The Experts below are selected from a list of 163512 Experts worldwide ranked by ideXlab platform
Anneclaude Camproux - One of the best experts on this subject based on the ideXlab platform.
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pockdrug server a new web server for predicting pocket druggability on holo and apo proteins
Nucleic Acids Research, 2015Co-Authors: Hiba Abi Hussein, Colette Geneix, Michel Petitjean, Leslie Regad, Alexandre Borrel, Anneclaude CamprouxAbstract:Predicting protein pocket's ability to bind drug-like molecules with high affinity, i.e. druggability, is of major interest in the target Identification Phase of drug discovery. Therefore, pocket druggability investigations represent a key step of compound clinical progression projects. Currently computational druggability prediction models are attached to one unique pocket estimation method despite pocket estimation uncertainties. In this paper, we propose ‘PockDrug-Server’ to predict pocket druggability, efficient on both (i) estimated pockets guided by the ligand proximity (extracted by proximity to a ligand from a holo protein structure) and (ii) estimated pockets based solely on protein structure information (based on amino atoms that form the surface of potential binding cavities). PockDrug-Server provides consistent druggability results using different pocket estimation methods. It is robust with respect to pocket boundary and estimation uncertainties, thus efficient using apo pockets that are challenging to estimate. It clearly distinguishes druggable from less druggable pockets using different estimation methods and outperformed recent druggability models for apo pockets. It can be carried out from one or a set of apo/holo proteins using different pocket estimation methods proposed by our web server or from any pocket previously estimated by the user. PockDrug-Server is publicly available at: http://pockdrug.rpbs.univ-paris-diderot.fr.
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pockdrug a model for predicting pocket druggability that overcomes pocket estimation uncertainties
Journal of Chemical Information and Modeling, 2015Co-Authors: Michel Petitjean, Anneclaude Camproux, Leslie Regad, Alexandre Borrel, Henri XhaardAbstract:Predicting protein druggability is a key interest in the target Identification Phase of drug discovery. Here, we assess the pocket estimation methods’ influence on druggability predictions by comparing statistical models constructed from pockets estimated using different pocket estimation methods: a proximity of either 4 or 5.5 A to a cocrystallized ligand or DoGSite and fpocket estimation methods. We developed PockDrug, a robust pocket druggability model that copes with uncertainties in pocket boundaries. It is based on a linear discriminant analysis from a pool of 52 descriptors combined with a selection of the most stable and efficient models using different pocket estimation methods. PockDrug retains the best combinations of three pocket properties which impact druggability: geometry, hydrophobicity, and aromaticity. It results in an average accuracy of 87.9% ± 4.7% using a test set and exhibits higher accuracy (∼5–10%) than previous studies that used an identical apo set. In conclusion, this study co...
Alexandre Borrel - One of the best experts on this subject based on the ideXlab platform.
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pockdrug server a new web server for predicting pocket druggability on holo and apo proteins
Nucleic Acids Research, 2015Co-Authors: Hiba Abi Hussein, Colette Geneix, Michel Petitjean, Leslie Regad, Alexandre Borrel, Anneclaude CamprouxAbstract:Predicting protein pocket's ability to bind drug-like molecules with high affinity, i.e. druggability, is of major interest in the target Identification Phase of drug discovery. Therefore, pocket druggability investigations represent a key step of compound clinical progression projects. Currently computational druggability prediction models are attached to one unique pocket estimation method despite pocket estimation uncertainties. In this paper, we propose ‘PockDrug-Server’ to predict pocket druggability, efficient on both (i) estimated pockets guided by the ligand proximity (extracted by proximity to a ligand from a holo protein structure) and (ii) estimated pockets based solely on protein structure information (based on amino atoms that form the surface of potential binding cavities). PockDrug-Server provides consistent druggability results using different pocket estimation methods. It is robust with respect to pocket boundary and estimation uncertainties, thus efficient using apo pockets that are challenging to estimate. It clearly distinguishes druggable from less druggable pockets using different estimation methods and outperformed recent druggability models for apo pockets. It can be carried out from one or a set of apo/holo proteins using different pocket estimation methods proposed by our web server or from any pocket previously estimated by the user. PockDrug-Server is publicly available at: http://pockdrug.rpbs.univ-paris-diderot.fr.
