Etchebest et al., 2005 - Google Patents
A structural alphabet for local protein structures: improved prediction methodsEtchebest et al., 2005
- Document ID
- 8078509299640088683
- Author
- Etchebest C
- Benros C
- Hazout S
- de Brevern A
- Publication year
- Publication venue
- Proteins: Structure, Function, and Bioinformatics
External Links
Snippet
Three‐dimensional protein structures can be described with a library of 3D fragments that define a structural alphabet. We have previously proposed such an alphabet, composed of 16 patterns of five consecutive amino acids, called Protein Blocks (PBs). These PBs have …
- 108090000623 proteins and genes 0 title abstract description 111
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F19/00—Digital computing or data processing equipment or methods, specially adapted for specific applications
- G06F19/10—Bioinformatics, i.e. methods or systems for genetic or protein-related data processing in computational molecular biology
- G06F19/16—Bioinformatics, i.e. methods or systems for genetic or protein-related data processing in computational molecular biology for molecular structure, e.g. structure alignment, structural or functional relations, protein folding, domain topologies, drug targeting using structure data, involving two-dimensional or three-dimensional structures
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F19/00—Digital computing or data processing equipment or methods, specially adapted for specific applications
- G06F19/10—Bioinformatics, i.e. methods or systems for genetic or protein-related data processing in computational molecular biology
- G06F19/22—Bioinformatics, i.e. methods or systems for genetic or protein-related data processing in computational molecular biology for sequence comparison involving nucleotides or amino acids, e.g. homology search, motif or SNP [Single-Nucleotide Polymorphism] discovery or sequence alignment
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F19/00—Digital computing or data processing equipment or methods, specially adapted for specific applications
- G06F19/10—Bioinformatics, i.e. methods or systems for genetic or protein-related data processing in computational molecular biology
- G06F19/28—Bioinformatics, i.e. methods or systems for genetic or protein-related data processing in computational molecular biology for programming tools or database systems, e.g. ontologies, heterogeneous data integration, data warehousing or computing architectures
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F19/00—Digital computing or data processing equipment or methods, specially adapted for specific applications
- G06F19/10—Bioinformatics, i.e. methods or systems for genetic or protein-related data processing in computational molecular biology
- G06F19/18—Bioinformatics, i.e. methods or systems for genetic or protein-related data processing in computational molecular biology for functional genomics or proteomics, e.g. genotype-phenotype associations, linkage disequilibrium, population genetics, binding site identification, mutagenesis, genotyping or genome annotation, protein-protein interactions or protein-nucleic acid interactions
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F19/00—Digital computing or data processing equipment or methods, specially adapted for specific applications
- G06F19/10—Bioinformatics, i.e. methods or systems for genetic or protein-related data processing in computational molecular biology
- G06F19/24—Bioinformatics, i.e. methods or systems for genetic or protein-related data processing in computational molecular biology for machine learning, data mining or biostatistics, e.g. pattern finding, knowledge discovery, rule extraction, correlation, clustering or classification
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F19/00—Digital computing or data processing equipment or methods, specially adapted for specific applications
- G06F19/10—Bioinformatics, i.e. methods or systems for genetic or protein-related data processing in computational molecular biology
- G06F19/20—Bioinformatics, i.e. methods or systems for genetic or protein-related data processing in computational molecular biology for hybridisation or gene expression, e.g. microarrays, sequencing by hybridisation, normalisation, profiling, noise correction models, expression ratio estimation, probe design or probe optimisation
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F19/00—Digital computing or data processing equipment or methods, specially adapted for specific applications
- G06F19/70—Chemoinformatics, i.e. data processing methods or systems for the retrieval, analysis, visualisation, or storage of physicochemical or structural data of chemical compounds
- G06F19/708—Chemoinformatics, i.e. data processing methods or systems for the retrieval, analysis, visualisation, or storage of physicochemical or structural data of chemical compounds for data visualisation, e.g. molecular structure representations, graphics generation, display of maps or networks or other visual representations
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F19/00—Digital computing or data processing equipment or methods, specially adapted for specific applications
- G06F19/70—Chemoinformatics, i.e. data processing methods or systems for the retrieval, analysis, visualisation, or storage of physicochemical or structural data of chemical compounds
- G06F19/705—Chemoinformatics, i.e. data processing methods or systems for the retrieval, analysis, visualisation, or storage of physicochemical or structural data of chemical compounds for database search of chemical structures, e.g. full structure search, substructure search, similarity search, pharmacophore search, 3D structure search
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
- G06F17/30—Information retrieval; Database structures therefor; File system structures therefor
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computer systems based on biological models
- G06N3/12—Computer systems based on biological models using genetic models
- G06N3/126—Genetic algorithms, i.e. information processing using digital simulations of the genetic system
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N99/00—Subject matter not provided for in other groups of this subclass
- G06N99/005—Learning machines, i.e. computer in which a programme is changed according to experience gained by the machine itself during a complete run
Similar Documents
Publication | Publication Date | Title |
---|---|---|
Etchebest et al. | A structural alphabet for local protein structures: improved prediction methods | |
Zhang et al. | TOUCHSTONE II: a new approach to ab initio protein structure prediction | |
Hamelryck et al. | Sampling realistic protein conformations using local structural bias | |
de Brevern et al. | Bayesian probabilistic approach for predicting backbone structures in terms of protein blocks | |
de Brevern et al. | Extension of a local backbone description using a structural alphabet: a new approach to the sequence‐structure relationship | |
Camproux et al. | A hidden markov model derived structural alphabet for proteins | |
Offmann et al. | Local protein structures | |
Xu et al. | Toward optimal fragment generations for ab initio protein structure assembly | |
Lindorff-Larsen et al. | Protein folding and the organization of the protein topology universe | |
Karakaş et al. | BCL:: Fold-de novo prediction of complex and large protein topologies by assembly of secondary structure elements | |
Fourrier et al. | Use of a structural alphabet for analysis of short loops connecting repetitive structures | |
Benros et al. | Assessing a novel approach for predicting local 3D protein structures from sequence | |
Martin et al. | Analysis of an optimal hidden Markov model for secondary structure prediction | |
Yao et al. | Efficient algorithms to explore conformation spaces of flexible protein loops | |
Olson et al. | Prediction of protein loop conformations using multiscale modeling methods with physical energy scoring functions | |
Yang et al. | Construction of a deep neural network energy function for protein physics | |
De Brevern et al. | “Pinning strategy”: a novel approach for predicting the backbone structure in terms of protein blocks from sequence | |
Bornot et al. | A new prediction strategy for long local protein structures using an original description | |
Tuffery et al. | Dependency between consecutive local conformations helps assemble protein structures from secondary structures using Go potential and greedy algorithm | |
Li et al. | Fragment‐based local statistical potentials derived by combining an alphabet of protein local structures with secondary structures and solvent accessibilities | |
Liu et al. | Improving the orientation‐dependent statistical potential using a reference state | |
Rossi et al. | A self-consistent knowledge-based approach to protein design | |
Lee et al. | Benchmarking of TASSER_2. 0: an improved protein structure prediction algorithm with more accurate predicted contact restraints | |
Yang et al. | Genetic algorithms for protein conformation sampling and optimization in a discrete backbone dihedral angle space | |
Dong et al. | Analysis and prediction of protein local structure based on structure alphabets |