• Genomics & Informatics
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• v.11 no.3
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• pp.155-160
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• 2013

Structural information has been a major concern for biological and pharmaceutical studies for its intimate relationship to the function of a protein. Three-dimensional representation of the positions of protein atoms is utilized among many structural information repositories that have been published. The reliability of the torsional system, which represents the native processes of structural change in the structural analysis, was partially proven with previous structural alignment studies. Here, a web server providing structural information and analysis based on the backbone torsional representation of a protein structure is newly introduced. The web server offers functions of secondary structure database search, secondary structure calculation, and pair-wise protein structure comparison, based on a backbone torsion angle representation system. Application of the implementation in pair-wise structural alignment showed highly accurate results. The information derived from this web server might be further utilized in the field of ab initio protein structure modeling or protein homology-related analyses.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.20 no.9
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• pp.1816-1821
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• 2016

Protein secondary structure is important for the study of protein evolution, structure and function of proteins which play crucial roles in most of biological processes. This paper try to effectively extract protein secondary structure information from the large protein structure database in order to predict the protein secondary structure of a query protein sequence. To find more remote homologous sequences of a query sequence in the protein database, we used PSI-BLAST which can perform gapped iterative searches and use profiles consisting of homologous protein sequences of a query protein. The secondary structures of the homologous sequences are weighed combined to the secondary structure prediction according to their relative degree of similarity to the query sequence. When homologous sequences with a neural network predictor were used, the accuracies were higher than those of current state-of-art techniques, achieving a Q3 accuracy of 92.28% and a Q8 accuracy of 88.79%.

• The KIPS Transactions:PartA
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• v.16A no.3
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• pp.153-158
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• 2009

Analysis of 3-dimensional (3D) protein structure plays an important role of structural bioinformatics. The protein structure visualization is the one of the structural bioinformatics and the most fundamental problem. As the number of known protein structure increases rapidly and the study of protein-protein interaction is prevalent, the fast visualization of large scale protein structure becomes essential. The fast protein structure visualization system we proposed is sophisticated and well designed visualization system using geometry instancing technique. Because this system is optimized for recent 3D graphics hardware using geometry instancing technique, its rendering speed is faster than other visualization tools.

• BMB Reports
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• v.37 no.1
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• pp.28-34
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• 2004

The discovery of biochemical and cellular functions of unannotated gene products begins with a database search of proteins with structure/sequence homologues based on known genes. Very recently, a number of frontier groups in structural biology proposed a new paradigm to predict biological functions of an unknown protein on the basis of its three-dimensional structure on a genomic scale. Structural proteomics (genomics), a research area for structure-based functional discovery, aims to complete the protein-folding universe of all gene products in a cell. It would lead us to a complete understanding of a living organism from protein structure. Two major complementary experimental techniques, X-ray crystallography and NMR spectroscopy, combined with recently developed high throughput methods have played a central role in structural proteomics research; however, an integration of these methodologies together with comparative modeling and electron microscopy would speed up the goal for completing a full dictionary of protein folding space in the near future.

• Proceedings of the Korean Society for Bioinformatics Conference
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• 2000.11a
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• pp.17-20
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• 2000

Structural genomics aims to provide a good experimental structure or computational model of every tractable protein in a complete genome. Underlying this goal is the immense value of protein structure, especially in permitting recognition of distant evolutionary relationships for proteins whose sequence analysis has failed to find any significant homolog. A considerable fraction of the genes in all sequenced genomes have no known function, and structure determination provides a direct means of revealing homology that may be used to infer their putative molecular function. The solved structures will be similarly useful for elucidating the biochemical or biophysical role of proteins that have been previously ascribed only phenotypic functions. More generally, knowledge of an increasingly complete repertoire of protein structures will aid structure prediction methods, improve understanding of protein structure, and ultimately lend insight into molecular interactions and pathways. We use computational methods to select families whose structures cannot be predicted and which are likely to be amenable to experimental characterization. Methods to be employed included modern sequence analysis and clustering algorithms. A critical component is consultation of the presage database for structural genomics, which records the community's experimental work underway and computational predictions. The protein families are ranked according to several criteria including taxonomic diversity and known functional information. Individual proteins, often homologs from hyperthermophiles, are selected from these families as targets for structure determination. The solved structures are examined for structural similarity to other proteins of known structure. Homologous proteins in sequence databases are computationally modeled, to provide a resource of protein structure models complementing the experimentally solved protein structures.

• Proceedings of the Korean Society for Bioinformatics Conference
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• 2000.11a
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• pp.28-31
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• 2000

We describe a hidden Markov model, HMMTIR, for general protein sequence based on the I-sites library of sequence-structure motifs. Unlike the linear HMMs used to model individual protein families, HMMSTR has a highly branched topology and captures recurrent local features of protein sequences and structures that transcend protein family boundaries. The model extends the I-sites library by describing the adjacencies of different sequence-structure motifs as observed in the database, and achieves a great reduction in parameters by representing overlapping motifs in a much more compact form. The HMM attributes a considerably higher probability to coding sequence than does an equivalent dipeptide model, predicts secondary structure with an accuracy of 74.6% and backbone torsion angles better than any previously reported method, and predicts the structural context of beta strands and turns with an accuracy that should be useful for tertiary structure prediction. HMMSTR has been incorporated into a public, fully-automated protein structure prediction server.

