SABBAC: online Structural Alphabet-based protein BackBone reconstruction from Alpha-Carbon trace

SABBAC is an on-line service devoted to protein backbone reconstruction from alpha-carbon trace. It is based on the assembly of fragments taken from a library of reduced size, selected from the encoding of the protein trace in a hidden Markov model-derived structural alphabet. The assembly of the fragments is achieved by a greedy algorithm, using an energy-based scoring. Alpha-carbon coordinates remain unaffected. SABBAC simply positions the missing backbone atoms, no further refinement is performed. From our tests, SABBAC performs equal or better than other similar on-line approach and is robust to deviations on the alpha-carbon coordinates. It can be accessed at

SA-Mot: a web server for the identification of motifs of interest extracted from protein loops

The detection of functional motifs is an important step for the determination of protein functions. We present here a new web server SA-Mot (Structural Alphabet Motif) for the extraction and location of structural motifs of interest from protein loops. Contrary to other methods, SA-Mot does not focus only on functional motifs, but it extracts recurrent and conserved structural motifs involved in structural redundancy of loops. SA-Mot uses the structural word notion to extract all structural motifs from uni-dimensional sequences corresponding to loop structures. Then, SA-Mot provides a description of these structural motifs using statistics computed in the loop data set and in SCOP superfamily, sequence and structural parameters. SA-Mot results correspond to an interactive table listing all structural motifs extracted from a target structure and their associated descriptors. Using this information, the users can easily locate loop regions that are important for the protein folding and function. The SA-Mot web server is available at http://sa-mot.mti.univ-paris-diderot.fr

galaxy-tourbuilder

This Galaxy Tour Builder web extension allows you to easily create new interactive tours for the Galaxy framework. You can record and replay any tour, update its content and preview your changes in live. Once you are satisfied, you can export your tour as a YAML file and deploy it to your Galaxy instance and/or propose it to the community

Candidate Fragments Prediction and their Assembly with a Greedy Algorithm and a Coarse-Grained Force Field to solve Protein Folding

Abstract: Prediction of proteins tertiary structure, starting from its amino acids sequence, still remains a challenge in computational biology. We are developing a method to fold proteins in silico, starting from a HMM based structural alphabet which consists of a local 3D description of the structure. Candidate protein fragments are selected by SAFrAN, a new original approach combining SA-Search and profile prediction conditionned by PSIPRED results. Selected fragments cover the target sequence with more than 90 % and can approximate the native structure at high accuracy (less than 2 ˚A). Fragment assembly is performed by an improved greedy algorithm, and the relevance of the models is evaluated by a simplified version of the coarse-grained Optimized Potential for Efficient structure Prediction (sOPEP). We discuss the effectiveness of the approach to generate de novo 3D models of proteins

Improved PEP-FOLD Approach for Peptide and Miniprotein Structure Prediction

International audienceno abstrac