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Due to the rapid release of new data from genome sequencing projects, the majority of protein sequences in public databases have not been experimentally characterized; rather, sequences are annotated using computational analysis. The level of misannotation and the types of misannotation in large public databases are currently unknown and have not been analyzed in depth. We have investigated the misannotation levels for molecular function in four public protein sequence databases (UniProtKB/Swiss-Prot, GenBank NR, UniProtKB/TrEMBL, and KEGG) for a model set of 37 enzyme families for which extensive experimental information is available. The manually curated database Swiss-Prot shows the lowest annotation error levels (close to 0% for most families); the two other protein sequence databases (GenBank NR and TrEMBL) and the protein sequences in the KEGG pathways database exhibit similar and surprisingly high levels of misannotation that average 5%-63% across the six superfamilies studied. For 10 of the 37 families examined, the level of misannotation in one or more of these databases is >80%. Examination of the NR database over time shows that misannotation has increased from 1993 to 2005. The types of misannotation that were found fall into several categories, most associated with \"overprediction\" of molecular function. These results suggest that misannotation in enzyme superfamilies containing multiple families that catalyze different reactions is a larger problem than has been recognized. Strategies are suggested for addressing some of the systematic problems contributing to these high levels of misannotation.

Marchantia polymorpha is a basal terrestrial land plant, which like most liverworts accumulates structurally diverse terpenes believed to serve in deterring disease and herbivory. Previous studies have suggested that the mevalonate and methylerythritol phosphate pathways, present in evolutionarily diverged plants, are also operative in liverworts. However, the genes and enzymes responsible for the chemical diversity of terpenes have yet to be described. In this study, we resorted to a HMMER search tool to identify 17 putative terpene synthase genes from M. polymorpha transcriptomes. Functional characterization identified four diterpene synthase genes phylogenetically related to those found in diverged plants and nine rather unusual monoterpene and sesquiterpene synthase-like genes. The presence of separate monofunctional diterpene synthases for ent-copalyl diphosphate and ent-kaurene biosynthesis is similar to orthologs found in vascular plants, pushing the date of the underlying gene duplication and neofunctionalization of the ancestral diterpene synthase gene family to >400 million years ago. By contrast, the mono- and sesquiterpene synthases represent a distinct class of enzymes, not related to previously described plant terpene synthases and only distantly so to microbial-type terpene synthases. The absence of a Mg2+ binding, aspartate-rich, DDXXD motif places these enzymes in a noncanonical family of terpene synthases.

The rapid advance in genome sequencing presents substantial challenges for protein functional assignment, with half or more of new protein sequences inferred from these genomes having uncertain assignments. The assignment of enzyme function in functionally diverse superfamilies represents a particular challenge, which we address through a combination of computational predictions, enzymology, and structural biology. Here we describe the results of a focused investigation of a group of enzymes in the enolase superfamily that are involved in epimerizing dipeptides. The first members of this group to be functionally characterized were Ala-Glu epimerases in Eschericiha coli and Bacillus subtilis, based on the operon context and enzymological studies; these enzymes are presumed to be involved in peptidoglycan recycling. We have subsequently studied more than 65 related enzymes by computational methods, including homology modeling and metabolite docking, which suggested that many would have divergent specificities;, i.e., they are likely to have different (unknown) biological roles. In addition to the Ala-Phe epimerase specificity reported previously, we describe the prediction and experimental verification of: (i) a new group of presumed Ala-Glu epimerases; (ii) several enzymes with specificity for hydrophobic dipeptides, including one from Cytophaga hutchinsonii that epimerizes D-Ala-D-Ala; and (iii) a small group of enzymes that epimerize cationic dipeptides. Crystal structures for certain of these enzymes further elucidate the structural basis of the specificities. The results highlight the potential of computational methods to guide experimental characterization of enzymes in an automated, large-scale fashion.

To study the substrate specificity of enzymes, we use the amidohydrolase and enolase superfamilies as model systems; members of these superfamilies share a common TIM barrel fold and catalyze a wide range of chemical reactions. Here, we describe a collaboration between the Enzyme Specificity Consortium (ENSPEC) and the New York SGX Research Center for Structural Genomics (NYSGXRC) that aims to maximize the structural coverage of the amidohydrolase and enolase superfamilies. Using sequence- and structure-based protein comparisons, we first selected 535 target proteins from a variety of genomes for high-throughput structure determination by X-ray crystallography; 63 of these targets were not previously annotated as superfamily members. To date, 20 unique amidohydrolase and 41 unique enolase structures have been determined, increasing the fraction of sequences in the two superfamilies that can be modeled based on at least 30% sequence identity from 45% to 73%. We present case studies of proteins related to uronate isomerase (an amidohydrolase superfamily member) and mandelate racemase (an enolase superfamily member), to illustrate how this structure-focused approach can be used to generate hypotheses about sequence-structure-function relationships.

