EzMechanism: an automated tool to propose catalytic mechanisms of enzyme reactions.

Over the years, hundreds of enzyme reaction mechanisms have been studied using experimental and simulation methods. This rich literature on biological catalysis is now ripe for use as the foundation of new knowledge-based approaches to investigate enzyme mechanisms. Here, we present a tool able to automatically infer mechanistic paths for a given three-dimensional active site and enzyme reaction, based on a set of catalytic rules compiled from the Mechanism and Catalytic Site Atlas, a database of enzyme mechanisms. EzMechanism (pronounced as 'Easy' Mechanism) is available to everyone through a web user interface. When studying a mechanism, EzMechanism facilitates and improves the generation of hypotheses, by making sure that relevant information is considered, as derived from the literature on both related and unrelated enzymes. We validated EzMechanism on a set of 62 enzymes and have identified paths for further improvement, including the need for additional and more generic catalytic rules.

The mission to ensure continued funding for excellent basic research.

The surprising decision by Novo Nordisk Foundation (NNF) to discontinue funding for the Center for Protein Research in Copenhagen should prompt discussions about public and private commitment to support basic research.

Enzyme function and evolution through the lens of bioinformatics.

Enzymes have been shaped by evolution over billions of years to catalyse the chemical reactions that support life on earth. Dispersed in the literature, or organised in online databases, knowledge about enzymes can be structured in distinct dimensions, either related to their quality as biological macromolecules, such as their sequence and structure, or related to their chemical functions, such as the catalytic site, kinetics, mechanism, and overall reaction. The evolution of enzymes can only be understood when each of these dimensions is considered. In addition, many of the properties of enzymes only make sense in the light of evolution. We start this review by outlining the main paradigms of enzyme evolution, including gene duplication and divergence, convergent evolution, and evolution by recombination of domains. In the second part, we overview the current collective knowledge about enzymes, as organised by different types of data and collected in several databases. We also highlight some increasingly powerful computational tools that can be used to close gaps in understanding, in particular for types of data that require laborious experimental protocols. We believe that recent advances in protein structure prediction will be a powerful catalyst for the prediction of binding, mechanism, and ultimately, chemical reactions. A comprehensive mapping of enzyme function and evolution may be attainable in the near future.

GRaSP-web: a machine learning strategy to predict binding sites based on residue neighborhood graphs.

Proteins are essential macromolecules for the maintenance of living systems. Many of them perform their function by interacting with other molecules in regions called binding sites. The identification and characterization of these regions are of fundamental importance to determine protein function, being a fundamental step in processes such as drug design and discovery. However, identifying such binding regions is not trivial due to the drawbacks of experimental methods, which are costly and time-consuming. Here we propose GRaSP-web, a web server that uses GRaSP (Graph-based Residue neighborhood Strategy to Predict binding sites), a residue-centric method based on graphs that uses machine learning to predict putative ligand binding site residues. The method outperformed 6 state-of-the-art residue-centric methods (MCC of 0.61). Also, GRaSP-web is scalable as it takes 10-20 seconds to predict binding sites for a protein complex (the state-of-the-art residue-centric method takes 2-5h on the average). It proved to be consistent in predicting binding sites for bound/unbound structures (MCC 0.61 for both) and for a large dataset of multi-chain proteins (4500 entries, MCC 0.61). GRaSPWeb is freely available at https://grasp.ufv.br.

Cell type-specific modulation of healthspan by Forkhead family transcription factors in the nervous system.

Reduced activity of insulin/insulin-like growth factor signaling (IIS) increases healthy lifespan among diverse animal species. Downstream of IIS, multiple evolutionarily conserved transcription factors (TFs) are required; however, distinct TFs are likely responsible for these effects in different tissues. Here we have asked which TFs can extend healthy lifespan within distinct cell types of the adult nervous system in Drosophila Starting from published single-cell transcriptomic data, we report that forkhead (FKH) is endogenously expressed in neurons, whereas forkhead-box-O (FOXO) is expressed in glial cells. Accordingly, we find that neuronal FKH and glial FOXO exert independent prolongevity effects. We have further explored the role of neuronal FKH in a model of Alzheimer's disease-associated neuronal dysfunction, where we find that increased neuronal FKH preserves behavioral function and reduces ubiquitinated protein aggregation. Finally, using transcriptomic profiling, we identify Atg17, a member of the Atg1 autophagy initiation family, as one FKH-dependent target whose neuronal overexpression is sufficient to extend healthy lifespan. Taken together, our results underscore the importance of cell type-specific mapping of TF activity to preserve healthy function with age.

