Functional Classification Of Proteins Research Articles

PROSES: A Web Server for Sequence-Based Protein Encoding.

Recently, the number of the amino acid sequences shared in online databases is growing rapidly in huge amounts. By using sequence-derived features, machine learning algorithms are successfully applied to prediction of protein functional classes, protein-protein interactions, subcellular location, and peptides of specific properties in many studies. Protein Sequence Encoding System (PROSES) is a web server designed as freely and easily accessible for all researchers who want to use computational methods on protein sequence data. That is, PROSES provides users to encode their protein sequences easily without writing any programming code.

FusoPortal: an Interactive Repository of Hybrid MinION-Sequenced Fusobacterium Genomes Improves Gene Identification and Characterization.

Here we present FusoPortal, an interactive repository of Fusobacterium genomes that were sequenced using a hybrid MinION long-read sequencing pipeline, followed by assembly and annotation using a diverse portfolio of predominantly open-source software. Significant efforts were made to provide genomic and bioinformatic data as downloadable files, including raw sequencing reads, genome maps, gene annotations, protein functional analysis and classifications, and a custom BLAST server for FusoPortal genomes. FusoPortal has been initiated with eight complete genomes, of which seven were previously only drafts that ranged from 24 to 67 contigs. We have showcased that the genomes in FusoPortal provide accurate open reading frame annotations and have corrected a number of large (>3-kb) genes that were previously misannotated due to contig boundaries. In summary, FusoPortal (http://fusoportal.org) is the first database of MinION-sequenced and completely assembled Fusobacterium genomes, and this central Fusobacterium genomic and bioinformatic resource will aid the scientific community in developing a deeper understanding of how this human pathogen contributes to an array of diseases, including periodontitis and colorectal cancer.IMPORTANCE In this report, we describe a hybrid MinION whole-genome sequencing pipeline and the genomic characteristics of the first eight Fusobacterium strains deposited in the FusoPortal database. This collection of highly accurate and complete genomes drastically improves upon previous multicontig assemblies by correcting and newly identifying a significant number of open reading frames. We believe that the availability of this resource will result in the discovery of proteins and molecular mechanisms used by an oral pathogen, with the potential to further our understanding of how Fusobacterium nucleatum contributes to a repertoire of diseases, including periodontitis, preterm birth, and colorectal cancer.

Root proteomic and metabolic analyses reveal specific responses to drought stress in differently tolerant grapevine rootstocks

BackgroundRoots play a central role in plant response to water stress (WS). They are involved in its perception and signalling to the leaf as well as in allowing the plant to adapt to maintaining an adequate water balance. Only a few studies have investigated the molecular/biochemical responses to WS in roots of perennial plants, such as grapevine. This study compares two grapevine rootstock genotypes (i.e. 101.14 and M4) with different tolerance to WS, evaluating the responses at proteomic and metabolite levels.ResultsWS induced changes in the abundance of several proteins in both genotypes (17 and 22% of the detected proteins in 101.14 and M4, respectively). The proteomic analysis revealed changes in many metabolic pathways that fitted well with the metabolite data. M4 showed metabolic responses which were potentially able to counteract the WS effects, such as the drop in cell turgor, increased oxidative stress and loss of cell structure integrity/functionality. However, in 101.14 it was evident that the roots were suffering more severely from these effects. We found that many proteins classified as active in energy metabolism, hormone metabolism, protein, secondary metabolism and stress functional classes showed particular differences between the two rootstocks.ConclusionThe proteomic/metabolite comparative analysis carried out provides new information on the possible biochemical and molecular strategies adopted by grapevine roots to counteract WS. Although further work is needed to define in detail the role(s) of the proteins and metabolites that characterize WS response, this study, involving the M4 rootstock genotype, highlights that osmotic responses, modulations of C metabolism, mitochondrial functionality and some specific responses to stress occurring in the roots play a primary role in Vitis spp. tolerance to this type of abiotic stress.

