Protein Peeling 2: a web server to convert protein structures into series of protein units

Protein Peeling 2 (PP2) is a web server for the automatic identification of protein units (PUs) given the 3D coordinates of a protein. PUs are an intermediate level of protein structure description between protein domains and secondary structures. It is a new tool to better understand and analyze the organization of protein structures. PP2 uses only the matrices of protein contact probabilities and cuts the protein structures optimally using Matthews' coefficient correlation. An index assesses the compactness quality of each PU. Results are given both textually and graphically using JMol and PyMol softwares. The server can be accessed from

PBxplore: a tool to analyze local protein structure and deformability with Protein Blocks

This paper describes the development and application of a suite of tools, called PBxplore, to analyze the dynamics and deformability of protein structures using Protein Blocks (PBs). Proteins are highly dynamic macromolecules, and a classical way to analyze their inherent flexibility is to perform molecular dynamics simulations. The advantage of using small structural prototypes such as PBs is to give a good approximation of the local structure of the protein backbone. More importantly, by reducing the conformational complexity of protein structures, PBs allow analysis of local protein deformability which cannot be done with other methods and had been used efficiently in different applications. PBxplore is able to process large amounts of data such as those produced by molecular dynamics simulations. It produces frequencies, entropy and information logo outputs as text and graphics. PBxplore is available at https://github.com/pierrepo/PBxplore and is released under the open-source MIT license

Protein structural motif prediction in multidimensional φ-ψ space leads to improved secondary structure prediction

A significant step towards establishing the structure and function of a protein is the prediction of the local conformation of the polypeptide chain. In this article, we present systems for the prediction of three new alphabets of local structural motifs. The motifs are built by applying multidimensional scaling (MDS) and clustering to pair-wise angular distances for multiple φ-ψ angle values collected from high-resolution protein structures. The predictive systems, based on ensembles of bidirectional recurrent neural network architectures, and trained on a large non-redundant set of protein structures, achieve 72%, 66%, and 60% correct motif prediction on an independent test set for di-peptides (six classes), tri-peptides (eight classes) and tetra-peptides (14 classes), respectively, 28–30% above baseline statistical predictors. We then build a further system, based on ensembles of two-layered bidirectional recurrent neural networks, to map structural motif predictions into a traditional 3-class (helix, strand, coil) secondary structure. This system achieves 79.5% correct prediction using the “hard” CASP 3-class assignment, and 81.4% with a more lenient assignment, outper- forming a sophisticated state-of-the-art predictor (Porter) trained in the same experimental conditions. The structural motif predictor is publicly available at: http://distill.ucd.ie/porter+/.Science Foundation IrelandIrish Research Council for Science, Engineering and TechnologyHealth Research Boardau, ti, ke, ab - kpw28/11/1

Representation of protein secondary structure using bond-orientational order parameters

Structural studies of proteins for motif mining and other pattern recognition techniques require the abstraction of the structure into simpler elements for robust matching. In this study, we propose the use of bond-orientational order parameters, a well-established metric usually employed to compare atom packing in crystals and liquids. Creating a vector of orientational order parameters of residue centers in a sliding window fashion provides us with a descriptor of local structure and connectivity around each residue that is easy to calculate and compare. To test whether this representation is feasible and applicable to protein structures, we tried to predict the secondary structure of protein segments from those descriptors, resulting in 0.99 AUC (area under the ROC curve). Clustering those descriptors to 6 clusters also yield 0.93 AUC, showing that these descriptors can be used to capture and distinguish local structural information