firestar--advances in the prediction of functionally important residues

ProCogGraph: a graph-based mapping of cognate ligand domain interactions

Motivation

Mappings of domain-cognate ligand interactions can enhance our understanding of the core concepts of evolution and be used to aid docking and protein design. Since the last available cognate-ligand domain database was released, the PDB has grown significantly and new tools are available for measuring similarity and determining contacts.

Results

We present ProCogGraph, a graph database of cognate-ligand domain mappings in PDB structures. Building upon the work of the predecessor database, PROCOGNATE, we use data-driven approaches to develop thresholds and interaction modes. We explore new aspects of domain-cognate ligand interactions, including the chemical similarity of bound cognate ligands and how domain combinations influence cognate ligand binding. Finally, we use the graph to add specificity to partial EC IDs, showing that ProCogGraph can complete partial annotations systematically through assigned cognate ligands.

Availability and implementation

The ProCogGraph pipeline, database and flat files are available at https://github.com/bashton-lab/ProCogGraph and https://doi.org/10.5281/zenodo.13165851.

GPR176 promotes fibroblast-to-myofibroblast transition in organ fibrosis progression

Fibrosis is characterized by excessive deposition of extracellular matrix proteins, particularly collagen, caused by myofibroblasts in response to chronic inflammation. Although G protein-coupled receptors (GPCRs) are among the targets of current antifibrotic drugs, no drug has yet been approved to stop fibrosis progression. Herein, we aimed to identify GPCRs with profibrotic effects. In gene expression analysis of mouse lungs with induced fibrosis, eight GPCRs were identified, showing a >2-fold increase in mRNA expression after fibrosis induction. Among them, we focused on Gpr176 owing to its significant correlation with a myofibroblast marker α-smooth muscle actin (αSMA), the profibrotic factor transforming growth factor β1 (TGFβ1), and collagen in a human lung gene expression database. Similar to the lung fibrosis model, increased Gpr176 expression was also observed in other organs affected by fibrosis, including the kidney, liver, and heart, suggesting its role in fibrosis across various organs. Furthermore, fibroblasts abundantly expressed Gpr176 compared to alveolar epithelial cells, endothelial cells, and macrophages in the fibrotic lung. GPR176 expression was unaffected by TGFβ1 stimulation in rat renal fibroblast NRK-49 cells, whereas knockdown of Gpr176 by siRNA reduced TGFβ1-induced expression of αSMA, fibronectin, and collagen as well as Smad2 phosphorylation. This suggested that Gpr176 regulates fibroblast activation. Consequently, Gpr176 acts in a profibrotic manner, and inhibiting its activity could potentially prevent myofibroblast differentiation and improve fibrosis. Developing a GPR176 inverse agonist or allosteric modulator is a promising therapeutic approach for fibrosis.

Bioinformatics and bioactive peptides from foods: Do they work together?

We live in the Big Data Era which affects many aspects of science, including research on bioactive peptides derived from foods, which during the last few decades have been a focus of interest for scientists. These two issues, i.e., the development of computer technologies and progress in the discovery of novel peptides with health-beneficial properties, are closely interrelated. This Chapter presents the example applications of bioinformatics for studying biopeptides, focusing on main aspects of peptide analysis as the starting point, including: (i) the role of peptide databases; (ii) aspects of bioactivity prediction; (iii) simulation of peptide release from proteins. Bioinformatics can also be used for predicting other features of peptides, including ADMET, QSAR, structure, and taste. To answer the question asked "bioinformatics and bioactive peptides from foods: do they work together?", currently it is almost impossible to find examples of peptide research with no bioinformatics involved. However, theoretical predictions are not equivalent to experimental work and always require critical scrutiny. The aspects of compatibility of in silico and in vitro results are also summarized herein.

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.

An Outstandingly Rare Occurrence of Mycoviruses in Soil Strains of the Plant-Beneficial Fungi from the Genus Trichoderma and a Novel Polymycoviridae Isolate

In fungi, viral infections frequently remain cryptic causing little or no phenotypic changes. It can indicate either a long history of coevolution or a strong immune system of the host. Some fungi are outstandingly ubiquitous and can be recovered from a great diversity of habitats. However, the role of viral infection in the emergence of environmental opportunistic species is not known. The genus of filamentous and mycoparasitic fungi Trichoderma (Hypocreales, Ascomycota) consists of more than 400 species, which mainly occur on dead wood, other fungi, or as endo- and epiphytes. However, some species are environmental opportunists because they are cosmopolitan, can establish in a diversity of habitats, and can also become pests on mushroom farms and infect immunocompromised humans. In this study, we investigated the library of 163 Trichoderma strains isolated from grassland soils in Inner Mongolia, China, and found only four strains with signs of the mycoviral nucleic acids, including a strain of T. barbatum infected with a novel strain of the Polymycoviridae and named and characterized here as Trichoderma barbatum polymycovirus 1 (TbPMV1). Phylogenetic analysis suggested that TbPMV1 was evolutionarily distinct from the Polymycoviridae isolated either from Eurotialean fungi or from the order Magnaportales. Although the Polymycoviridae viruses were also known from Hypocrealean Beauveria bassiana, the phylogeny of TbPMV1 did not reflect the phylogeny of the host. Our analysis lays the groundwork for further in-depth characterization of TbPMV1 and the role of mycoviruses in the emergence of environmental opportunism in Trichoderma. IMPORTANCE Although viruses infect all organisms, our knowledge of some groups of eukaryotes remains limited. For instance, the diversity of viruses infecting fungi-mycoviruses-is largely unknown. However, the knowledge of viruses associated with industrially relevant and plant-beneficial fungi, such as Trichoderma spp. (Hypocreales, Ascomycota), may shed light on the stability of their phenotypes and the expression of beneficial traits. In this study, we screened the library of soilborne Trichoderma strains because these isolates may be developed into bioeffectors for plant protection and sustainable agriculture. Notably, the diversity of endophytic viruses in soil Trichoderma was outstandingly low. Only 2% of 163 strains contained traces of dsRNA viruses, including the new Trichoderma barbatum polymycovirus 1 (TbPMV1) characterized in this study. TbPMV1 is the first mycovirus found in Trichoderma. Our results indicate that the limited data prevent the in-depth study of the evolutionary relationship between soilborne fungi and is worth further investigation.

