Interaction Study of Soybean mosaic virus Proteins with Soybean Proteins using the Yeast-Two Hybrid System

Seo, Jang-Kyun,Hwang, Sung-Hyun,Kang, Sung-Hwan,Choi, Hong-Soo,Lee, Su-Heon,Sohn, Seong-Han,Kim, Kook-Hyung The Korean Society of Plant Pathology 2007 Plant Pathology Journal Vol.23 No.4

Interactions between viral proteins and host proteins are essential for virus replication. Especially, translation of viral genes completely depends on the host machinery. In potyviruses, interactions of genome-linked viral protein (VPg) with host translation factors including eIF4E, eIF(iso)4E, and poly(A)-binding protein (PABP) has previously been characterized. In this study, we investigated interactions between Soybean mosaic virus (SMV) viral proteins and host translation factors by yeast two-hybrid system. SMV VPg interacted with eIF4E, eIF(iso)4E, and PABP in yeast two-hybrid system, while SMV helper component proteinase (HC-pro) interacted with neither of those proteins. The interaction between SMV NIb and PABP was also detected. These results are consistent with those reported previously in other potyviruses. Interestingly, we found reproducible and specific interactions between SMV coat protein (CP) and PABP. Deletion analysis showed that the region of CP comprising amino acids 116 to 206 and the region of PABP comprising amino acids 520 to 580 are involved in CP/PABP interactions. Soybean library screening with SMV NIb by yeast two-hybrid assay also identified several soybean proteins including chlorophyll a/b binding preprotein, photo-system I-N subunit, ribulose 1,5-biphosphate carboxylase, ST-LSI protein, translation initiation factor 1, TIR-NBS type R protein, RNA binding protein, ubiquitin, and LRR protein kinase. Altogether, these results suggest that potyviral replicase may comprise a multi-protein complex with PABP, CP, and other host factors.

Web-Based Computational System for Protein-Protein Interaction Inference

Kim, Ki-Bong Korea Information Processing Society 2012 Journal of information processing systems Vol.8 No.3

Recently, high-throughput technologies such as the two-hybrid system, protein chip, Mass Spectrometry, and the phage display have furnished a lot of data on protein-protein interactions (PPIs), but the data has not been accurate so far and the quantity has also been limited. In this respect, computational techniques for the prediction and validation of PPIs have been developed. However, existing computational methods do not take into account the fact that a PPI is actually originated from the interactions of domains that each protein contains. So, in this work, the information on domain modules of individual proteins has been employed in order to find out the protein interaction relationship. The system developed here, WASPI (Web-based Assistant System for Protein-protein interaction Inference), has been implemented to provide many functional insights into the protein interactions and their domains. To achieve those objectives, several preprocessing steps have been taken. First, the domain module information of interacting proteins was extracted by taking advantage of the InterPro database, which includes protein families, domains, and functional sites. The InterProScan program was used in this preprocess. Second, the homology comparison with the GO (Gene Ontology) and COG (Clusters of Orthologous Groups) with an E-value of $10^{-5}$, $10^{-3}$ respectively, was employed to obtain the information on the function and annotation of each interacting protein of a secondary PPI database in the WASPI. The BLAST program was utilized for the homology comparison.

Interaction Study of Soybean mosaic virus Proteins with Soybean Proteins usingthe Yeast-Two Hybrid System

서장균,황성현,강성환,최홍수,이수헌,손승한,김국형 한국식물병리학회 2007 Plant Pathology Journal Vol.23 No.4

Interactions between viral proteins and host proteins are essential for virus replication. Especially, translation of viral genes completely depends on the host machinery. In potyviruses, interactions of genome-linked viral protein (VPg) with host translation factors including eIF4E, eIF(iso)4E, and poly(A)-binding protein (PABP) has previously been characterized. In this study, we investigated interactions between Soybean mosaic virus (SMV) viral proteins and host translation factors by yeast two-hybrid system. SMV VPg interacted with eIF4E, eIF(iso)4E, and PABP in yeast two-hybrid system, while SMV helper component proteinase (HCpro) interacted with neither of those proteins. The interaction between SMV NIb and PABP was also detected. These results are consistent with those reported previously in other potyviruses. Interestingly, we found reproducible and specific interactions between SMV coat protein (CP) and PABP. Deletion analysis showed that the region of CP comprising amino acids 116 to 206 and the region of PABP comprising amino acids 520 to 580 are involved in CP/PABP interactions. Soybean library screening with SMV NIb by yeast twohybrid assay also identified several soybean proteins including chlorophyll a/b binding preprotein, photosystem I-N subunit, ribulose 1,5-biphosphate carboxylase, ST-LS1 protein, translation initiation factor 1, TIRNBS type R protein, RNA binding protein, ubiquitin, and LRR protein kinase. Altogether, these results suggest that potyviral replicase may comprise a multiprotein complex with PABP, CP, and other host factors.

