• Journal of KIISE:Software and Applications
• /
• v.37 no.11
• /
• pp.803-811
• /
• 2010

Since protein subcellular location and biological function are highly correlated, the prediction of protein subcellular localization can provide information about the function of a protein. In order to enhance the prediction performance, external information other than amino acids sequence information is actively exploited in many researches. This paper compares the prediction capabilities resided in amino acid sequence similarity, protein profile, gene ontology, motif, and textual information. In the experiments using PLOC dataset which has proteins less than 80% sequence similarity, sequence similarity information and gene ontology are effective information, achieving a classification accuracy of 94.8%. In the experiments using BaCelLo IDS dataset with low sequence similarity less than 30%, using gene ontology gives the best prediction accuracies, 93.2% for animals and 86.6% for fungi.

• Genomics & Informatics
• /
• v.21 no.2
• /
• pp.25.1-25.12
• /
• 2023

Adaptation of infections and hosts has resulted in several metabolic mechanisms adopted by intracellular pathogens to combat the defense responses and the lack of fuel during infection. Human tuberculosis caused by Mycobacterium tuberculosis (MTB) is the world's first cause of mortality tied to a single disease. This study aims to characterize and anticipate potential antigen characteristics for promising vaccine candidates for the hypothetical protein of MTB through computational strategies. The protein is associated with the catalyzation of dithiol oxidation and/or disulfide reduction because of the protein's anticipated disulfide oxidoreductase properties. This investigation analyzed the protein's physicochemical characteristics, protein-protein interactions, subcellular locations, anticipated active sites, secondary and tertiary structures, allergenicity, antigenicity, and toxicity properties. The protein has significant active amino acid residues with no allergenicity, elevated antigenicity, and no toxicity.

• Interdisciplinary Bio Central
• /
• v.2 no.3
• /
• pp.8.1-8.5
• /
• 2010

Introduction: Recently identified human homologues of ALKB protein have shown the activity of DNA damaging drugs, used for cancer therapy. Bioinformatics study of hABH2 and hABH3 had led to the discovery of a novel DNA repair mechanism. Very little is known about structure and function of hABH4, one of the members of this superfamily. Therefore, in present study we are intended to predict its structure and function through various bioinformatics tools. Materials and Methods: Modeling was done with modeler 9v7 to predict the 3D structure of the hABH4 protein. This model was validated with the program Procheck using Ramachandran plot statistics and was submitted to PMDB with ID PM0076284. The 3d2GO server was used to predict the functions. Residues at protein ligand and protein RNA binding sites were predicted with 3dLigandSite and KYG programs respectively. Results and Discussion: 3-D model of hABH4, ALKBH4.B99990003.pdb was predicted and evaluated. Validation result showed that 96.4 % residues lies in favored and additional allowed region of Ramachandran plot. Ligand binding residues prediction showed four Ligand clusters, having 24 ligands in cluster 1. Importantly, conserved pattern of Glu196-X-Pro198- Xn-His254 in the functional domain was detected. DNA and RNA binding sites were also predicted in the model. Conclusion and Prospects: The predicted and validated model of human homologue hABH4 resulted from this study may unveil the mechanism of DNA damage repair in human and accelerate the research on designing of appropriate inhibitors aiding in chemotherapy and cancer related diseases.

• Journal of the Korea Society of Computer and Information
• /
• v.27 no.8
• /
• pp.49-59
• /
• 2022

In this paper, we designed a new enzyme function prediction model PSCREM based on a study that compared and evaluated CNN and LSTM/GRU models, which are the most widely used deep learning models in the field of predicting functions and structures using protein sequences in 2020, under the same conditions. Sequence evolution information was used to preserve detailed patterns which would miss in CNN convolution, and the relationship information between amino acids with functional significance was extracted through overlapping RNNs. It was referenced to feature map production. The RNN family of algorithms used in small CNN-RNN models are LSTM algorithms and GRU algorithms, which are usually stacked two to three times over 100 units, but in this paper, small RNNs consisting of 10 and 20 units are overlapped. The model used the PSSM profile, which is transformed from protein sequence data. The experiment proved 86.4% the performance for the problem of predicting the main classes of enzyme number, and it was confirmed that the performance was 84.4% accurate up to the sub-sub classes of enzyme number. Thus, PSCREM better identifies unique patterns related to protein function through overlapped RNN, and Overlapped RNN is proposed as a novel methodology for protein function and structure prediction extraction.

