An Expanded Evaluation of Protein Function Prediction Methods Shows an Improvement In Accuracy

Background: A major bottleneck in our understanding of the molecular underpinnings of life is the assignment of function to proteins. While molecular experiments provide the most reliable annotation of proteins, their relatively low throughput and restricted purview have led to an increasing role for computational function prediction. However, assessing methods for protein function prediction and tracking progress in the field remain challenging. Results: We conducted the second critical assessment of functional annotation (CAFA), a timed challenge to assess computational methods that automatically assign protein function. We evaluated 126 methods from 56 research groups for their ability to predict biological functions using Gene Ontology and gene-disease associations using Human Phenotype Ontology on a set of 3681 proteins from 18 species. CAFA2 featured expanded analysis compared with CAFA1, with regards to data set size, variety, and assessment metrics. To review progress in the field, the analysis compared the best methods from CAFA1 to those of CAFA2. Conclusions: The top-performing methods in CAFA2 outperformed those from CAFA1. This increased accuracy can be attributed to a combination of the growing number of experimental annotations and improved methods for function prediction. The assessment also revealed that the definition of top-performing algorithms is ontology specific, that different performance metrics can be used to probe the nature of accurate predictions, and the relative diversity of predictions in the biological process and human phenotype ontologies. While there was methodological improvement between CAFA1 and CAFA2, the interpretation of results and usefulness of individual methods remain context-dependent

An expanded evaluation of protein function prediction methods shows an improvement in accuracy

Background: A major bottleneck in our understanding of the molecular underpinnings of life is the assignment of function to proteins. While molecular experiments provide the most reliable annotation of proteins, their relatively low throughput and restricted purview have led to an increasing role for computational function prediction. However, assessing methods for protein function prediction and tracking progress in the field remain challenging. Results: We conducted the second critical assessment of functional annotation (CAFA), a timed challenge to assess computational methods that automatically assign protein function. We evaluated 126 methods from 56 research groups for their ability to predict biological functions using Gene Ontology and gene-disease associations using Human Phenotype Ontology on a set of 3681 proteins from 18 species. CAFA2 featured expanded analysis compared with CAFA1, with regards to data set size, variety, and assessment metrics. To review progress in the field, the analysis compared the best methods from CAFA1 to those of CAFA2. Conclusions: The top-performing methods in CAFA2 outperformed those from CAFA1. This increased accuracy can be attributed to a combination of the growing number of experimental annotations and improved methods for function prediction. The assessment also revealed that the definition of top-performing algorithms is ontology specific, that different performance metrics can be used to probe the nature of accurate predictions, and the relative diversity of predictions in the biological process and human phenotype ontologies. While there was methodological improvement between CAFA1 and CAFA2, the interpretation of results and usefulness of individual methods remain context-dependent. Keywords: Protein function prediction, Disease gene prioritizationpublishedVersio

Prolyl 4-hydroxylase:structural and functional characterization of the peptide-substrate-binding domain of the human enzyme, and cloning and characterization of a plant enzyme with unique properties

Abstract Collagen prolyl 4-hydroxylase is the key enzyme in the biosynthesis of collagens, a family of extracellular matrix proteins. Vertebrate collagen prolyl 4-hydroxylases are α2β2 tetramers, the β subunit being identical to the multifunctional protein disulphide isomerase (PDI). Several isoforms of the catalytic α subunit have been identified in various organisms. Prolyl 4-hydroxylases have also been isolated from plants, where they hydroxylate proline-rich structural glycoproteins of the cell walls. The structural and functional properties of the peptide-substrate-binding domain of human collagen prolyl 4-hydroxylase are characterized here. Data obtained from NMR studies indicate that the domain consists of five α helices and one short β strand, this structure being quite different from those of other proline-rich peptide-binding modules. Several residues involved in the binding of a short synthetic peptide were also identified by NMR. Kd values for the binding of several synthetic peptides to the α(I) and α(II) domains were determined by surface plasmon resonance and isothermal calorimetry, and the results indicated that the binding properties of the type I and type II collagen prolyl 4-hydroxylase tetramers can mainly be explained by the binding of peptides to this domain rather than to the catalytic domain. The peptide-substrate-binding domain of human type I collagen prolyl 4-hydroxylase was also crystallized. The crystals were well ordered and diffracted to at least 3 Å, the asymmetric unit most probably containing a domain dimer. The genome of Arabidopsis thaliana was found to encode at least six putative prolyl 4-hydroxylase polypeptides, one of which was cloned and characterized here as a recombinant protein. All the catalytically critical residues identified in animal prolyl 4-hydroxylases were also conserved in this plant prolyl 4-hydroxylase, and their mutagenesis led to inactivation of the enzyme. The recombinant plant enzyme was effective in hydroxylating poly(L-proline) and several synthetic proline-rich peptides. Surprisingly, contrary to previous reports on plant prolyl 4-hydroxylases, the collagen-like peptides were found to be good substrates, the enzyme preferentially hydroxylating prolines in the Y positions of the -X-Y-Gly- triplets, thus resembling the vertebrate collagen prolyl 4-hydroxylases even in this respect. The recombinant plant prolyl 4-hydroxylase also hydroxylated peptides representing the N and C-terminal hydroxylation sites present in the hypoxia-inducible transcription factor α. The fact that these peptides contain only one proline residue indicated that a poly(L-proline) type II conformation was not required for hydroxylation