Web-Based Computational System for Protein- Protein Interaction Inference

김기봉 한국정보처리학회 2012 Journal of information processing systems Vol.8 No.3

Recently, high-throughput technologies such as the two-hybrid system,protein chip, Mass Spectrometry, and the phage display have furnished a lot of data on protein-protein interactions (PPIs), but the data has not been accurate so far and the quantity has also been limited. In this respect, computational techniques for the prediction and validation of PPIs have been developed. However, existing computational methods do not take into account the fact that a PPI is actually originated from the interactions of domains that each protein contains. So, in this work, the information on domain modules of individual proteins has been employed in order to find out the protein interaction relationship. The system developed here, WASPI (Web-based Assistant System for Protein-protein interaction Inference), has been implemented to provide many functional insights into the protein interactions and their domains. To achieve those objectives,several preprocessing steps have been taken. First, the domain module information of interacting proteins was extracted by taking advantage of the InterPro database, which includes protein families, domains, and functional sites. The InterProScan program was used in this preprocess. Second, the homology comparison with the GO (Gene Ontology)and COG (Clusters of Orthologous Groups) with an E-value of 10-5, 10-3 respectively, was employed to obtain the information on the function and annotation of each interacting protein of a secondary PPI database in the WASPI. The BLAST program was utilized for the homology comparison

Web-Based Computational System for Protein-Protein Interaction Inference

( Ki Bong Kim ) 한국정보처리학회 2012 Journal of information processing systems Vol.8 No.3

Recently, high-throughput technologies such as the two-hybrid system, protein chip, Mass Spectrometry, and the phage display have furnished a lot of data on protein-protein interactions (PPIs), but the data has not been accurate so far and the quantity has also been limited. In this respect, computational techniques for the prediction and validation of PPIs have been developed. However, existing computational methods do not take into account the fact that a PPI is actually originated from the interactions of domains that each protein contains. So, in this work, the information on domain modules of individual proteins has been employed in order to find out the protein interaction relationship. The system developed here, WASPI (Web-based Assistant System for Protein-protein interaction Inference), has been implemented to provide many functional insights into the protein interactions and their domains. To achieve those objectives, several preprocessing steps have been taken. First, the domain module information of interacting proteins was extracted by taking advantage of the InterPro database, which includes protein families, domains, and functional sites. The InterProScan program was used in this preprocess. Second, the homology comparison with the GO (Gene Ontology) and COG (Clusters of Orthologous Groups) with an E-value of iO, i03 respectively, was employed to obtain the information on the function and annotation of each interacting protein of a secondary PPI database in the WASPI. The BLAST program was utilized for the homology comparison.

워드 임베딩 기반의 단백질 간 유사도 측정을 통한 단백질 상호작용과 약물-단백질 상호작용 예측

정이수,김윤비,조영래 한국정보과학회 2023 정보과학회 컴퓨팅의 실제 논문지 Vol.29 No.4

Accurate measurement of functional similarity between proteins is crucial for the prediction of protein-protein interactions and drug-target interactions. In this study, the functional similarity between proteins was computed using Gene Ontology (GO). The similarity of terms was measured by word embedding methods, Word2vec and TF-IDF, which consider the term features and by a semantic similarity method that considers the relationship between terms. Thereafter, the measured similarity of terms was expanded to protein-protein similarity using annotations in GO. We proposed an integrated similarity that considers both features and relationships of the terms. Protein similarities measured by semantic similarity, Word2vec, TF-IDF, and integrated methods were applied to the prediction of protein-protein interactions and drug-target interactions. As a result, it was confirmed that the integrated similarity showed the best performance. 단백질 상호작용과 약물-단백질 상호작용에 대한 예측을 위하여 단백질 간의 정확한 기능적 유사도 측정이 중요하다. 본 연구에서는 유전자 온톨로지를 사용하여 단백질 간 기능적 유사도를 계산하였다. 용어 간 유사도는 용어의 특성을 고려하는 워드 임베딩 방법인 Word2vec과 TF-IDF, 그리고 용어의 연결성을 고려하는 의미론적 방법으로 측정되었다. 측정된 용어의 유사도는 유전자 온톨로지 내 어노테이션 데이터를 사용하여 단백질 간의 기능적 유사도로 확장되었다. 또한, 용어의 특성과 용어 간의 연결성을 모두 고려할 수 있는 통합된 유사도를 제안하여, 의미론적 방법, Word2vec, TF-IDF, 통합 방법으로 측정된 단백질 간 유사도를 단백질 상호작용과 약물-단백질 상호작용 예측에 적용하였다. 결과적으로 통합 방법에서 가장 우수한 성능을 보이는 것을 확인하였다.

