Spherical polar Fourier assembly of protein complexes with arbitrary point group symmetry

International audienceA novel fast Fourier transform-based ab inito docking algorithm called SAM is presented, for building perfectly symmetrical models of protein complexes with arbitrary point group symmetry. The basic approach uses a novel and very fast one-dimensional symmetry-constrained spherical polar Fourier search to assemble cyclic Cn systems from a given protein monomer. Structures with higher-order (Dn, T, O and I) point group symmetries may be built using a subsequent symmetry-constrained Fourier domain search to assemble trimeric sub-units. The results reported here show that the SAM algorithm can correctly assemble monomers of up to around 500 residues to produce a near-native complex structure with the given point group symmetry in 17 out of 18 test cases. The SAM program may be downloaded for academic use at http://sam.loria.fr/

HexServer: an FFT-based protein docking server powered by graphics processors

HexServer (http://hexserver.loria.fr/) is the first Fourier transform (FFT)-based protein docking server to be powered by graphics processors. Using two graphics processors simultaneously, a typical 6D docking run takes ∼15 s, which is up to two orders of magnitude faster than conventional FFT-based docking approaches using comparable resolution and scoring functions. The server requires two protein structures in PDB format to be uploaded, and it produces a ranked list of up to 1000 docking predictions. Knowledge of one or both protein binding sites may be used to focus and shorten the calculation when such information is available. The first 20 predictions may be accessed individually, and a single file of all predicted orientations may be downloaded as a compressed multi-model PDB file. The server is publicly available and does not require any registration or identification by the user

Evaluation of individual-tree and disaggregative prediction methods for Douglas-fir stands in western Oregon

The efficiency of six disaggregative methods and two individual-tree methods was evaluated in terms of their ability to predict 5-year basal area increment for Douglas-fir (Pseudotsuga menziesii (Mirb.) Franco) stands in western Oregon. Models were developed for predicting gross stand basal-area increment and individual-tree diameter increment. In addition, models were developed to disaggregate the active increment prediction methods to the tree level. Passive and active prediction schemes were evaluated for both the tree and stand levels. Generally, the individual-tree approach was superior to the disaggregative approach for prediction of both stand and tree growth. This was less evident, however, when crown ratio was eliminated from the individual-tree models. This suggests that at least some of the disparity between the two is due to the presence of crown ratio in an individual-tree passive aggregation approach. The additive disaggregation approach appeared to be best suited to young stands (less than 50 years of age). The linearity assumption required for this particular model appeared to be violated for older stands with larger trees. Generally, the two whole-stand, gross-growth models used in this study were inferior to the individual-tree method for predicting gross basal area growth for one period.Keywords: individual-tree prediction methods, basal-area increment, disaggregative prediction methods, tree growth, stand growth, crown ratio, Douglas-fir (Pseudotsuga menziesii)Keywords: individual-tree prediction methods, basal-area increment, disaggregative prediction methods, tree growth, stand growth, crown ratio, Douglas-fir (Pseudotsuga menziesii

Unraveling the molecular architecture of a G protein-coupled receptor/β-arrestin/Erk module complex

International audienceβ-arrestins serve as signaling scaffolds downstream of G protein-coupled receptors, and thus play a crucial role in a plethora of cellular processes. Although it is largely accepted that the ability of β-arrestins to interact simultaneously with many protein partners is key in G protein-independent signaling of GPCRs, only the precise knowledge of these multimeric arrangements will allow a full understanding of the dynamics of these interactions and their functional consequences. However, current experimental procedures for the determination of the three-dimensional structures of protein-protein complexes are not well adapted to analyze these short-lived, multi-component assemblies. We propose a model of the receptor/β-arrestin/Erk1 signaling module, which is consistent with most of the available experimental data. Moreover, for the β-arrestin/Raf1 and the β-arrestin/ERK interactions, we have used the model to design interfering peptides and shown that they compete with both partners, hereby demonstrating the validity of the predicted interaction regions

