[논문]다발성 골수종 적용을 위한 HLA-A*0201 제한 항원성 펩타이드 예측

강윤중

doi:10.22156/cs4smb.2020.10.06.209

[국내논문] **다발성 골수종 적용을 위한 HLA-A0201 제한 항원성 펩타이드 예측
Prediction of HLA-A0201-Restricted Antigenic Epitopes Targeting Multiple Myeloma** 원문보기

융합정보논문지 = Journal of Convergence for Information Technology, v.10 no.6, 2020년, pp.209 - 216

강윤중 (중원대학교 의생명과학과)

초록
AI-Helper

단백질 항원에 존재하는 에피토프는 에피토프를 기반으로 한 백신 개발의 표적이 되고 있다. 인간의 주조 직적합 복합체 (MHC-1)에 결합하는 펩타이드를 확인할 수 있는 여러 서버들이 보고되고 있으나 인간의 MHC-I 분자의 수가 매우 많고 각 서버 검색 방법의 표준화 부재 등의 문제로 인해 펩타이드 예측에 적절한 서버를 선정하는 것이 쉽지 않다. 본 논문에서는 MHC-I 결합 펩타이드를 예측하는 서버 30 종을 비교하였으며, 다발성 골수종에 적용하기 위해 survivin 단백질로부터 사람의 HLA-A2 제한 펩타이드를 예측하였다. 본 연구의 결과는 MHC-I 결합 예측의 표준화된 방법을 제시하고 펩타이드 에피토프를 예측하는데 도움을 줄 것이다.

Abstract ▼ AI-Helper

Protein antigens and their epitopes are targets for epitope based vaccines. There are many prediction servers which can be used for identification of binding peptides to MHC molecules. However, choosing of appropriate prediction servers is difficult. This study compared data obtained from prediction servers and evaluate them in scope of binding affinity to MHC-I molecules. Here we predicted HLA-A2-restricted cytotoxic T lymphocyte epitopes from survivin as a potential target for multiple myeloma. We suggest a procedure for prediction of antigenic peptides which could bind to MHC-I molecule. The results of this study will assist researchers in selection and prediction of noble antigenic peptides.

주제어

표/그림 (7)

그림 Fig. 1. AROC values used in predictions.
그림 Fig. 3. Predictions using AROC values with CMV test set.
그림 Fig. 2. Predictions using AROC values with survivin test set.
그림 Fig. 4. The correlation coefficients of prediction servers for survivin, CMV, and combined data sets.
표 Table 1. Prediction ability of selected representative servers in order to predict T cell peptide epitopes
표 Table 2. Prediction ability of selected representative servers in order to predict the majority of binding peptides
표 Table 3. Prediction ability of selected representative servers in order to exclude false positives peptides

AI 본문요약
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* AI 자동 식별 결과로 적합하지 않은 문장이 있을 수 있으니, 이용에 유의하시기 바랍니다.

제안 방법

By using iTopia™ binding assay as the experimental control, we obtained correlation coefficients for all available prediction servers for CMV construct, survivin, and the combined data set.
We also measured the threshold at which the first false positive appears. The predictor with higher thresholds was used for further analysis.
We also done the analysis of survivin and CMV construct test sets. The results shown in Figures 2 and 3 were very similar to the previous result.

데이터처리

4. The correlation coefficients of prediction servers for survivin, CMV, and combined data sets. Black bars represents survivin, gray bars represents CMV construct, and white bars represents the combined set.

성능/효과

We used the most simple prediction method available at each predictor. After doing all predictions, we obtained the Pearson correlation coefficient for all the servers and found out that MAPPP (SYFPEITHI) and MAPPP (BIMAS) showed identical results (r=1) to SYFPEITHI and BIMAS, respectively. The BIMAS and ProPred1 results showed r ≥ 0.
The accuracy of binder classification was assessed using the cutoff of 30 (measured binding affinity of ≥ 30% of the binding affinity of a positive control) for binders. As a results, we had 39 excellent servers , 47 good servers, 33 marginal servers, and 28 poor servers. The AROC values used in these predictions are shown in Fig.
The best predictors in this result was NETM_ANN, followed by IEDB_SMM and IEDB_ANN also showed good predictions. The best servers we recommend for binder/non-binder classification prediction are shown in Fig.

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