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pockdrug a model for predicting pocket druggability that overcomes pocket estimation uncertainties
Journal of Chemical Information and Modeling, 2015Co-Authors: Michel Petitjean, Anneclaude Camproux, Leslie Regad, Alexandre Borrel, Henri XhaardAbstract:Predicting protein druggability is a key interest in the target Identification Phase of drug discovery. Here, we assess the pocket estimation methods’ influence on druggability predictions by comparing statistical models constructed from pockets estimated using different pocket estimation methods: a proximity of either 4 or 5.5 A to a cocrystallized ligand or DoGSite and fpocket estimation methods. We developed PockDrug, a robust pocket druggability model that copes with uncertainties in pocket boundaries. It is based on a linear discriminant analysis from a pool of 52 descriptors combined with a selection of the most stable and efficient models using different pocket estimation methods. PockDrug retains the best combinations of three pocket properties which impact druggability: geometry, hydrophobicity, and aromaticity. It results in an average accuracy of 87.9% ± 4.7% using a test set and exhibits higher accuracy (∼5–10%) than previous studies that used an identical apo set. In conclusion, this study co...
Philippe Colomban - One of the best experts on this subject based on the ideXlab platform.
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raman spectroscopy of nanomaterials how spectra relate to disorder particle size and mechanical properties
Progress in Crystal Growth and Characterization of Materials, 2007Co-Authors: Gwenael Gouadec, Philippe ColombanAbstract:The purpose of this review is to provide non-specialists with a basic understanding of the information micro-Raman Spectroscopy (mRS) may yield when this characterization tool is applied to nanomaterials, a generic term for describing nano-sized crystals and bulk homogeneous materials with a structural disorder at the nanoscale e typically nanoceramics, nanocomposites, glassy materials and relaxor ferroelectrics. The selected materials include advanced and ancient ceramics, semiconductors and polymers developed in the form of dots, wires, films, fibres or composites for applications in the energy, electronic and aeronauticseaerospace industries. The text is divided into five sections: � Section 1 is a general introduction. � Section 2 outlines the principles of conventional mRS. � Section 3 introduces the main effects for nanomaterials, with special emphasis on two models that connect Raman spectra features to ‘‘grain size’’, namely the Phonon Confinement Model (PCM) and the Elastic Sphere Model (ESM). � Section 4 presents the experimental versatility of mRS applied to nanomaterials (Phase Identification, Phase transition monitoring, grain size determination, defect concentration assessment, etc.).
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raman spectroscopy of nanomaterials how spectra relate to disorder particle size and mechanical properties
Progress in Crystal Growth and Characterization of Materials, 2007Co-Authors: Gwenael Gouadec, Philippe ColombanAbstract:The purpose of this review is to provide non-specialists with a basic understanding of the information micro-Raman Spectroscopy (mRS) may yield when this characterization tool is applied to nanomaterials, a generic term for describing nano-sized crystals and bulk homogeneous materials with a structural disorder at the nanoscale e typically nanoceramics, nanocomposites, glassy materials and relaxor ferroelectrics. The selected materials include advanced and ancient ceramics, semiconductors and polymers developed in the form of dots, wires, films, fibres or composites for applications in the energy, electronic and aeronauticseaerospace industries. The text is divided into five sections: � Section 1 is a general introduction. � Section 2 outlines the principles of conventional mRS. � Section 3 introduces the main effects for nanomaterials, with special emphasis on two models that connect Raman spectra features to ‘‘grain size’’, namely the Phonon Confinement Model (PCM) and the Elastic Sphere Model (ESM). � Section 4 presents the experimental versatility of mRS applied to nanomaterials (Phase Identification, Phase transition monitoring, grain size determination, defect concentration assessment, etc.).
Jinhua Zhao - One of the best experts on this subject based on the ideXlab platform.
