Relevant bibliographies by topics / Structural Classification of Proteins (SCOP)

Academic literature on the topic 'Structural Classification of Proteins (SCOP)'

Author: Grafiati
Published: 10 December 2022
Last updated: 28 January 2023

Create a spot-on reference in APA, MLA, Chicago, Harvard, and other styles

Select a source type:

Contents

Consult the lists of relevant articles, books, theses, conference reports, and other scholarly sources on the topic 'Structural Classification of Proteins (SCOP).'

Next to every source in the list of references, there is an 'Add to bibliography' button. Press on it, and we will generate automatically the bibliographic reference to the chosen work in the citation style you need: APA, MLA, Harvard, Chicago, Vancouver, etc.

You can also download the full text of the academic publication as pdf and read online its abstract whenever available in the metadata.

Journal articles on the topic "Structural Classification of Proteins (SCOP)"

1

Barton, Geoffrey J. "scop: structural classification of proteins." Trends in Biochemical Sciences 19, no. 12 (December 1994): 554–55. http://dx.doi.org/10.1016/0968-0004(94)90060-4.

Full text
APA, Harvard, Vancouver, ISO, and other styles
2

Hubbard, T. J. P., B. Ailey, S. E. Brenner, A. G. Murzin, and C. Chothia. "SCOP: a Structural Classification of Proteins database." Nucleic Acids Research 27, no. 1 (January 1, 1999): 254–56. http://dx.doi.org/10.1093/nar/27.1.254.

Full text
APA, Harvard, Vancouver, ISO, and other styles
3

Lo Conte, L. "SCOP: a Structural Classification of Proteins database." Nucleic Acids Research 28, no. 1 (January 1, 2000): 257–59. http://dx.doi.org/10.1093/nar/28.1.257.

Full text
APA, Harvard, Vancouver, ISO, and other styles
4

Hubbard, T. J. P., A. G. Murzin, S. E. Brenner, and C. Chothia. "SCOP: a Structural Classification of Proteins database." Nucleic Acids Research 25, no. 1 (January 1, 1997): 236–39. http://dx.doi.org/10.1093/nar/25.1.236.

Full text
APA, Harvard, Vancouver, ISO, and other styles
5

ANGADI, ULAVAPPA B., and M. VENKATESULU. "FUZZYART NEURAL NETWORK FOR PROTEIN CLASSIFICATION." Journal of Bioinformatics and Computational Biology 08, no. 05 (October 2010): 825–41. http://dx.doi.org/10.1142/s0219720010004951.

Full text
Abstract:
One of the major research directions in bioinformatics is that of predicting the protein superfamily in large databases and classifying a given set of protein domains into superfamilies. The classification reflects the structural, evolutionary and functional relatedness. These relationships are embodied in hierarchical classification such as Structural Classification of Protein (SCOP), which is manually curated. Such classification is essential for the structural and functional analysis of proteins. Yet, a large number of proteins remain unclassified. We have proposed an unsupervised machine-learning FuzzyART neural network algorithm to classify a given set of proteins into SCOP superfamilies. The proposed method is fast learning and uses an atypical non-linear pattern recognition technique. In this approach, we have constructed a similarity matrix from p-values of BLAST all-against-all, trained the network with FuzzyART unsupervised learning algorithm using the similarity matrix as input vectors and finally the trained network offers SCOP superfamily level classification. In this experiment, we have evaluated the performance of our method with existing techniques on six different datasets. We have shown that the trained network is able to classify a given similarity matrix of a set of sequences into SCOP superfamilies at high classification accuracy.
APA, Harvard, Vancouver, ISO, and other styles
6

Fox, Naomi K., Steven E. Brenner, and John-Marc Chandonia. "SCOPe: Structural Classification of Proteins—extended, integrating SCOP and ASTRAL data and classification of new structures." Nucleic Acids Research 42, no. D1 (December 3, 2013): D304—D309. http://dx.doi.org/10.1093/nar/gkt1240.

Full text
APA, Harvard, Vancouver, ISO, and other styles
7

Andreeva, Antonina, Eugene Kulesha, Julian Gough, and Alexey G. Murzin. "The SCOP database in 2020: expanded classification of representative family and superfamily domains of known protein structures." Nucleic Acids Research 48, no. D1 (November 14, 2019): D376—D382. http://dx.doi.org/10.1093/nar/gkz1064.

