Journal articles: '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

11

CASAGRANDE, ALBERTO, and FRANCESCO FABRIS. "FAMILY FINGERPRINTS: A GLOBAL APPROACH TO STRUCTURAL CLASSIFICATION." Journal of Bioinformatics and Computational Biology 10, no. 03 (June 2012): 1242001. http://dx.doi.org/10.1142/s0219720012420012.

Full text

Abstract:

Protein domain classification is a useful tool to deduce functional properties of proteins. Many software to classify domains according to available databases have been proposed so far. This paper introduces the notion of "fingerprint" as an easy and readable digest of the similarities between a protein fragment and an entire set of sequences. This concept offers us a rationale for building an automatic SCOP classifier which assigns a query sequence to the most likely family. Fingerprint-based analysis has been implemented in a software tool and we report some experimental validations for it.

APA, Harvard, Vancouver, ISO, and other styles

12

Murzin, Alexey G., Steven E. Brenner, Tim Hubbard, and Cyrus Chothia. "SCOP: A structural classification of proteins database for the investigation of sequences and structures." Journal of Molecular Biology 247, no. 4 (April 1995): 536–40. http://dx.doi.org/10.1016/s0022-2836(05)80134-2.

Full text

APA, Harvard, Vancouver, ISO, and other styles

13

Rudnev, Vladimir R., Denis V. Petrovsky, Kirill S. Nikolsky, Liudmila I. Kulikova, Alexander A. Stepanov, Kristina A. Malsagova, Anna L. Kaysheva, and Alexander V. Efimov. "Biological Role of the 3β-Corner Structural Motif in Proteins." Processes 10, no. 11 (October 22, 2022): 2159. http://dx.doi.org/10.3390/pr10112159.

Full text

Abstract:

In this study, we analyze the occurrence of the unique structural motif, the 3β-corner, belonging to the Structural Classification of Proteins (SCOP) folds, in proteins of various origins. We further assess the structural and functional role of this motif as well as the clustering of the biological functions of proteins in which it occurs. It has been shown previously that the 3β-corner occurs with different probabilities in all beta proteins, alpha and beta proteins (α + β and α/β), and alpha classes occur most often in the composition of β-proteins. The 3β-corner is often found as a building block in protein structures, such as β-barrels, -sandwiches, and -sheets/-layers.

APA, Harvard, Vancouver, ISO, and other styles

14

Pitulice, Laura, Adriana Isvoran, and Adrian Chiriac. "Structural features of proteins as reflected by statistical scaling laws." Journal of the Serbian Chemical Society 73, no. 8-9 (2008): 805–13. http://dx.doi.org/10.2298/jsc0809805p.

Full text

Abstract:

Within this paper, statistical scaling laws for the radius of gyration with the residues number, the surface area with the probe radii and the back- bone length with the interval of residues for a set of 60 proteins are revealed. The proteins belong to three different structural classes: alpha, beta and alpha plus beta class (20 proteins for each) according to the SCOP database classification, which takes into account the composition in the elements of their secondary structure. The shape and the surface roughness of proteins seem to be independent of the protein content in the secondary structure elements. On the contrary, the protein packing density shows a strong correlation with this composition.

APA, Harvard, Vancouver, ISO, and other styles

15

Gerstein, Mark, and Michael Levitt. "Comprehensive assessment of automatic structural alignment against a manual standard, the scop classification of proteins." Protein Science 7, no. 2 (February 1998): 445–56. http://dx.doi.org/10.1002/pro.5560070226.

Full text

APA, Harvard, Vancouver, ISO, and other styles

16

Plewczynski, Dariusz, Jakub Pas, Marcin Von Grotthuss, and Leszek Rychlewski. "Comparison of proteins based on segments structural similarity." Acta Biochimica Polonica 51, no. 1 (March 31, 2004): 161–72. http://dx.doi.org/10.18388/abp.2004_3608.

Full text

Abstract:

We present here a simple method for fast and accurate comparison of proteins using their structures. The algorithm is based on structural alignment of segments of Calpha chains (with size of 99 or 199 residues). The method is optimized in terms of speed and accuracy. We test it on 97 representative proteins with the similarity measure based on the SCOP classification. We compare our algorithm with the LGscore2 automatic method. Our method has the same accuracy as the LGscore2 algorithm with much faster processing of the whole test set, which is promising. A second test is done using the ToolShop structure prediction evaluation program and shows that our tool is on average slightly less sensitive than the DALI server. Both algorithms give a similar number of correct models, however, the final alignment quality is better in the case of DALI. Our method was implemented under the name 3D-Hit as a web server at http://3dhit.bioinfo.pl/ free for academic use, with a weekly updated database containing a set of 5000 structures from the Protein Data Bank with non-homologous sequences.

