms except for two protista mitochondrial rRNAs of Trypanosoma brucei and Plasmodium falciparum. We found that quite similar relationships for the two species hold under the assumption presented in the present paper. The complementarity between T-loop of tRNA and the region 1103-1107 of 16S rRNA suggests that the first interaction of a ribosome with aminoacyl-tRNAEF-TuGTP ternary complex or EF-GGDP complex could occur at the region 1103-1107 of 16S rRNA with the T-loop-D-loop contact region of the ternary complex or the domain IV-V bridge region of the EF-GGDP complex. The second interaction should occur between the A-site codon and the anticodon loop or between the anticodon stem/loop of A-site tRNA and the tip of domain IV of EF-G. The above stepwise interactions would facilitate the collision of the region 1064-1077 of 16S rRNA with the region around A2660 at the alpha-sarcin/ricin loop of 23S rRNA. In this way, the universal rule is capable of explaining how spectinomycin-binding region of 16S rRNA takes part in translocation, how GTPases such as EF-Tu and EF-G can be introduced into their binding site on the large subunit ribosome in proper orientation efficiently and also how driving forces for tRNA movement are produced in translocation and codon recognition. The analysis of T-loops of all tRNAs also presents an evolutionary trend from a random and seemingly primitive sequence, as defined to be Y type, to the most developed structure, such as either 5G7 or 5A7 types in the present definition.

Systematic comparison of catalytic mechanisms of hydrolysis and transfer reactions classified in the EzCatDB database.

Catalytic mechanisms of 270 enzymes from 131 superfamilies, mainly hydrolases and transferases, were analyzed based on their enzyme structures. A method of systematic comparison and classification of the catalytic reactions was developed. Hydrolysis and transfer reactions closely resemble one another, displaying common mechanisms, single displacement, and double displacement. These displacement mechanisms might be further subclassified according to the type of catalytic factors and nucleophilic substitution involved. Several types of catalytic factors exist: nucleophile, acid, base, stabilizer, modulator, cofactors. Nucleophilic substitution might be categorized as S(N)1/S(N)2 (or dissociative/associative) reactions. The classification indicates that some mechanisms favor particular types of catalytic factors. In hydrolyses of amide bonds and phosphoric ester bonds, mechanisms with single displacement tend to use inorganic cofactors such as zinc and magnesium ions as important catalysts, whereas those with double displacement frequently do not use such cofactors. In contrast, hydrolyses of O-glycoside bond rarely use such cofactors, with one exception. The trypsin-like hydrolytic reaction, which is catalyzed by the classic catalytic triad comprising serine/histidine/aspartate, can be considered as a "super-reaction" because it is observed in at least three nonhomologous enzymes, whereas most reactions are singlets without any nonhomologous enzymes. By dividing complex reactions into several reactions, correlations between active site structures and catalytic functions can be suggested. This classification method is applicable to other reactions such as elimination and isomerization. Furthermore, it will facilitate annotation of enzyme functions from 3D patterns of enzyme active sites. The classification is available at http://mbs.cbrc.jp/EzCatDB/RLCP/index.html.

EzCatDB: the Enzyme Catalytic-mechanism Database.

The EzCatDB (Enzyme Catalytic-mechanism Database) specifically includes catalytic mechanisms of enzymes in terms of sequences and tertiary structures of enzymes, and proposed catalytic mechanisms, along with ligand structures. The EzCatDB groups enzyme data in the Protein Data Bank (PDB) and the SWISS-PROT database with identical domain compositions, Enzyme Commission (EC) numbers and catalytic mechanisms. The EzCatDB can be queried by the type of catalytic residue, name and type of ligand molecule that interacts with an enzyme as a cofactor, substrate or product. It can provide literature information, other database codes and EC numbers. The EzCatDB provides ligand annotation for enzymes in the PDB as well as literature information on structure and catalytic mechanisms. Furthermore, the EzCatDB also provides a hierarchic classification of catalytic mechanisms. This classification incorporates catalytic mechanisms and active-site structures of enzymes as well as basic reactions and reactive parts of ligand molecules. The EzCatDB is available at http://mbs.cbrc.jp/EzCatDB/.