Full text
Abstract:
Abstract The Structural Classification of Proteins (SCOP) database is a classification of protein domains organised according to their evolutionary and structural relationships. We report a major effort to increase the coverage of structural data, aiming to provide classification of almost all domain superfamilies with representatives in the PDB. We have also improved the database schema, provided a new API and modernised the web interface. This is by far the most significant update in coverage since SCOP 1.75 and builds on the advances in schema from the SCOP 2 prototype. The database is accessible from http://scop.mrc-lmb.cam.ac.uk.
APA, Harvard, Vancouver, ISO, and other styles
8

Andreeva, Antonina. "Lessons from making the Structural Classification of Proteins (SCOP) and their implications for protein structure modelling." Biochemical Society Transactions 44, no. 3 (June 9, 2016): 937–43. http://dx.doi.org/10.1042/bst20160053.

Full text
Abstract:
The Structural Classification of Proteins (SCOP) database has facilitated the development of many tools and algorithms and it has been successfully used in protein structure prediction and large-scale genome annotations. During the development of SCOP, numerous exceptions were found to topological rules, along with complex evolutionary scenarios and peculiarities in proteins including the ability to fold into alternative structures. This article reviews cases of structural variations observed for individual proteins and among groups of homologues, knowledge of which is essential for protein structure modelling.
APA, Harvard, Vancouver, ISO, and other styles
9

Hubbard, Tim J. P., Bart Ailey, Steven E. Brenner, Alexey G. Murzin, and Cyrus Chothia. "SCOP, Structural Classification of Proteins Database: Applications to Evaluation of the Effectiveness of Sequence Alignment Methods and Statistics of Protein Structural Data." Acta Crystallographica Section D Biological Crystallography 54, no. 6 (November 1, 1998): 1147–54. http://dx.doi.org/10.1107/s0907444998009172.

Full text
Abstract:
The Structural Classification of Proteins (SCOP) database provides a detailed and comprehensive description of the relationships of all known protein structures. The classification is on hierarchical levels: the first two levels, family and superfamily, describe near and far evolutionary relationships; the third, fold, describes geometrical relationships. The distinction between evolutionary relationships and those that arise from the physics and chemistry of proteins is a feature that is unique to this database, so far. The database can be used as a source of data to calibrate sequence search algorithms and for the generation of population statistics on protein structures. The database and its associated files are freely accessible from a number of WWW sites mirrored from URL http://scop.mrc-lmb.cam.ac.uk/scop/.
APA, Harvard, Vancouver, ISO, and other styles
10

Newaz, Khalique, Mahboobeh Ghalehnovi, Arash Rahnama, Panos J. Antsaklis, and Tijana Milenković. "Network-based protein structural classification." Royal Society Open Science 7, no. 6 (June 2020): 191461. http://dx.doi.org/10.1098/rsos.191461.

Full text
Abstract:
Experimental determination of protein function is resource-consuming. As an alternative, computational prediction of protein function has received attention. In this context, protein structural classification (PSC) can help, by allowing for determining structural classes of currently unclassified proteins based on their features, and then relying on the fact that proteins with similar structures have similar functions. Existing PSC approaches rely on sequence-based or direct three-dimensional (3D) structure-based protein features. By contrast, we first model 3D structures of proteins as protein structure networks (PSNs). Then, we use network-based features for PSC. We propose the use of graphlets, state-of-the-art features in many research areas of network science, in the task of PSC. Moreover, because graphlets can deal only with unweighted PSNs, and because accounting for edge weights when constructing PSNs could improve PSC accuracy, we also propose a deep learning framework that automatically learns network features from weighted PSNs. When evaluated on a large set of approximately 9400 CATH and approximately 12 800 SCOP protein domains (spanning 36 PSN sets), the best of our proposed approaches are superior to existing PSC approaches in terms of accuracy, with comparable running times. Our data and code are available at https://doi.org/10.5281/zenodo.3787922
APA, Harvard, Vancouver, ISO, and other styles
More sources

Dissertations / Theses on the topic "Structural Classification of Proteins (SCOP)"

1

Valenta, Martin. "Predikce proteinových domén." Master's thesis, Vysoké učení technické v Brně. Fakulta informačních technologií, 2013. http://www.nusl.cz/ntk/nusl-236163.