APA, Harvard, Vancouver, ISO, and other styles

17

Lampros, Christos, Thomas Simos, Themis P. Exarchos, Konstantinos P. Exarchos, Costas Papaloukas, and Dimitrios I. Fotiadis. "Assessment of optimized Markov models in protein fold classification." Journal of Bioinformatics and Computational Biology 12, no. 04 (August 2014): 1450016. http://dx.doi.org/10.1142/s0219720014500164.

Full text

Abstract:

Protein fold classification is a challenging task strongly associated with the determination of proteins' structure. In this work, we tested an optimization strategy on a Markov chain and a recently introduced Hidden Markov Model (HMM) with reduced state-space topology. The proteins with unknown structure were scored against both these models. Then the derived scores were optimized following a local optimization method. The Protein Data Bank (PDB) and the annotation of the Structural Classification of Proteins (SCOP) database were used for the evaluation of the proposed methodology. The results demonstrated that the fold classification accuracy of the optimized HMM was substantially higher compared to that of the Markov chain or the reduced state-space HMM approaches. The proposed methodology achieved an accuracy of 41.4% on fold classification, while Sequence Alignment and Modeling (SAM), which was used for comparison, reached an accuracy of 38%.

APA, Harvard, Vancouver, ISO, and other styles

18

Casbon, James, and Mansoor Saqi. "Functional diversity within protein superfamilies." Journal of Integrative Bioinformatics 3, no. 2 (December 1, 2006): 295–304. http://dx.doi.org/10.1515/jib-2006-46.

Full text

Abstract:

Summary Structural genomics projects are leading to the discovery of relationships between proteins that would not have been anticipated from consideration of sequence alone. However the assignment of function via structure remains difficult as some structures are compatible with a variety of functions. In this study we explore the relationships between structural diversity and functional diversity within distantly related members of SCOP superfamilies. We use the Gene Ontology functional classification scheme and Greens path entropy to measure functional diversity. We observe a negative correlation between the functional entropy of a superfamily and the size of the conserved core.

APA, Harvard, Vancouver, ISO, and other styles

19

Xu, Jinrui, and Yang Zhang. "How significant is a protein structure similarity with TM-score = 0.5?" Bioinformatics 26, no. 7 (February 17, 2010): 889–95. http://dx.doi.org/10.1093/bioinformatics/btq066.

Full text

Abstract:

Abstract Motivation: Protein structure similarity is often measured by root mean squared deviation, global distance test score and template modeling score (TM-score). However, the scores themselves cannot provide information on how significant the structural similarity is. Also, it lacks a quantitative relation between the scores and conventional fold classifications. This article aims to answer two questions: (i) what is the statistical significance of TM-score? (ii) What is the probability of two proteins having the same fold given a specific TM-score? Results: We first made an all-to-all gapless structural match on 6684 non-homologous single-domain proteins in the PDB and found that the TM-scores follow an extreme value distribution. The data allow us to assign each TM-score a P-value that measures the chance of two randomly selected proteins obtaining an equal or higher TM-score. With a TM-score at 0.5, for instance, its P-value is 5.5 × 10−7, which means we need to consider at least 1.8 million random protein pairs to acquire a TM-score of no less than 0.5. Second, we examine the posterior probability of the same fold proteins from three datasets SCOP, CATH and the consensus of SCOP and CATH. It is found that the posterior probability from different datasets has a similar rapid phase transition around TM-score=0.5. This finding indicates that TM-score can be used as an approximate but quantitative criterion for protein topology classification, i.e. protein pairs with a TM-score >0.5 are mostly in the same fold while those with a TM-score <0.5 are mainly not in the same fold. Contact: zhng@umich.edu Supplementary information: Supplementary data are available at Bioinformatics online.

APA, Harvard, Vancouver, ISO, and other styles

20

Chandonia, John-Marc, Naomi K. Fox, and Steven E. Brenner. "SCOPe: classification of large macromolecular structures in the structural classification of proteins—extended database." Nucleic Acids Research 47, no. D1 (November 30, 2018): D475—D481. http://dx.doi.org/10.1093/nar/gky1134.

Full text

APA, Harvard, Vancouver, ISO, and other styles

21

Rother, Kristian, Mathias Dunkel, Elke Michalsky, Silke Trissl, Andrean Goede, Ulf Leser, and Robert Preissner. "A structural keystone for drug design." Journal of Integrative Bioinformatics 3, no. 1 (June 1, 2006): 21–31. http://dx.doi.org/10.1515/jib-2006-19.