• Journal of KIISE:Computing Practices and Letters
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• v.11 no.2
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• pp.133-148
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• 2005

Since understanding of similarities and differences among protein structures is very important for the study of the relationship between structure and function, many protein structure comparison systems have been developed. Hut, unfortunately, these systems introduce their own protein data derived from the PDB(Protein Data Bank), which are needed in their algorithms for comparing protein structures. In addition, according to the rapid increase in the size of PDB, these systems require much more computation to search for common substructures in their databases. In this paper, we introduce a protein structure comparison system named WS4E(A Web-Based Searching Substructures of Secondary Structure Elements) based on a PSAML database which stores PSAML documents using the eXist open XML DBMS. PSAML(Protein Structure Abstraction Markup Language) is an XML representation of protein data, describing a protein structure as the secondary structures of the protein and their relationships. Using the PSAML database, the WS4E provides web services searching for common substructures among proteins represented in PSAML. In addition, to reduce the number of candidate protein structures to be compared in the PSAML database, we used topology strings which contain the spatial information of secondary structures in a protein.

• Proceedings of the Korean Society for Bioinformatics Conference
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• 2003.10a
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• pp.133-138
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• 2003

The prediction of protein secondary structure has been an important bioinformatics tool that is an essential component of the template-based protein tertiary structure prediction process. It has been known that the predicted secondary structure information improves both the fold recognition performance and the alignment accuracy. In this paper, we describe several novel ideas that may improve the prediction accuracy. The main idea is motivated by an observation that the protein's structural information, especially when it is combined with the evolutionary information, significantly improves the accuracy of the predicted tertiary structure. From the non-redundant set of protein structures, we derive the 'potential' parameters for the protein secondary structure prediction that contains the structural information of proteins, by following the procedure similar to the way to derive the directional information table of GOR method. Those potential parameters are combined with the frequency matrices obtained by running PSI-BLAST to construct the feature vectors that are used to train the support vector machines (SVM) to build the secondary structure classifiers. Moreover, the problem of huge model file size, which is one of the known shortcomings of SVM, is partially overcome by reducing the size of training data by filtering out the redundancy not only at the protein level but also at the feature vector level. A preliminary result measured by the average three-state prediction accuracy is encouraging.

• Biotechnology and Bioprocess Engineering:BBE
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• v.9 no.5
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• pp.414-418
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• 2004

A 'minimally complex problem set' for ab initio protein Structure prediction has been proposed. As well as consisting of non-redundant and crystallographically determined high-resolution protein structures, without disulphide bonds, modified residues, unusual connectivities and heteromolecules, it is more importantly a collection of protein structures. with a high probability of being the same in the crystal form as in solution. To our knowledge, this is the first attempt at this kind of dataset. Considering the lattice constraint in crystals, and the possible flexibility in solution of crystallographically determined protein structures, our dataset is thought to be the safest starting points for an ab initio protein structure prediction study.

• Genomics & Informatics
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• v.21 no.1
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• pp.7.1-7.11
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• 2023

The gram-positive bacterium Listeria monocytogenes is an important foodborne intracellular pathogen that is widespread in the environment. The functions of hypothetical proteins (HP) from various pathogenic bacteria have been successfully annotated using a variety of bioinformatics strategies. In this study, a HP Imo0888 (NP_464414.1) from the Listeria monocytogenes EGD-e strain was annotated using several bioinformatics tools. Various techniques, including CELLO, PSORTb, and SOSUIGramN, identified the candidate protein as cytoplasmic. Domain and motif analysis revealed that the target protein is a PemK/MazF-like toxin protein of the type II toxin-antitoxin system (TAS) which was consistent with BLASTp analysis. Through secondary structure analysis, we found the random coil to be the most frequent. The Alpha Fold 2 Protein Structure Prediction Database was used to determine the three-dimensional (3D) structure of the HP using the template structure of a type II TAS PemK/MazF family toxin protein (DB ID_AFDB: A0A4B9HQB9) with 99.1% sequence identity. Various quality evaluation tools, such as PROCHECK, ERRAT, Verify 3D, and QMEAN were used to validate the 3D structure. Following the YASARA energy minimization method, the target protein's 3D structure became more stable. The active site of the developed 3D structure was determined by the CASTp server. Most pathogens that harbor TAS create a crucial risk to human health. Our aim to annotate the HP Imo088 found in Listeria could offer a chance to understand bacterial pathogenicity and identify a number of potential targets for drug development.