Full text
Abstract:
The work is focused on the area of the proteins and their domains. It also briefly describes gathering methods of the protein´s structure at the various levels of the hierarchy. This is followed by examining of existing tools for protein´s domains prediction and databases consisting of domain´s information. In the next part of the work selected representatives of prediction methods are introduced.  These methods work with the information about the internal structure of the molecule or the amino acid sequence. The appropriate chapter outlines applied procedure of domains´ boundaries prediction. The prediction is derived from the primary structure of the protein, using a neural network  The implemented procedure and its possibility of further development in the related thesis are introduced at the conclusion of this work.
APA, Harvard, Vancouver, ISO, and other styles
2

Schroeder, Michael, Annalisa Marsico, Andreas Henschel, Christof Winter, Anne Tuukkanen, Boris Vassilev, and Kerstin Scheubert. "Structural fragment clustering reveals novel structural and functional motifs in α-helical transmembrane proteins." BioMed Central, 2010. https://tud.qucosa.de/id/qucosa%3A28887.

Full text
Abstract:
Background A large proportion of an organism's genome encodes for membrane proteins. Membrane proteins are important for many cellular processes, and several diseases can be linked to mutations in them. With the tremendous growth of sequence data, there is an increasing need to reliably identify membrane proteins from sequence, to functionally annotate them, and to correctly predict their topology. Results We introduce a technique called structural fragment clustering, which learns sequential motifs from 3D structural fragments. From over 500,000 fragments, we obtain 213 statistically significant, non-redundant, and novel motifs that are highly specific to α-helical transmembrane proteins. From these 213 motifs, 58 of them were assigned to function and checked in the scientific literature for a biological assessment. Seventy percent of the motifs are found in co-factor, ligand, and ion binding sites, 30% at protein interaction interfaces, and 12% bind specific lipids such as glycerol or cardiolipins. The vast majority of motifs (94%) appear across evolutionarily unrelated families, highlighting the modularity of functional design in membrane proteins. We describe three novel motifs in detail: (1) a dimer interface motif found in voltage-gated chloride channels, (2) a proton transfer motif found in heme-copper oxidases, and (3) a convergently evolved interface helix motif found in an aspartate symporter, a serine protease, and cytochrome b. Conclusions Our findings suggest that functional modules exist in membrane proteins, and that they occur in completely different evolutionary contexts and cover different binding sites. Structural fragment clustering allows us to link sequence motifs to function through clusters of structural fragments. The sequence motifs can be applied to identify and characterize membrane proteins in novel genomes.
APA, Harvard, Vancouver, ISO, and other styles
3

Teyra, i. Canaleta Joan. "Entwicklung von rechnergestützten Ansätzen für strukturelle Klassifikation, Analyse und Vorhersage von molekularen Erkennungsregionen in Proteinen." Doctoral thesis, Saechsische Landesbibliothek- Staats- und Universitaetsbibliothek Dresden, 2010. http://nbn-resolving.de/urn:nbn:de:bsz:14-qucosa-62163.