Fine-tuning autophagy maximises lifespan and is associated with changes in mitochondrial gene expression in Drosophila.

Increased cellular degradation by autophagy is a feature of many interventions that delay ageing. We report here that increased autophagy is necessary for reduced insulin-like signalling (IIS) to extend lifespan in Drosophila and is sufficient on its own to increase lifespan. We first established that the well-characterised lifespan extension associated with deletion of the insulin receptor substrate chico was completely abrogated by downregulation of the essential autophagy gene Atg5. We next directly induced autophagy by over-expressing the major autophagy kinase Atg1 and found that a mild increase in autophagy extended lifespan. Interestingly, strong Atg1 up-regulation was detrimental to lifespan. Transcriptomic and metabolomic approaches identified specific signatures mediated by varying levels of autophagy in flies. Transcriptional upregulation of mitochondrial-related genes was the signature most specifically associated with mild Atg1 upregulation and extended lifespan, whereas short-lived flies, possessing strong Atg1 overexpression, showed reduced mitochondrial metabolism and up-regulated immune system pathways. Increased proteasomal activity and reduced triacylglycerol levels were features shared by both moderate and high Atg1 overexpression conditions. These contrasting effects of autophagy on ageing and differential metabolic profiles highlight the importance of fine-tuning autophagy levels to achieve optimal healthspan and disease prevention.

Differential Complex Formation via Paralogs in the Human Sin3 Protein Interaction Network.

Despite the continued analysis of HDAC inhibitors in clinical trials, the heterogeneous nature of the protein complexes they target limits our understanding of the beneficial and off-target effects associated with their application. Among the many HDAC protein complexes found within the cell, Sin3 complexes are conserved from yeast to humans and likely play important roles as regulators of transcriptional activity. The presence of two Sin3 paralogs in humans, SIN3A and SIN3B, may result in a heterogeneous population of Sin3 complexes and contributes to our poor understanding of the functional attributes of these complexes. Here, we profile the interaction networks of SIN3A and SIN3B to gain insight into complex composition and organization. In accordance with existing data, we show that Sin3 paralog identity influences complex composition. Additionally, chemical cross-linking MS identifies domains that mediate interactions between Sin3 proteins and binding partners. The characterization of rare SIN3B proteoforms provides additional evidence for the existence of conserved and divergent elements within human Sin3 proteins. Together, these findings shed light on both the shared and divergent properties of human Sin3 proteins and highlight the heterogeneous nature of the complexes they organize.

Context dependency of Set1/COMPASS-mediated histone H3 Lys4 trimethylation.

The stimulation of trimethylation of histone H3 Lys4 (H3K4) by H2B monoubiquitination (H2Bub) has been widely studied, with multiple mechanisms having been proposed for this form of histone cross-talk. Cps35/Swd2 within COMPASS (complex of proteins associated with Set1) is considered to bridge these different processes. However, a truncated form of Set1 (762-Set1) is reported to function in H3K4 trimethylation (H3K4me3) without interacting with Cps35/Swd2, and such cross-talk is attributed to the n-SET domain of Set1 and its interaction with the Cps40/Spp1 subunit of COMPASS. Here, we used biochemical, structural, in vivo, and chromatin immunoprecipitation (ChIP) sequencing (ChIP-seq) approaches to demonstrate that Cps40/Spp1 and the n-SET domain of Set1 are required for the stability of Set1 and not the cross-talk. Furthermore, the apparent wild-type levels of H3K4me3 in the 762-Set1 strain are due to the rogue methylase activity of this mutant, resulting in the mislocalization of H3K4me3 from the promoter-proximal regions to the gene bodies and intergenic regions. We also performed detailed screens and identified yeast strains lacking H2Bub but containing intact H2Bub enzymes that have normal levels of H3K4me3, suggesting that monoubiquitination may not directly stimulate COMPASS but rather works in the context of the PAF and Rad6/Bre1 complexes. Our study demonstrates that the monoubiquitination machinery and Cps35/Swd2 function to focus COMPASS's H3K4me3 activity at promoter-proximal regions in a context-dependent manner.

A global analysis of function and conservation of catalytic residues in enzymes.