Initiating Events in Direct Cardiomyocyte Reprogramming

Direct reprogramming of fibroblasts into cardiomyocyte-like cells (iCM) holds great potential for heart regeneration and disease modeling and may lead to future therapeutic applications. Currently, application of this technology is limited by our lack of understanding of the molecular mechanisms that drive direct iCM reprogramming. Using a quantitative mass spectrometry-based proteomic approach, we identified the temporal global changes in protein abundance that occur during initial phases of iCM reprogramming. Collectively, our results show systematic and temporally distinct alterations in levels of specific functional classes of proteins during the initiating steps of reprogramming including extracellular matrix proteins, translation factors, and chromatin-binding proteins. We have constructed protein relational networks associated with the initial transition of a fibroblast into an iCM. These findings demonstrate the presence of an orchestrated series of temporal steps associated with dynamic changes inprotein abundance in a defined group of protein pathways during the initiating events of direct reprogramming.

Site Specific Modification of Adeno-Associated Virus Enables Both Fluorescent Imaging of Viral Particles and Characterization of the Capsid Interactome

Adeno-associated viruses (AAVs) are attractive gene therapy vectors due to their low toxicity, high stability, and rare integration into the host genome. Expressing ligands on the viral capsid can re-target AAVs to new cell types, but limited sites have been identified on the capsid that tolerate a peptide insertion. Here, we incorporated a site-specific tetracysteine sequence into the AAV serotype 9 (AAV9) capsid, to permit labelling of viral particles with either a fluorescent dye or biotin. We demonstrate that fluorescently labelled particles are detectable in vitro, and explore the utility of the method in vivo in mice with time-lapse imaging. We exploit the biotinylated viral particles to generate two distinct AAV interactomes, and identify several functional classes of proteins that are highly represented: actin/cytoskeletal protein binding, RNA binding, RNA splicing/processing, chromatin modifying, intracellular trafficking and RNA transport proteins. To examine the biological relevance of the capsid interactome, we modulated the expression of two proteins from the interactomes prior to AAV transduction. Blocking integrin αVβ6 receptor function reduced AAV9 transduction, while reducing histone deacetylase 4 (HDAC4) expression enhanced AAV transduction. Our method demonstrates a strategy for inserting motifs into the AAV capsid without compromising viral titer or infectivity.

Proteome analysis ofLilium lancifoliumin response to cold stress and abscisic acid induction

Ecological balance and metabolism activity are caused by changes in the expression of differential genes and proteins when plants are subjected to various stresses. Lilium lancifolium is a very important cold-resistant wild flower for lily cold resistance breeding. In order to study the response mechanism to stresses (cold and ABA) in-depth, a proteome study was conducted on the basis of 2-D gel electrophoresis. One hundred and forty-nine significantly differential protein spots were found by DIGE technology, 21 spots with at least 2-fold altered expressions were analyzed by MALDI-TOF and ESI-MS/MS, and 19 proteins were successfully identified. Moreover, the proteins were classified into 'metabolism process' (35.1%), 'response to stress' (20.7%), 'energy' (12.5%), 'signal transduction mechanism' (10.1%), 'others' (10.0%), 'protein binding' (6.2%), and 'biogenesis of cellular component' (5.4%). The functional classification of proteins was almost in accordance with that of genes by RNA-seq, especially the change and damage to the protein structure caused by low temperature, which could be relieved after being treated with ABA. Our study of differentially expressed proteins associated with metabolic pathways furthered the research of cold-resistance proteins and confirmed the consistency in functional classification between differential genes and differential proteins in L. lancifolium. This will be helpful for further study and deeper research of cold-resistance breeding in lily.