Full text
Abstract:
The vast and growing volume of 3D protein structural data stored in the PDB contains abundant information about macromolecular complexes, and hence, data about protein interfaces. Non-covalent contacts between amino acids are the basis of protein interactions, and they are responsible for binding afinity and specificity in biological processes. In addition, water networks in protein interfaces can also complement direct interactions contributing significantly to molecular recognition, although their exact role is still not well understood. It is estimated that protein complexes in the PDB are substantially underrepresented due to their crystallization dificulties. Methods for automatic classifification and description of the protein complexes are essential to study protein interfaces, and to propose putative binding regions. Due to this strong need, several protein-protein interaction databases have been developed. However, most of them do not take into account either protein-peptide complexes, solvent information or a proper classification of the binding regions, which are fundamental components to provide an accurate description of protein interfaces. In the firest stage of my thesis, I developed the SCOWLP platform, a database and web application that structurally classifies protein binding regions at family level and defines accurately protein interfaces at atomic detail. The analysis of the results showed that protein-peptide complexes are substantially represented in the PDB, and are the only source of interacting information for several families. By clustering the family binding regions, I could identify 9,334 binding regions and 79,803 protein interfaces in the PDB. Interestingly, I observed that 65% of protein families interact to other molecules through more than one region and in 22% of the cases the same region recognizes different protein families. The database and web application are open to the research community (www.scowlp.org) and can tremendously facilitate high-throughput comparative analysis of protein binding regions, as well as, individual analysis of protein interfaces. SCOWLP and the other databases collect and classify the protein binding regions at family level, where sequence and structure homology exist. Interestingly, it has been observed that many protein families also present structural resemblances within each other, mostly across folds. Likewise, structurally similar interacting motifs (binding regions) have been identified among proteins with different folds and functions. For these reasons, I decided to explore the possibility to infer protein binding regions independently of their fold classification. Thus, I performed the firest systematic analysis of binding region conservation within all protein families that are structurally similar, calculated using non-sequential structural alignment methods. My results indicate there is a substantial molecular recognition information that could be potentially inferred among proteins beyond family level. I obtained a 6 to 8 fold enrichment of binding regions, and identified putative binding regions for 728 protein families that lack binding information. Within the results, I found out protein complexes from different folds that present similar interfaces, confirming the predictive usage of the methodology. The data obtained with my approach may complement the SCOWLP family binding regions suggesting alternative binding regions, and can be used to assist protein-protein docking experiments and facilitate rational ligand design. In the last part of my thesis, I used the interacting information contained in the SCOWLP database to help understand the role that water plays in protein interactions in terms of affinity and specificity. I carried out one of the firest high-throughput analysis of solvent in protein interfaces for a curated dataset of transient and obligate protein complexes. Surprisingly, the results highlight the abundance of water-bridged residues in protein interfaces (40.1% of the interfacial residues) that reinforces the importance of including solvent in protein interaction studies (14.5% extra residues interacting only water- mediated). Interestingly, I also observed that obligate and transient interfaces present a comparable amount of solvent, which contrasts the old thoughts saying that obligate protein complexes are expected to exhibit similarities to protein cores having a dry and hydrophobic interfaces. I characterized novel features of water-bridged residues in terms of secondary structure, temperature factors, residue composition, and pairing preferences that differed from direct residue-residue interactions. The results also showed relevant aspects in the mobility and energetics of water-bridged interfacial residues. Collectively, my doctoral thesis work can be summarized in the following points: 1. I developed SCOWLP, an improved framework that identiffies protein interfaces and classifies protein binding regions at family level. 2. I developed a novel methodology to predict alternative binding regions among structurally similar protein families independently of the fold they belong to. 3. I performed a high-throughput analysis of water-bridged interactions contained in SCOWLP to study the role of solvent in protein interfaces. These three components of my thesis represent novel methods for exploiting existing structural information to gain insights into protein- protein interactions, key mechanisms to understand biological processes.
APA, Harvard, Vancouver, ISO, and other styles
4

Shiou-Ling, Wang, and 王秀綾. "Structural Signature of Protein Folds for SCOP Classification:A Building-Block-based Hidden Markov Model." Thesis, 2003. http://ndltd.ncl.edu.tw/handle/30690976971548675536.

Full text
Abstract:
碩士
國立臺灣大學
醫學工程學研究所
91
Protein folds follow certain stereo-chemical or functional constrains to adopt particular three-dimensional configurations. These constrains, which might be local or global in the spatial arrangement of protein structures, may contribute significantly their uniqueness. In our study, we aim to find fold signatures composed of features in terms of building blocks of 5-residue fragments presented in one-dimensional string of structural alphabets. (One alphabet is a building block). We then trained these strings of structural alphabets using Hidden Markov Model to uncover their grammars, i.e. signature of structural fold. Such an approach is similar to that used widely to construct profiles of protein sequence families. Our study focused on discovering structural patterns or signatures of 43 populated folds in SCOP, a prestigious database warehousing and classifying proteins structures. The accuracy of our result, which is defined as the rate of being correctly classification against SCOP, is 71%. For a number of well known folds, including Globin-like, Immunoglobuilin-like beta-sandwich and TIM barrel, we achieved > 90% correct classification. Our work showed that structural signature can be extracted for protein folds even though they were broken into local fragments and transformed into one-dimensional strings.
APA, Harvard, Vancouver, ISO, and other styles
5

Kaiser, Florian. "Structural Bioinformatics to Understand the Origin of the Genetic Code: Structural Motif Detection in Aminoacyl-tRNA Synthetases." Doctoral thesis, 2018. https://tud.qucosa.de/id/qucosa%3A31991.