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Demand management of congested public transport systems: a conceptual framework and application using smart card data
Transportation, 2019Co-Authors: Anne Halvorsen, Zhenliang Ma, Haris N Koutsopoulos, Jinhua ZhaoAbstract:Transportation demand management, long used to reduce car traffic, is receiving attention among public transport operators as a means to reduce congestion in crowded public transportation systems. Though far less studied, a more structured approach to public transport demand management (PTDM) can help agencies make informed decisions on the combination of PTDM and infrastructure investments that best manage crowding. Automated fare collection data, readily available in many public transport agencies, provide a unique platform to advance systematic approaches for the design and evaluation of PTDM strategies. The paper discusses the main steps for developing PTDM programs: (a) problem Identification and formulation of program goals; (b) program design; (c) evaluation; and (d) monitoring. The problem Identification Phase examines bottlenecks in the system based on a spatiotemporal passenger flow analysis. The design Phase identifies the main design parameters based on a categorization of potential interventions along spatial, temporal, modal, and targeted user group parameters. Evaluation takes place at the system, group, and individual levels, taking advantage of the detailed information obtained from smart card transaction data. The monitoring Phase addresses the long-term sustainability of the intervention and informs potential changes to improve its effectiveness. A case study of a pre-peak fare discount policy in Hong Kong’s MTR network is used to illustrate the application of the various steps with focus on evaluation and analysis of the impacts from a behavioral point of view. Smart card data from before and after the implementation of the scheme from a panel of users was used to study policy-induced behavior shifts. A cluster analysis inferred customer groups relevant to the analysis based on their usage patterns. Users who shifted their behavior were identified based on a change point analysis and a logit model was estimated to identify the main factors that contribute to this change: the amount of time a user needed to shift his/her departure time, departure time variability, fare savings, and price sensitivity. User heterogeneity suggests that future incentives may be improved if they target specific groups.
Leslie Regad - One of the best experts on this subject based on the ideXlab platform.
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pockdrug server a new web server for predicting pocket druggability on holo and apo proteins
Nucleic Acids Research, 2015Co-Authors: Hiba Abi Hussein, Colette Geneix, Michel Petitjean, Leslie Regad, Alexandre Borrel, Anneclaude CamprouxAbstract:Predicting protein pocket's ability to bind drug-like molecules with high affinity, i.e. druggability, is of major interest in the target Identification Phase of drug discovery. Therefore, pocket druggability investigations represent a key step of compound clinical progression projects. Currently computational druggability prediction models are attached to one unique pocket estimation method despite pocket estimation uncertainties. In this paper, we propose ‘PockDrug-Server’ to predict pocket druggability, efficient on both (i) estimated pockets guided by the ligand proximity (extracted by proximity to a ligand from a holo protein structure) and (ii) estimated pockets based solely on protein structure information (based on amino atoms that form the surface of potential binding cavities). PockDrug-Server provides consistent druggability results using different pocket estimation methods. It is robust with respect to pocket boundary and estimation uncertainties, thus efficient using apo pockets that are challenging to estimate. It clearly distinguishes druggable from less druggable pockets using different estimation methods and outperformed recent druggability models for apo pockets. It can be carried out from one or a set of apo/holo proteins using different pocket estimation methods proposed by our web server or from any pocket previously estimated by the user. PockDrug-Server is publicly available at: http://pockdrug.rpbs.univ-paris-diderot.fr.
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pockdrug a model for predicting pocket druggability that overcomes pocket estimation uncertainties
Journal of Chemical Information and Modeling, 2015Co-Authors: Michel Petitjean, Anneclaude Camproux, Leslie Regad, Alexandre Borrel, Henri XhaardAbstract:Predicting protein druggability is a key interest in the target Identification Phase of drug discovery. Here, we assess the pocket estimation methods’ influence on druggability predictions by comparing statistical models constructed from pockets estimated using different pocket estimation methods: a proximity of either 4 or 5.5 A to a cocrystallized ligand or DoGSite and fpocket estimation methods. We developed PockDrug, a robust pocket druggability model that copes with uncertainties in pocket boundaries. It is based on a linear discriminant analysis from a pool of 52 descriptors combined with a selection of the most stable and efficient models using different pocket estimation methods. PockDrug retains the best combinations of three pocket properties which impact druggability: geometry, hydrophobicity, and aromaticity. It results in an average accuracy of 87.9% ± 4.7% using a test set and exhibits higher accuracy (∼5–10%) than previous studies that used an identical apo set. In conclusion, this study co...
