BUSCA: An integrative web server to predict subcellular localization of proteins

Here, we present BUSCA (http://busca.biocomp.unibo.it), a novel web server that integrates different computational tools for predicting protein subcellular localization. BUSCA combines methods for identifying signal and transit peptides (DeepSig and TPpred3), GPI-anchors (PredGPI) and transmembrane domains (ENSEMBLE3.0 and BetAware) with tools for discriminating subcellular localization of both globular and membrane proteins (BaCelLo, MemLoci and SChloro). Outcomes from the different tools are processed and integrated for annotating subcellular localization of both eukaryotic and bacterial protein sequences. We benchmark BUSCA against protein targets derived from recent CAFA experiments and other specific data sets, reporting performance at the state-of-the-art. BUSCA scores better than all other evaluated methods on 2732 targets from CAFA2, with a F1 value equal to 0.49 and among the best methods when predicting targets from CAFA3. We propose BUSCA as an integrated and accurate resource for the annotation of protein subcellular localization

Protein function annotation using protein domain family resources

As a result of the genome sequencing and structural genomics initiatives, we have a wealth of protein sequence and structural data. However, only about 1% of these proteins have experimental functional annotations. As a result, computational approaches that can predict protein functions are essential in bridging this widening annotation gap. This article reviews the current approaches of protein function prediction using structure and sequence based classification of protein domain family resources with a special focus on functional families in the CATH-Gene3D resource

A daily-updated tree of (sequenced) life as a reference for genome research

We report a daily-updated sequenced/species Tree Of Life (sTOL) as a reference for the increasing number of cellular organisms with their genomes sequenced. The sTOL builds on a likelihood-based weight calibration algorithm to consolidate NCBI taxonomy information in concert with unbiased sampling of molecular characters from whole genomes of all sequenced organisms. Via quantifying the extent of agreement between taxonomic and molecular data, we observe there are many potential improvements that can be made to the status quo classification, particularly in the Fungi kingdom; we also see that the current state of many animal genomes is rather poor. To augment the use of sTOL in providing evolutionary contexts, we integrate an ontology infrastructure and demonstrate its utility for evolutionary understanding on: nuclear receptors, stem cells and eukaryotic genomes. The sTOL (http://supfam.org/SUPERFAMILY/ sTOL) provides a binary tree of (sequenced) life, and contributes to an analytical platform linking genome evolution, function and phenotype

Combining learning and constraints for genome-wide protein annotation

BackgroundThe advent of high-throughput experimental techniques paved the way to genome-wide computational analysis and predictive annotation studies. When considering the joint annotation of a large set of related entities, like all proteins of a certain genome, many candidate annotations could be inconsistent, or very unlikely, given the existing knowledge. A sound predictive framework capable of accounting for this type of constraints in making predictions could substantially contribute to the quality of machine-generated annotations at a genomic scale.ResultsWe present Ocelot, a predictive pipeline which simultaneously addresses functional and interaction annotation of all proteins of a given genome. The system combines sequence-based predictors for functional and protein-protein interaction (PPI) prediction with a consistency layer enforcing (soft) constraints as fuzzy logic rules. The enforced rules represent the available prior knowledge about the classification task, including taxonomic constraints over each GO hierarchy (e.g. a protein labeled with a GO term should also be labeled with all ancestor terms) as well as rules combining interaction and function prediction. An extensive experimental evaluation on the Yeast genome shows that the integration of prior knowledge via rules substantially improves the quality of the predictions. The system largely outperforms GoFDR, the only high-ranking system at the last CAFA challenge with a readily available implementation, when GoFDR is given access to intra-genome information only (as Ocelot), and has comparable or better results (depending on the hierarchy and performance measure) when GoFDR is allowed to use information from other genomes. Our system also compares favorably to recent methods based on deep learning

The mRNA-bound proteome of the human malaria parasite Plasmodium falciparum.

BackgroundGene expression is controlled at multiple levels, including transcription, stability, translation, and degradation. Over the years, it has become apparent that Plasmodium falciparum exerts limited transcriptional control of gene expression, while at least part of Plasmodium's genome is controlled by post-transcriptional mechanisms. To generate insights into the mechanisms that regulate gene expression at the post-transcriptional level, we undertook complementary computational, comparative genomics, and experimental approaches to identify and characterize mRNA-binding proteins (mRBPs) in P. falciparum.ResultsClose to 1000 RNA-binding proteins are identified by hidden Markov model searches, of which mRBPs encompass a relatively large proportion of the parasite proteome as compared to other eukaryotes. Several abundant mRNA-binding domains are enriched in apicomplexan parasites, while strong depletion of mRNA-binding domains involved in RNA degradation is observed. Next, we experimentally capture 199 proteins that interact with mRNA during the blood stages, 64 of which with high confidence. These captured mRBPs show a significant overlap with the in silico identified candidate RBPs (p < 0.0001). Among the experimentally validated mRBPs are many known translational regulators active in other stages of the parasite's life cycle, such as DOZI, CITH, PfCELF2, Musashi, and PfAlba1-4. Finally, we also detect several proteins with an RNA-binding domain abundant in Apicomplexans (RAP domain) that is almost exclusively found in apicomplexan parasites.ConclusionsCollectively, our results provide the most complete comparative genomics and experimental analysis of mRBPs in P. falciparum. A better understanding of these regulatory proteins will not only give insight into the intricate parasite life cycle but may also provide targets for novel therapeutic strategies

Learning a Functional Grammar of Protein Domains using Natural Language Word Embedding Techniques

In this paper, using word2vec, a widely-used natural language processing method, we demonstrate that proteins domains may have a learnable implicit semantic "meaning" in the context of their functional contributions to multi-domain proteins in which they are found. Word2vec is a group of models which can be used to produce semantically meaningful embeddings of words or tokens in a fixed-dimension vector space. In this work, we treat multi-domain proteins as "sentences" where domain identifiers are tokens which may be considered as "words". Using all InterPro [1] pfam domain assignments we observe that the embedding could be used to suggest putative GO assignments for Pfam [2] Domains of Unknown Function. This article is protected by copyright. All rights reserved

Enzyme function and its evolution

With rapid increases over recent years in the determination of protein sequence and structure, alongside knowledge of thousands of enzyme functions and hundreds of chemical mechanisms, it is now possible to combine breadth and depth in our understanding of enzyme evolution. Phylogenetics continues to move forward, though determining correct evolutionary family trees is not trivial. Protein function prediction has spawned a variety of promising methods that offer the prospect of identifying enzymes across the whole range of chemical functions and over numerous species. This knowledge is essential to understand antibiotic resistance, as well as in protein re-engineering and de novo enzyme design.PostprintPeer reviewe

Diversity in protein domain superfamilies

Whilst ∼93% of domain superfamilies appear to be relatively structurally and functionally conserved based on the available data from the CATH-Gene3D domain classification resource, the remainder are much more diverse. In this review, we consider how domains in some of the most ubiquitous and promiscuous superfamilies have evolved, in particular the plasticity in their functional sites and surfaces which expands the repertoire of molecules they interact with and actions performed on them. To what extent can we identify a core function for these superfamilies which would allow us to develop a 'domain grammar of function' whereby a protein's biological role can be proposed from its constituent domains? Clearly the first step is to understand the extent to which these components vary and how changes in their molecular make-up modifies function