Luisa Castagnoli

MINT, the molecular interaction database: 2012 update

MINT (http://mint.bio.uniroma2.it/mint) is a public repository for molecular interactions reported in peer-reviewed journals. Since its last report, MINT has grown considerably in size and evolved in scope to meet the requirements of its users. The main changes include a more precise definition of the curation policy and the development of an enhanced and user-friendly interface to facilitate the analysis of the ever-growing interaction dataset. MINT has adopted the PSI-MI standards for the annotation and for the representation of molecular interactions and is a member of the IMEx consortium.

Selection of antibody ligands from a large library of oligopeptides expressed on a multivalent exposition vector

Practically any oligopeptide can be exposed on the surface of the bacteriophage capsid by fusion to the major coat protein of filamentous bacteriophages. A phage expressing a particular peptide tag can be selected from a mixture of tens of millions of clones, exposing oligopeptides of random sequence, by affinity purification with a protein ligand. In this respect, pVIII can be used as an alternative and complement to the exposition vectors based on the product of gene III (pIII). We have constructed a phagemid vector that contains gene VIII under the control of the pLac promoter. This vector can be conveniently used to construct libraries of oligopeptides with a random amino acid sequence. An antipeptide monoclonal antibody was used to affinity-purify phagemids exposing oligopeptides which can interact with the monoclonal antibody. DNA sequencing of the amino terminus of gene VIII of the recovered clones predicts the synthesis of hybrid proteins whose aminoterminal amino acid sequence is related to that of the oligopeptide used to raise the antibody. In other words, only oligopeptides that bind a very small portion of the immunoglobulin G surface are affinity-purified by this method, implying that the antigen binding site possesses molecular properties that renders it much stickier than the remainder of the molecule.

Intersectin, a Novel Adaptor Protein with Two Eps15 Homology and Five Src Homology 3 Domains

We screened a Xenopus laevis oocyte cDNA expression library with a Src homology 3 (SH3) class II peptide ligand and identified a 1270-amino acid-long protein containing two Eps15 homology (EH) domains, a central coiled-coil region, and five SH3 domains. We named this protein Intersectin, because it potentially brings together EH and SH3 domain-binding proteins into a macromolecular complex. The ligand preference of the EH domains were deduced to be asparajine-proline-phenylalanine (NPF) or cyclized NPF (CX 1–2NPFXXC), depending on the type of phage-displayed combinatorial peptide library used. Screens of a mouse embryo cDNA library with the EH domains of Intersectin yielded clones for the Rev-associated binding/Rev-interacting protein (RAB/Rip) and two novel proteins, which we named Intersectin-binding proteins (Ibps) 1 and 2. All three proteins contain internal and C-terminal NPF peptide sequences, and Ibp1 and Ibp2 also contain putative clathrin-binding sites. Deletion of the C-terminal sequence, NPFL-COOH, from RAB/Rip eliminated EH domain binding, whereas fusion of the same peptide sequence to glutathione S-transferase generated strong binding to the EH domains of Intersectin. Several experiments support the conclusion that the free carboxylate group contributes to binding of the NPFL motif at the C terminus of RAB/Rip to the EH domains of Intersectin. Finally, affinity selection experiments with the SH3 domains of Intersectin identified two endocytic proteins, dynamin and synaptojanin, as potential interacting proteins. We propose that Intersectin is a component of the endocytic machinery.

Physical and Functional Interaction between p53 Mutants and Different Isoforms of p73

p53 is the most frequently inactivated tumor suppressor gene in human cancer, whereas its homologue, p73, is rarely mutated. Similarly to p53, p73 can promote growth arrest or apoptosis when overexpressed in certain p53-null tumor cells. It has previously been shown that some human tumor-derived p53 mutants can exert gain of function activity. The molecular mechanism underlying this activity remains to be elucidated. We show here that human tumor-derived p53 mutants (p53His175 and p53Gly281) associate in vitro andin vivo with p73α, β, γ, and δ. This association occurs under physiological conditions, as verified in T47D and SKBR3 breast cancer cell lines. The core domain of mutant p53 is sufficient for the association with p73, whereas both the specific DNA binding and the oligomerization domains of p73 are required for the association with mutant p53. Furthermore, p53His175 and p53Gly281 mutants markedly reduce the transcriptional activity of the various isoforms of p73. Thus, human tumor-derived p53 mutants can associate with p73 not only physically but also functionally. These findings define a network involving mutant p53 and the various spliced isoforms of p73 that may confer upon tumor cells a selective survival advantage.