Full text

Abstract:

Abstract 3D-structures of proteins and potential ligands are the cornerstones of rational drug design. The first brick to build upon is selecting a protein target and finding out whether biologically active compounds are known. Both tasks require more information than the structures themselves provide. For this purpose we have built a web resource bridging protein and ligand databases. It consists of three parts: i) A data warehouse on annotation of protein structures that integrates many well-known databases such as Swiss-Prot, SCOP, ENZYME and others. ii) A conformational library of structures of approved drugs. iii) A conformational library of ligands from the PDB, linking the realms of proteins and small molecules. The data collection contains structures of 30,000 proteins, 5,000 different ligands from 70,000 ligand-protein complexes, and 2,500 known drugs. Sets of protein structures can be refined by criteria like protein fold, family, metabolic pathway, resolution and textual annotation. The structures of organic compounds (drugs and ligands) can be searched considering chemical formula, trivial and trade names as well as medical classification codes for drugs (ATC). Retrieving structures by 2D-similarity has been implemented for all small molecules using Tanimoto coefficients. For the drug structures, 110,000 structural conformers have been calculated to account for structural flexibility. Two substances can be compared online by 3D-superimposition, where the pair of conformers that fits best is detected. Together, these web-accessible resources can be used to identify promising drug candidates. They have been used in-house to find alternatives to substances with a known binding activity but adverse side effects.

APA, Harvard, Vancouver, ISO, and other styles

22

Chandonia, John-Marc, Naomi K. Fox, and Steven E. Brenner. "SCOPe: Manual Curation and Artifact Removal in the Structural Classification of Proteins – extended Database." Journal of Molecular Biology 429, no. 3 (February 2017): 348–55. http://dx.doi.org/10.1016/j.jmb.2016.11.023.

Full text

APA, Harvard, Vancouver, ISO, and other styles

23

Narykov, Oleksandr, Dmytro Bogatov, and Dmitry Korkin. "DISPOT: a simple knowledge-based protein domain interaction statistical potential." Bioinformatics 35, no. 24 (July 27, 2019): 5374–78. http://dx.doi.org/10.1093/bioinformatics/btz587.

Full text

Abstract:

Abstract Motivation The complexity of protein–protein interactions (PPIs) is further compounded by the fact that an average protein consists of two or more domains, structurally and evolutionary independent subunits. Experimental studies have demonstrated that an interaction between a pair of proteins is not carried out by all domains constituting each protein, but rather by a select subset. However, determining which domains from each protein mediate the corresponding PPI is a challenging task. Results Here, we present domain interaction statistical potential (DISPOT), a simple knowledge-based statistical potential that estimates the propensity of an interaction between a pair of protein domains, given their structural classification of protein (SCOP) family annotations. The statistical potential is derived based on the analysis of >352 000 structurally resolved PPIs obtained from DOMMINO, a comprehensive database of structurally resolved macromolecular interactions. Availability and implementation DISPOT is implemented in Python 2.7 and packaged as an open-source tool. DISPOT is implemented in two modes, basic and auto-extraction. The source code for both modes is available on GitHub: https://github.com/korkinlab/dispot and standalone docker images on DockerHub: https://hub.docker.com/r/korkinlab/dispot. The web server is freely available at http://dispot.korkinlab.org/. Supplementary information Supplementary data are available at Bioinformatics online.

APA, Harvard, Vancouver, ISO, and other styles

24

Chandonia, John-Marc, Lindsey Guan, Shiangyi Lin, Changhua Yu, Naomi K. Fox, and Steven E. Brenner. "SCOPe: improvements to the structural classification of proteins – extended database to facilitate variant interpretation and machine learning." Nucleic Acids Research 50, no. D1 (December 1, 2021): D553—D559. http://dx.doi.org/10.1093/nar/gkab1054.

Full text

Abstract:

Abstract The Structural Classification of Proteins—extended (SCOPe, https://scop.berkeley.edu) knowledgebase aims to provide an accurate, detailed, and comprehensive description of the structural and evolutionary relationships amongst the majority of proteins of known structure, along with resources for analyzing the protein structures and their sequences. Structures from the PDB are divided into domains and classified using a combination of manual curation and highly precise automated methods. In the current release of SCOPe, 2.08, we have developed search and display tools for analysis of genetic variants we mapped to structures classified in SCOPe. In order to improve the utility of SCOPe to automated methods such as deep learning classifiers that rely on multiple alignment of sequences of homologous proteins, we have introduced new machine-parseable annotations that indicate aberrant structures as well as domains that are distinguished by a smaller repeat unit. We also classified structures from 74 of the largest Pfam families not previously classified in SCOPe, and we improved our algorithm to remove N- and C-terminal cloning, expression and purification sequences from SCOPe domains. SCOPe 2.08-stable classifies 106 976 PDB entries (about 60% of PDB entries).