Interactive Analysis of Functional Residues in Protein Families

A protein's function depends on functional residues that determine its binding specificity or its catalytic activity, but these residues are typically not considered when annotating a protein's function. To help biologists investigate the functional residues of proteins, we developed two interactive web-based tools, SitesBLAST and Sites on a Tree. Given a protein sequence, SitesBLAST finds homologs that have known functional residues and shows whether the functional residues are conserved. Sites on a Tree shows how functional residues vary across a protein family by showing them on a phylogenetic tree. These tools are available at http://papers.genomics.lbl.gov/sites. IMPORTANCE For most microbes of interest, a genome sequence is available, but the function of its proteins is not known. Instead, proteins' functions are predicted from their similarity to other protein sequences. Within a protein's sequence, a few key residues are most important for function, such as catalyzing a chemical reaction or determining what it binds. But most function prediction tools do not take these key residues into account. We developed interactive tools for identifying functional residues in a protein sequence by comparing it to proteins with known functional residues. Our tools also make it easy to compare key residues across many similar proteins. This should help biologists check if a protein's function is predicted correctly, or to predict if groups of similar proteins have conserved functions.

Origins and Evolution of Human Tandem Duplicated Exon Substitution Events

The mutually exclusive splicing of tandem duplicated exons produces protein isoforms that are identical save for a homologous region that allows for the fine tuning of protein function. Tandem duplicated exon substitution events are rare, yet highly important alternative splicing events. Most events are ancient, their isoforms are highly expressed, and they have significantly more pathogenic mutations than other splice events. Here, we analyzed the physicochemical properties and functional roles of the homologous polypeptide regions produced by the 236 tandem duplicated exon substitutions annotated in the human gene set. We find that the most important structural and functional residues in these homologous regions are maintained, and that most changes are conservative rather than drastic. Three quarters of the isoforms produced from tandem duplicated exon substitution events are tissue-specific, particularly in nervous and cardiac tissues, and tandem duplicated exon substitution events are enriched in functional terms related to structures in the brain and skeletal muscle. We find considerable evidence for the convergent evolution of tandem duplicated exon substitution events in vertebrates, arthropods, and nematodes. Twelve human gene families have orthologues with tandem duplicated exon substitution events in both Drosophila melanogaster and Caenorhabditis elegans. Six of these gene families are ion transporters, suggesting that tandem exon duplication in genes that control the flow of ions into the cell has an adaptive benefit. The ancient origins, the strong indications of tissue-specific functions, and the evidence of convergent evolution suggest that these events may have played important roles in the evolution of animal tissues and organs.

APPRIS principal isoforms and MANE Select transcripts define reference splice variants

MOTIVATION

Selecting the splice variant that best represents a coding gene is a crucial first step in many experimental analyses, and vital for mapping clinically relevant variants. This study compares the longest isoforms, MANE Select transcripts, APPRIS principal isoforms, and expression data, and aims to determine which method is best for selecting biological important reference splice variants for large-scale analyses.

RESULTS

Proteomics analyses and human genetic variation data suggest that most coding genes have a single main protein isoform. We show that APPRIS principal isoforms and MANE Select transcripts best describe these main cellular isoforms, and find that using the longest splice variant as the representative is a poor strategy. Exons unique to the longest splice isoforms are not under selective pressure, and so are unlikely to be functionally relevant. Expression data are also a poor means of selecting the main splice variant. APPRIS principal and MANE Select exons are under purifying selection, while exons specific to alternative transcripts are not. There are MANE and APPRIS representatives for almost 95% of genes, and where they agree they are particularly effective, coinciding with the main proteomics isoform for over 98.2% of genes.

AVAILABILITY AND IMPLEMENTATION

APPRIS principal isoforms for human, mouse and other model species can be downloaded from the APPRIS database (https://appris.bioinfo.cnio.es), GENCODE genes (https://www.gencodegenes.org/) and the Ensembl website (https://www.ensembl.org). MANE Select transcripts for the human reference set are available from the Ensembl, GENCODE and RefSeq databases (https://www.ncbi.nlm.nih.gov/refseq/). Lists of splice variants where MANE and APPRIS coincide are available from the APPRIS database.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

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.

3DLigandSite: structure-based prediction of protein-ligand binding sites

3DLigandSite is a web tool for the prediction of ligand-binding sites in proteins. Here, we report a significant update since the first release of 3DLigandSite in 2010. The overall methodology remains the same, with candidate binding sites in proteins inferred using known binding sites in related protein structures as templates. However, the initial structural modelling step now uses the newly available structures from the AlphaFold database or alternatively Phyre2 when AlphaFold structures are not available. Further, a sequence-based search using HHSearch has been introduced to identify template structures with bound ligands that are used to infer the ligand-binding residues in the query protein. Finally, we introduced a machine learning element as the final prediction step, which improves the accuracy of predictions and provides a confidence score for each residue predicted to be part of a binding site. Validation of 3DLigandSite on a set of 6416 binding sites obtained 92% recall at 75% precision for non-metal binding sites and 52% recall at 75% precision for metal binding sites. 3DLigandSite is available at https://www.wass-michaelislab.org/3dligandsite. Users submit either a protein sequence or structure. Results are displayed in multiple formats including an interactive Mol* molecular visualization of the protein and the predicted binding sites.

APPRIS: selecting functionally important isoforms

APPRIS (https://appris.bioinfo.cnio.es) is a well-established database housing annotations for protein isoforms for a range of species. APPRIS selects principal isoforms based on protein structure and function features and on cross-species conservation. Most coding genes produce a single main protein isoform and the principal isoforms chosen by the APPRIS database best represent this main cellular isoform. Human genetic data, experimental protein evidence and the distribution of clinical variants all support the relevance of APPRIS principal isoforms. APPRIS annotations and principal isoforms have now been expanded to 10 model organisms. In this paper we highlight the most recent updates to the database. APPRIS annotations have been generated for two new species, cow and chicken, the protein structural information has been augmented with reliable models from the EMBL-EBI AlphaFold database, and we have substantially expanded the confirmatory proteomics evidence available for the human genome. The most significant change in APPRIS has been the implementation of TRIFID functional isoform scores. TRIFID functional scores are assigned to all splice isoforms, and APPRIS uses the TRIFID functional scores and proteomics evidence to determine principal isoforms when core methods cannot.

Evidence for the preferential reuse of sub-domain motifs in primordial protein folds

A comparison of protein backbones makes clear that not more than approximately 1400 different folds exist, each specifying the three-dimensional topology of a protein domain. Large proteins are composed of specific domain combinations and many domains can accommodate different functions. These findings confirm that the reuse of domains is key for the evolution of multi-domain proteins. If reuse was also the driving force for domain evolution, ancestral fragments of sub-domain size exist that are shared between domains possessing significantly different topologies. For the fully automated detection of putatively ancestral motifs, we developed the algorithm Fragstatt that compares proteins pairwise to identify fragments, that is, instantiations of the same motif. To reach maximal sensitivity, Fragstatt compares sequences by means of cascaded alignments of profile Hidden Markov Models. If the fragment sequences are sufficiently similar, the program determines and scores the structural concordance of the fragments. By analyzing a comprehensive set of proteins from the CATH database, Fragstatt identified 12 532 partially overlapping and structurally similar motifs that clustered to 134 unique motifs. The dissemination of these motifs is limited: We found only two domain topologies that contain two different motifs and generally, these motifs occur in not more than 18% of the CATH topologies. Interestingly, motifs are enriched in topologies that are considered ancestral. Thus, our findings suggest that the reuse of sub-domain sized fragments was relevant in early phases of protein evolution and became less important later on.