Exploring general-purpose protein features for distinguishing enzymes and non-enzymes within the twilight zone

Computational prediction of protein function constitutes one of the more complex problems in Bioinformatics, because of the diversity of functions and mechanisms in that proteins exert in nature. This issue is reinforced especially for proteins that share very low primary or tertiary structure similarity to existing annotated proteomes. In this sense, new alignment-free (AF) tools are needed to overcome the inherent limitations of classic alignment-based approaches to this issue. We have recently introduced AF protein-numerical-encoding programs (TI2BioP and ProtDCal), whose sequence-based features have been successfully applied to detect remote protein homologs, post-translational modifications and antibacterial peptides. Here we aim to demonstrate the applicability of 4 AF protein descriptor families, implemented in our programs, for the identification enzyme-like proteins. At the same time, the use of our novel family of 3D–structure-based descriptors is introduced for the first time. The Dobson & Doig (D&D) benchmark dataset is used for the evaluation of our AF protein descriptors, because of its proven structural diversity that permits one to emulate an experiment within the twilight zone of alignment-based methods (pair-wise identity <30%). The performance of our sequence-based predictor was further assessed using a subset of formerly uncharacterized proteins which currently represent a benchmark annotation dataset. Four protein descriptor families (sequence-composition-based (0D), linear-topology-based (1D), pseudo-fold-topology-based (2D) and 3D–structure features (3D), were assessed using the D&D benchmark dataset. We show that only the families of ProtDCal’s descriptors (0D, 1D and 3D) encode significant information for enzymes and non-enzymes discrimination. The obtained 3D–structure-based classifier ranked first among several other SVM-based methods assessed in this dataset. Furthermore, the model leveraging 1D descriptors, showed a higher success rate than EzyPred on a benchmark annotation dataset from the Shewanella oneidensis proteome. The applicability of ProtDCal as a general-purpose-AF protein modelling method is illustrated through the discrimination between two comprehensive protein functional classes. The observed performances using the highly diverse D&D dataset, and the set of formerly uncharacterized (hard-to-annotate) proteins of Shewanella oneidensis, places our methodology on the top range of methods to model and predict protein function using alignment-free approaches.

A three-way approach for protein function classification

The knowledge of protein functions plays an essential role in understanding biological cells and has a significant impact on human life in areas such as personalized medicine, better crops and improved therapeutic interventions. Due to expense and inherent difficulty of biological experiments, intelligent methods are generally relied upon for automatic assignment of functions to proteins. The technological advancements in the field of biology are improving our understanding of biological processes and are regularly resulting in new features and characteristics that better describe the role of proteins. It is inevitable to neglect and overlook these anticipated features in designing more effective classification techniques. A key issue in this context, that is not being sufficiently addressed, is how to build effective classification models and approaches for protein function prediction by incorporating and taking advantage from the ever evolving biological information. In this article, we propose a three-way decision making approach which provides provisions for seeking and incorporating future information. We considered probabilistic rough sets based models such as Game-Theoretic Rough Sets (GTRS) and Information-Theoretic Rough Sets (ITRS) for inducing three-way decisions. An architecture of protein functions classification with probabilistic rough sets based three-way decisions is proposed and explained. Experiments are carried out on Saccharomyces cerevisiae species dataset obtained from Uniprot database with the corresponding functional classes extracted from the Gene Ontology (GO) database. The results indicate that as the level of biological information increases, the number of deferred cases are reduced while maintaining similar level of accuracy.

Proteome scale identification, classification and structural analysis of iron-binding proteins in bread wheat

Bread wheat is one of the major staple foods of worldwide population and iron plays a significant role in growth and development of the plant. In this report, we are presenting the genome wide identification of iron-binding proteins in bread wheat. The wheat genome derived putative proteome was screened for identification of iron-binding sequence motifs. Out of 602 putative iron-binding proteins, 130 were able to produce reliable structural models by homology techniques and further analyzed for the presence of iron-binding structural motifs. The computationally identified proteins appear to bind to ferrous and ferric ions and showed diverse coordination geometries. Glu, His, Asp and Cys amino acid residues were found to be mostly involved in iron binding. We have classified these proteins on the basis of their localization in the different cellular compartments. The identified proteins were further classified into their protein folds, families and functional classes ranging from structure maintenance of cellular components, regulation of gene expression, post translational modification, membrane proteins, enzymes, signaling and storage proteins. This comprehensive report regarding structural iron binding proteome provides useful insights into the diversity of iron binding proteins of wheat plants and further utilized to study their roles in plant growth, development and physiology.