Due to the rapid release of new data from genome sequencing projects, the majority of protein sequences in public databases have not been experimentally characterized; rather, sequences are annotated using computational analysis. The level of misannotation and the types of misannotation in large public databases are currently unknown and have not been analyzed in depth. We have investigated the misannotation levels for molecular function in four public protein sequence databases (UniProtKB/Swiss-Prot, GenBank NR, UniProtKB/TrEMBL, and KEGG) for a model set of 37 enzyme families for which extensive experimental information is available. The manually curated database Swiss-Prot shows the lowest annotation error levels (close to 0% for most families); the two other protein sequence databases (GenBank NR and TrEMBL) and the protein sequences in the KEGG pathways database exhibit similar and surprisingly high levels of misannotation that average 5%-63% across the six superfamilies studied. For 10 of the 37 families examined, the level of misannotation in one or more of these databases is >80%. Examination of the NR database over time shows that misannotation has increased from 1993 to 2005. The types of misannotation that were found fall into several categories, most associated with \"overprediction\" of molecular function. These results suggest that misannotation in enzyme superfamilies containing multiple families that catalyze different reactions is a larger problem than has been recognized. Strategies are suggested for addressing some of the systematic problems contributing to these high levels of misannotation.

Empirical audit and review is an approach to assessing the evidentiary value of a research area. It involves identifying a topic and selecting a cross-section of studies for replication. We apply the method to research on the psychological consequences of scarcity. Starting with the papers citing a seminal publication in the field, we conducted replications of 20 studies that evaluate the role of scarcity priming in pain sensitivity, resource allocation, materialism, and many other domains. There was considerable variability in the replicability, with some strong successes and other undeniable failures. Empirical audit and review does not attempt to assign an overall replication rate for a heterogeneous field, but rather facilitates researchers seeking to incorporate strength of evidence as they refine theories and plan new investigations in the research area. This method allows for an integration of qualitative and quantitative approaches to review and enables the growth of a cumulative science.

Pathway docking ( in silico docking of metabolites to several enzymes and binding proteins in a metabolic pathway) enables the discovery of a catabolic pathway for the osmolyte trans -4-hydroxy- l -proline betaine. Structural key to predicting enzyme function Overprediction and database annotation errors in genome-sequencing projects have caused much confusion because of the difficulty of assigning valid functions to the proteins identified. These authors use structure-guided approaches for predicting the substrate specificities of several enzymes encoded by a bacterial gene cluster to correctly predict the in vitro activity of an enzyme of unknown function and identify the catabolic pathway in which it participates in cells. The substrate-liganded pose predicted by virtual library screening was confirmed experimentally, enzyme activities in the predicted pathway were confirmed by in vitro assays and genetic analyses, the intermediates were identified by metabolomics, and repression of the genes encoding the pathway by high salt concentrations was established by transcriptomics. This study establishes the utility of structure-guided functional predictions for the discovery of new metabolic pathways. Assigning valid functions to proteins identified in genome projects is challenging: overprediction and database annotation errors are the principal concerns 1 . We and others 2 are developing computation-guided strategies for functional discovery with ‘metabolite docking’ to experimentally derived 3 or homology-based 4 three-dimensional structures. Bacterial metabolic pathways often are encoded by ‘genome neighbourhoods’ (gene clusters and/or operons), which can provide important clues for functional assignment. We recently demonstrated the synergy of docking and pathway context by ‘predicting’ the intermediates in the glycolytic pathway in Escherichia coli 5 . Metabolite docking to multiple binding proteins and enzymes in the same pathway increases the reliability of in silico predictions of substrate specificities because the pathway intermediates are structurally similar. Here we report that structure-guided approaches for predicting the substrate specificities of several enzymes encoded by a bacterial gene cluster allowed the correct prediction of the in vitro activity of a structurally characterized enzyme of unknown function (PDB 2PMQ), 2-epimerization of trans -4-hydroxy- l -proline betaine (tHyp-B) and cis -4-hydroxy- d -proline betaine (cHyp-B), and also the correct identification of the catabolic pathway in which Hyp-B 2-epimerase participates. The substrate-liganded pose predicted by virtual library screening (docking) was confirmed experimentally. The enzymatic activities in the predicted pathway were confirmed by in vitro assays and genetic analyses; the intermediates were identified by metabolomics; and repression of the genes encoding the pathway by high salt concentrations was established by transcriptomics, confirming the osmolyte role of tHyp-B. This study establishes the utility of structure-guided functional predictions to enable the discovery of new metabolic pathways.