Genetic variants of small airways and interstitial pulmonary disease in children

Genetic variants of small airways and interstitial pulmonary disease have not been comprehensively studied. This cluster of respiratory disorders usually manifests from early infancy ('lung disease in utero'). In this study, 24 variants linked to these entities are described. The variants involved two genes associated with surfactant metabolism dysfunction (ABCA3 and CSF2RB), two with pulmonary fibrosis (MUC5B and SFTP), one with bronchiectasis (SCNN1B), and one with alpha-1-antitrypsin deficiency (SERPINA1). A nonsense variant, MUC5B:c.16861G > T, p.Glu5621*, was found in homozygous state in two siblings with severe respiratory disease from birth. One of the siblings also had heterozygous SFTPA1:c.675C > G, p.Asn225Lys, which resulted in a more severe respiratory disease. The sibling with only the homozygous MUC5B variant had lung biopsy, which showed alveolar simplification, interstitial fibrosis, intra-alveolar lipid-laden macrophages, and foci of foreign body giant cell reaction in distal airspaces. Two missense variants, MUC5B:c.14936 T > C, p.Ile4979Thr (rs201287218) and MUC5B:c.16738G > A, p.Gly5580Arg (rs776709402), were also found in compound heterozygous state in two siblings with severe respiratory disease from birth. Overall, the results emphasize the need for genetic studies for patients with complex respiratory problems. Identifying pathogenic variants, such as those presented here, assists in effective family counseling aimed at genetic prevention. In addition, results of genetic studies improve the clinical care and provide opportunities for participating in clinical trials, such as those involving molecularly-targeted therapies.

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.

APPRIS 2017: principal isoforms for multiple gene sets

The APPRIS database (http://appris-tools.org) uses protein structural and functional features and information from cross-species conservation to annotate splice isoforms in protein-coding genes. APPRIS selects a single protein isoform, the ‘principal’ isoform, as the reference for each gene based on these annotations. A single main splice isoform reflects the biological reality for most protein coding genes and APPRIS principal isoforms are the best predictors of these main proteins isoforms. Here, we present the updates to the database, new developments that include the addition of three new species (chimpanzee, Drosophila melangaster and Caenorhabditis elegans), the expansion of APPRIS to cover the RefSeq gene set and the UniProtKB proteome for six species and refinements in the core methods that make up the annotation pipeline. In addition APPRIS now provides a measure of reliability for individual principal isoforms and updates with each release of the GENCODE/Ensembl and RefSeq reference sets. The individual GENCODE/Ensembl, RefSeq and UniProtKB reference gene sets for six organisms have been merged to produce common sets of splice variants.

Loose ends: almost one in five human genes still have unresolved coding status

Seventeen years after the sequencing of the human genome, the human proteome is still under revision. One in eight of the 22 210 coding genes listed by the Ensembl/GENCODE, RefSeq and UniProtKB reference databases are annotated differently across the three sets. We have carried out an in-depth investigation on the 2764 genes classified as coding by one or more sets of manual curators and not coding by others. Data from large-scale genetic variation analyses suggests that most are not under protein-like purifying selection and so are unlikely to code for functional proteins. A further 1470 genes annotated as coding in all three reference sets have characteristics that are typical of non-coding genes or pseudogenes. These potential non-coding genes also appear to be undergoing neutral evolution and have considerably less supporting transcript and protein evidence than other coding genes. We believe that the three reference databases currently overestimate the number of human coding genes by at least 2000, complicating and adding noise to large-scale biomedical experiments. Determining which potential non-coding genes do not code for proteins is a difficult but vitally important task since the human reference proteome is a fundamental pillar of most basic research and supports almost all large-scale biomedical projects.

Environmental conditions shape the nature of a minimal bacterial genome

Of the 473 genes in the genome of the bacterium with the smallest genome generated to date, 149 genes have unknown function, emphasising a universal problem; less than 1% of proteins have experimentally determined annotations. Here, we combine the results from state-of-the-art in silico methods for functional annotation and assign functions to 66 of the 149 proteins. Proteins that are still not annotated lack orthologues, lack protein domains, and/ or are membrane proteins. Twenty-four likely transporter proteins are identified indicating the importance of nutrient uptake into and waste disposal out of the minimal bacterial cell in a nutrient-rich environment after removal of metabolic enzymes. Hence, the environment shapes the nature of a minimal genome. Our findings also show that the combination of multiple different state-of-the-art in silico methods for annotating proteins is able to predict functions, even for difficult to characterise proteins and identify crucial gaps for further development.

Predicting protein-ligand binding residues with deep convolutional neural networks

Background

Ligand-binding proteins play key roles in many biological processes. Identification of protein-ligand binding residues is important in understanding the biological functions of proteins. Existing computational methods can be roughly categorized as sequence-based or 3D-structure-based methods. All these methods are based on traditional machine learning. In a series of binding residue prediction tasks, 3D-structure-based methods are widely superior to sequence-based methods. However, due to the great number of proteins with known amino acid sequences, sequence-based methods have considerable room for improvement with the development of deep learning. Therefore, prediction of protein-ligand binding residues with deep learning requires study.

Results

In this study, we propose a new sequence-based approach called DeepCSeqSite for ab initio protein-ligand binding residue prediction. DeepCSeqSite includes a standard edition and an enhanced edition. The classifier of DeepCSeqSite is based on a deep convolutional neural network. Several convolutional layers are stacked on top of each other to extract hierarchical features. The size of the effective context scope is expanded as the number of convolutional layers increases. The long-distance dependencies between residues can be captured by the large effective context scope, and stacking several layers enables the maximum length of dependencies to be precisely controlled. The extracted features are ultimately combined through one-by-one convolution kernels and softmax to predict whether the residues are binding residues. The state-of-the-art ligand-binding method COACH and some of its submethods are selected as baselines. The methods are tested on a set of 151 nonredundant proteins and three extended test sets. Experiments show that the improvement of the Matthews correlation coefficient (MCC) is no less than 0.05. In addition, a training data augmentation method that slightly improves the performance is discussed in this study.

Conclusions

Without using any templates that include 3D-structure data, DeepCSeqSite significantlyoutperforms existing sequence-based and 3D-structure-based methods, including COACH. Augmentation of the training sets slightly improves the performance. The model, code and datasets are available at https://github.com/yfCuiFaith/DeepCSeqSite.

