1 Structural Bioinformatics Identification of amyloid fibril-forming segments based on structure and residue-based statistical potential

Motivation: Experimental evidence suggests that certain short pro-tein segments have stronger amyloidogenic propensities than oth-ers. Identification of the fibril-forming segments of proteins is crucial for understanding diseases associated with protein misfolding and for finding favorable targets for therapeutic strategies. Result: In this study we used the microcrystal structure of the NNQQNY peptide from yeast prion protein and residue-based statis-tical potentials to establish an algorithm to identify the amyloid fibril-forming segment of proteins. Using the same sets of sequences, a comparable prediction performance was obtained from this study to that from 3D profile method based on the physical atomic-level po-tential ROSETTADESIGN. The predicted results are consistent with experiments for several representative proteins associated with amyloidosis, and also agree with the idea that peptides that can form fibrils may have strong sequence signatures. Application of the residue-based statistical potentials is computationally more efficient than using atomic-level potentials and can be applied in whole pro-teome analysis to investigate the evolutionary pressure effect or forecast other latent diseases related to amyloid deposits. Availability: The fibril prediction program is available a