Full text
Abstract:
One of the most profound open questions in biology is how the genetic code developed. The blueprints for proteins are encoded by triplets of nucleic acids, which in turn require proteins for interpretation and replication. The mere existence of this self-referencing system is a chicken-and-egg dilemma. Aminoacyl-tRNA synthetases are key players in the transfer of genetic information and reflect the earliest episode of life. These enzymes are responsible for loading tRNA molecules with the correct amino acid. Two protein superfamilies of aminoacyl-tRNA synthetases emerged, each responsible for ten amino acids. Despite sequence and structure similarity, the delicate balance between these superfamilies is manifested in two structural motifs, which were identified in the context of this thesis: the Backbone Brackets and the Arginine Tweezers. Both motifs realize constant ligand recognition and can be found in almost all protein structures of aminoacyl-tRNA synthetases. In this thesis, I thoroughly characterized Backbone Brackets and Arginine Tweezers. The specific characteristics of these motifs require high-precision methods for their detection and analysis. However, existing algorithms do not feature an adequate computational representation of structural motifs at the atom level and the support of isofunctional residue mutations. In order to address these limitations, I designed the Fit3D algorithm for template-based and template-free detection of structural motifs. I show that proper computational representation of structural motifs is crucial and improves accuracy up to 26% for a benchmark dataset. Fit3D is a general-purpose tool for structural motif detection in high-resolution protein structure data. In conjunction with the accelerating progress in experimental methods, the demand for such tools will increase rapidly over the next years. I applied Fit3D to structures of aminoacyl-tRNA synthetases to investigate whether Backbone Brackets and Arginine Tweezers are universal building blocks for ligand recognition, and to quantify structural changes upon ligand binding. While the Arginine Tweezers motif is exclusively found in aminoacyl-tRNA synthetases and paralogs, the Backbone Brackets seem to be a general pattern to recognize functional groups of certain ligands. The results show subtle differences in side chain orientation for one structural motif and a backbone shift for the other. This suggests a structural rearrangement to be a general mechanism in some aminoacyl-tRNA synthetases. The detailed level of these analyses would not have been possible without high-precision structural motif detection with Fit3D. The results emphasize the importance of structural motifs, which consist of only a few residues, for the global function of the enzyme. Furthermore, the stunning conservation of the structural motifs located in the core domains of aminoacyl-tRNA synthetases suggests their presence in the earliest predecessors of these enzymes. Both motifs might have played a fundamental role in shaping the genetic code as we know it.
APA, Harvard, Vancouver, ISO, and other styles
6

Sontheimer, Jana. "Functional characterization of proteins involved in cell cycle by structure-based computational methods." Doctoral thesis, 2011. https://tud.qucosa.de/id/qucosa%3A25989.

