Rufino et al., 1997 - Google Patents
Predicting the conformational class of short and medium size loops connecting regular secondary structures: application to comparative modellingRufino et al., 1997
- Document ID
- 9405873941173026308
- Author
- Rufino S
- Donate L
- Canard L
- Blundell T
- Publication year
- Publication venue
- Journal of Molecular Biology
External Links
Snippet
Loops are regions of non-repetitive conformation connecting regular secondary structures. They are both the most difficult and error prone regions of a protein to solve by X-ray crystallography and the hardest regions to model using comparative procedures. Although a …
- 230000000052 comparative effect 0 title abstract description 16
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
- G06F19/70—Chemoinformatics, i.e. data processing methods or systems for the retrieval, analysis, visualisation, or storage of physicochemical or structural data of chemical compounds
- G06F19/706—Chemoinformatics, i.e. data processing methods or systems for the retrieval, analysis, visualisation, or storage of physicochemical or structural data of chemical compounds for drug design with the emphasis on a therapeutic agent, e.g. ligand-biological target interactions, pharmacophore generation
-
- C—CHEMISTRY; METALLURGY
- C07—ORGANIC CHEMISTRY
- C07K—PEPTIDES
- C07K14/00—Peptides having more than 20 amino acids; Gastrins; Somatostatins; Melanotropins; Derivatives thereof
- C07K14/435—Peptides having more than 20 amino acids; Gastrins; Somatostatins; Melanotropins; Derivatives thereof from animals; from humans
- C07K14/46—Peptides having more than 20 amino acids; Gastrins; Somatostatins; Melanotropins; Derivatives thereof from animals; from humans from vertebrates
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Rufino et al. | Predicting the conformational class of short and medium size loops connecting regular secondary structures: application to comparative modelling | |
| Melo et al. | Novel knowledge-based mean force potential at atomic level | |
| Blundell et al. | Knowledge-based prediction of protein structures and the design of novel molecules | |
| Wintjens et al. | Automatic classification and analysis of αα-turn motifs in proteins | |
| Sun et al. | A simple protein folding algorithm using a binary code and secondary structure constraints | |
| Smith et al. | The relationship between the flexibility of proteins and their conformational states on forming protein–protein complexes with an application to protein–protein docking | |
| Johnson et al. | Knowledge-based protein modeling | |
| Tsai et al. | Protein-protein interfaces: architectures and interactions in protein-protein interfaces and in protein cores. Their similarities and differences | |
| Payne et al. | Molecular recognition using a binary genetic search algorithm | |
| Meyer et al. | Hydrogen bonding and molecular surface shape complementarity as a basis for protein docking | |
| Jones | Successful ab initio prediction of the tertiary structure of NK‐lysin using multiple sequences and recognized supersecondary structural motifs | |
| Defay et al. | Evaluation of current techniques for ab initio protein structure prediction | |
| US7092825B1 (en) | Protein engineering | |
| US20130013279A1 (en) | Apparatus and method for structure-based prediction of amino acid sequences | |
| Summers et al. | Modeling of globular proteins: A distance-based data search procedure for the construction of insertion/deletion regions and pronon-pro mutations | |
| Burke et al. | Browsing the SLoop database of structurally classified loops connecting elements of protein secondary structure | |
| Zheng et al. | Protein structure prediction constrained by solution X-ray scattering data and structural homology identification | |
| Rayan et al. | Exploring the conformational space of cyclic peptides by a stochastic search method | |
| Lahfa et al. | The structural landscape and diversity of Pyricularia oryzae MAX effectors revisited | |
| Wilmanns et al. | Inverse protein folding by the residue pair preference profile method: estimating the correctness of alignments of structurally compatible sequences | |
| Scheraga et al. | The protein folding problem: Global optimization of force fields | |
| Evans et al. | De novo prediction of polypeptide conformations using dihedral probability grid Monte Carlo methodology | |
| Das et al. | Optimization of solvation models for predicting the structure of surface loops in proteins | |
| Mathiowetz et al. | Building proteins from Cα coordinates using the dihedral probability grid Monte Carlo method | |
| US6721663B1 (en) | Method for manipulating protein or DNA sequence data in order to generate complementary peptide ligands |