title: Intrinsic Disorder and Semi-disorder Prediction by SPINE-D.
abstract: Over the past decade, it has become evident that a large proportion of proteins contain intrinsically disordered regions, which play important roles in pivotal cellular functions. Many computational tools have been developed with the aim of identifying the level and location of disorder within a protein. In this chapter, we describe a neural network based technique called SPINE-D that employs a unique three-state design and can accurately capture disordered residues in both short and long disordered regions. SPINE-D was trained on a large database of 4229 non-redundant proteins, and yielded an AUC of 0.86 on a cross-validation test and 0.89 on an independent test. SPINE-D can also detect a semi-disordered state that is associated with induced folders and aggregation-prone regions in disordered proteins and weakly stable or locally unfolded regions in structured proteins. We implement an online web service and an offline stand-alone program for SPINE-D, they are freely available at http://sparks-lab.org/SPINE-D/ . We then walk you through how to use the online and offline SPINE-D in making disorder predictions, and examine the disorder and semi-disorder prediction in a case study on the p53 protein.
authors: Department of Microbiology and Immunology, Weill Cornell Medical College, New York, NY, 10065, USA.
authors: Tuo
authors: T
authors: Zhang
authors: Department of Biochemistry and Molecular Biology, Indiana University School of Medicine, Indianapolis, IN, 46032, USA.|Research and Information Systems, LLC, Indianapolis, IN, USA.
authors: Eshel
authors: E
authors: Faraggi
authors: Translational Genomics Group, Institute of Health and Biomedical Innovation, Queensland University of Technology at Translational Research Institute, 37 Kent Street, Woolloongabba, QLD, 4102, Australia.
authors: Zhixiu
authors: Z
authors: Li
authors: Institute for Glycomics and School of Information and Communication Technology, Griffith University, Gold Coast Campus, Science 1 (G24) 2.10, Parklands Drive, Southport, QLD, 4222, Australia. yaoqi.zhou@griffith.edu.au.
authors: Yaoqi
authors: Y
authors: Zhou
mesh_terms: D000465:Algorithms; D000595:Amino Acid Sequence / Q000235:genetics; D019295:Computational Biology; D030562:Databases, Protein; D016571:Neural Networks, Computer; D011487:Protein Conformation; D011506:Proteins / Q000737:chemistry / Q000235:genetics*; D012984:Software
chemical_list: D011506:Proteins
keywords: Intrinsically disorder; Neural network; P53; Prediction; Protein aggregation; Protein induced folding; SPINE-D; Semi-disorder