Full text
Abstract:
In the recent years, a rapidly increasing amount of experimental data has been generated by high-throughput technologies. Despite of these large quantities of protein-related data and the development of computational prediction methods, the function of many proteins is still unknown. In the human proteome, at least 20% of the annotated proteins are not characterized. Thus, the question, how to predict protein function from its amino acid sequence, remains to be answered for many proteins. Classical bioinformatics approaches for function prediction are based on inferring function from well-characterized homologs, which are identified based on sequence similarity. However, these methods fail to identify distant homologs with low sequence similarity. As protein structure is more conserved than sequence in protein families, structure-based methods (e.g. fold recognition) may recognize possible structural similarities even at low sequence similarity and therefore provide information for function inference. These fold recognition methods have already been proven to be successful for individual proteins, but their automation for high-throughput application is difficult due to intrinsic challenges of these techniques, mainly caused by a high false positive rate. Automated identification of remote homologs based on fold recognition methods would allow a signi cant improvement in functional annotation of proteins. My approach was to combine structure-based computational prediction methods with experimental data from genome-wide RNAi screens to support the establishment of functional hypotheses by improving the analysis of protein structure prediction results. In the first part of my thesis, I characterized proteins from the Ska complex by computational methods. I showed the benefit of including experimental information to identify remote homologs: Integration of functional data helped to reduce the number of false positives in fold recognition results and made it possible to establish interesting functional hypotheses based on high con dence structural predictions. Based on the structural hypothesis of a GLEBS motif in c13orf3 (Ska3), I could derive a potential molecular mechanism that could explain the observed phenotype. In the second part of my thesis, my goal was to develop computational tools and automated analysis techniques to be able to perform structure-based functional annotation in a high-throughput way. I designed and implemented key tools that were successfully integrated into a computational platform, called StrAnno, which I set up together with my colleagues. These novel computational modules include a domain prediction algorithm and a graphical overview that facilitates and accelerates the analysis of results. StrAnno can be seen as a first step towards automatic functional annotation of proteins by structure-based methods. First, the analysis of long hit lists to identify promising candidates for further analysis is substantially facilitated by integration and combination of various sequence-based computational tools and data from functional databases. Second, the developed post-processing tools accelerate the evaluation of structural and functional hypotheses. False positives from the threading result lists are removed by various filters, and analysis of the possible true positives is greatly enhanced by the graphical overview. With these two essential benefits, fold recognition techniques are applicable to large-scale approaches. By applying this developed methodology to hits from a genome-wide cell cycle RNAi screen and evaluating structural hypotheses by molecular modeling techniques, I aimed to associate biological functions to human proteins and link the RNAi phenotype to a molecular function. For two selected human proteins, c20orf43 and HJURP, I could establish interesting structural and functional hypotheses. These predictions were based on templates with low sequence identity (10-20%). The uncharacterized human protein c20orf43 might be a E3 SUMO-ligase that could be involved either in DNA repair or rRNA regulatory processes. Based on the structural hypotheses of two domains of HJURP, I predicted a potential link to ubiquitylation processes and direct DNA binding. In addition, I substantiated the cell cycle arrest phenotype of these two genes upon RNAi knockdown. Fold recognition methods are a promising alternative for functional annotation of proteins that escape sequence-based annotation due to their low sequence identity to well-characterized protein families. The structural and functional hypotheses I established in my thesis open the door to investigate the molecular mechanisms of previously uncharacterized proteins, which may provide new insights into cellular mechanisms.
APA, Harvard, Vancouver, ISO, and other styles
7

Towse, Clare-Louise, and V. Daggett. "When a domain is not a domain, and why it is important to properly filter proteins in databases: conflicting definitions and fold classification systems for structural domains make filtering of such databases imperative." 2012. http://hdl.handle.net/10454/11548.

Full text
Abstract:
No
Membership in a protein domain database does not a domain make; a feature we realized when generating a consensus view of protein fold space with our consensus domain dictionary (CDD). This dictionary was used to select representative structures for characterization of the protein dynameome: the Dynameomics initiative. Through this endeavor we rejected a surprising 40% of the 1,695 folds in the CDD as being non-autonomous folding units. Although some of this was due to the challenges of grouping similar fold topologies, the dissonance between the cataloguing and structural qualification of protein domains remains surprising. Another potential factor is previously overlooked intrinsic disorder; predictions suggest that 40% of proteins have either local or global disorder. One thing is clear, filtering a structural database and ensuring a consistent definition for protein domains is crucial, and caution is prescribed when generalizations of globular domains are drawn from unfiltered protein domain datasets.
NIH
APA, Harvard, Vancouver, ISO, and other styles
8

Teyra, i. Canaleta Joan [Verfasser]. "Entwicklung von rechnergestützten Ansätzen für strukturelle Klassifikation, Analyse und Vorhersage von molekularen Erkennungsregionen in Proteinen = Development of computational approaches for structural classification, analysis and prediction of molecular recognition regions in proteins / von Joan Teyra i Canaleta." 2010. http://d-nb.info/101069054X/34.

