Philippe Chassignet

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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(More)
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(More)
Transmembrane 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(More)
Mechanical stresses play a key role in regulating cell growth and cell differentiation. Using mechanical and physiological data available in the literature, we are able to construct a growth curve of a child, which we compare to the standard curve. It appears likely that the impact of hormones on pubertal growth rate sprout followed by growth arrest can be(More)
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(More)
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(More)
Background Transmembrane proteins are divided into two main classes based upon their conformation: b-barrels and ahelical bundles. Computational methods based on learning are poorly tractable since the transmembrane structures are difficult to be determined by standard experimental methods. Generally, those structures are not only a series of b-strands or(More)
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