Associating mutations causing cystinuria with disease severity with the aim of providing precision medicine

Background

Cystinuria is an inherited disease that results in the formation of cystine stones in the kidney, which can have serious health complications. Two genes (SLC7A9 and SLC3A1) that form an amino acid transporter are known to be responsible for the disease. Variants that cause the disease disrupt amino acid transport across the cell membrane, leading to the build-up of relatively insoluble cystine, resulting in formation of stones. Assessing the effects of each mutation is critical in order to provide tailored treatment options for patients. We used various computational methods to assess the effects of cystinuria associated mutations, utilising information on protein function, evolutionary conservation and natural population variation of the two genes. We also analysed the ability of some methods to predict the phenotypes of individuals with cystinuria, based on their genotypes, and compared this to clinical data.

Results

Using a literature search, we collated a set of 94 SLC3A1 and 58 SLC7A9 point mutations known to be associated with cystinuria. There are differences in sequence location, evolutionary conservation, allele frequency, and predicted effect on protein function between these mutations and other genetic variants of the same genes that occur in a large population. Structural analysis considered how these mutations might lead to cystinuria. For SLC7A9, many mutations swap hydrophobic amino acids for charged amino acids or vice versa, while others affect known functional sites. For SLC3A1, functional information is currently insufficient to make confident predictions but mutations often result in the loss of hydrogen bonds and largely appear to affect protein stability. Finally, we showed that computational predictions of mutation severity were significantly correlated with the disease phenotypes of patients from a clinical study, despite different methods disagreeing for some of their predictions.

Conclusions

The results of this study are promising and highlight the areas of research which must now be pursued to better understand how mutations in SLC3A1 and SLC7A9 cause cystinuria. The application of our approach to a larger data set is essential, but we have shown that computational methods could play an important role in designing more effective personalised treatment options for patients with cystinuria.

Electronic supplementary material

The online version of this article (doi:10.1186/s12864-017-3913-1) contains supplementary material, which is available to authorized users.

Annotating Protein Functional Residues by Coupling High-Throughput Fitness Profile and Homologous-Structure Analysis

Identification and annotation of functional residues are fundamental questions in protein sequence analysis. Sequence and structure conservation provides valuable information to tackle these questions. It is, however, limited by the incomplete sampling of sequence space in natural evolution. Moreover, proteins often have multiple functions, with overlapping sequences that present challenges to accurate annotation of the exact functions of individual residues by conservation-based methods. Using the influenza A virus PB1 protein as an example, we developed a method to systematically identify and annotate functional residues. We used saturation mutagenesis and high-throughput sequencing to measure the replication capacity of single nucleotide mutations across the entire PB1 protein. After predicting protein stability upon mutations, we identified functional PB1 residues that are essential for viral replication. To further annotate the functional residues important to the canonical or noncanonical functions of viral RNA-dependent RNA polymerase (vRdRp), we performed a homologous-structure analysis with 16 different vRdRp structures. We achieved high sensitivity in annotating the known canonical polymerase functional residues. Moreover, we identified a cluster of noncanonical functional residues located in the loop region of the PB1 β-ribbon. We further demonstrated that these residues were important for PB1 protein nuclear import through the interaction with Ran-binding protein 5. In summary, we developed a systematic and sensitive method to identify and annotate functional residues that are not restrained by sequence conservation. Importantly, this method is generally applicable to other proteins about which homologous-structure information is available.

IMPORTANCE

To fully comprehend the diverse functions of a protein, it is essential to understand the functionality of individual residues. Current methods are highly dependent on evolutionary sequence conservation, which is usually limited by sampling size. Sequence conservation-based methods are further confounded by structural constraints and multifunctionality of proteins. Here we present a method that can systematically identify and annotate functional residues of a given protein. We used a high-throughput functional profiling platform to identify essential residues. Coupling it with homologous-structure comparison, we were able to annotate multiple functions of proteins. We demonstrated the method with the PB1 protein of influenza A virus and identified novel functional residues in addition to its canonical function as an RNA-dependent RNA polymerase. Not limited to virology, this method is generally applicable to other proteins that can be functionally selected and about which homologous-structure information is available.

In silico Identification and Characterization of Protein-Ligand Binding Sites

Protein-ligand binding site prediction methods aim to predict, from amino acid sequence, protein-ligand interactions, putative ligands, and ligand binding site residues using either sequence information, structural information, or a combination of both. In silico characterization of protein-ligand interactions has become extremely important to help determine a protein's functionality, as in vivo-based functional elucidation is unable to keep pace with the current growth of sequence databases. Additionally, in vitro biochemical functional elucidation is time-consuming, costly, and may not be feasible for large-scale analysis, such as drug discovery. Thus, in silico prediction of protein-ligand interactions must be utilized to aid in functional elucidation. Here, we briefly discuss protein function prediction, prediction of protein-ligand interactions, the Critical Assessment of Techniques for Protein Structure Prediction (CASP) and the Continuous Automated EvaluatiOn (CAMEO) competitions, along with their role in shaping the field. We also discuss, in detail, our cutting-edge web-server method, FunFOLD for the structurally informed prediction of protein-ligand interactions. Furthermore, we provide a step-by-step guide on using the FunFOLD web server and FunFOLD3 downloadable application, along with some real world examples, where the FunFOLD methods have been used to aid functional elucidation.

Structure-based function analysis of putative conserved proteins with isomerase activity from Haemophilus influenzae

Haemophilus influenzae, a Gram-negative bacterium and a member of the family Pasteurellaceae, causes chronic bronchitis, bacteremia, meningitis, etc. The H. influenzae is the first organism whose genome was completely sequenced and annotated. Here, we have extensively analyzed the genome of H. influenzae using available proteins structure and function analysis tools. The objective of this analysis is to assign a precise function to hypothetical proteins (HPs) whose functions are not determined so far. Function prediction of these proteins is helpful in precise understanding of mechanisms of pathogenesis and biochemical pathways important for selecting novel therapeutic target. After an extensive analysis of H. Influenzae genome we have found 13 HPs showing high level of sequence and structural similarity to the enzyme isomerase. Consequently, the structures of HPs have been modeled and analyzed to determine their precise functions. We found these HPs are alanine racemase, lysine 2, 3-aminomutase, topoisomerase DNA-binding C4 zinc finger, pseudouridine synthase B, C and E (Rlu B, C and E), hydroxypyruvate isomerase, nucleoside-diphosphate-sugar epimerase, amidophosphoribosyltransferase, aldose-1-epimerase, tautomerase/MIF, Xylose isomerase-like, have TIM barrel domain and sedoheptulose-7-phosphate isomerase like activity, signifying their corresponding functions in the H. influenzae. This work provides a better understanding of the role HPs with isomerase activities in the survival and pathogenesis of H. influenzae.