A Novel Method for Functional Prediction of Proteins from a Protein-Protein Interaction Network

Tae-Ho Kang,여명호,김학용,Jean S. Chung,Jae-Soo Yoo 한국물리학회 2009 THE JOURNAL OF THE KOREAN PHYSICAL SOCIETY Vol.54 No.4

Functional prediction of unannotated proteins is one of the most important tasks in yeast genomics. Analysis of a protein-protein interaction network leads to a better understanding of the functions of unannotated proteins. Much research has been performed for the functional prediction of unannotated proteins from a protein-protein interaction network. A chi-square method is one of the existing methods for the functional prediction of unannotated proteins from a protein- protein interaction network, but the method does not consider the topology of network. In this paper, we propose a novel method that is able to predict specific molecular functions for unanno- tated proteins from a protein-protein interaction network. To do this, we investigated all protein interaction databases of yeast in public sites such as MIPS, DIP and SGD. For the prediction of unannotated proteins, we employed a modified chi-square measure based on neighborhood counting and we assessed the prediction accuracy of the protein function from a protein-protein interaction network. Functional prediction of unannotated proteins is one of the most important tasks in yeast genomics. Analysis of a protein-protein interaction network leads to a better understanding of the functions of unannotated proteins. Much research has been performed for the functional prediction of unannotated proteins from a protein-protein interaction network. A chi-square method is one of the existing methods for the functional prediction of unannotated proteins from a protein- protein interaction network, but the method does not consider the topology of network. In this paper, we propose a novel method that is able to predict specific molecular functions for unanno- tated proteins from a protein-protein interaction network. To do this, we investigated all protein interaction databases of yeast in public sites such as MIPS, DIP and SGD. For the prediction of unannotated proteins, we employed a modified chi-square measure based on neighborhood counting and we assessed the prediction accuracy of the protein function from a protein-protein interaction network.

박테리오파아지 T7 의 기능에 관한 연구;복제단백질간의 단백질 상호작용

김학준,김영태 한국생명과학회 1996 생명과학회지 Vol.6 No.3

박테리오파지 T7 gene 2.5 단백질은 single-stranded DNA 결합 단백질로 박태리오파지 T7의 DNA복제, 재조합, 및 수선에 필수적으로 요구된다. Gene 2.5 protein은 T7의 DNA 합성과 성장에 필수적인 단백질이다. Gene 2.5 Protein이 중요시 되는 이유는 이 단백질이 T7의 다른 복제 필수단백질인 T7의 다른 복제 필수단백질인 T7 DNA polymerase 와 gene 4 protein(helicase/primase)와 서로 상호작용할 것으로 제안되었기 때문이다. (Kim and Richardson, J. Biol. Chem., 1992;1994). 이 단백질의 단백질 상호작용을 가능하게 하는 domain은 carboxyl-terminal domain일 것으로 여러 실험에서 대두되었기에, 이 domain의 특성을 파악하기 위해 야생형과 변이체 gene 2.5 단백질들을 각각 GST에 융합한후 fusion 단백질을 정제하였다. 정제된 이 융합 단백질들의 carboxyl-terminal domain이 T7 복제 단백질들과 상호작용을 조사하는지를 조사하기 위해 affinity chromatography로 이용하였다. 실험 결과, 아생형 GST-gene 2.5 융합단잭질(GST-2.5 (WT))는 T7 DNA polymerase 와 상호작용을 하였지만. 변이형 융합단백질(GST-2.5$\Delta$21C)는 interaction을 하지 못했다. 이 결과는 carbohyl-terminal domain이 단백질-단백질 상호작용을 하는데 직접적으로 관여하는 것을 증명하였다. 또한,GST2.5(WT)는 gene 4 protein(helicase/primase)와 직접 상호작용을 하나. GST2.5$\Delta$21C는 상호작용을 하지 못하는 것으로 나타났다. 따라서 gene 4 proteins와의 상호작용에도 gene 2.5 protein의 carboxyl-terminal domain이 직접 관여 한다는 것이 증명되었다. 이상의 결과에서 gene 2.5 protein은 박테리오파지 T7 의 유전자 목제 시 단백질-단백질 상호작용에 관혀아며, 특히 gene 2.5 protein의 carboxyl-terminal domain이 이러한 상호작용에 직접적으로 관여하는 domain이라는 것을 알 수가 있었다. Bacteriophage T7 gene 2.5 protein, a single-stranded DNA binding protein, is required for T7 DNA replication, recombination, and repair. T7 gene 2.5 protein has two distinctive domains, DNA binding and C-terminal domain, directly involved in protein-protein interaction. Gene 2.5 protein participates in the DNA replication of Bacteriophage T7, which makes this protein essential for the T7 growth and DNA replication. What gene 2.5 protein makes important at T7 growth and DNA replication is its binding affinity to single-stranded DNA and the protein-protein important at T7 DNA replication proteins which are essential for the T7 DNA synthesis. We have constructed pGST2.5(WT) encoding the wild-type gene 2.5 protein and pGST2.5$\Delta $21C lacking C-terminal 21 amino acid residues. The purified GST-fusion proteins, GST2.5(WT) and GST2.5(WT)$\Delta$21C, were used for whether the carboxyl-terminal domain participates in the protein-protein interactions or not. GST2.5(WT) and GST2.5$\Delta$21C showed the difference in the protein-protein interaction. GST2.5(WT) interacted with T7 DNA polymerase and gene 4 protein, but GST2.5$\Delta$21C did not interact with either protein. Secondly, GST2.5(WT) interacts with gene 4 proteins (helicase/primase) but not GST2.5$\Delta$21C. these results proved the involvement of the carboxyl-terminal domain of gene 2.5 protein in the protein-protein interaction. We clearly conclude that carboxy-terminal domain of gene 2.5 protein is firmly involved in protein-protein interactions in T7 replication proteins.