APA, Harvard, Vancouver, ISO, and other styles

25

Apiletti, Daniele, Giulia Bruno, Elisa Ficarra, and Elena Baralis. "Data Cleaning and Semantic Improvement in Biological Databases." Journal of Integrative Bioinformatics 3, no. 2 (December 1, 2006): 219–29. http://dx.doi.org/10.1515/jib-2006-40.

Full text

Abstract:

Summary Public genomic and proteomic databases can be affected by a variety of errors. These errors may involve either the description or the meaning of data (namely, syntactic or semantic errors). We focus our analysis on the detection of semantic errors, in order to verify the accuracy of the stored information. In particular, we address the issue of data constraints and functional dependencies among attributes in a given relational database. Constraints and dependencies show semantics among attributes in a database schema and their knowledge may be exploited to improve data quality and integration in database design, and to perform query optimization and dimensional reduction. We propose a method to discover data constraints and functional dependencies by means of association rule mining. Association rules are extracted among attribute values and allow us to find causality relationships among them. Then, by analyzing the support and confidence of each rule, (probabilistic) data constraints and functional dependencies may be detected. With our method we can both show the presence of erroneous data and highlight novel semantic information. Moreover, our method is database-independent because it infers rules from data. In this paper, we report the application of our techniques to the SCOP (Structural Classification of Proteins) and CATH Protein Structure Classification databases.

APA, Harvard, Vancouver, ISO, and other styles

26

Chen, Daozheng, Xiaoyu Tian, Bo Zhou, and Jun Gao. "ProFold: Protein Fold Classification with Additional Structural Features and a Novel Ensemble Classifier." BioMed Research International 2016 (2016): 1–10. http://dx.doi.org/10.1155/2016/6802832.

Full text

Abstract:

Protein fold classification plays an important role in both protein functional analysis and drug design. The number of proteins in PDB is very large, but only a very small part is categorized and stored in the SCOPe database. Therefore, it is necessary to develop an efficient method for protein fold classification. In recent years, a variety of classification methods have been used in many protein fold classification studies. In this study, we propose a novel classification method called proFold. We import protein tertiary structure in the period of feature extraction and employ a novel ensemble strategy in the period of classifier training. Compared with existing similar ensemble classifiers using the same widely used dataset (DD-dataset), proFold achieves 76.2% overall accuracy. Another two commonly used datasets, EDD-dataset and TG-dataset, are also tested, of which the accuracies are 93.2% and 94.3%, higher than the existing methods. ProFold is available to the public as a web-server.

APA, Harvard, Vancouver, ISO, and other styles

27

COMIN, MATTEO, CARLO FERRARI, and CONCETTINA GUERRA. "GRID DEPLOYMENT OF BIOINFORMATICS APPLICATIONS: A CASE STUDY IN PROTEIN SIMILARITY DETERMINATION." Parallel Processing Letters 14, no. 02 (June 2004): 163–76. http://dx.doi.org/10.1142/s0129626404001817.

Full text

Abstract:

In this paper we present a scenario for the grid immersion of the procedures that solve the protein structural similarity determination problem. The emphasis is on the way various computational components and data resources are tied together into a workflow to be executed on a grid. The grid deployment has been organized according to the bag-of-service model: a set of different modules (with their data set) is made available to the application designers. Each module deals with a specific subproblem using a proper protein data representation. At the design level, the process of task selection produces a first general workflow that establishes which subproblems need to be solved and their temporal relations. A further refinement requires to select a procedure for each previously identified task that solves it: the choice is made among different available methods and representations. The final outcome is an instance of the workflow ready for execution on a grid. Our approach to protein structure comparison is based on a combination of indexing and dynamic programming techniques to achieve fast and reliable matching. All the components have been implemented on a grid infrastructure using Globus, and the overall tool has been tested by choosing proteins from different fold classes. The obtained results are compared against SCOP, a standard tool for the classification of known proteins.

APA, Harvard, Vancouver, ISO, and other styles

28

Rudakova, Tetyana, Antonina Minorova, Nataliia Krushelnytska, and Sergiy Narizhnyy. "Scientific approaches to classifying dairy dessert products." FOOD RESOURCES 9, no. 16 (June 25, 2021): 164–79. http://dx.doi.org/10.31073/foodresources2021-16-16.