Functional Constraint Profiling of a Viral Protein Reveals Discordance of Evolutionary Conservation and Functionality

Viruses often encode proteins with multiple functions due to their compact genomes. Existing approaches to identify functional residues largely rely on sequence conservation analysis. Inferring functional residues from sequence conservation can produce false positives, in which the conserved residues are functionally silent, or false negatives, where functional residues are not identified since they are species-specific and therefore non-conserved. Furthermore, the tedious process of constructing and analyzing individual mutations limits the number of residues that can be examined in a single study. Here, we developed a systematic approach to identify the functional residues of a viral protein by coupling experimental fitness profiling with protein stability prediction using the influenza virus polymerase PA subunit as the target protein. We identified a significant number of functional residues that were influenza type-specific and were evolutionarily non-conserved among different influenza types. Our results indicate that type-specific functional residues are prevalent and may not otherwise be identified by sequence conservation analysis alone. More importantly, this technique can be adapted to any viral (and potentially non-viral) protein where structural information is available.

The analysis of sequence conservation is a common approach to identify functional residues within a protein. However, not all functional residues are conserved as natural evolution and species diversification permit continuous innovation of protein functionality through the retention of advantageous mutations. Non-conserved functional residues, which are often species-specific, may not be identified by conventional analysis of sequence conservation despite being biologically important. Here we described a novel approach to identify functional residues within a protein by coupling a high-throughput experimental fitness profiling approach with computational protein modeling. Our methodology is independent of sequence conservation and is applicable to any protein where structural information is available. In this study, we systematically mapped the functional residues on the influenza A PA protein and revealed that non-conserved functional residues are prevalent. Our results not only have significant implication on how functionality evolves during natural evolution, but also highlight the caveats when applying conservation-based approaches to identify functional residues within a protein.

Alternatively Spliced Homologous Exons Have Ancient Origins and Are Highly Expressed at the Protein Level

Alternative splicing of messenger RNA can generate a wide variety of mature RNA transcripts, and these transcripts may produce protein isoforms with diverse cellular functions. While there is much supporting evidence for the expression of alternative transcripts, the same is not true for the alternatively spliced protein products. Large-scale mass spectroscopy experiments have identified evidence of alternative splicing at the protein level, but with conflicting results. Here we carried out a rigorous analysis of the peptide evidence from eight large-scale proteomics experiments to assess the scale of alternative splicing that is detectable by high-resolution mass spectroscopy. We find fewer splice events than would be expected: we identified peptides for almost 64% of human protein coding genes, but detected just 282 splice events. This data suggests that most genes have a single dominant isoform at the protein level. Many of the alternative isoforms that we could identify were only subtly different from the main splice isoform. Very few of the splice events identified at the protein level disrupted functional domains, in stark contrast to the two thirds of splice events annotated in the human genome that would lead to the loss or damage of functional domains. The most striking result was that more than 20% of the splice isoforms we identified were generated by substituting one homologous exon for another. This is significantly more than would be expected from the frequency of these events in the genome. These homologous exon substitution events were remarkably conserved—all the homologous exons we identified evolved over 460 million years ago—and eight of the fourteen tissue-specific splice isoforms we identified were generated from homologous exons. The combination of proteomics evidence, ancient origin and tissue-specific splicing indicates that isoforms generated from homologous exons may have important cellular roles.

Alternative splicing is thought to be one means for generating the protein diversity necessary for the whole range of cellular functions. While the presence of alternatively spliced transcripts in the cell has been amply demonstrated, the same cannot be said for alternatively spliced proteins. The quest for alternative protein isoforms has focused primarily on the analysis of peptides from large-scale mass spectroscopy experiments, but evidence for alternative isoforms has been patchy and contradictory. A careful analysis of the peptide evidence is needed to fully understand the scale of alternative splicing detectable at the protein level. Here we analysed peptides from eight large-scale data sets, identifying just 282 splice events among 12,716 genes. This suggests that most genes have a single dominant isoform. Many of the alternative isoforms that we identified were only subtly different from the main splice variant, and one in five was generated by substitution of homologous exons by swapping one related exon for another. Remarkably, the alternative isoforms generated from homologous exons were highly conserved, first appearing 460 million years ago, and several appear to have tissue-specific roles in the brain and heart. Our results suggest that these particular isoforms are likely to have important cellular roles.

APPRIS WebServer and WebServices

This paper introduces the APPRIS WebServer (http://appris.bioinfo.cnio.es) and WebServices (http://apprisws.bioinfo.cnio.es). Both the web servers and the web services are based around the APPRIS Database, a database that presently houses annotations of splice isoforms for five different vertebrate genomes. The APPRIS WebServer and WebServices provide access to the computational methods implemented in the APPRIS Database, while the APPRIS WebServices also allows retrieval of the annotations. The APPRIS WebServer and WebServices annotate splice isoforms with protein structural and functional features, and with data from cross-species alignments. In addition they can use the annotations of structure, function and conservation to select a single reference isoform for each protein-coding gene (the principal protein isoform). APPRIS principal isoforms have been shown to agree overwhelmingly with the main protein isoform detected in proteomics experiments. The APPRIS WebServer allows for the annotation of splice isoforms for individual genes, and provides a range of visual representations and tools to allow researchers to identify the likely effect of splicing events. The APPRIS WebServices permit users to generate annotations automatically in high throughput mode and to interrogate the annotations in the APPRIS Database. The APPRIS WebServices have been implemented using REST architecture to be flexible, modular and automatic.

I-TASSER server: new development for protein structure and function predictions

The I-TASSER server (http://zhanglab.ccmb.med.umich.edu/I-TASSER) is an online resource for automated protein structure prediction and structure-based function annotation. In I-TASSER, structural templates are first recognized from the PDB using multiple threading alignment approaches. Full-length structure models are then constructed by iterative fragment assembly simulations. The functional insights are finally derived by matching the predicted structure models with known proteins in the function databases. Although the server has been widely used for various biological and biomedical investigations, numerous comments and suggestions have been reported from the user community. In this article, we summarize recent developments on the I-TASSER server, which were designed to address the requirements from the user community and to increase the accuracy of modeling predictions. Focuses have been made on the introduction of new methods for atomic-level structure refinement, local structure quality estimation and biological function annotations. We expect that these new developments will improve the quality of the I-TASSER server and further facilitate its use by the community for high-resolution structure and function prediction.

Most highly expressed protein-coding genes have a single dominant isoform

Although eukaryotic cells express a wide range of alternatively spliced transcripts, it is not clear whether genes tend to express a range of transcripts simultaneously across cells, or produce dominant isoforms in a manner that is either tissue-specific or regardless of tissue. To date, large-scale investigations into the pattern of transcript expression across distinct tissues have produced contradictory results. Here, we attempt to determine whether genes express a dominant splice variant at the protein level. We interrogate peptides from eight large-scale human proteomics experiments and databases and find that there is a single dominant protein isoform, irrespective of tissue or cell type, for the vast majority of the protein-coding genes in these experiments, in partial agreement with the conclusions from the most recent large-scale RNAseq study. Remarkably, the dominant isoforms from the experimental proteomics analyses coincided overwhelmingly with the reference isoforms selected by two completely orthogonal sources, the consensus coding sequence variants, which are agreed upon by separate manual genome curation teams, and the principal isoforms from the APPRIS database, predicted automatically from the conservation of protein sequence, structure, and function.