Resource: Mapping the Triticum aestivum proteome.

Yield and quality improvement of bread wheat (Triticum aestivum) is a focus in efforts to meet new demands from population growth and changing human diets. As the complexity of the wheat genome is unravelled, determining how it is used to build the protein machinery of wheat plants is a key next step in explaining detailed aspects of wheat growth and development. The specific functions of wheat organs during vegetative development and the role of metabolism, protein degradation and remobilisation in driving grain production are the foundations of crop performance and have recently become accessible through studies of the wheat proteome. We present a large scale, publicly accessible proteome mapping of wheat consisting of 24 organ and developmental samples. Tissue specific sub-proteomes and ubiquitously expressed markers of the wheat proteome are identified, alongside hierarchical assessment of protein functional classes, their presence in different tissues and correlations between the abundance of functional classes of proteins. Gene-specific identifications and protein family relationships are accounted for in the organisation of the data and 202 new protein-coding transcripts identified by proteogenomic mapping. The interactive database will serve as a vehicle to build, refine and deposit confirmed targeted proteomic assays for wheat proteins and protein families to assess function (www.wheatproteome.org).

CDD/SPARCLE: functional classification of proteins via subfamily domain architectures.

NCBI's Conserved Domain Database (CDD) aims at annotating biomolecular sequences with the location of evolutionarily conserved protein domain footprints, and functional sites inferred from such footprints. An archive of pre-computed domain annotation is maintained for proteins tracked by NCBI's Entrez database, and live search services are offered as well. CDD curation staff supplements a comprehensive collection of protein domain and protein family models, which have been imported from external providers, with representations of selected domain families that are curated in-house and organized into hierarchical classifications of functionally distinct families and sub-families. CDD also supports comparative analyses of protein families via conserved domain architectures, and a recent curation effort focuses on providing functional characterizations of distinct subfamily architectures using SPARCLE: Subfamily Protein Architecture Labeling Engine. CDD can be accessed at https://www.ncbi.nlm.nih.gov/Structure/cdd/cdd.shtml.

Comparative transcriptome analysis of three color variants of the sea cucumber Apostichopus japonicus

The sea cucumber Apostichopus japonicus Selenka 1867 represents an important resource in biomedical research, traditional medicine, and the seafood industry. Much of the commercial value of A. japonicus is determined by dorsal/ventral color variation (red, green, and black), yet the taxonomic relationships between these color variants are not clearly understood. We performed the first comparative analysis of de novo assembled transcriptome data from three color variants of A. japonicus. Using the Illumina platform, we sequenced nearly 177,596,774 clean reads representing a total of 18.2Gbp of sea cucumber transcriptome. A comparison of over 0.3 million transcript scaffolds against the Uniprot/Swiss-Prot database yielded 8513, 8602, and 8588 positive matches for green, red, and black body color transcriptomes, respectively. Using the Panther gene classification system, we assessed an extensive and diverse set of expressed genes in three color variants and found that (1) among the three color variants of A. japonicus, genes associated with RNA binding protein, oxidoreductase, nucleic acid binding, transferase, and KRAB box transcription factor were most commonly expressed; and (2) the main protein functional classes are differently regulated in all three color variants (extracellular matrix protein and phosphatase for green color, transporter and potassium channel for red color, and G-protein modulator and enzyme modulator for black color). This work will assist in the discovery and annotation of novel genes that play significant morphological and physiological roles in color variants of A. japonicus, and these sequence data will provide a useful set of resources for the rapidly growing sea cucumber aquaculture industry.