The catalytic residues of an enzyme comprise the amino acids located in the active center responsible for accelerating the enzyme-catalyzed reaction. These residues lower the activation energy of reactions by performing several catalytic functions. Decades of enzymology research has established general themes regarding the roles of specific residues in these catalytic reactions, but it has been more difficult to explore these roles in a more systematic way. Here, we review the data on the catalytic residues of 648 enzymes, as annotated in the Mechanism and Catalytic Site Atlas (M-CSA), and compare our results with those in previous studies. We structured this analysis around three key properties of the catalytic residues: amino acid type, catalytic function, and sequence conservation in homologous proteins. As expected, we observed that catalysis is mostly accomplished by a small set of residues performing a limited number of catalytic functions. Catalytic residues are typically highly conserved, but to a smaller degree in homologues that perform different reactions or are nonenzymes (pseudoenzymes). Cross-analysis yielded further insights revealing which residues perform particular functions and how often. We obtained more detailed specificity rules for certain functions by identifying the chemical group upon which the residue acts. Finally, we show the mutation tolerance of the catalytic residues based on their roles. The characterization of the catalytic residues, their functions, and conservation, as presented here, is key to understanding the impact of mutations in evolution, disease, and enzyme design. The tools developed for this analysis are available at the M-CSA website and allow for user specific analysis of the same data.

GRaSP: a graph-based residue neighborhood strategy to predict binding sites.

MOTIVATION: The discovery of protein-ligand-binding sites is a major step for elucidating protein function and for investigating new functional roles. Detecting protein-ligand-binding sites experimentally is time-consuming and expensive. Thus, a variety of in silico methods to detect and predict binding sites was proposed as they can be scalable, fast and present low cost. RESULTS: We proposed Graph-based Residue neighborhood Strategy to Predict binding sites (GRaSP), a novel residue centric and scalable method to predict ligand-binding site residues. It is based on a supervised learning strategy that models the residue environment as a graph at the atomic level. Results show that GRaSP made compatible or superior predictions when compared with methods described in the literature. GRaSP outperformed six other residue-centric methods, including the one considered as state-of-the-art. Also, our method achieved better results than the method from CAMEO independent assessment. GRaSP ranked second when compared with five state-of-the-art pocket-centric methods, which we consider a significant result, as it was not devised to predict pockets. Finally, our method proved scalable as it took 10-20 s on average to predict the binding site for a protein complex whereas the state-of-the-art residue-centric method takes 2-5 h on average. AVAILABILITY AND IMPLEMENTATION: The source code and datasets are available at https://github.com/charles-abreu/GRaSP. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Functional conservation in genes and pathways linking ageing and immunity.

At first glance, longevity and immunity appear to be different traits that have not much in common except the fact that the immune system promotes survival upon pathogenic infection. Substantial evidence however points to a molecularly intertwined relationship between the immune system and ageing. Although this link is well-known throughout the animal kingdom, its genetic basis is complex and still poorly understood. To address this question, we here provide a compilation of all genes concomitantly known to be involved in immunity and ageing in humans and three well-studied model organisms, the nematode worm Caenorhabditis elegans, the fruit fly Drosophila melanogaster, and the house mouse Mus musculus. By analysing human orthologs among these species, we identified 7 evolutionarily conserved signalling cascades, the insulin/TOR network, three MAPK (ERK, p38, JNK), JAK/STAT, TGF-ß, and Nf-κB pathways that act pleiotropically on ageing and immunity. We review current evidence for these pathways linking immunity and lifespan, and their role in the detrimental dysregulation of the immune system with age, known as immunosenescence. We argue that the phenotypic effects of these pathways are often context-dependent and vary, for example, between tissues, sexes, and types of pathogenic infection. Future research therefore needs to explore a higher temporal, spatial and environmental resolution to fully comprehend the connection between ageing and immunity.

CATH: expanding the horizons of structure-based functional annotations for genome sequences.

This article provides an update of the latest data and developments within the CATH protein structure classification database (http://www.cathdb.info). The resource provides two levels of release: CATH-B, a daily snapshot of the latest structural domain boundaries and superfamily assignments, and CATH+, which adds layers of derived data, such as predicted sequence domains, functional annotations and functional clustering (known as Functional Families or FunFams). The most recent CATH+ release (version 4.2) provides a huge update in the coverage of structural data. This release increases the number of fully- classified domains by over 40% (from 308 999 to 434 857 structural domains), corresponding to an almost two- fold increase in sequence data (from 53 million to over 95 million predicted domains) organised into 6119 superfamilies. The coverage of high-resolution, protein PDB chains that contain at least one assigned CATH domain is now 90.2% (increased from 82.3% in the previous release). A number of highly requested features have also been implemented in our web pages: allowing the user to view an alignment between their query sequence and a representative FunFam structure and providing tools that make it easier to view the full structural context (multi-domain architecture) of domains and chains.