• The KIPS Transactions:PartD
• /
• v.10D no.4
• /
• pp.679-688
• /
• 2003

Motif databases are used in the function and structure prediction of proteins which appear on new and rapid release of raw data from genome sequencing projects. Recently, the frequency of use about these databases increases continuously. However, existing motif databases were developed and extended independently and were integrated mainly by using a web-based cross-reference, thus these databases have a heterogeneous search result problem, a complex query process problem and a duplicate database entry handling problem. Therefore, in this paper, we suppose physical motif resource integration and describe the integrated search method about a family-based protein prediction for solving above these problems. Finally, we estimate our implementation of the motif integration database and prediction system for predicting protein motifs.

• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.18 no.10
• /
• pp.2562-2570
• /
• 2014

One of the important hints for inferring the function of unknown proteins is the knowledge about protein subcellular localization. Recently, there are considerable researches on the prediction of subcellular localization of proteins which simultaneously exist at multiple subcellular localization. In this paper, label power-set classification is improved for the accurate prediction of multiple subcellular localization. The predicted multi-labels from the label power-set classifier are combined with their prediction probability to give the final result. To find the accurate probability estimates of multi-classes, this paper employs pair-wise comparison and error-correcting output codes frameworks. Prediction experiments on protein subcellular localization show significant performance improvement.

• Korean Journal of Microbiology
• /
• v.39 no.2
• /
• pp.67-75
• /
• 2003

One large topic in comparative genomics is to predict functional annotation by classifying protein sequences. Computational approaches for function prediction include protein structure prediction, sequence alignment and domain prediction or binding site prediction. This paper is on another computational approach searching for sets of homologous sequences from sequence similarity graph. Methods based on similarity graph do not need previous knowledges about sequences, but largely depend on the researcher's subjective threshold settings. In this paper, we propose a genome sequence clustering method of iterative testing and graph decomposition, and a simple method to calculate a strict threshold having biochemical meaning. Proposed method was applied to known bacterial genome sequences and the result was shown with the BAG algorithm's. Result clusters are lacking some completeness, but the confidence level is very high and the method does not need user-defined thresholds.

• Proceedings of the Korean Society for Bioinformatics Conference
• /
• 2005.09a
• /
• pp.5-5
• /
• 2005

• Proceedings of the Korean Society for Bioinformatics Conference
• /
• 2005.09a
• /
• pp.237-242
• /
• 2005

Predicting the cellular location of an unknown protein gives a valuable information for inferring the possible function of the protein. For more accurate prediction system, we need a good feature extraction method that transforms the raw sequence data into the numerical feature vector, minimizing information loss. In this paper, we propose new methods of extracting underlying features only from the sequence data by computing pairwise sequence alignment scores. In addition, we use composition based features to improve prediction accuracy. To construct an SVM ensemble from separately trained SVM classifiers, we propose specificity based weighted majority voting. The overall prediction accuracy evaluated by the 5-fold cross-validation reached 88.53% for the eukaryotic animal data set. By comparing the prediction accuracy of various feature extraction methods, we could get the biological insight on the location of targeting information. Our numerical experiments confirm that our new feature extraction methods are very useful for predicting subcellular localization of proteins.

• Proceedings of the Korean Society for Bioinformatics Conference
• /
• 2004.11a
• /
• pp.250-261
• /
• 2004

A novel method for ab initio prediction of protein tertiary structures, PROFESY (PROFile Enumerating SYstem), is introduced. This method utilizes secondary structure prediction information and fragment assembly. The secondary structure prediction of proteins is performed with the PREDICT method which uses PSI-BLAST to generate profiles and a distance measure in the pattern space. In order to predict the tertiary structure of a protein sequence, we assemble fragments in the fragment library constructed as a byproduct of PREDICT. The tertiary structure is obtained by minimizing the potential energy using the conformational space annealing method which enables one to sample diverse low lying minima of the energy function. We apply PROFESY for prediction of some proteins with known structures, which shows good performances. We also participated in CASP5 and applied PROFESY to new fold targets for blind predictions. The results were quite promising, despite the fact that PROFESY was in its early stage of development. In particular, the PROFESY result is the best for the hardest target T0161.