background: Provision of nutrition care for patients with cancer represents a key component of holistic oncology care. However, information is limited about the use and perceptions of registered dietitian-led nutrition care in the oncology setting. objectives: This study aimed to better understand the experiences and expectations of patients and healthcare workers regarding nutrition care during outpatient cancer treatment. methods: Oncology care team members (N = 55) and patients (N = 90) completed a survey about their knowledge of and interest in nutrition care. A subset of participants completed semistructured interviews to capture experiences with and perspectives on nutrition care practices. findings: The majority of patients (n = 73) reported experiencing at least one nutrition impact symptom, but only 14 indicated that they freguently discussed nutrition during provider visits. In addition, 40 oncology care team members indicated freguently discussing nutrition at visits, although 13 were unaware of local nutrition resources.

Metabolic pathways in eubacteria and archaea often are encoded by operons and/or gene clusters (genome neighborhoods) that provide important clues for assignment of both enzyme functions and metabolic pathways. We describe a bioinformatic approach (genome neighborhood network; GNN) that enables large scale prediction of the in vitro enzymatic activities and in vivo physiological functions (metabolic pathways) of uncharacterized enzymes in protein families. We demonstrate the utility of the GNN approach by predicting in vitro activities and in vivo functions in the proline racemase superfamily (PRS; InterPro IPR008794). The predictions were verified by measuring in vitro activities for 51 proteins in 12 families in the PRS that represent ~85% of the sequences; in vitro activities of pathway enzymes, carbon/nitrogen source phenotypes, and/or transcriptomic studies confirmed the predicted pathways. The synergistic use of sequence similarity networks3 and GNNs will facilitate the discovery of the components of novel, uncharacterized metabolic pathways in sequenced genomes. DNA molecules are polymers in which four nucleotides—guanine, adenine, thymine, and cytosine—are arranged along a sugar backbone. The sequence of these four nucleotides along the DNA strand determines the genetic code of the organism, and can be deciphered using various genome sequencing techniques. Microbial genomes are particularly easy to sequence as they contain fewer than several million nucleotides, compared with the 3 billion or so nucleotides that are present in the human genome. Reading a genome sequence is straight forward, but predicting the physiological functions of the proteins encoded by the genes in the sequence can be challenging. In a process called genome annotation, the function of protein is predicted by comparing the relevant gene to the genes of proteins with known functions. However, microbial genomes and proteins are hugely diverse and over 50% of the microbial genomes that have been sequenced have not yet been related to any physiological function. With thousands of microbial genomes waiting to be deciphered, large scale approaches are needed. Zhao et al. take advantage of a particular characteristic of microbial genomes. DNA sequences that code for two proteins required for the same task tend to be closer to each other in the genome than two sequences that code for unrelated functions. Operons are an extreme example; an operon is a unit of DNA that contains several genes that are expressed as proteins at the same time. Zhao et al. have developed a bioinformatic method called the genome neighbourhood network approach to work out the function of proteins based on their position relative to other proteins in the genome. When applied to the proline racemase superfamily (PRS), which contains enzymes with similar sequences that can catalyze three distinct chemical reactions, the new approach was able to assign a function to the majority of proteins in a public database of PRS enzymes, and also revealed new members of the PRS family. Experiments confirmed that the proteins behaved as predicted. The next challenge is to develop the genome neighbourhood network approach so that it can be applied to more complex systems.

PurposeThe purpose of this study was to evaluate patient, oncologist and nurse perspectives on side effects and patient reported outcomes (PROs) with the question of how to optimize side effect management and PRO tools in this unique population.MethodsThis pilot study utilized a mixed method explanatory design. Patients receiving intravenous (IV) chemotherapy from June to August 2020 were surveyed about side effect burden and PRO system preferences. Providers and nurses (PN) completed complementary surveys. Semi-structured phone interviews were conducted among a subset of each group.ResultsOf 90 patient surveys collected; 51.1% minority, 35.6% rural, and 40.0% income < $30,000, 48% felt side effect management was a significant issue. All patients reported access to a communication device but 12.2% did not own a cell phone; 68% smart phone, 20% cell phone, 22% landline, 53% computer, and 39% tablet. Patients preferred a response to reported side effects within 0–3 h (73%) while only 29% of the 55 PN surveyed did (p < 0.0001). Interviews reinforced that side effect burden was a significant issue, the varied communication devices, and a PRO system could improve side effect management.ConclusionIn a non-White, rural and low-income patient population, 87.8% of patients reported owning a cell phone. Although all agreed side effect management was a prominent issue, expectations between patients and PN differed substantially. Qualitative data echoed the above and providing concrete suggestions to inform development of a PRO program and side effect mitigation strategies among a diverse patient population.