Full text

Abstract:

Subject of study. At the present stage of technology development, dairy dessert products acquire special significance due to its high sensorial properties, a wide range of components, the possibility of varying the nutritional and energy value. However, the range of desserts made from milk using different types of fillers and structurants is very diverse. Therefore, there is a need for their systematization and classification. The aim of the research was to systematize up-dated information on the composition and technology of dairy desserts using non-traditional structurants from raw materials of plant and animal origin to develop a scientifically ground classification of dairy desserts. Materials and methods. Modern normative and analytical data on the range of dairy desserts. Results and discussion. Analytical studies have shown that dairy-based desserts can be divided into three groups – foam, gel and desserts with a complex dispersed structure. To give a certain structure to dairy desserts, various structurants and fillers are used, due to which the final consumer properties of the finished product are formed. It is noted that the use of such structural additives as milk proteins in milk concentrates, milk powder or whey powder, egg whites, various types of flour, starch, hydrocolloids, dietary fiber, etc., which play an important role in shaping the structural and mechanical properties of dairy desserts, is promising. The use of natural animal and vegetable raw materials will not only improve the quality and expand the range of dairy desserts, but also rationally use local raw materials. Scope of research results. The obtained results of analytical research will be used for the development of technologies of dairy dessert products with a combined composition of raw materials.

APA, Harvard, Vancouver, ISO, and other styles

29

Angadi, Ulavappa B., and M. Venkatesulu. "Structural SCOP Superfamily Level Classification Using Unsupervised Machine Learning." IEEE/ACM Transactions on Computational Biology and Bioinformatics 9, no. 2 (March 2012): 601–8. http://dx.doi.org/10.1109/tcbb.2011.114.

Full text

APA, Harvard, Vancouver, ISO, and other styles

30

Joseph, Agnel Praveen, Hélène Valadié, Narayanaswamy Srinivasan, and Alexandre G. de Brevern. "Local Structural Differences in Homologous Proteins: Specificities in Different SCOP Classes." PLoS ONE 7, no. 6 (June 22, 2012): e38805. http://dx.doi.org/10.1371/journal.pone.0038805.

Full text

APA, Harvard, Vancouver, ISO, and other styles

31

Malhotra, Sony, and Ramanathan Sowdhamini. "Collation and analyses of DNA-binding protein domain families from sequence and structural databanks." Mol. BioSyst. 11, no. 4 (2015): 1110–18. http://dx.doi.org/10.1039/c4mb00629a.

Full text

Abstract:

The distribution of GO molecular functions across different SCOP DNA-binding folds was studied. Majority of the folds were observed to perform more than one molecular function. This supports the notion that majority of DNA-binding proteins might follow divergent evolution.

APA, Harvard, Vancouver, ISO, and other styles

32

ÇAMOĞLU, ORHAN, TOLGA CAN, AMBUJ K. SINGH, and YUAN-FANG WANG. "DECISION TREE BASED INFORMATION INTEGRATION FOR AUTOMATED PROTEIN CLASSIFICATION." Journal of Bioinformatics and Computational Biology 03, no. 03 (June 2005): 717–42. http://dx.doi.org/10.1142/s0219720005001259.

Full text

Abstract:

We propose a novel technique for automatically generating the SCOP classification of a protein structure with high accuracy. We achieve accurate classification by combining the decisions of multiple methods using the consensus of a committee (or an ensemble) classifier. Our technique, based on decision trees, is rooted in machine learning which shows that by judicially employing component classifiers, an ensemble classifier can be constructed to outperform its components. We use two sequence- and three structure-comparison tools as component classifiers. Given a protein structure and using the joint hypothesis, we first determine if the protein belongs to an existing category (family, superfamily, fold) in the SCOP hierarchy. For the proteins that are predicted as members of the existing categories, we compute their family-, superfamily-, andfold-level classifications using the consensus classifier. We show that we can significantly improve the classification accuracy compared to the individual component classifiers. In particular, we achieve error rates that are 3–12 times less than the individual classifiers' error rates at the family level, 1.5–4.5 times less at the superfamily level, and 1.1–2.4 times less at the fold level.

APA, Harvard, Vancouver, ISO, and other styles

33

Lo, Sheng C., Zhong-Ru Xie, and Kuan Y. Chang. "Structural and Functional Enrichment Analyses for Antimicrobial Peptides." International Journal of Molecular Sciences 21, no. 22 (November 20, 2020): 8783. http://dx.doi.org/10.3390/ijms21228783.