Full text
APA, Harvard, Vancouver, ISO, and other styles

Book chapters on the topic "Structural Classification of Proteins (SCOP)"

1

Cantoni, Virginio, Alessio Ferone, Alfredo Petrosino, and Gabriella Sanniti di Baja. "A Supervised Approach to 3D Structural Classification of Proteins." In New Trends in Image Analysis and Processing – ICIAP 2013, 326–35. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-41190-8_35.

Full text
APA, Harvard, Vancouver, ISO, and other styles
2

Andreeva, Antonina. "Classification of Proteins: Available Structural Space for Molecular Modeling." In Methods in Molecular Biology, 1–31. Totowa, NJ: Humana Press, 2011. http://dx.doi.org/10.1007/978-1-61779-588-6_1.

Full text
APA, Harvard, Vancouver, ISO, and other styles
3

Shi, Jian-Yu, and Yan-Ning Zhang. "Fast SCOP Classification of Structural Class and Fold Using Secondary Structure Mining in Distance Matrix." In Pattern Recognition in Bioinformatics, 344–53. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-04031-3_30.

Full text
APA, Harvard, Vancouver, ISO, and other styles
4

Schuchhardt, Johannes, Gisbert Schneider, Joachim Reichelt, Dietmar Schomburg, and Paul Wrede. "Classification of Local Protein Structural Motifs by Kohonen Networks." In Bioinformatics: From Nucleic Acids and Proteins to Cell Metabolism, 85–92. Weinheim, Germany: Wiley-VCH Verlag GmbH, 2007. http://dx.doi.org/10.1002/9783527615193.ch7.

Full text
APA, Harvard, Vancouver, ISO, and other styles
5

Kalajdziski, Slobodan, Bojan Pepik, Ilinka Ivanovska, Georgina Mirceva, Kire Trivodaliev, and Danco Davcev. "Automated Structural Classification of Proteins by Using Decision Trees and Structural Protein Features." In ICT Innovations 2009, 135–44. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-10781-8_15.

Full text
APA, Harvard, Vancouver, ISO, and other styles
6

"Structural Classification of Proteins." In Encyclopedia of Genetics, Genomics, Proteomics and Informatics, 1891. Dordrecht: Springer Netherlands, 2008. http://dx.doi.org/10.1007/978-1-4020-6754-9_16266.

Full text
APA, Harvard, Vancouver, ISO, and other styles
7

Sj��lander, Kimmen, and Chelsea Specht. "Functional prediction through phylogenetic inference and structural classification of proteins." In Encyclopedia of Genetics, Genomics, Proteomics and Bioinformatics. Chichester, UK: John Wiley & Sons, Ltd, 2006. http://dx.doi.org/10.1002/047001153x.g306320.

Full text
APA, Harvard, Vancouver, ISO, and other styles
8

DASSA, ELIE. "PHYLOGENETIC AND FUNCTIONAL CLASSIFICATION OF ABC (ATP-BINDING CASSETTE) SYSTEMS**ABSCISSE, a database of ABC systems, which includes functional, sequence and structural information, is available on the internet at the following address: www.pasteur.fr/recherche/unites/pmtg/abc/index.html." In ABC Proteins, 3–35. Elsevier, 2003. http://dx.doi.org/10.1016/b978-012352551-2/50002-0.

Full text
APA, Harvard, Vancouver, ISO, and other styles
9

Fackovec, Boris, and Jiri Vondrasek. "Decomposition of Intramolecular Interactions Between Amino-Acids in Globular Proteins - A Consequence for Structural Classes of Proteins and Methods of Their Classification." In Systems and Computational Biology - Molecular and Cellular Experimental Systems. InTech, 2011. http://dx.doi.org/10.5772/20277.

Full text
APA, Harvard, Vancouver, ISO, and other styles
10

M. Harvey, Evan, Murad Almasri, and Hugo R. Martinez. "Genetics of Cardiomyopathy." In Cardiomyopathy - Disease of the Heart Muscle [Working Title]. IntechOpen, 2021. http://dx.doi.org/10.5772/intechopen.97010.