An automated protocol for modelling peptide substrates to proteases.

BACKGROUND: Proteases are key drivers in many biological processes, in part due to their specificity towards their substrates. However, depending on the family and molecular function, they can also display substrate promiscuity which can also be essential. Databases compiling specificity matrices derived from experimental assays have provided valuable insights into protease substrate recognition. Despite this, there are still gaps in our knowledge of the structural determinants. Here, we compile a set of protease crystal structures with bound peptide-like ligands to create a protocol for modelling substrates bound to protease structures, and for studying observables associated to the binding recognition. RESULTS: As an application, we modelled a subset of protease-peptide complexes for which experimental cleavage data are available to compare with informational entropies obtained from protease-specificity matrices. The modelled complexes were subjected to conformational sampling using the Backrub method in Rosetta, and multiple observables from the simulations were calculated and compared per peptide position. We found that some of the calculated structural observables, such as the relative accessible surface area and the interaction energy, can help characterize a protease's substrate recognition, giving insights for the potential prediction of novel substrates by combining additional approaches. CONCLUSION: Overall, our approach provides a repository of protease structures with annotated data, and an open source computational protocol to reproduce the modelling and dynamic analysis of the protease-peptide complexes.

Influence of aminoacyl-tRNA synthetase complex-interacting multifunctional protein 1 on epithelial differentiation and organization during lung development.

Proper development of the respiratory bronchiole and alveolar epithelium proceeds through coordinated cross talk between the interface of epithelium and neighboring mesenchyme. Signals that facilitate and coordinate the cross talk as the bronchial forming canalicular stage transitions to construction of air-exchanging capillary-alveoli niche in the alveolar stage are poorly understood. Expressed within this decisive region, levels of aminoacyl-tRNA synthetase complex-interacting multifunctional protein 1 (AIMP1) inversely correlate with the maturation of the lung. The present study addresses the role of AIMP1 in lung development through the generation and characterization of Aimp1-/- mutant mice. Mating of Aimp1+/- produced offspring in expected Mendelian ratios throughout embryonic development. However, newborn Aimp1-/- pups exhibited neonatal lethality with mild cyanosis. Imaging both structure and ultrastructure of Aimp1-/- lungs showed disorganized bronchial epithelium, decreased type I but not type II cell differentiation, increased distal vessels, and disruption of E-cadherin deposition in cell-cell junctions. Supporting the in vivo findings of disrupted epithelial cell-cell junctions, in vitro biochemical experiments show that a portion of AIMP1 binds to phosphoinositides, the lipid anchor of proteins that have a fundamental role in both cellular membrane and actin cytoskeleton organization; a dramatic disruption in F-actin cytoskeleton was observed in Aimp1-/- mouse embryonic fibroblasts. Such observed structural defects may lead to disrupted cell-cell boundaries. Together, these results suggest a requirement of AIMP1 in epithelial cell differentiation in proper lung development.

Finding enzyme cofactors in Protein Data Bank.

MOTIVATION: Cofactors are essential for many enzyme reactions. The Protein Data Bank (PDB) contains >67 000 entries containing enzyme structures, many with bound cofactor or cofactor-like molecules. This work aims to identify and categorize these small molecules in the PDB and make it easier to find them. RESULTS: The Protein Data Bank in Europe (PDBe; pdbe.org) has implemented a pipeline to identify enzyme cofactor and cofactor-like molecules, which are now part of the PDBe weekly release process. AVAILABILITY AND IMPLEMENTATION: Information is made available on the individual PDBe entry pages at pdbe.org and programmatically through the PDBe REST API (pdbe.org/api). SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

VarMap: a web tool for mapping genomic coordinates to protein sequence and structure and retrieving protein structural annotations.