Full text

Abstract:

Whether there is any inclination between structures and functions of antimicrobial peptides (AMPs) is a mystery yet to be unraveled. AMPs have various structures associated with many different antimicrobial functions, including antibacterial, anticancer, antifungal, antiparasitic and antiviral activities. However, none has yet reported any antimicrobial functional tendency within a specific category of protein/peptide structures nor any structural tendency of a specific antimicrobial function with respect to AMPs. Here, we examine the relationships between structures categorized by three structural classification methods (CATH, SCOP, and TM) and seven antimicrobial functions with respect to AMPs using an enrichment analysis. The results show that antifungal activities of AMPs were tightly related to the two-layer sandwich structure of CATH, the knottin fold of SCOP, and the first structural cluster of TM. The associations with knottin and TM Cluster 1 even sustained through the AMPs with a low sequence identity. Moreover, another significant mutual enrichment was observed between the third cluster of TM and anti-Gram-positive-bacterial/anti-Gram-negative-bacterial activities. The findings of the structure–function inclination further our understanding of AMPs and could help us design or discover new therapeutic potential AMPs.

APA, Harvard, Vancouver, ISO, and other styles

34

Murzin, Alexey G. "Structural classification of proteins: new superfamilies." Current Opinion in Structural Biology 6, no. 3 (June 1996): 386–94. http://dx.doi.org/10.1016/s0959-440x(96)80059-5.

Full text

APA, Harvard, Vancouver, ISO, and other styles

35

Dubchak, Inna, Ilya Muchnik, Christopher Mayor, Igor Dralyuk, and Sung-Hou Kim. "Recognition of a protein fold in the context of the SCOP classification." Proteins: Structure, Function, and Genetics 35, no. 4 (June 1, 1999): 401–7. http://dx.doi.org/10.1002/(sici)1097-0134(19990601)35:4<401::aid-prot3>3.0.co;2-k.

Full text

APA, Harvard, Vancouver, ISO, and other styles

36

Suwa, M. "Structural recognition and classification of membrane proteins." Seibutsu Butsuri 40, supplement (2000): S106. http://dx.doi.org/10.2142/biophys.40.s106_1.

Full text

APA, Harvard, Vancouver, ISO, and other styles

37

Berntsson, Ronnie P. A., Sander H. J. Smits, Lutz Schmitt, Dirk-Jan Slotboom, and Bert Poolman. "A structural classification of substrate-binding proteins." FEBS Letters 584, no. 12 (April 20, 2010): 2606–17. http://dx.doi.org/10.1016/j.febslet.2010.04.043.

Full text

APA, Harvard, Vancouver, ISO, and other styles

38

Chen, Yingfei, Armando Elizondo-Noriega, David C. Cantu, and Peter J. Reilly. "Structural classification of biotin carboxyl carrier proteins." Biotechnology Letters 34, no. 10 (June 20, 2012): 1869–75. http://dx.doi.org/10.1007/s10529-012-0978-4.

Full text

APA, Harvard, Vancouver, ISO, and other styles

39

Qi, Yuan, and Nick V. Grishin. "Structural classification of thioredoxin-like fold proteins." Proteins: Structure, Function, and Bioinformatics 58, no. 2 (November 19, 2004): 376–88. http://dx.doi.org/10.1002/prot.20329.

Full text

APA, Harvard, Vancouver, ISO, and other styles

40

Bonet, Jaume, Joan Planas-Iglesias, Javier Garcia-Garcia, Manuel A. Marín-López, Narcis Fernandez-Fuentes, and Baldo Oliva. "ArchDB 2014: structural classification of loops in proteins." Nucleic Acids Research 42, no. D1 (November 21, 2013): D315—D319. http://dx.doi.org/10.1093/nar/gkt1189.

Full text

APA, Harvard, Vancouver, ISO, and other styles

41

Murzin, Alexey G., and Alex Bateman. "Distant homology recognition using structural classification of proteins." Proteins: Structure, Function, and Genetics 29, S1 (1997): 105–12. http://dx.doi.org/10.1002/(sici)1097-0134(1997)1+<105::aid-prot14>3.0.co;2-s.

Full text

APA, Harvard, Vancouver, ISO, and other styles

42

Scheepers, Giel H., Jelger A. Lycklama a Nijeholt, and Bert Poolman. "An updated structural classification of substrate-binding proteins." FEBS Letters 590, no. 23 (October 23, 2016): 4393–401. http://dx.doi.org/10.1002/1873-3468.12445.