Decreasing O‐GlcNAc Levels Reverses Diabetic Nephropathy‐Associated Phenotypes

Diabetic nephropathy (DN) is a leading cause of morbidity and mortality in diabetic patients. DN is characterized by structural changes of the glomeruli in the kidney, such as mesangial cell expansion, podocyte foot process effacement, and apoptosis, with accompanying glomerular hyperfiltration. Hyperglycemia has been shown to initiate these glomeruli structural changes by stimulating flux through the hexosamine biosynthetic pathway (HBP). The major endpoint of the HBP is the formation of uridine diphosphate β‐D‐N‐acetylglucosamine (UDP‐GlcNAc), the donor for protein O‐GlcNAcylation, and complex extracellular glycosylation. O‐GlcNAcylation is a dynamic nutrient sensitive post‐translational modification that is characterized by the addition of single β‐D‐N‐acetylglucosamine (O‐GlcNAc) to the serine and/or threonine residues of almost every functional class of protein. O‐GlcNAc serves as a nutrient/stress sensor regulating signaling, transcription, cytoskeletal dynamics, and cell division. Altered O‐GlcNAc signaling is directly involved in the pathogenesis of diabetes and may have a role in diabetic complications. However, the molecular mechanisms of how hyperglycemia leads to glomerular dysfunction are largely unknown. Here we report that a dynamic sugar‐protein modification, O‐GlcNAcylation, which serves as a nutrient sensor, links hyperglycemia to defects in glomerular structure and function. By targeting the enzymes that either add or remove O‐GlcNAc (OGT and OGA, respectively), we show that O‐GlcNAcylation regulates mesangial ECM production. Increasing O‐GlcNAc distorts gene expression, protein expression, and subcellular distribution of key slit diaphragm specific proteins, whereas decreasing O‐GlcNAcylation, even in hyperglycemic conditions, restore podocytes to normal. Electron microscopy of the slit diaphragm reveals that decreasing O‐GlcNAc rescues the DN‐associated phenotype. This study provides evidence that O‐GlcNAc plays a role in glomerular structure and function and may contribute to the molecular etiology of DN‐associated phenotypes.Support or Funding InformationThis work is supported by NIH grants F32DK100199 to S. Peterson and R01DK61671, P01HL107153, and N01‐HV‐00240 to G. Hart.

PDP-CON: prediction of domain/linker residues in protein sequences using a consensus approach.

The prediction of domain/linker residues in protein sequences is a crucial task in the functional classification of proteins, homology-based protein structure prediction, and high-throughput structural genomics. In this work, a novel consensus-based machine-learning technique was applied for residue-level prediction of the domain/linker annotations in protein sequences using ordered/disordered regions along protein chains and a set of physicochemical properties. Six different classifiers—decision tree, Gaussian naïve Bayes, linear discriminant analysis, support vector machine, random forest, and multilayer perceptron—were exhaustively explored for the residue-level prediction of domain/linker regions. The protein sequences from the curated CATH database were used for training and cross-validation experiments. Test results obtained by applying the developed PDP-CON tool to the mutually exclusive, independent proteins of the CASP-8, CASP-9, and CASP-10 databases are reported. An n-star quality consensus approach was used to combine the results yielded by different classifiers. The average PDP-CON accuracy and F-measure values for the CASP targets were found to be 0.86 and 0.91, respectively. The dataset, source code, and all supplementary materials for this work are available at https://cmaterju.org/cmaterbioinfo/ for noncommercial use.Electronic supplementary materialThe online version of this article (doi:10.1007/s00894-016-2933-0) contains supplementary material, which is available to authorized users.

Cell wall proteome of sugarcane stems: comparison of a destructive and a non-destructive extraction method showed differences in glycoside hydrolases and peroxidases.

