• 한국정보통신학회논문지
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• 제20권9호
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• pp.1816-1821
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• 2016

단백질은 대부분의 생물학적 과정에서 중대한 역할을 수행하고 있으므로, 단백질 진화, 구조와 기능을 알아내기 위하여 많은 연구가 수행되고 있는데, 단백질의 이차 구조는 이러한 연구의 중요한 기본적 정보이다. 본 연구는 대규모 단백질 구조 자료로부터 단백질 이차 구조 정보를 효과적으로 추출하여 미지의 단백질 서열이 가지는 이차 구조를 예측하려 한다. 질의 서열과 상동관계에 있는 단백질 구조자료내의 서열들을 광범위하게 찾아내기 위하여, 탐색에 사용하는 프로파일의 구성에 질의 서열과 유사한 서열들을 사용하고 갭을 허용하여 반복적인 탐색이 가능한 PSI-BLAST를 사용하였다. 상동 단백질들의 이차구조는 질의 서열과의 상동 관계의 강도에 따라 가중되어 이차 구조 예측에 기여되었다. 이차 구조를 각각 세 개와 여덟 개로 분류하는 예측 실험에서 상동 서열들과 신경망을 동시에 사용하여 93.28%와 88.79%의 정확도를 얻어서 기존 방법보다 성능이 향상되었다.

• Genomics & Informatics
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• 제11권3호
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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.

• 한국생물정보학회:학술대회논문집
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• 한국생물정보시스템생물학회 2003년도 제2차 연례학술대회 발표논문집
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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.

• 한국자기공명학회논문지
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• 제18권1호
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• pp.36-40
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• 2014

Predicting secondary structure of protein through assigned backbone chemical shifts has been used widely because of its convenience and flexibility. In spite of its usefulness, chemical shift based analysis has some defects including isotopic shifts and solvent interaction. Here, it is shown that corrected chemical shift analysis for secondary structure of protein. It is included chemical shift correction through consideration of deuterium isotopic effect and calculate chemical shift index using probability-based methods. Enhanced method was applied successfully to one of the proteins from Mycobacterium tuberculosis. It is suggested that correction of chemical shift analysis could increase accuracy of secondary structure prediction of protein and small molecule in solution.

• 한국정보통신학회논문지
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• 제22권5호
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• pp.728-733
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• 2018

단백질을 구성하는 아미노산의 서열 정보만으로 단백질 이차 구조를 예측하기 위하여 심층 학습이 활발히 연구되고 있다. 본 논문에서는 단백질 이차 구조를 예측하기 위하여 다양한 구조의 합성곱 신경망의 성능을 비교하였다. 단백질 이차 구조의 예측에 적합한 신경망의 층의 깊이를 알아내기 위하여 층의 개수에 따른 성능을 조사하였다. 또한 이미지 분류 분야의 많은 방법들이 기반 하는 GoogLeNet과 ResNet의 구조를 적용하였는데, 이러한 방법은 입력 자료에서 다양한 특성을 추출하거나, 깊은 층을 사용하여도 학습과정에서 그래디언트 전달을 원활하게 한다. 합성곱 신경망의 여러 구조를 단백질 자료의 특성에 적합하게 변경하여 성능을 향상시켰다.

• Journal of Microbiology and Biotechnology
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• 제9권5호
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• pp.562-567
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• 1999

Single-stranded DNA binding protein (SSB) is well-characterized as having a helix-destabilizing activity. The helix-destabilizing capability of SSB has been re-examined in this study. The results of restriction endonuclease protection assays and titration experiments suggest that the stimulatory effect of SSB on strand exchange acts by melting out the secondary structure which is inaccessible to RecA protein binding; however, SSB is excluded from regions of secondary structure present in native single-stranded DNA. Complexes of SSB and RecA protein are required for eliminating the secondary structure barriers under optimal conditions for strand exchange.

• Journal of Microbiology and Biotechnology
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• 제11권1호
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• pp.164-166
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• 2001

To examine the characteristics of the recombinant thin aggregative fimbriae of Salmonella, the AgfA subunit gene was amplified from Salmonella enteritidis using a PCR. The maltose binding protein (MBP)-AgfA fusion protein was overproduced in E. coli and purified. The secondary structure of AgfA was then elucidated from the difference CD spectra. An estimation of the secondary structure of AgfA using the self-consistent method revealed a mostly ${\beta}-sheet$ structure.

• 한국정보통신학회논문지
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• 제22권1호
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• pp.26-32
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• 2018

단백질의 이차구조는 단백질의 진화, 구조, 기능을 연구하는데 중요한 정보이다. 단백질 서열 정보만을 이용하여 단백질의 이차 구조를 예측하는 분야에 심층 학습 방법들이 최근 들어 활발히 적용되고 있다. 이러한 방법에서 널리 사용되는 입력은 단백질 서열을 변환하여 만들어진 단백질 프로파일이다. 본 논문에서는 효과적인 단백질 프로파일을 얻기 위하여 단백질 서열 탐색 방법으로 PSI-BLAST와 더불어서 HHblits를 사용하였다. 단백질 프로파일의 구성에 사용되는 상동 단백질 서열을 결정하기 위한 유사도 문턱치와 상동 단백질 서열 정보를 반복적으로 사용하는 회수를 조절하였다. 합성곱 신경망과 순환 신경망을 사용하여 단백질 이차구조를 예측하였는데, 진화적 정보를 한번만 추가하여 만들어진 단백질 프로파일이 효과적이었다.

• International Journal of Fuzzy Logic and Intelligent Systems
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• 제10권4호
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• pp.314-318
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• 2010

Predicting Alpha-helicies, Beta-sheets and Turns of a proteins secondary structure is a complex non-linear task that has been approached by several techniques such as Neural Networks, Genetic Algorithms, Decision Trees and other statistical or heuristic methods. This project introduces a new machine learning method by combining Bayesian Inference with offline trained Multilayered Perceptron (MLP) models as the likelihood for secondary structure prediction of proteins. With varying window sizes of neighboring amino acid information, the information is extracted and passed back and forth between the Neural Net and the Bayesian Inference process until the posterior probability of the secondary structure converges.

• 한국생물물리학회:학술대회논문집
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• 한국생물물리학회 2003년도 정기총회 및 학술발표회
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• pp.65-65
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• 2003

After many genome projects, algorithms and software to process explosively growing biological information have been developed. To process huge amount of biological information, high performance computing equipments are essential. If we use the remote resources such as computing power, storages etc., through a Grid to share the resources in the Internet environment, we will be able to obtain great efficiency to process data at a low cost. Here we present the performance improvement of the protein secondary structure prediction (PSIPred) by using the Grid platform, distributing protein sequence data on the Grid where each computer node analyzes its own part of protein sequence data to speed up the structure prediction. On the Grid, genome scale secondary structure prediction for Mycoplasma genitalium, Escherichia coli, Helicobacter pylori, Saccharomyces cerevisiae and Caenorhabditis slogans were performed and analyzed by a statistical way to show the protein structural deviation and comparison between the genomes. Experimental results show that the Grid is a viable platform to speed up the protein structure prediction and from the predicted structures.