• Journal of Information Processing Systems
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• 제8권3호
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• pp.459-470
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• 2012

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.

• Mass Spectrometry Letters
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• 제9권2호
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• pp.51-55
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• 2018

Capture of non-glycoproteins during lectin affinity chromatography is frequently observed, although it would seem to be anomalous. In actuality, lectin affinity chromatography works at post-translational modification (PTM) sites on a glycoprotein which is not involved in protein-protein interactions (PPIs). In this study, serial affinity column set (SACS) using lectins followed by proteomics methods was used to identify PPI mechanisms of captured proteins in human plasma. MetaCore, STRING, Ingenuity Pathway Analysis (IPA), and IntAct were individually used to elucidate the interactions of the identified abundant proteins and to obtain the corresponding interaction maps. The abundant non-glycoproteins were captured with the binding to the selected glycoproteins. Therefore, depletion process in sample pretreatment for abundant protein removal should be considered with more caution because it may lose precious disease-related low abundant proteins through PPIs of the removed abundant proteins in human plasma during the depletion process in biomarker discovery. Glycoproteins bearing specific glycans are frequently associated with cancer and can be specifically isolated by lectin affinity chromatography. Therefore, SACS using Lycopersicon esculentum lectin (LEL) can also be used to study disease interactomes.

• BMB Reports
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• 제51권10호
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• pp.526-531
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• 2018

Protein-Protein Interactions (PPIs) play essential roles in diverse biological processes and their misregulations are associated with a wide range of diseases. Especially, the growing attention to PPIs as a new class of therapeutic target is increasing the need for an efficient method of cell-based PPI analysis. Thus, we newly developed a robust PPI assay (SeePPI) based on the co-translocation of interacting proteins to the discrete subcellular compartment 'processing body' (p-body) inside living cells, enabling a facile analysis of PPI by the enriched fluorescent signal. The feasibility and strength of SeePPI (${\underline{S}}ignal$ ${\underline{e}}nhancement$ ${\underline{e}}xclusively$ on ${\underline{P}}-body$ for ${\underline{P}}rotein-protein$ ${\underline{I}}nteraction$) assay was firmly demonstrated with FKBP12/FRB interaction induced by rapamycin within seconds in real-time analysis of living cells, indicating its recapitulation of physiological PPI dynamics. In addition, we applied p53/MDM2 interaction and its dissociation by Nutlin-3 to SeePPI assay and further confirmed that SeePPI was quantitative and well reflected the endogenous PPI. Our SeePPI assay will provide another useful tool to achieve an efficient analysis of PPIs and their modulators in cells.

• International Journal of Computer Science & Network Security
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• 제21권3호
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• pp.41-47
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• 2021

Detection of protein-protein interactions (PPIs) remains essential for the development of therapies against diseases. Experimental studies to detect PPI are longer and more expensive. Today, with the availability of PPI data, several computer models for predicting PPIs have been proposed. One of the big challenges in this task is feature extraction. The relevance of the information extracted by some extraction techniques remains limited. In this work, we first propose an extraction method based on correlation relationships between the physicochemical properties of amino acids. The proposed method uses a correlation matrix obtained from the hydrophobicity and hydrophilicity properties that it then integrates in the calculation of the bigram. Then, we use the SVM algorithm to detect the presence of an interaction between 2 given proteins. Experimental results show that the proposed method obtains better performances compared to the approaches in the literature. It obtains performances of 94.75% in accuracy, 95.12% in precision and 96% in sensitivity on human HPRD protein data.

• International Journal of Stem Cells
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• 제17권3호
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• pp.298-308
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• 2024

Sine oculis homeobox 1 (Six1) is an important factor for embryonic development and carcinoma malignancy. However, the localization of Six1 varies due to protein size and cell types in different organs. In this study, we focus on the expression and localization of Six1 in male reproductive organ via bioinformatics analysis and immunofluorescent detection. The potential interacted proteins with Six1 were also predicted by protein-protein interactions (PPIs) and Enrichr analysis. Bioinformatic data from The Cancer Genome Atlas and Genotype-Tissue Expression project databases showed that SIX1 was highly expressed in normal human testis, but low expressed in the testicular germ cell tumor sample. Human Protein Atlas examination verified that SIX1 level was higher in normal than that in cancer samples. The sub-localization of SIX1 in different reproductive tissues varies but specifically in the cytoplasm and membrane in testicular cells. In mouse cells, single cell RNA-sequencing data analysis indicated that Six1 expression level was higher in mouse spermatogonial stem cells (mSSCs) and differentiating spermatogonial than in other somatic cells. Immunofluorescence staining showed the cytoplasmic localization of Six1 in mouse testis and mSSCs. Further PPIs and Enrichr examination showed the potential interaction of Six1 with bone morphogenetic protein 4 (Bmp4) and catenin Beta-1 (CtnnB1) and stem cell signal pathways. Cytoplasmic localization of Six1 in male testis and mSSCs was probably associated with stem cell related proteins Bmp4 and CtnnB1 for stem cell development.

