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Henri Orland

dblp:40/5718 · DBLP profile ↗
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3ranked-venue papers
0as first author
0since 2021 · last 2015
0000-0002-6983-2951ORCID · verified

Domains — the database's venue-derived domains; a paper can count in several

Applied, interdisciplinary, general and emerging computing · 3

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Interdisciplinary, comprehensive, and emerging computing
2 papers
Bioinformatics and computational biology · 92% Medical and health informatics · 8%

Topics — the 5 heaviest of 6, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Bioinformatics and computational biology
protein design
0.212015
Probing the binding affinity of amyloids to reduce toxicity of oligomers in diabetes · Bioinform. 2015
Bioinformatics and computational biology
protein structure analysis
0.212015
Probing the binding affinity of amyloids to reduce toxicity of oligomers in diabetes · Bioinform. 2015
Bioinformatics and computational biology › protein structure analysis › protein structure alignment
multiple structure alignment
0.112009
MISTRAL: a tool for energy-based multiple structural alignment of proteins · Bioinform. 2009
Bioinformatics and computational biology
protein structure prediction
0.112009
MISTRAL: a tool for energy-based multiple structural alignment of proteins · Bioinform. 2009
Bioinformatics and computational biology › protein structure analysis
structural alignment
0.112009
MISTRAL: a tool for energy-based multiple structural alignment of proteins · Bioinform. 2009

Methods — techniques the papers use, named apart from their topics

mathematical modeling · 0.2binding affinity scoring · 0.2statistical significance estimation · 0.1energy minimization · 0.1
YearPublicationVenuePosition
2015 Probing the binding affinity of amyloids to reduce toxicity of oligomers in diabetes
abstract
MOTIVATION: Amyloids play a role in the degradation of β-cells in diabetes patients. In particular, short amyloid oligomers inject themselves into the membranes of these cells and create pores that disrupt the strictly controlled flow of ions through the membranes. This leads to cell death. Getting rid of the short oligomers either by a deconstruction process or by elongating them into longer fibrils will reduce this toxicity and allow the β-cells to live longer. RESULTS: We develop a computational method to probe the binding affinity of amyloid structures and produce an amylin analog that binds to oligomers and extends their length. The binding and extension lower toxicity and β-cell death. The amylin analog is designed through a parsimonious selection of mutations and is to be administered with the pramlintide drug, but not to interact with it. The mutations (T9K L12K S28H T30K) produce a stable native structure, strong binding affinity to oligomers, and long fibrils. We present an extended mathematical model for the insulin-glucose relationship and demonstrate how affecting the concentration of oligomers with such analog is strictly coupled with insulin release and β-cell fitness. AVAILABILITY AND IMPLEMENTATION: SEMBA, the tool to probe the binding affinity of amyloid proteins and generate the binding affinity scoring matrices and R-scores is available at: http://amyloid.cs.mcgill.ca
Mohamed Raef Smaoui, Henri Orland, Jérôme Waldispühl
Bioinform.2
2010 Knotted vs. Unknotted Proteins: Evidence of Knot-Promoting Loops
abstract
Knotted proteins, because of their ability to fold reversibly in the same topologically entangled conformation, are the object of an increasing number of experimental and theoretical studies. The aim of the present investigation is to assess, on the basis of presently available structural data, the extent to which knotted proteins are isolated instances in sequence or structure space, and to use comparative schemes to understand whether specific protein segments can be associated to the occurrence of a knot in the native state. A significant sequence homology is found among a sizeable group of knotted and unknotted proteins. In this family, knotted members occupy a primary sub-branch of the phylogenetic tree and differ from unknotted ones only by additional loop segments. These "knot-promoting" loops, whose virtual bridging eliminates the knot, are found in various types of knotted proteins. Valuable insight into how knots form, or are encoded, in proteins could be obtained by targeting these regions in future computational studies or excision experiments.
Raffaello Potestio, Cristian Micheletti, Henri Orland
PLoS Comput. Biol.3
2009 MISTRAL: a tool for energy-based multiple structural alignment of proteins
abstract
MOTIVATION: The steady growth of the number of available protein structures has constantly motivated the development of new algorithms for detecting structural correspondences in proteins. Detecting structural equivalences in two or more proteins is computationally demanding as it typically entails the exploration of the combinatorial space of all possible amino acid pairings in the parent proteins. The search is often aided by the introduction of various constraints such as considering protein fragments, rather than single amino acids, and/or seeking only sequential correspondences in the given proteins. An additional challenge is represented by the difficulty of associating to a given alignment, a reliable a priori measure of its statistical significance. RESULTS: Here, we present and discuss MISTRAL (Multiple STRuctural ALignment), a novel strategy for multiple protein alignment based on the minimization of an energy function over the low-dimensional space of the relative rotations and translations of the molecules. The energy minimization avoids combinatorial searches and returns pairwise alignment scores for which a reliable a priori statistical significance can be given. AVAILABILITY: MISTRAL is freely available for academic users as a standalone program and as a web service at http://ipht.cea.fr/protein.php.
Cristian Micheletti, Henri Orland
Bioinform.2