SVM model for Predicting Human Proteins Interacting with HCV Proteins

방초(Chao Fang),최광우(Guangyu Cui),한경숙(Kyungsook Han) 한국정보과학회 2011 정보과학회 컴퓨팅의 실제 논문지 Vol.17 No.4

단백질 상호작용을 예측하기 위하여 몇몇 계산 학적 방법들이 개발되었으나, 대부분 한 종류의 생명체에서의 단백질 상호작용에 국한된 것이다. 동종 단백질간의 상호작용 예측 기법은 단백질 종류를 구별하지 않기 때문에 이종 단백질간의 상호작용을 예측하는데 적합하지 않다. 본 논문은 단백질 서열 데이터를 이용하여C형 간염 바이러스 (HCV) 단백질과 인간 단백질의 상호작용을 예측하는 support vector machine (SVM) 모델의 개발을 소개한다. 이 SVM 모델의 예측 정확도는 평균적으로 81.5%임을 보였다. 이 SVM 모델과 단백질의 유전자 온톨로지 정보를 이용하여, HCV단백질과 인간 단백질 사이의 새로운 상호작용을 456개 예측하였다. Several computational methods have been developed for predicting protein-protein interactions, but most of these methods are intended for finding the protein-protein interactions within a species rather than for the interactions across different species. Methods for predicting the interactions between homogeneous proteins are not appropriate for predicting the interactions between heterogeneous proteins since they do not distinguish the interactions between proteins of the same species from those of different species. In this paper we present the development of a support vector machine (SVM) model that predicts the interactions between hepatitis C virus (HCV) proteins and human proteins using the sequence data. The average accuracy of the SVM model in predicting the interactions between HCV proteins and human proteins is 81.5%. Using the SVM model and the Gene Ontology (GO) annotations of proteins, we also predicted a total of 456 new interactions between HCV and human proteins.

Essentiality of Hub Proteins in Protein-protein Interaction Networks of Yeast

Jea Woon Ryu,이윤경,강태호,유재수,정진수,박별나,김학용,여명호 한국물리학회 2010 THE JOURNAL OF THE KOREAN PHYSICAL SOCIETY Vol.56 No.5

Scale-free protein interaction networks contain a small number of highly connected proteins, called hubs, and a large number of poorly connected proteins. Recently, several independent studies have elucidated that hub proteins are more likely to be essential to cell function than non-hub proteins. Deletion of a hub protein is more likely to be lethal than deletion of a non-hub protein. This concept defines the centrality-lethality rule; it indicates the importance of hub proteins in a complex protein network and the significance of the network architecture. Determination of the link number for a hub protein is obscure. Therefore, it is important to decide how many link numbers the hub proteins have. Here, we propose a new approach for determining the link number of hub proteins. Hub links were counted by locating the intersection point between the power-law distributions of essential and non-essential proteins. Application of this method to the Uetz database yielded an estimate of seven for the minimum number of hub protein links in yeast. Other public database (Ito, DIP,SGD, and BioGRID) predicted a different number of hub protein links. To assess the reliability of the centrality-lethality rule, we examined the essentiality of hub proteins in the protein interaction networks defined within each of the five public datasets: Uetz, Ito, DIP, SGD, and BioGRID. All five sites indicated that hub proteins were more likely to be essential than were non-hub proteins. This new method for determining the number of hub links is a useful tool for hub proteins.