Methods for predicting protein-ligand binding sites

Ligand binding is required for many proteins to function properly. A large number of bioinformatics tools have been developed to predict ligand binding sites as a first step in understanding a protein's function or to facilitate docking computations in virtual screening based drug design. The prediction usually requires only the three-dimensional structure (experimentally determined or computationally modeled) of the target protein to be searched for ligand binding site(s), and Web servers have been built, allowing the free and simple use of prediction tools. In this chapter, we review the underlying concepts of the methods used by various tools, and discuss their different features and the related issues of ligand binding site prediction. Some cautionary notes about the use of these tools are also provided.

GASS: identifying enzyme active sites with genetic algorithms

MOTIVATION

Currently, 25% of proteins annotated in Pfam have their function unknown. One way of predicting proteins function is by looking at their active site, which has two main parts: the catalytic site and the substrate binding site. The active site is more conserved than the other residues of the protein and can be a rich source of information for protein function prediction. This article presents a new heuristic method, named genetic active site search (GASS), which searches for given active site 3D templates in unknown proteins. The method can perform non-exact amino acid matches (conservative mutations), is able to find amino acids in different chains and does not impose any restrictions on the active site size.

RESULTS

GASS results were compared with those catalogued in the catalytic site atlas (CSA) in four different datasets and compared with two other methods: amino acid pattern search for substructures and motif and catalytic site identification. The results show GASS can correctly identify >90% of the templates searched. Experiments were also run using data from the substrate binding sites prediction competition CASP 10, and GASS is ranked fourth among the 18 methods considered.

Multiple evidence strands suggest that there may be as few as 19,000 human protein-coding genes

Determining the full complement of protein-coding genes is a key goal of genome annotation. The most powerful approach for confirming protein-coding potential is the detection of cellular protein expression through peptide mass spectrometry (MS) experiments. Here, we mapped peptides detected in seven large-scale proteomics studies to almost 60% of the protein-coding genes in the GENCODE annotation of the human genome. We found a strong relationship between detection in proteomics experiments and both gene family age and cross-species conservation. Most of the genes for which we detected peptides were highly conserved. We found peptides for >96% of genes that evolved before bilateria. At the opposite end of the scale, we identified almost no peptides for genes that have appeared since primates, for genes that did not have any protein-like features or for genes with poor cross-species conservation. These results motivated us to describe a set of 2001 potential non-coding genes based on features such as weak conservation, a lack of protein features, or ambiguous annotations from major databases, all of which correlated with low peptide detection across the seven experiments. We identified peptides for just 3% of these genes. We show that many of these genes behave more like non-coding genes than protein-coding genes and suggest that most are unlikely to code for proteins under normal circumstances. We believe that their inclusion in the human protein-coding gene catalogue should be revised as part of the ongoing human genome annotation effort.

GalaxySite: ligand-binding-site prediction by using molecular docking

Knowledge of ligand-binding sites of proteins provides invaluable information for functional studies, drug design and protein design. Recent progress in ligand-binding-site prediction methods has demonstrated that using information from similar proteins of known structures can improve predictions. The GalaxySite web server, freely accessible at http://galaxy.seoklab.org/site, combines such information with molecular docking for more precise binding-site prediction for non-metal ligands. According to the recent critical assessments of structure prediction methods held in 2010 and 2012, this server was found to be superior or comparable to other state-of-the-art programs in the category of ligand-binding-site prediction. A strong merit of the GalaxySite program is that it provides additional predictions on binding ligands and their binding poses in terms of the optimized 3D coordinates of the protein–ligand complexes, whereas other methods predict only identities of binding-site residues or copy binding geometry from similar proteins. The additional information on the specific binding geometry would be very useful for applications in functional studies and computer-aided drug discovery.

Computational identification of natural peptides based on analysis of molecular evolution

MOTIVATION

Many secretory peptides are synthesized as inactive precursors that must undergo post-translational processing to become biologically active peptides. Attempts to predict natural peptides are limited by the low performance of proteolytic site predictors and by the high combinatorial complexity of pairing such sites. To overcome these limitations, we analyzed the site-wise evolutionary mutation rates of peptide hormone precursors, calculated using the Rate4Site algorithm.

RESULTS

Our analysis revealed that within their precursors, peptide residues are significantly more conserved than the pro-peptide residues. This disparity enables the prediction of peptides with a precision of ∼60% at a recall of 40% [receiver-operating characteristic curve (ROC) AUC 0.79]. Subsequently, combining the Rate4Site score with additional features and training a Random Forest classifier enable the prediction of natural peptides hidden within secreted human proteins at a precision of ∼90% at a recall of 50% (ROC AUC 0.96). The high performance of our method allows it to be applied to full secretomes and to predict naturally occurring active peptides. Our prediction on Homo sapiens revealed several putative peptides in the human secretome that are currently unannotated. Furthermore, the unique expression of some of these peptides implies a potential hormone function, including peptides that are highly expressed in endocrine glands.

AVAILABILITY AND IMPLEMENTATION

A pseudocode is available in the SUPPLEMENTARY INFORMATION.

CONTACT

doron.gerber@biu.ac.il or kliger@cgen.com

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

FireDB: a compendium of biological and pharmacologically relevant ligands

FireDB (http://firedb.bioinfo.cnio.es) is a curated inventory of catalytic and biologically relevant small ligand-binding residues culled from the protein structures in the Protein Data Bank. Here we present the important new additions since the publication of FireDB in 2007. The database now contains an extensive list of manually curated biologically relevant compounds. Biologically relevant compounds are informative because of their role in protein function, but they are only a small fraction of the entire ligand set. For the remaining ligands, the FireDB provides cross-references to the annotations from publicly available biological, chemical and pharmacological compound databases. FireDB now has external references for 95% of contacting small ligands, making FireDB a more complete database and providing the scientific community with easy access to the pharmacological annotations of PDB ligands. In addition to the manual curation of ligands, FireDB also provides insights into the biological relevance of individual binding sites. Here, biological relevance is calculated from the multiple sequence alignments of related binding sites that are generated from all-against-all comparison of each FireDB binding site. The database can be accessed by RESTful web services and is available for download via MySQL.

CLIPS-4D: a classifier that distinguishes structurally and functionally important residue-positions based on sequence and 3D data

MOTIVATION

The precise identification of functionally and structurally important residues of a protein is still an open problem, and state-of-the-art classifiers predict only one or at most two different categories.