BackgroundSugarcane has been used as the main crop for ethanol production for more than 40 years in Brazil. Recently, the production of bioethanol from bagasse and straw, also called second generation (2G) ethanol, became a reality with the first commercial plants started in the USA and Brazil. However, the industrial processes still need to be improved to generate a low cost fuel. One possibility is the remodeling of cell walls, by means of genetic improvement or transgenesis, in order to make the bagasse more accessible to hydrolytic enzymes. We aimed at characterizing the cell wall proteome of young sugarcane culms, to identify proteins involved in cell wall biogenesis. Proteins were extracted from the cell walls of 2-month-old culms using two protocols, non-destructive by vacuum infiltration vs destructive. The proteins were identified by mass spectrometry and bioinformatics.ResultsA predicted signal peptide was found in 84 different proteins, called cell wall proteins (CWPs). As expected, the non-destructive method showed a lower percentage of proteins predicted to be intracellular than the destructive one (33 % vs 44 %). About 19 % of CWPs were identified with both methods, whilst the infiltration protocol could lead to the identification of 75 % more CWPs. In both cases, the most populated protein functional classes were those of proteins related to lipid metabolism and oxido-reductases. Curiously, a single glycoside hydrolase (GH) was identified using the non-destructive method whereas 10 GHs were found with the destructive one. Quantitative data analysis allowed the identification of the most abundant proteins.ConclusionsThe results highlighted the importance of using different protocols to extract proteins from cell walls to expand the coverage of the cell wall proteome. Ten GHs were indicated as possible targets for further studies in order to obtain cell walls less recalcitrant to deconstruction. Therefore, this work contributed to two goals: enlarge the coverage of the sugarcane cell wall proteome, and provide target proteins that could be used in future research to facilitate 2G ethanol production.Electronic supplementary materialThe online version of this article (doi:10.1186/s12870-015-0677-0) contains supplementary material, which is available to authorized users.

PANTHER version 10: expanded protein families and functions, and analysis tools.

PANTHER (Protein Analysis THrough Evolutionary Relationships, http://pantherdb.org) is a widely used online resource for comprehensive protein evolutionary and functional classification, and includes tools for large-scale biological data analysis. Recent development has been focused in three main areas: genome coverage, functional information (‘annotation’) coverage and accuracy, and improved genomic data analysis tools. The latest version of PANTHER, 10.0, includes almost 5000 new protein families (for a total of over 12 000 families), each with a reference phylogenetic tree including protein-coding genes from 104 fully sequenced genomes spanning all kingdoms of life. Phylogenetic trees now include inference of horizontal transfer events in addition to speciation and gene duplication events. Functional annotations are regularly updated using the models generated by the Gene Ontology Phylogenetic Annotation Project. For the data analysis tools, PANTHER has expanded the number of different ‘functional annotation sets’ available for functional enrichment testing, allowing analyses to access all Gene Ontology annotations—updated monthly from the Gene Ontology database—in addition to the annotations that have been inferred through evolutionary relationships. The Prowler (data browser) has been updated to enable users to more efficiently browse the entire database, and to create custom gene lists using the multiple axes of classification in PANTHER.

A Review of Class Imbalance Learning Methods in Bioinformatics

In recent years, research on bioinformatics has increasingly focused on the problem of class imbalance. A classification task is called class imbalance when the number of instances belonging to one class or several classes exceeds that of the other classes. Class imbalance often underestimates the performance of minority classes. This article provides a review of the most widely used class imbalance learning methods and their applications in various bioinformatic problems, including disease diagnosis based on gene expression data and protein mass spectrometry data, translation initiation site recognition based on DNA sequences, protein function classification using amino acid sequences, activities prediction of drug molecules, recognition of precursor microRNA (pre-miRNAs), etc. This article also summarizes the current challenges and future possible trends of class imbalance learning methods in Bioinformatics. Keywords: Activities prediction of drug molecules, bioinformatics, class imbalance, gene expression, protein function classification, protein mass spectrometry, recognition of precursor microRNA, translation initiation site recognition.

Comparative phosphoproteomic analysis of intestinal phosphorylated proteins in active versus aestivating sea cucumbers