Full text

APA, Harvard, Vancouver, ISO, and other styles

43

Chen, Chang, Haipeng Liu, Shadi Zabad, Nina Rivera, Emily Rowin, Maheen Hassan, Stephanie M. Gomez De Jesus, et al. "MoonProt 3.0: an update of the moonlighting proteins database." Nucleic Acids Research 49, no. D1 (November 27, 2020): D368—D372. http://dx.doi.org/10.1093/nar/gkaa1101.

Full text

Abstract:

Abstract MoonProt 3.0 (http://moonlightingproteins.org) is an updated open-access database storing expert-curated annotations for moonlighting proteins. Moonlighting proteins have two or more physiologically relevant distinct biochemical or biophysical functions performed by a single polypeptide chain. Here, we describe an expansion in the database since our previous report in the Database Issue of Nucleic Acids Research in 2018. For this release, the number of proteins annotated has been expanded to over 500 proteins and dozens of protein annotations have been updated with additional information, including more structures in the Protein Data Bank, compared with version 2.0. The new entries include more examples from humans, plants and archaea, more proteins involved in disease and proteins with different combinations of functions. More kinds of information about the proteins and the species in which they have multiple functions has been added, including CATH and SCOP classification of structure, known and predicted disorder, predicted transmembrane helices, type of organism, relationship of the protein to disease, and relationship of organism to cause of disease.

APA, Harvard, Vancouver, ISO, and other styles

44

Rubinstein, Rotem, Udupi A. Ramagopal, Stanley G. Nathenson, Steven C. Almo, and Andras Fiser. "Functional Classification of Immune Regulatory Proteins." Structure 21, no. 5 (May 2013): 766–76. http://dx.doi.org/10.1016/j.str.2013.02.022.

Full text

APA, Harvard, Vancouver, ISO, and other styles

45

Malik, Ashar J., Anthony M. Poole, and Jane R. Allison. "Structural Phylogenetics with Confidence." Molecular Biology and Evolution 37, no. 9 (April 17, 2020): 2711–26. http://dx.doi.org/10.1093/molbev/msaa100.

Full text

Abstract:

Abstract For evaluating the deepest evolutionary relationships among proteins, sequence similarity is too low for application of sequence-based homology search or phylogenetic methods. In such cases, comparison of protein structures, which are often better conserved than sequences, may provide an alternative means of uncovering deep evolutionary signal. Although major protein structure databases such as SCOP and CATH hierarchically group protein structures, they do not describe the specific evolutionary relationships within a hierarchical level. Structural phylogenies have the potential to fill this gap. However, it is difficult to assess evolutionary relationships derived from structural phylogenies without some means of assessing confidence in such trees. We therefore address two shortcomings in the application of structural data to deep phylogeny. First, we examine whether phylogenies derived from pairwise structural comparisons are sensitive to differences in protein length and shape. We find that structural phylogenetics is best employed where structures have very similar lengths, and that shape fluctuations generated during molecular dynamics simulations impact pairwise comparisons, but not so drastically as to eliminate evolutionary signal. Second, we address the absence of statistical support for structural phylogeny. We present a method for assessing confidence in a structural phylogeny using shape fluctuations generated via molecular dynamics or Monte Carlo simulations of proteins. Our approach will aid the evolutionary reconstruction of relationships across structurally defined protein superfamilies. With the Protein Data Bank now containing in excess of 158,000 entries (December 2019), we predict that structural phylogenetics will become a useful tool for ordering the protein universe.

APA, Harvard, Vancouver, ISO, and other styles

46

Min, Seungsik, and Kyungsik Kim. "Topological properties of networks in structural classification of proteins." Journal of the Korean Physical Society 65, no. 7 (October 2014): 1164–69. http://dx.doi.org/10.3938/jkps.65.1164.

Full text

APA, Harvard, Vancouver, ISO, and other styles

47

Kinjo, Akira R., and Haruki Nakamura. "Comprehensive Structural Classification of Ligand-Binding Motifs in Proteins." Structure 17, no. 2 (February 2009): 234–46. http://dx.doi.org/10.1016/j.str.2008.11.009.

Full text

APA, Harvard, Vancouver, ISO, and other styles

48

PAI, TUN-WEN, RUEI-HSIANG CHANG, CHIEN-MING CHEN, PO-HAN SU, LEE-JYI WANG, KUEN-TSAIR LAY, and KUO-TORNG LAN. "MULTIPLE STRUCTURE ALIGNMENT BASED ON GEOMETRICAL CORRELATION OF SECONDARY STRUCTURE ELEMENTS." New Mathematics and Natural Computation 06, no. 01 (March 2010): 77–95. http://dx.doi.org/10.1142/s1793005710001621.