MOTIVATION: Understanding the protein structural context and patterning on proteins of genomic variants can help to separate benign from pathogenic variants and reveal molecular consequences. However, mapping genomic coordinates to protein structures is non-trivial, complicated by alternative splicing and transcript evidence. RESULTS: Here we present VarMap, a web tool for mapping a list of chromosome coordinates to canonical UniProt sequences and associated protein 3D structures, including validation checks, and annotating them with structural information. AVAILABILITY AND IMPLEMENTATION: https://www.ebi.ac.uk/thornton-srv/databases/VarMap. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Using the drug-protein interactome to identify anti-ageing compounds for humans.

Advancing age is the dominant risk factor for most of the major killer diseases in developed countries. Hence, ameliorating the effects of ageing may prevent multiple diseases simultaneously. Drugs licensed for human use against specific diseases have proved to be effective in extending lifespan and healthspan in animal models, suggesting that there is scope for drug repurposing in humans. New bioinformatic methods to identify and prioritise potential anti-ageing compounds for humans are therefore of interest. In this study, we first used drug-protein interaction information, to rank 1,147 drugs by their likelihood of targeting ageing-related gene products in humans. Among 19 statistically significant drugs, 6 have already been shown to have pro-longevity properties in animal models (p < 0.001). Using the targets of each drug, we established their association with ageing at multiple levels of biological action including pathways, functions and protein interactions. Finally, combining all the data, we calculated a ranked list of drugs that identified tanespimycin, an inhibitor of HSP-90, as the top-ranked novel anti-ageing candidate. We experimentally validated the pro-longevity effect of tanespimycin through its HSP-90 target in Caenorhabditis elegans.

A Structured Workflow for Mapping Human Sin3 Histone Deacetylase Complex Interactions Using Halo-MudPIT Affinity-Purification Mass Spectrometry.

Although a variety of affinity purification mass spectrometry (AP-MS) strategies have been used to investigate complex interactions, many of these are susceptible to artifacts because of substantial overexpression of the exogenously expressed bait protein. Here we present a logical and systematic workflow that uses the multifunctional Halo tag to assess the correct localization and behavior of tagged subunits of the Sin3 histone deacetylase complex prior to further AP-MS analysis. Using this workflow, we modified our tagging/expression strategy with 21.7% of the tagged bait proteins that we constructed, allowing us to quickly develop validated reagents. Specifically, we apply the workflow to map interactions between stably expressed versions of the Sin3 subunits SUDS3, SAP30, or SAP30L and other cellular proteins. Here we show that the SAP30 and SAP30L paralogues strongly associate with the core Sin3 complex, but SAP30L has unique associations with the proteasome and the myelin sheath. Next, we demonstrate an advancement of the complex NSAF (cNSAF) approach, in which normalization to the scaffold protein SIN3A accounts for variations in the proportion of each bait capturing Sin3 complexes and allows a comparison among different baits capturing the same protein complex. This analysis reveals that although the Sin3 subunit SUDS3 appears to be used in both SIN3A and SIN3B based complexes, the SAP30 subunit is not used in SIN3B based complexes. Intriguingly, we do not detect the Sin3 subunits SAP18 and SAP25 among the 128 high-confidence interactions identified, suggesting that these subunits may not be common to all versions of the Sin3 complex in human cells. This workflow provides the framework for building validated reagents to assemble quantitative interaction networks for chromatin remodeling complexes and provides novel insights into focused protein interaction networks.

Mechanism and Catalytic Site Atlas (M-CSA): a database of enzyme reaction mechanisms and active sites.

M-CSA (Mechanism and Catalytic Site Atlas) is a database of enzyme active sites and reaction mechanisms that can be accessed at www.ebi.ac.uk/thornton-srv/m-csa. Our objectives with M-CSA are to provide an open data resource for the community to browse known enzyme reaction mechanisms and catalytic sites, and to use the dataset to understand enzyme function and evolution. M-CSA results from the merging of two existing databases, MACiE (Mechanism, Annotation and Classification in Enzymes), a database of enzyme mechanisms, and CSA (Catalytic Site Atlas), a database of catalytic sites of enzymes. We are releasing M-CSA as a new website and underlying database architecture. At the moment, M-CSA contains 961 entries, 423 of these with detailed mechanism information, and 538 with information on the catalytic site residues only. In total, these cover 81% (195/241) of third level EC numbers with a PDB structure, and 30% (840/2793) of fourth level EC numbers with a PDB structure, out of 6028 in total. By searching for close homologues, we are able to extend M-CSA coverage of PDB and UniProtKB to 51 993 structures and to over five million sequences, respectively, of which about 40% and 30% have a conserved active site.