Bayesian modeling of the yeast SH3 domain interactome predicts spatiotemporal dynamics of endocytosis proteins.

A genome-scale specificity and interaction map for yeast SH3 domain-containing proteins reveal how family members show selective binding to target proteins and predicts the dynamic localization of new candidate endocytosis proteins.

The Protein-Protein Interaction tasks of BioCreative III: classification/ranking of articles and linking bio-ontology concepts to full text

BackgroundDetermining usefulness of biomedical text mining systems requires realistic task definition and data selection criteria without artificial constraints, measuring performance aspects that go beyond traditional metrics. The BioCreative III Protein-Protein Interaction (PPI) tasks were motivated by such considerations, trying to address aspects including how the end user would oversee the generated output, for instance by providing ranked results, textual evidence for human interpretation or measuring time savings by using automated systems. Detecting articles describing complex biological events like PPIs was addressed in the Article Classification Task (ACT), where participants were asked to implement tools for detecting PPI-describing abstracts. Therefore the BCIII-ACT corpus was provided, which includes a training, development and test set of over 12,000 PPI relevant and non-relevant PubMed abstracts labeled manually by domain experts and recording also the human classification times. The Interaction Method Task (IMT) went beyond abstracts and required mining for associations between more than 3,500 full text articles and interaction detection method ontology concepts that had been applied to detect the PPIs reported in them.ResultsA total of 11 teams participated in at least one of the two PPI tasks (10 in ACT and 8 in the IMT) and a total of 62 persons were involved either as participants or in preparing data sets/evaluating these tasks. Per task, each team was allowed to submit five runs offline and another five online via the BioCreative Meta-Server. From the 52 runs submitted for the ACT, the highest Matthews Correlation Coefficient (MCC) score measured was 0.55 at an accuracy of 89% and the best AUC iP/R was 68%. Most ACT teams explored machine learning methods, some of them also used lexical resources like MeSH terms, PSI-MI concepts or particular lists of verbs and nouns, some integrated NER approaches. For the IMT, a total of 42 runs were evaluated by comparing systems against manually generated annotations done by curators from the BioGRID and MINT databases. The highest AUC iP/R achieved by any run was 53%, the best MCC score 0.55. In case of competitive systems with an acceptable recall (above 35%) the macro-averaged precision ranged between 50% and 80%, with a maximum F-Score of 55%.ConclusionsThe results of the ACT task of BioCreative III indicate that classification of large unbalanced article collections reflecting the real class imbalance is still challenging. Nevertheless, text-mining tools that report ranked lists of relevant articles for manual selection can potentially reduce the time needed to identify half of the relevant articles to less than 1/4 of the time when compared to unranked results. Detecting associations between full text articles and interaction detection method PSI-MI terms (IMT) is more difficult than might be anticipated. This is due to the variability of method term mentions, errors resulting from pre-processing of articles provided as PDF files, and the heterogeneity and different granularity of method term concepts encountered in the ontology. However, combining the sophisticated techniques developed by the participants with supporting evidence strings derived from the articles for human interpretation could result in practical modules for biological annotation workflows.