RESULT

We have implemented the classifier CLIPS-4D, which predicts in a mutually exclusively manner a role in catalysis, ligand-binding or protein stability for each residue-position of a protein. Each prediction is assigned a P-value, which enables the statistical assessment and the selection of predictions with similar quality. CLIPS-4D requires as input a multiple sequence alignment and a 3D structure of one protein in PDB format. A comparison with existing methods confirmed state-of-the-art prediction quality, even though CLIPS-4D classifies more specifically than other methods. CLIPS-4D was implemented as a multiclass support vector machine, which exploits seven sequence-based and two structure-based features, each of which was shown to contribute to classification quality. The classification of ligand-binding sites profited most from the 3D features, which were the assessment of the solvent accessible surface area and the identification of surface pockets. In contrast, five additionally tested 3D features did not increase the classification performance achieved with evolutionary signals deduced from the multiple sequence alignment.

Protein-ligand binding site recognition using complementary binding-specific substructure comparison and sequence profile alignment

MOTIVATION

Identification of protein-ligand binding sites is critical to protein function annotation and drug discovery. However, there is no method that could generate optimal binding site prediction for different protein types. Combination of complementary predictions is probably the most reliable solution to the problem.

RESULTS

We develop two new methods, one based on binding-specific substructure comparison (TM-SITE) and another on sequence profile alignment (S-SITE), for complementary binding site predictions. The methods are tested on a set of 500 non-redundant proteins harboring 814 natural, drug-like and metal ion molecules. Starting from low-resolution protein structure predictions, the methods successfully recognize >51% of binding residues with average Matthews correlation coefficient (MCC) significantly higher (with P-value <10(-9) in student t-test) than other state-of-the-art methods, including COFACTOR, FINDSITE and ConCavity. When combining TM-SITE and S-SITE with other structure-based programs, a consensus approach (COACH) can increase MCC by 15% over the best individual predictions. COACH was examined in the recent community-wide COMEO experiment and consistently ranked as the best method in last 22 individual datasets with the Area Under the Curve score 22.5% higher than the second best method. These data demonstrate a new robust approach to protein-ligand binding site recognition, which is ready for genome-wide structure-based function annotations.

AVAILABILITY

http://zhanglab.ccmb.med.umich.edu/COACH/

eFindSite: improved prediction of ligand binding sites in protein models using meta-threading, machine learning and auxiliary ligands

Molecular structures and functions of the majority of proteins across different species are yet to be identified. Much needed functional annotation of these gene products often benefits from the knowledge of protein-ligand interactions. Towards this goal, we developed eFindSite, an improved version of FINDSITE, designed to more efficiently identify ligand binding sites and residues using only weakly homologous templates. It employs a collection of effective algorithms, including highly sensitive meta-threading approaches, improved clustering techniques, advanced machine learning methods and reliable confidence estimation systems. Depending on the quality of target protein structures, eFindSite outperforms geometric pocket detection algorithms by 15-40 % in binding site detection and by 5-35 % in binding residue prediction. Moreover, compared to FINDSITE, it identifies 14 % more binding residues in the most difficult cases. When multiple putative binding pockets are identified, the ranking accuracy is 75-78 %, which can be further improved by 3-4 % by including auxiliary information on binding ligands extracted from biomedical literature. As a first across-genome application, we describe structure modeling and binding site prediction for the entire proteome of Escherichia coli. Carefully calibrated confidence estimates strongly indicate that highly reliable ligand binding predictions are made for the majority of gene products, thus eFindSite holds a significant promise for large-scale genome annotation and drug development projects. eFindSite is freely available to the academic community at http://www.brylinski.org/efindsite .

The FunFOLD2 server for the prediction of protein-ligand interactions

The FunFOLD2 server is a new independent server that integrates our novel protein-ligand binding site and quality assessment protocols for the prediction of protein function (FN) from sequence via structure. Our guiding principles were, first, to provide a simple unified resource to make our function prediction software easily accessible to all via a simple web interface and, second, to produce integrated output for predictions that can be easily interpreted. The server provides a clean web interface so that results can be viewed on a single page and interpreted by non-experts at a glance. The output for the prediction is an image of the top predicted tertiary structure annotated to indicate putative ligand-binding site residues. The results page also includes a list of the most likely binding site residues and the types of predicted ligands and their frequencies in similar structures. The protein-ligand interactions can also be interactively visualized in 3D using the Jmol plug-in. The raw machine readable data are provided for developers, which comply with the Critical Assessment of Techniques for Protein Structure Prediction data standards for FN predictions. The FunFOLD2 webserver is freely available to all at the following web site: http://www.reading.ac.uk/bioinf/FunFOLD/FunFOLD_form_2_0.html.

MODexplorer: an integrated tool for exploring protein sequence, structure and function relationships

Summary: MODexplorer is an integrated tool aimed at exploring the sequence, structural and functional diversity in protein families useful in homology modeling and in analyzing protein families in general. It takes as input either the sequence or the structure of a protein and provides alignments with its homologs along with a variety of structural and functional annotations through an interactive interface. The annotations include sequence conservation, similarity scores, ligand-, DNA- and RNA-binding sites, secondary structure, disorder, crystallographic structure resolution and quality scores of models implied by the alignments to the homologs of known structure.

MODexplorer can be used to analyze sequence and structural conservation among the structures of similar proteins, to find structures of homologs solved in different conformational state or with different ligands and to transfer functional annotations. Furthermore, if the structure of the query is not known, MODexplorer can be used to select the modeling templates taking all this information into account and to build a comparative model.

Availability and implementation: Freely available on the web at http://modorama.biocomputing.it/modexplorer. Website implemented in HTML and JavaScript with all major browsers supported.

Contact:anna.tramontano@uniroma1.it

Supplementary information:Supplementary data are available at Bioinformatics online

GENCODE: the reference human genome annotation for The ENCODE Project

The GENCODE Consortium aims to identify all gene features in the human genome using a combination of computational analysis, manual annotation, and experimental validation. Since the first public release of this annotation data set, few new protein-coding loci have been added, yet the number of alternative splicing transcripts annotated has steadily increased. The GENCODE 7 release contains 20,687 protein-coding and 9640 long noncoding RNA loci and has 33,977 coding transcripts not represented in UCSC genes and RefSeq. It also has the most comprehensive annotation of long noncoding RNA (lncRNA) loci publicly available with the predominant transcript form consisting of two exons. We have examined the completeness of the transcript annotation and found that 35% of transcriptional start sites are supported by CAGE clusters and 62% of protein-coding genes have annotated polyA sites. Over one-third of GENCODE protein-coding genes are supported by peptide hits derived from mass spectrometry spectra submitted to Peptide Atlas. New models derived from the Illumina Body Map 2.0 RNA-seq data identify 3689 new loci not currently in GENCODE, of which 3127 consist of two exon models indicating that they are possibly unannotated long noncoding loci. GENCODE 7 is publicly available from gencodegenes.org and via the Ensembl and UCSC Genome Browsers.

