Philippe Chassignet

A graph-theoretic approach for classification and structure prediction of transmembrane β-barrel proteins

BackgroundTransmembrane β-barrel proteins are a special class of transmembrane proteins which play several key roles in human body and diseases. Due to experimental difficulties, the number of transmembrane β-barrel proteins with known structures is very small. Over the years, a number of learning-based methods have been introduced for recognition and structure prediction of transmembrane β-barrel proteins. Most of these methods emphasize on homology search rather than any biological or chemical basis.ResultsWe present a novel graph-theoretic model for classification and structure prediction of transmembrane β-barrel proteins. This model folds proteins based on energy minimization rather than a homology search, avoiding any assumption on availability of training dataset. The ab initio model presented in this paper is the first method to allow for permutations in the structure of transmembrane proteins and provides more structural information than any known algorithm. The model is also able to recognize β-barrels by assessing the pseudo free energy. We assess the structure prediction on 41 proteins gathered from existing databases on experimentally validated transmembrane β-barrel proteins. We show that our approach is quite accurate with over 90% F-score on strands and over 74% F-score on residues. The results are comparable to other algorithms suggesting that our pseudo-energy model is close to the actual physical model. We test our classification approach and show that it is able to reject α-helical bundles with 100% accuracy and β-barrel lipocalins with 97% accuracy.ConclusionsWe show that it is possible to design models for classification and structure prediction for transmembrane β-barrel proteins which do not depend essentially on training sets but on combinatorial properties of the structures to be proved. These models are fairly accurate, robust and can be run very efficiently on PC-like computers. Such models are useful for the genome screening.

Prediction of permuted super-secondary structures in β-barrel proteins

Computational structure prediction methods based on learning are poorly tractable for transmembrane β-barrel (TMB) proteins, for it is difficult to observe them with standard experimental techniques. Generally, those structures are not only a series of β-strands where each is bonded to the preceding and succeeding ones in the primary sequence, but they may contain Greek key or Jelly roll motifs as well. This may be described as a permutation on the order of the bonded segments. We model the protein folding problem with minimum energy into the search of the longest closed path in a weighted graph with respect to a given permutation. With dynamic programming, the algorithm runs in O(N2) for an identity permutation, and at most O(N4) for the Greek key motifs, where N is the number of amino acids. The prediction accuracy as well as the discrimination ability is favorably comparable with existing works.

Energy-based classification and structure prediction of transmembrane beta-barrel proteins

Transmembrane β-barrel (TMB) proteins are a special class of transmembrane proteins which play several key roles in human body and diseases. Due to experimental difficulties, the number of TMB proteins with known structures is very small. Over the years, a number of learning-based methods have been introduced for recognition and structure prediction of TMB proteins. Most of these methods emphasize on homology search rather than any biological or chemical basis. We present a novel graph-theoretic model for classification and structure prediction of TMB proteins. This model folds proteins based on energy minimization rather than a homology search, avoiding any assumption on availability of training dataset. The ab initio model presented in this paper is the first method to allow for permutations in the structure of transmembrane proteins and provides more structural information than any known algorithm. The model is also able to recognize β-barrels by assessing the pseudo free energy. We assess the structure prediction on 42 proteins gathered from existing databases on experimentally validated TMB proteins. We show that our approach is quite accurate with over 90% F-score on strands and over 74% F-score on residues. The results are comparable to other algorithms suggesting that our pseudo-energy model is close to the actual physical model. We test our classification approach and show that it is able to reject β-helical bundles with 100% accuracy and β-barrel lipocalins with 97% accuracy.

Prediction of super-secondary structure in α-helical and β-barrel transmembrane proteins

A dynamic programming algorithm is proposed to predict the structure of different families of proteins and is tested with the b-barrel transmembrane proteins.

Accurate Prediction of the Statistics of Repetitions in Random Sequences: A Case Study in Archaea Genomes

Repetitive patterns in genomic sequences have a great biological significance and also algorithmic implications. Analytic combinatorics allow to derive formula for the expected length of repetitions in a random sequence. Asymptotic results, which generalize previous works on a binary alphabet, are easily computable. Simulations on random sequences show their accuracy. As an application, the sample case of Archaea genomes illustrates how biological sequences may differ from random sequences.

On permuted super-secondary structures of transmembrane β-barrel proteins

Transmembrane β-barrel (TMB) proteins constitute a special class of proteins, which are located in outer membranes of Gram-negative bacteria, mitochondria, and chloroplasts. These proteins play diverse important roles in biological organisms. However, only a small number of TMB protein structures are currently known due to difficulties with experimental techniques. Hence, computational structure prediction methods based on learning are poorly tractable for these proteins. We introduce here a graph-theoretic ab initio model for predicting structures of TMB proteins by free energy minimization. TMB super-secondary structures with permuted arrangements are taken into consideration in this model. We show that finding a permuted structure is an NP-hard problem and then analyze the complexity of a tree decomposition-based algorithm to search for the optimal structure of a TMB protein corresponding to a given permutation. The robust performance of the model has been proven in our previously published results.

Supersecondary Structure Prediction of Transmembrane Beta-Barrel Proteins

We introduce a graph-theoretic model for predicting the supersecondary structure of transmembrane β-barrel proteins--a particular class of proteins that performs diverse important functions but it is difficult to determine their structure with experimental methods. This ab initio model resolves the protein folding problem based on pseudo-energy minimization with the aid of a simple probabilistic filter. It also allows for determining structures whose barrel follows a given permutation on the arrangement of β-strands, and allows for rapidly discriminating the transmembrane β-barrels from other kinds of proteins. The model is fairly accurate, robust and can be run very efficiently on PC-like computers, thus proving useful for genome screening.

Hand radiograph analysis for fully automatic bone age assessment

This paper describes a method for the fully automatic and reliable segmentation of the bones in a radiograph of the childs hand. The problem consists in identifying the contours of the bones and the difficulty lies in the large variability of the anatomical structures, according to age, hand pose or individual. The model shall not force any standard interpretation, hence we use a simple hierarchical geometric model that provides the only information required for the identification of the chunks of contours. The phalangeal and metacarpal resulting segmentation is proved robust over a set of many hundred of images and measurements of shapes, sizes, areas, ..., are now quite allowed. The next step consists in extending the model for more accurate measurements and also for the localization of the carpal bones.