Full text
Abstract:
Cardiomyopathies (CMs) encompass a heterogeneous group of structural and functional (systolic and diastolic) abnormalities of the myocardium and are either confined to the cardiovascular system or are part of a systemic disorder. CMs represent a leading cause of morbidity and mortality and account for a significant percentage of death and cardiac transplantation. The 2006 American Heart Association (AHA) classification grouped CMs into primary (genetic, mixed, or acquired) or secondary (i.e., infiltrative or autoimmune). In 2008, the European Society of Cardiology classification proposed subgrouping CM into familial or genetic and nonfamilial or nongenetic forms. In 2013, the World Heart Federation recommended the MOGES nosology system, which incorporates a morpho-functional phenotype (M), organ(s) involved (O), the genetic inheritance pattern (G), an etiological annotation (E) including genetic defects or underlying disease/substrates, and the functional status (S) of a particular patient based on heart failure symptoms. Rapid advancements in the biology of cardio-genetics have revealed substantial genetic and phenotypic heterogeneity in myocardial disease. Given the variety of disciplines in the scientific and clinical fields, any desired classification may face challenges to obtaining consensus. Nonetheless, the heritable phenotype-based CM classification offers the possibility of a simple, clinically useful diagnostic scheme. In this chapter, we will describe the genetic basis of dilated cardiomyopathy (DCM), hypertrophic cardiomyopathy (HCM), arrhythmogenic cardiomyopathy (ACM), LV noncompaction cardiomyopathy (LVNC), and restrictive cardiomyopathy (RCM). Although the descriptive morphologies of these types of CM differ, an overlapping phenotype is frequently encountered within the CM types and arrhythmogenic pathology in clinical practice. CMs appear to originate secondary to disruption of “final common pathways.” These disruptions may have purely genetic causes. For example, single gene mutations result in dysfunctional protein synthesis causing downstream dysfunctional protein interactions at the level of the sarcomere and a CM phenotype. The sarcomere is a complex with multiple protein interactions, including thick myofilament proteins, thin myofilament proteins, and myosin-binding proteins. In addition, other proteins are involved in the surrounding architecture of the sarcomere such as the Z-disk and muscle LIM proteins. One or multiple genes can exhibit tissue-specific function, development, and physiologically regulated patterns of expression for each protein. Alternatively, multiple mutations in the same gene (compound heterozygosity) or in different genes (digenic heterozygosity) may lead to a phenotype that may be classic, more severe, or even overlapping with other disease forms.
APA, Harvard, Vancouver, ISO, and other styles

Conference papers on the topic "Structural Classification of Proteins (SCOP)"

1

Casagrande, A., and F. Fabris. "SCOP family fingerprints: An information theoretic approach to structural classification of protein domains." In 2011 IEEE International Conference on Bioinformatics and Biomedicine Workshops (BIBMW). IEEE, 2011. http://dx.doi.org/10.1109/bibmw.2011.6112408.

Full text
APA, Harvard, Vancouver, ISO, and other styles
2

Nguyen, Thanh, Abbas Khosravi, Douglas Creighton, and Saeid Nahavandi. "Structural classification of proteins through amino acid sequence using interval type-2 fuzzy logic system." In 2014 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE). IEEE, 2014. http://dx.doi.org/10.1109/fuzz-ieee.2014.6891741.

Full text
APA, Harvard, Vancouver, ISO, and other styles
3

Barucci, Andrea, Cristiano D'Andrea, Edoardo Farnesi, Martina Banchelli, Chiara Amicucci, Marella De Angelis, Chiara Marzi, Roberto Pini, Byungil Hwang, and Paolo Matteini. "A Machine Learning approach to the classification of chemo-structural determinants in label-free SERS detection of proteins." In 2022 Italian Conference on Optics and Photonics (ICOP). IEEE, 2022. http://dx.doi.org/10.1109/icop56156.2022.9911735.

Full text
APA, Harvard, Vancouver, ISO, and other styles
You might also be interested in the extended bibliographies on the topic 'Structural Classification of Proteins (SCOP)' for particular source types:
Journal articles
We offer discounts on all premium plans for authors whose works are included in thematic literature selections. Contact us to get a unique promo code!

To the bibliography