VirusMINT: a viral protein interaction database

Understanding the consequences on host physiology induced by viral infection requires complete understanding of the perturbations caused by virus proteins on the cellular protein interaction network. The VirusMINT database (http://mint.bio.uniroma2.it/virusmint/) aims at collecting all protein interactions between viral and human proteins reported in the literature. VirusMINT currently stores over 5000 interactions involving more than 490 unique viral proteins from more than 110 different viral strains. The whole data set can be easily queried through the search pages and the results can be displayed with a graphical viewer. The curation effort has focused on manuscripts reporting interactions between human proteins and proteins encoded by some of the most medically relevant viruses: papilloma viruses, human immunodeficiency virus 1, Epstein–Barr virus, hepatitis B virus, hepatitis C virus, herpes viruses and Simian virus 40.

Can we infer peptide recognition specificity mediated by SH3 domains

Protein interaction domain families that modulate the formation of macromolecular complexes recognize specific sequence or structural motifs. For instance SH3 and WW domains bind to polyproline peptides while SH2 and FHA domains bind to peptides phosphorylated in Tyr and Thr respectively. Within each family, variations in the chemical characteristics of the domain binding pocket modulate a finer peptide recognition specificity and, as a consequence, determine the selection of functional protein partners in vivo. In the proteomic era there is the need for reliable inference methods to help restricting the sequence space of the putative targets to be confirmed experimentally by more laborious experimental approaches. Here we will review the published data about the peptide recognition specificity of the SH3 domain family and we will propose a classification of SH3 domains into eight classes. Finally, we will discuss whether the available information is sufficient to infer the recognition specificity of any uncharacterized SH3 domain.

The SH3 Domains of Endophilin and Amphiphysin Bind to the Proline-rich Region of Synaptojanin 1 at Distinct Sites That Display an Unconventional Binding Specificity

The proline-rich domain of synaptojanin 1, a synaptic protein with phosphatidylinositol phosphatase activity, binds to amphiphysin and to a family of recently discovered proteins known as the SH3p4/8/13, the SH3-GL, or the endophilin family. These interactions are mediated by SH3 domains and are believed to play a regulatory role in synaptic vesicle recycling. We have precisely mapped the target peptides on human synaptojanin that are recognized by the SH3 domains of endophilins and amphiphysin and proven that they are distinct. By a combination of different approaches, selection of phage displayed peptide libraries, substitution analyses of peptides synthesized on cellulose membranes, and a peptide scan spanning a 252-residue long synaptojanin fragment, we have concluded that amphiphysin binds to two sites, PIRPSR and PTIPPR, whereas endophilin has a distinct preferred binding site, PKRPPPPR. The comparison of the results obtained by phage display and substitution analysis permitted the identification of proline and arginine at positions 4 and 6 in the PIRPSR and PTIPPR target sequence as the major determinants of the recognition specificity mediated by the SH3 domain of amphiphysin 1. More complex is the structural rationalization of the preferred endophilin ligands where SH3 binding cannot be easily interpreted in the framework of the “classical” type I or type II SH3 binding models. Our results suggest that the binding repertoire of SH3 domains may be more complex than originally predicted.

Recognition specificity of individual EH domains of mammals and yeast

The Eps homology (EH) domain is a recently described protein binding module that is found, in multiple or single copies, in several proteins in species as diverse as human and yeast. In this work, we have investigated the molecular details of recognition specificity mediated by this domain family by characterizing the peptide‐binding preference of 11 different EH domains from mammal and yeast proteins. Ten of the eleven EH domains could bind at least some peptides containing an Asn‐Pro‐Phe (NPF) motif. By contrast, the first EH domain of End3p preferentially binds peptides containing an His‐Thr/Ser‐Phe (HT/SF) motif. Domains that have a low affinity for the majority of NPF peptides reveal some affinity for a third class of peptides that contains two consecutive amino acids with aromatic side chains (FW or WW). This is the case for the third EH domain of Eps15 and for the two N‐terminal domains of YBL47c. The consensus sequences derived from the peptides selected from phage‐displayed peptide libraries allows for grouping of EH domains into families that are characterized by different NPF‐context preference. Finally, comparison of the primary sequence of EH domains with similar or divergent specificity identifies a residue at position +3 following a conserved tryptophan, whose chemical characteristics modulate binding preference.