Pixel: a content management platform for quantitative omics data.

Background: In biology, high-throughput experimental technologies, also referred as "omics" technologies, are increasingly used in research laboratories. Several thousands of gene expression measurements can be obtained in a single experiment. Researchers are routinely facing the challenge to annotate, store, explore and mine all the biological information they have at their disposal.

Improved PEP-FOLD Approach for Peptide and Miniprotein Structure Prediction.

Peptides and mini proteins have many biological and biomedical implications, which motivates the development of accurate methods, suitable for large-scale experiments, to predict their experimental or native conformations solely from sequences. In this study, we report PEP-FOLD2, an improved coarse grained approach for peptide de novo structure prediction and compare it with PEP-FOLD1 and the state-of-the-art Rosetta program. Using a benchmark of 56 structurally diverse peptides with 25-52 amino acids and a total of 600 simulations for each system, PEP-FOLD2 generates higher quality models than PEP-FOLD1, and PEP-FOLD2 and Rosetta generate near-native or native models for 95% and 88% of the targets, respectively.

Isotype modulates epitope specificity, affinity, and antiviral activities of anti-HIV-1 human broadly neutralizing 2F5 antibody.

The constant heavy chain (CH1) domain affects antibody affinity and fine specificity, challenging the paradigm that only variable regions contribute to antigen binding. To investigate the role of the CH1 domain, we constructed IgA2 from the broadly neutralizing anti-HIV-1 2F5 IgG1, and compared 2F5 IgA2 and IgG binding affinity and functional activities. We found that 2F5 IgA2 bound to the gp41 membrane proximal external region with higher affinity than IgG1.

PEP-FOLD: an updated de novo structure prediction server for both linear and disulfide bonded cyclic peptides.

In the context of the renewed interest of peptides as therapeutics, it is important to have an on-line resource for 3D structure prediction of peptides with well-defined structures in aqueous solution. We present an updated version of PEP-FOLD allowing the treatment of both linear and disulphide bonded cyclic peptides with 9-36 amino acids. The server makes possible to define disulphide bonds and any residue-residue proximity under the guidance of the biologists.

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.

The FAF-Drugs2 server: a multistep engine to prepare electronic chemical compound collections.

Summary: The FAF-Drugs2 server is a web application that prepares chemical compound libraries prior to virtual screening or that assists hit selection/lead optimization before chemical synthesis or ordering. The FAF-Drugs2 web server is an enhanced version of the FAF-Drugs2 package that now includes Pan Assay Interference Compounds detection. This online toolkit has been designed through a user-centered approach with emphasis on user-friendliness.

fpocket: online tools for protein ensemble pocket detection and tracking.

Computational small-molecule binding site detection has several important applications in the biomedical field. Notable interests are the identification of cavities for structure-based drug discovery or functional annotation of structures. fpocket is a small-molecule pocket detection program, relying on the geometric alpha-sphere theory.

Mobyle: a new full web bioinformatics framework.

Motivation: For the biologist, running bioinformatics analyses involves a time-consuming management of data and tools. Users need support to organize their work, retrieve parameters and reproduce their analyses. They also need to be able to combine their analytic tools using a safe data flow software mechanism.

A fast method for large-scale de novo peptide and miniprotein structure prediction.

Although peptides have many biological and biomedical implications, an accurate method predicting their equilibrium structural ensembles from amino acid sequences and suitable for large-scale experiments is still missing. We introduce a new approach-PEP-FOLD-to the de novo prediction of peptides and miniproteins. It first predicts, in the terms of a Hidden Markov Model-derived structural alphabet, a limited number of local conformations at each position of the structure.

PEP-FOLD: an online resource for de novo peptide structure prediction.

Rational peptide design and large-scale prediction of peptide structure from sequence remain a challenge for chemical biologists. We present PEP-FOLD, an online service, aimed at de novo modelling of 3D conformations for peptides between 9 and 25 amino acids in aqueous solution. Using a hidden Markov model-derived structural alphabet (SA) of 27 four-residue letters, PEP-FOLD first predicts the SA letter profiles from the amino acid sequence and then assembles the predicted fragments by a greedy procedure driven by a modified version of the OPEP coarse-grained force field.

A coarse-grained protein force field for folding and structure prediction.

We have revisited the protein coarse-grained optimized potential for efficient structure prediction (OPEP). The training and validation sets consist of 13 and 16 protein targets. Because optimization depends on details of how the ensemble of decoys is sampled, trial conformations are generated by molecular dynamics, threading, greedy, and Monte Carlo simulations, or taken from publicly available databases.

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.