Full text

Abstract:

Protein structure alignment facilitates the analysis of protein functionality. Through superimposed structures and the comparison of variant components, common or specific features of proteins can be identified. Several known protein families exhibit analogous tertiary structures but divergent primary sequences. These proteins in the same structural class are unable to be aligned by sequence-based methods. The main objective of the present study was to develop an efficient and effective algorithm for multiple structure alignment based on geometrical correlation of secondary structures, which are conserved in evolutionary heritage. The method utilizes mutual correlation analysis of secondary structure elements (SSEs) and selects representative segments as the key anchors for structural alignment. The system exploits a fast vector transformation technique to represent SSEs in vector format, and the mutual geometrical relationship among vectors is projected onto an angle-distance map. Through a scoring function and filtering mechanisms, the best candidates of vectors are selected, and an effective constrained multiple structural alignment module is performed. The correctness of the algorithm was verified by the multiple structure alignment of proteins in the SCOP database. Several protein sets with low sequence identities were aligned, and the results were compared with those obtained by three well-known structural alignment approaches. The results show that the proposed method is able to perform multiple structural alignments effectively and to obtain satisfactory results, especially for proteins possessing low sequence identity.

APA, Harvard, Vancouver, ISO, and other styles

49

CHEN, YUEHUI, FENG CHEN, JACK Y. YANG, and MARY QU YANG. "ENSEMBLE VOTING SYSTEM FOR MULTICLASS PROTEIN FOLD RECOGNITION." International Journal of Pattern Recognition and Artificial Intelligence 22, no. 04 (June 2008): 747–63. http://dx.doi.org/10.1142/s0218001408006454.

Full text

Abstract:

Protein structure classification is an important issue in understanding the associations between sequence and structure as well as possible functional and evolutionary relationships. Recently structural genomes initiatives and other high-throughput experiments have populated the biological databases at a rapid pace. In this paper, three types of classifiers, k nearest neighbors, class center and nearest neighbor and probabilistic neural networks and their homogenous ensemble for multiclass protein fold recognition problem are evaluated firstly, and then a heterogenous ensemble Voting System is designed for the same problem. The different features and/or their combinations extracted from the protein fold dataset are used in these classification models. The heterogenous classification results are then put into a voting system to get the final result. The experimental results show that the proposed method can improve prediction accuracy by 4%–10% on a benchmark dataset containing 27 SCOP folds.

APA, Harvard, Vancouver, ISO, and other styles

50

Do, Hackwon, Chang Woo Lee, and Jun Hyuck Lee. "Structure-based classification of ice-binding proteins." Acta Crystallographica Section A Foundations and Advances 70, a1 (August 5, 2014): C1054. http://dx.doi.org/10.1107/s2053273314089451.

Full text

Abstract:

Since the antifreeze protein (AFP) super family has low structural identity, classification standard of the AFPs is presently ambiguous. Newly identified ice-binding proteins (IBPs), named so after the function of the AFPs, have similar structural identity and function that interact to the ice. Identification and characterization of IBPs from the eukaryotic microorganisms Typhulaishikariensis (TisAFP) and Leucosporidium sp. (LeIBP) revealed that both are glycosylated and have irregular motif on the ice-binding site (IBS). The IBPs share a unique right-handed β-helix, which provides an advantage of broad-range interaction surface. The other IBP encoded by the Antarctic bacterium Flavobacterium frigoris PSI was determined at 2.1-Å resolution to gain insight into its ice-binding mechanism. The structure of FfIBP shows the presence of an intra-molecular disulfide bond in the loop region between α2 and α4 (capping region), unlike that of LeIBP and TisAFP. Electron density for this disulfide bond was seen between Cys107 and Cys124 during the structure refinement process and the Cβ–Cβ distance between Cys107 and Cys124 was 3.9 Å. By sequence alignments and structural comparisons of IBPs, we defined two groups within IBPs, depending on the sequence differences between the α2 and α4 loop regions and the presence of the disulfide bond. In addition, to investigate the effects of the capping region on the activity and stability of FfIBP, we determined the crystal structure and measured the thermal stability of mutants that swapped the capping region of FfIBP and LeIBP (mFfIBP and mLeIBP). In thermal denaturation experiments, it is clear that the capping-head region of FfIBP is more stable than that of LeIBP and is important for the overall stability of IBP, although it is not directly involved in the antifreeze activity.

APA, Harvard, Vancouver, ISO, and other styles

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

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