The sea cucumber Apostichopus japonicus is becoming an excellent model marine invertebrate for studies of environmentally-induced aestivation. Reversible protein phosphorylation as a regulatory mechanism in aestivation is known for some terrestrial aestivators but has never before been documented in sea cucumbers. The present study provides a global quantitative analysis of the role of reversible phosphorylation in sea cucumber aestivation by using tandem mass tag (TMT) labeling followed by an IMAC enrichment strategy to map aestivation-responsive changes in the phosphoproteome of sea cucumber intestine. We identified 2295 unique phosphosites derived from 1283 phosphoproteins and, of these, 211 hyperphosphorylated and 65 hypophosphorylated phosphoproteins were identified in intestine during deep aestivation compared with the active state based on the following criterion: quantitative ratios over 1.5 or less than 0.67 with corrected p-value <0.05. Six major functional classes of proteins exhibited changes in their phosphorylation status during aestivation: (1) protein synthesis, (2) transcriptional regulators, (3) kinases, (4) signaling, (5) transporter, (6) DNA binding. These data on the global involvement of phosphorylation in sea cucumber aestivation significantly improve our understanding of the regulatory mechanisms involved in metabolic arrest when marine invertebrates face environmental stress and provide substantial candidate phosphorylated proteins that could be important for identifying functionally adaptive variation in marine invertebrates. SignificanceSea cucumber Apostichopus japonicus is an excellent model organism for studies of environmentally-induced aestivation by a marine invertebrate. The present study provides the first quantitative phosphoproteomic analysis of sea cucumber aestivation using isobaric tag based TMT labeling followed by an IMAC enrichment strategy. These data on the global involvement of phosphorylation in sea cucumber aestivation significantly improve our understanding of the regulatory mechanism involved in metabolic arrest when marine invertebrates face environmental stress and provide substantial candidate phosphorylated proteins that could be important for identifying functionally adaptive variation in marine invertebrates. This study also demonstrates the usefulness of the TMT-based quantitative phosphoproteomics approach to explore the survival responses of a non-model marine invertebrate species to seasonal changes in its environment.

Functional classification of CATH superfamilies: a domain-based approach for protein function annotation.

Motivation: Computational approaches that can predict protein functions are essential to bridge the widening function annotation gap especially since <1.0% of all proteins in UniProtKB have been experimentally characterized. We present a domain-based method for protein function classification and prediction of functional sites that exploits functional sub-classification of CATH superfamilies. The superfamilies are sub-classified into functional families (FunFams) using a hierarchical clustering algorithm supervised by a new classification method, FunFHMMer.Results: FunFHMMer generates more functionally coherent groupings of protein sequences than other domain-based protein classifications. This has been validated using known functional information. The conserved positions predicted by the FunFams are also found to be enriched in known functional residues. Moreover, the functional annotations provided by the FunFams are found to be more precise than other domain-based resources. FunFHMMer currently identifies 110 439 FunFams in 2735 superfamilies which can be used to functionally annotate > 16 million domain sequences.Availability and implementation: All FunFam annotation data are made available through the CATH webpages (http://www.cathdb.info). The FunFHMMer webserver (http://www.cathdb.info/search/by_funfhmmer) allows users to submit query sequences for assignment to a CATH FunFam.Contact: sayoni.das.12@ucl.ac.ukSupplementary information: Supplementary data are available at Bioinformatics online.

A Universal Stress Protein (USP) in Mycobacteria Binds cAMP

Mycobacteria are endowed with rich and diverse machinery for the synthesis, utilization, and degradation of cAMP. The actions of cyclic nucleotides are generally mediated by binding of cAMP to conserved and well characterized cyclic nucleotide binding domains or structurally distinct cGMP-specific and -regulated cyclic nucleotide phosphodiesterase, adenylyl cyclase, and E. coli transcription factor FhlA (GAF) domain-containing proteins. Proteins with cyclic nucleotide binding and GAF domains can be identified in the genome of mycobacterial species, and some of them have been characterized. Here, we show that a significant fraction of intracellular cAMP is bound to protein in mycobacterial species, and by using affinity chromatography techniques, we identify specific universal stress proteins (USP) as abundantly expressed cAMP-binding proteins in slow growing as well as fast growing mycobacteria. We have characterized the biochemical and thermodynamic parameters for binding of cAMP, and we show that these USPs bind cAMP with a higher affinity than ATP, an established ligand for other USPs. We determined the structure of the USP MSMEG_3811 bound to cAMP, and we confirmed through structure-guided mutagenesis, the residues important for cAMP binding. This family of USPs is conserved in all mycobacteria, and we suggest that they serve as "sinks" for cAMP, making this second messenger available for downstream effectors as and when ATP levels are altered in the cell.