Structural changes are critical for biological functions of proteins and describing conformational changes in large macromolecular complexes is a major challenge. We have recently developed a hybrid method (HEMNMA) combining transmission electron microscopy (EM), normal mode analysis (NMA), and image analysis to study macromolecular dynamics. NMA is traditionally used to study macromolecular motions while HEMNMA provides insight into actual conformational changes seen by EM. HEMNMA uses normal modes to elastically align EM images with a reference structure in order to determine the conformations present in images and evaluate their pertinence. In this paper, we show how HEMNMA can be used with an atomic-resolution reference structure, using as an example the study of the conformational dynamics of Tomato Bushy Stunt Virus.

MOTIVATION: Describing domain architecture is a critical step in the functional characterization of proteins. However, some orphan domains do not match any profile stored in dedicated domain databases and are thereby difficult to analyze. RESULTS: We present here an original novel approach, called TREMOLO-HCA, for the analysis of orphan domain sequences and inspired from our experience in the use of Hydrophobic Cluster Analysis (HCA). Hidden relationships between protein sequences can be more easily identified from the PSI-BLAST results, using information on domain architecture, HCA plots and the conservation degree of amino acids that may participate in the protein core. This can lead to reveal remote relationships with known families of domains, as illustrated here with the identification of a hidden Tudor tandem in the human BAHCC1 protein and a hidden ET domain in the Saccharomyces cerevisiae Taf14p and human AF9 proteins. The results obtained in such a way are consistent with those provided by HHPRED, based on pairwise comparisons of HHMs. Our approach can, however, be applied even in absence of domain profiles or known 3D structures for the identification of novel families of domains. It can also be used in a reverse way for refining domain profiles, by starting from known protein domain families and identifying highly divergent members, hitherto considered as orphan. AVAILABILITY: We provide a possible integration of this approach in an open TREMOLO-HCA package, which is fully implemented in python v2.7 and is available on request. Instructions are available at http://www.impmc.upmc.fr/∼callebau/tremolohca.html. CONTACT: [email protected] SUPPLEMENTARY INFORMATION: Supplementary Data are available at Bioinformatics online.

In order to get a comprehensive repertoire of foldable domains within whole proteomes, including orphan domains, we developed a novel procedure, called SEG-HCA. From only the information of a single amino acid sequence, SEG-HCA automatically delineates segments possessing high densities in hydrophobic clusters, as defined by Hydrophobic Cluster Analysis (HCA). These hydrophobic clusters mainly correspond to regular secondary structures, which together form structured or foldable regions. Genome-wide analyses revealed that SEG-HCA is opposite of disorder predictors, both addressing distinct structural states. Interestingly, there is however an overlap between the two predictions, including small segments of disordered sequences, which undergo coupled folding and binding. SEG-HCA thus gives access to these specific domains, which are generally poorly represented in domain databases. Comparison of the whole set of SEG-HCA predictions with the Conserved Domain Database (CDD) also highlighted a wide proportion of predicted large (length >50 amino acids) segments, which are CDD orphan. These orphan sequences may either correspond to highly divergent members of already known families or belong to new families of domains. Their comprehensive description thus opens new avenues to investigate new functional and/or structural features, which remained so far uncovered. Altogether, the data described here provide new insights into the protein architecture and organization throughout the three kingdoms of life.

We describe here LOTUS, a hitherto uncharacterized small globular domain, which was identified using sensitive sequence profile analysis. The LOTUS domain is found in germline-specific proteins that are present in the nuage/polar granules of germ cells. TDRD5 and TDRD7, two mammalian members of the germline Tudor group, possess three copies of the LOTUS domain in their extreme N-termini. The Tudor domains of these proteins bind symmetric dimethyl arginines present on the germ cell-specific Piwi proteins, which form a particular clade of Argonaute proteins. Piwi proteins interact with a specific class of non-coding RNAs [piwi-interacting RNAs (piRNAs)] and play a key role in the repression (silencing) of transposons and possibly other germline-specific functions. A LOTUS domain is also present in the Oskar protein, a critical component of the pole plasm in the Drosophila oocyte, which is required for germ cell formation. LOTUS domains are found in various proteins from metazoans and plants, are often associated with RNA-specific modules and are likely to adopt a winged helix fold. This suggests a germline-specific role in the mRNA localization and/or translation or a specific function toward piRNAs.

CHS1/LYST, the causative protein of the Chediak-Higashi syndrome (CHS), belongs to the BEACH (named after BEige And Chediak-Higashi) family, which includes various large proteins sharing the same C-terminal domain architecture [a PH (Pleckstrin homology)-BEACH domain followed by WD repeats). Members of the BEACH family are generally defined as vesicle-trafficking regulatory proteins, but their functions remain to be determined at the molecular level. Here, using a panel of sensitive methods of sequence analysis, we show that the N-terminal regions of BEACH proteins contain an as yet not described domain, which shares striking similarities with clostridial neurotoxins and defines a novel family within the concanavalin A (ConA)-like lectin superfamily. These results suggest that the BEACH ConA-like lectin domain could be involved in oligosaccharide binding associated with protein traffic and sorting along the secretory pathway, especially in relation with components of the vesicle fusion machinery.

Zona Pellucida (ZP) domains have been found in a wide variety of extracellular proteins, in which they play essential role for polymerization. They are shared by the ZP proteins, which constitute the extracellular coat of animal eggs. Except from ZP3, constituting the primary sperm receptor, the ZP proteins possess, in addition to their C-terminal ZP domains, N-terminal extensions, which are thought to play an important role in the species-specific gamete recognition. Here, we show that these extensions are made of single or multiple copies of a small globular domain, which can be significantly related to the N-terminal region of ZP domains (ZP-N domains). This finding brings new insights into the molecular evolution of ZP proteins, which may have evolved around a common ZP-N architecture, and more generally into the noticeable sequence diversity of ZP-N domains, which can be found as isolated subunits or tightly associated with ZP-C domains to form complete, canonical ZP domains.

SUMMARY: In this study, we describe a novel domain, OCRE, which is shared by the recently identified angiogenic factor VG5Q and a specific family of RNA-binding motif proteins. The OCRE domain is characterized by a 5-fold, imperfectly repeated octameric sequence, which includes a triplet of often-conserved aromatic amino acids predicted to form a beta-strand and in which the slightly modified fifth repeat might act as a repeat terminator. Although the function of this domain remains to be elucidated, the domain architecture of OCRE containing proteins and experimental data suggest a role in RNA metabolism and/or in signalling pathways activated by the tumor necrosis factor superfamily of cytokines.

Automat is a novel program which finds exhaustively all the oligopeptide segments shared by a given protein of any size with the proteins of a whole databank. It allows the user to collect statistics on the composition of the sequence studied in reference to the databank used. We present here the rationale and the algorithm underlying this powerful software. Besides its immediate interest for identifying efficiently similarities between proteins (or DNAs), biological applications of this software have already been described in the case of HIV-1 viral proteins, and Automat should prove useful also for studying more generally autoimmune diseases.