Functional Annotation of Proteins using Domain Embedding based Sequence Classification

International audienceDue to the recent advancement in genomic sequencing technologies, the number of protein sequences in public databases is growing exponentially. The UniProt Knowledgebase (UniProtKB) is currently the largest and most comprehensive resource for protein sequence and annotation data. The May 2019 release of the Uniprot Knowledge base (UniprotKB) contains around 158 million protein sequences. For the complete exploitation of this huge knowledge base, protein sequences need to be annotated with functional properties such as Enzyme Commission (EC) numbers and Gene Ontology terms. However, there is only about half a million sequences (UniprotKB/SwissProt) are reviewed and functionally annotated by expert curators using information extracted from the published literature and computational analyses. The manual annotation by experts are expensive, slow and insufficient to fill the gap between the annotated and unannotated protein sequences. In this paper, we present an automatic functional annotation technique using neural network based based word embedding exploiting domain and family information of proteins. Domains are the most conserved regions in protein sequences and constitute the building blocks of 3D protein structures. To do the experiment, we used fastText a , a library for learning of word embeddings and text classification developed by Facebook's AI Research lab. The experimental results show that domain embeddings perform much better than k-mer based word embeddings. a https://github.com/facebookresearch/fasttex

Using Content-Based Filtering to Infer Direct Associations between the CATH, Pfam, and SCOP Domain Databases

International audienceProtein domain structure classification systems such as CATH and SCOP provide a useful way todescribe evolutionary structure-function relationships. Similarly, the Pfam sequence-basedclassification identifies sequence-function relationships. Nonetheless, there is no completedirect mapping from one classification to another. This means that functional annotations thathave been assigned to one classification cannot always be assigned to another. Here, wepresent a novel content-based filtering approach called CAPS (Computing direct Associationsbetween annotations of Protein Sequences and Structures) to systematically analyze multipleprotein-domain relationships in the SIFTS and UniProt databases in order to infer directmappings between CATH superfamilies, Pfam clans or families, and SCOP superfamilies. Wethen compare the result with existing mappings in Pfam, InterPro, and Genome3D

ECDomainMiner: discovering hidden associations between enzyme commission numbers and Pfam domains

International audienceBackgroundMany entries in the protein data bank (PDB) are annotated to show their component protein domains according to the Pfam classification, as well as their biological function through the enzyme commission (EC) numbering scheme. However, despite the fact that the biological activity of many proteins often arises from specific domain-domain and domain-ligand interactions, current on-line resources rarely provide a direct mapping from structure to function at the domain level. Since the PDB now contains many tens of thousands of protein chains, and since protein sequence databases can dwarf such numbers by orders of magnitude, there is a pressing need to develop automatic structure-function annotation tools which can operate at the domain level

Active metamaterial polarization modulators for the Terahertz frequency range

Abstract Active control of chirality in the terahertz frequency range is of great importance in many scientific areas, which include research into fundamental optical phenomena, investigation of novel materials, spectroscopy, imaging, wireless communications and chemistry. The lack of efficient, integrated and fast-reconfigurable polarization modulators has hindered, so far, the full exploitation of applications in all the aforementioned fields. Metamaterials are artificial resonant elements possessing unique remarkable properties such as high efficiency and miniaturization capability. The interplay of metallic metamaterial arrays with electrostatically tunable monolayer graphene has been demonstrated to be a valid approach for the realization of a novel class of THz devices. In this work, the realization of active chiral graphene/metamaterial modulator is presented. The versatility of this experimental approach allowed the device integration with broadband sources such as terahertz time domain spectrometers as well as with quantum cascade lasers. A continuous rotation of the polarization plane &gt; 30° has been reported with a reconfiguration speed &gt; 5 MHz. These results pave the way to the integration of fast terahertz polarization modulators in all the applications where these devices are in great demand.</jats:p