title: Prediction of extracellular matrix proteins based on distinctive sequence and domain characteristics.
abstract: Extracellular matrix (ECM) proteins are secreted to the exterior of the cell, and function as mediators between resident cells and the external environment. These proteins not only support cellular structure but also participate in diverse processes, including growth, hormonal response, homeostasis, and disease progression. Despite their importance, current knowledge of the number and functions of ECM proteins is limited. Here, we propose a computational method to predict ECM proteins. Specific features, such as ECM domain score and repetitive residues, were utilized for prediction. Based on previously employed and newly generated features, discriminatory characteristics for ECM protein categorization were determined, which significantly improved the performance of Random Forest and support vector machine (SVM) classification. We additionally predicted novel ECM proteins from non-annotated human proteins, validated with gene ontology and earlier literature. Our novel prediction method is available at biosoft.kaist.ac.kr/ecm.
authors.affiliation: Department of Bio and Brain Engineering , KAIST, Daejeon, Korea.
authors.forename: Juhyun
authors.initials: J
authors.lastname: Jung
authors.forename: Taewoo
authors.initials: T
authors.lastname: Ryu
authors.forename: Yongdeuk
authors.initials: Y
authors.lastname: Hwang
authors.forename: Eunjung
authors.initials: E
authors.lastname: Lee
authors.forename: Doheon
authors.initials: D
authors.lastname: Lee
mesh_terms: D000595:Amino Acid Sequence; D000818:Animals; D030562:Databases, Protein; D005109:Extracellular Matrix / Q000737:chemistry*; D006801:Humans; D008969:Molecular Sequence Data; D017434:Protein Structure, Tertiary; D011506:Proteins / Q000737:chemistry*; D040901:Proteomics / Q000379:methods*; D020449:Repetitive Sequences, Amino Acid
chemical_list: D011506:Proteins