natively unstructured loops differ from other loops本地非结构化循环不同于其他循环.pdfVIP

Natively Unstructured Loops Differ from Other Loops Avner Schlessinger1,2,3*, Jinfeng Liu1,2,3, Burkhard Rost1,2,3 1 Department of Biochemistry and Molecular Biophysics, Columbia University, New York, New York, United States of America, 2 Columbia University Center for Computational Biology and Bioinformatics (C2B2), New York, New York, United States of America, 3 Northeast Structural Genomics Consortium, Columbia University, New York, New York, United States of America Natively unstructured or disordered protein regions may increase the functional complexity of an organism; they are particularly abundant in eukaryotes and often evade structure determination. Many computational methods predict unstructured regions by training on outliers in otherwise well-ordered structures. Here, we introduce an approach that uses a neural network in a very different and novel way. We hypothesize that very long contiguous segments with nonregular secondary structure (NORS regions) differ significantly from regular, well-structured loops, and that a method detecting such features could predict natively unstructured regions. Training our new method, NORSnet, on predicted information rather than on experimental data yielded three major advantages: it removed the overlap between testing and training, it systematically covered entire proteomes, and it explicitly focused on one particular aspect of unstructured regions with a simple structural interpretation, namely that they are loops. Our hypothesis was correct: well-structured and unstructured loops differ so substantially that NORSnet succeeded in their distinction. Benchmarks on previously used and new experimental data of unstructured regions revealed that NORSnet performed very well. Although it was not the best single prediction method, NORSnet was sufficiently accurate to flag unstructured regions in proteins that were previous