The Structural Motifs for Substrate Binding and Dimerization of the α Subunit of Collagen Prolyl 4-Hydroxylase

SummaryCollagen prolyl 4-hydroxylase (C-P4H) catalyzes the proline hydroxylation of procollagen, an essential modification in the maturation of collagens. C-P4H consists of two catalytic α subunits and two protein disulfide isomerase β subunits. The assembly of these subunits is unknown. The α subunit contains an N domain (1–143), a peptide-substrate-binding-domain (PSB, 144–244) and a catalytic domain (245–517). Here, we report the dimeric structure of the N-terminal region (1–244) of the α subunit. It is shown that the N domain has an important role in the assembly of the C-P4H tetramer, by forming an extended four-helix bundle that includes an antiparallel coiled-coil dimerization motif between the two α subunits. Complexes of this construct with a C-P4H inhibitor and substrate show the mode of peptide-binding to the PSB domain. Both peptides adopt a poly-(L)-proline-type-II helix conformation and bind in a curved, asymmetric groove lined by conserved tyrosines and an Arg-Asp salt bridge

Constitutively active androgen receptor splice variants AR-V3, AR-V7 and AR-V9 are co-expressed in castration-resistant prostate cancer metastases

Background: A significant subset of prostate cancer (PC) patients with a castration-resistant form of the disease (CRPC) show primary resistance to androgen receptor (AR)-targeting drugs developed against CRPC. As one explanation could be the expression of constitutively active androgen receptor splice variants (AR-Vs), our current objectives were to study AR-Vs and other AR aberrations to better understand the emergence of CRPC. Methods: We analysed specimens from different stages of prostate cancer by next-generation sequencing and immunohistochemistry. Results: AR mutations and copy number variations were detected only in CRPC specimens. Genomic structural rearrangements of AR were observed in 5/30 metastatic CRPC patients, but they were not associated with expression of previously known AR-Vs. The predominant AR-Vs detected were AR-V3, AR-V7 and AR-V9, with the expression levels being significantly higher in CRPC cases compared to prostatectomy samples. Out of 25 CRPC metastases that expressed any AR variant, 17 cases harboured expression of all three of these AR-Vs. AR-V7 protein expression was highly heterogeneous and higher in CRPC compared to hormone-naïve tumours. Conclusions: AR-V3, AR-V7 and AR-V9 are co-expressed in CRPC metastases highlighting the fact that inhibiting AR function via regions common to all AR-Vs is likely to provide additional benefit to patients with CRPC

An expanded evaluation of protein function prediction methods shows an improvement in accuracy.

BackgroundA major bottleneck in our understanding of the molecular underpinnings of life is the assignment of function to proteins. While molecular experiments provide the most reliable annotation of proteins, their relatively low throughput and restricted purview have led to an increasing role for computational function prediction. However, assessing methods for protein function prediction and tracking progress in the field remain challenging.ResultsWe conducted the second critical assessment of functional annotation (CAFA), a timed challenge to assess computational methods that automatically assign protein function. We evaluated 126 methods from 56 research groups for their ability to predict biological functions using Gene Ontology and gene-disease associations using Human Phenotype Ontology on a set of 3681 proteins from 18 species. CAFA2 featured expanded analysis compared with CAFA1, with regards to data set size, variety, and assessment metrics. To review progress in the field, the analysis compared the best methods from CAFA1 to those of CAFA2.ConclusionsThe top-performing methods in CAFA2 outperformed those from CAFA1. This increased accuracy can be attributed to a combination of the growing number of experimental annotations and improved methods for function prediction. The assessment also revealed that the definition of top-performing algorithms is ontology specific, that different performance metrics can be used to probe the nature of accurate predictions, and the relative diversity of predictions in the biological process and human phenotype ontologies. While there was methodological improvement between CAFA1 and CAFA2, the interpretation of results and usefulness of individual methods remain context-dependent