• 정보과학회 컴퓨팅의 실제 논문지
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• 제23권3호
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• pp.194-198
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• 2017

본 논문에서는 학술 문헌에서 표현된 단백질 간 상호 작용(Protein-Protein Interaction) 정보를 자동으로 추출하기 위한 확장된 형태의 Convolutional Neural Network (CNN) 모델을 제안한다. 이 모델은 기존에 관계 추출(Relation Extraction)을 위해 고안된 단순 자질 기반의 CNN 모델을 확장하여 다양한 전역 자질들을 추가적으로 적용함으로써 성능을 개선할 수 있는 장점이 있다. PPI 추출 성능 평가를 위해서 많이 활용되고 있는 준거 평가 컬렉션인 AIMed를 이용한 실험에서 F-스코어 기준으로 78.0%를 나타내어 현재까지 도출된 세계 최고 성능에 비해 8.3% 높은 성능을 나타내었다. 추가적으로 CNN 모델이 복잡한 언어 처리를 통한 자질 추출 작업을 하지 않고도 단백질간 상호 작용 추출에 높은 성능을 나타냄을 보였다.

• Asian Pacific Journal of Cancer Prevention
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• 제17권3호
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• pp.1003-1008
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• 2016

Breast cancer is now the leading cause of cancer death in women worldwide. Cancer progression is driven not only by cancer cell intrinsic alterations and interactions with tumor microenvironment, but also by systemic effects. Integration of multiple profiling data may provide insights into the underlying molecular mechanisms of complex systemic processes. We performed a bioinformatic analysis of two public available microarray datasets for breast tumor stroma and peripheral blood mononuclear cells, featuring integrated transcriptomics data, protein-protein interactions (PPIs) and protein subcellular localization, to identify genes and biological pathways that contribute to dialogue between tumor stroma and the peripheral circulation. Genes of the integrin family as well as CXCR4 proved to be hub nodes of the crosstalk network and may play an important role in response to stroma-derived chemoattractants. This study pointed to potential for development of therapeutic strategies that target systemic signals travelling through the circulation and interdict tumor cell recruitment.

• 한국문헌정보학회지
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• 제45권2호
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• pp.251-275
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• 2011

본 논문에서는 단백질 간 상호작용 자동 추출을 위해서 기존에 연구되어 높은 성능을 나타낸 구문 트리 커널을 확장한 시맨틱 구문 트리 커널을 제안한다. 기존 구문 트리 커널의 문제점은 구문 트리의 단말 노드를 구성하는 개별 어휘에 대한 단순 외형적 비교로 인해, 실제 의미적으로는 유사한 두 구문 트리의 커널 값이 상대적으로 낮아지는 현상이며 결국 상호작용 자동 추출의 전체 성능에 악영향을 줄 수 있다는 점이다. 본 논문에서는 두 구문 트리의 구문적 유사도(syntactic similarity)와 어휘 의미적 유사도(lexical semantic similarity)를 동시에 효과적으로 계산하여 이를 결합하는 새로운 커널을 고안하였다. 어휘 의미적 유사도 계산을 위해서 문맥 및 워드넷 기반의 어휘 중의성 해소 시스템과 이 시스템의 출력으로 도출되는 어휘 개념(WordNet synset)의 추상화를 통한 기존 커널의 확장을 시도하였다. 실험에서는 단백질 간 상호작용 추출(PPII, PPIC) 성능의 심층적 최적화를 위해서 기존의 SVM에서 지원되던 정규화 매개변수 외에 구문 트리 커널의 소멸인자와 시맨틱 구문 트리 커널의 어휘 추상화 인자를 새롭게 도입하였다. 이를 통해 구문 트리 커널을 적용함에 있어서 소멸인자 역할의 중요성을 확인할 수 있었고, 시맨틱 구문 트리 커널이 기존 시스템의 성능향상에 도움을 줄 수 있음을 실험적으로 보여주었다. 특히 단백질 간 상호작용식별 문제보다도 비교적 난이도가 높은 상호작용 분류에 더욱 효과적임을 알 수 있었다.