APPRIS: annotation of principal and alternative splice isoforms

Here, we present APPRIS (http://appris.bioinfo.cnio.es), a database that houses annotations of human splice isoforms. APPRIS has been designed to provide value to manual annotations of the human genome by adding reliable protein structural and functional data and information from cross-species conservation. The visual representation of the annotations provided by APPRIS for each gene allows annotators and researchers alike to easily identify functional changes brought about by splicing events. In addition to collecting, integrating and analyzing reliable predictions of the effect of splicing events, APPRIS also selects a single reference sequence for each gene, here termed the principal isoform, based on the annotations of structure, function and conservation for each transcript. APPRIS identifies a principal isoform for 85% of the protein-coding genes in the GENCODE 7 release for ENSEMBL. Analysis of the APPRIS data shows that at least 70% of the alternative (non-principal) variants would lose important functional or structural information relative to the principal isoform.

FunFOLDQA: a quality assessment tool for protein-ligand binding site residue predictions

The estimation of prediction quality is important because without quality measures, it is difficult to determine the usefulness of a prediction. Currently, methods for ligand binding site residue predictions are assessed in the function prediction category of the biennial Critical Assessment of Techniques for Protein Structure Prediction (CASP) experiment, utilizing the Matthews Correlation Coefficient (MCC) and Binding-site Distance Test (BDT) metrics. However, the assessment of ligand binding site predictions using such metrics requires the availability of solved structures with bound ligands. Thus, we have developed a ligand binding site quality assessment tool, FunFOLDQA, which utilizes protein feature analysis to predict ligand binding site quality prior to the experimental solution of the protein structures and their ligand interactions. The FunFOLDQA feature scores were combined using: simple linear combinations, multiple linear regression and a neural network. The neural network produced significantly better results for correlations to both the MCC and BDT scores, according to Kendall’s τ, Spearman’s ρ and Pearson’s r correlation coefficients, when tested on both the CASP8 and CASP9 datasets. The neural network also produced the largest Area Under the Curve score (AUC) when Receiver Operator Characteristic (ROC) analysis was undertaken for the CASP8 dataset. Furthermore, the FunFOLDQA algorithm incorporating the neural network, is shown to add value to FunFOLD, when both methods are employed in combination. This results in a statistically significant improvement over all of the best server methods, the FunFOLD method (6.43%), and one of the top manual groups (FN293) tested on the CASP8 dataset. The FunFOLDQA method was also found to be competitive with the top server methods when tested on the CASP9 dataset. To the best of our knowledge, FunFOLDQA is the first attempt to develop a method that can be used to assess ligand binding site prediction quality, in the absence of experimental data.

PocketAnnotate: towards site-based function annotation

A computational pipeline PocketAnnotate for functional annotation of proteins at the level of binding sites has been proposed in this study. The pipeline integrates three in-house algorithms for site-based function annotation: PocketDepth, for prediction of binding sites in protein structures; PocketMatch, for rapid comparison of binding sites and PocketAlign, to obtain detailed alignment between pair of binding sites. A novel scheme has been developed to rapidly generate a database of non-redundant binding sites. For a given input protein structure, putative ligand-binding sites are identified, matched in real time against the database and the query substructure aligned with the promising hits, to obtain a set of possible ligands that the given protein could bind to. The input can be either whole protein structures or merely the substructures corresponding to possible binding sites. Structure-based function annotation at the level of binding sites thus achieved could prove very useful for cases where no obvious functional inference can be obtained based purely on sequence or fold-level analyses. An attempt has also been made to analyse proteins of no known function from Protein Data Bank. PocketAnnotate would be a valuable tool for the scientific community and contribute towards structure-based functional inference. The web server can be freely accessed at http://proline.biochem.iisc.ernet.in/pocketannotate/.

Ligand-binding site prediction using ligand-interacting and binding site-enriched protein triangles

MOTIVATION

Knowledge about the site at which a ligand binds provides an important clue for predicting the function of a protein and is also often a prerequisite for performing docking computations in virtual drug design and screening. We have previously shown that certain ligand-interacting triangles of protein atoms, called protein triangles, tend to occur more frequently at ligand-binding sites than at other parts of the protein.

RESULTS

In this work, we describe a new ligand-binding site prediction method that was developed based on binding site-enriched protein triangles. The new method was tested on 2 benchmark datasets and on 19 targets from two recent community-based studies of such predictions, and excellent results were obtained. Where comparisons were made, the success rates for the new method for the first predicted site were significantly better than methods that are not a meta-predictor. Further examination showed that, for most of the unsuccessful predictions, the pocket of the ligand-binding site was identified, but not the site itself, whereas for some others, the failure was not due to the method itself but due to the use of an incorrect biological unit in the structure examined, although using correct biological units would not necessarily improve the prediction success rates. These results suggest that the new method is a valuable new addition to a suite of existing structure-based bioinformatics tools for studies of molecular recognition and related functions of proteins in post-genomics research.

AVAILABILITY

The executable binaries and a web server for our method are available from http://sourceforge.net/projects/msdock/ and http://lise.ibms.sinica.edu.tw, respectively, free for academic users.

CLIPS-1D: analysis of multiple sequence alignments to deduce for residue-positions a role in catalysis, ligand-binding, or protein structure

Background

One aim of the in silico characterization of proteins is to identify all residue-positions, which are crucial for function or structure. Several sequence-based algorithms exist, which predict functionally important sites. However, with respect to sequence information, many functionally and structurally important sites are hard to distinguish and consequently a large number of incorrectly predicted functional sites have to be expected. This is why we were interested to design a new classifier that differentiates between functionally and structurally important sites and to assess its performance on representative datasets.

Results

We have implemented CLIPS-1D, which predicts a role in catalysis, ligand-binding, or protein structure for residue-positions in a mutually exclusive manner. By analyzing a multiple sequence alignment, the algorithm scores conservation as well as abundance of residues at individual sites and their local neighborhood and categorizes by means of a multiclass support vector machine. A cross-validation confirmed that residue-positions involved in catalysis were identified with state-of-the-art quality; the mean MCC-value was 0.34. For structurally important sites, prediction quality was considerably higher (mean MCC = 0.67). For ligand-binding sites, prediction quality was lower (mean MCC = 0.12), because binding sites and structurally important residue-positions share conservation and abundance values, which makes their separation difficult. We show that classification success varies for residues in a class-specific manner. This is why our algorithm computes residue-specific p-values, which allow for the statistical assessment of each individual prediction. CLIPS-1D is available as a Web service at http://www-bioinf.uni-regensburg.de/.

Conclusions

CLIPS-1D is a classifier, whose prediction quality has been determined separately for catalytic sites, ligand-binding sites, and structurally important sites. It generates hypotheses about residue-positions important for a set of homologous proteins and focuses on conservation and abundance signals. Thus, the algorithm can be applied in cases where function cannot be transferred from well-characterized proteins by means of sequence comparison.