Rapid assessment of contact‐dependent secondary structure propensity: Relevance to amyloidogenic sequences

We have previously demonstrated that calculation of contact‐dependent secondary structure propensity (CSSP) is highly sensitive in detecting non‐native β‐strand propensities in the core sequences of known amyloidogenic proteins. Here we describe a CSSP method based on an artificial neural network that rapidly and accurately quantifies the influence of tertiary contacts (TCs) on secondary structure propensity in local regions of protein sequences. The present method exhibited 72% accuracy in predicting the alternate secondary structure adopted by chameleon sequences located in highly disparate TC regions. Analysis of 1930 nonhomologous protein domains reveals that the α‐helix and the β‐strand largely share the same sequence context, and that tertiary context is a major determinant of the native conformation. Conversely, it appears that the propensity of random coils for either the α‐helix or the β‐strand is largely invariant to tertiary effects. The present CSSP method successfully reproduced the amyloidogenic character observed in local regions of the human islet amyloid polypeptide (hIAPP). Furthermore, CSSP profiles were strongly correlated (r = 0.76) with the observed mutational effects on the aggregation rate of acylphosphatase. Taken together, these results provide compelling evidence in support of the present CSSP approach as a sensitive probe useful for analysis of full‐length proteins and for detection of core sequences that may trigger amyloid fibril formation. The combined speed and simplicity of the CSSP method lends itself to proteome‐wide analysis of the amyloidogenic nature of common proteins. Proteins 2005. © 2005 Wiley‐Liss, Inc.

[1]  B. Rost PHD: predicting one-dimensional protein structure by profile-based neural networks. , 1996, Methods in enzymology.

[2]  P. S. Kim,et al.  Context-dependent secondary structure formation of a designed protein sequence , 1996, Nature.

[3]  Patrice Koehl,et al.  The ASTRAL compendium for protein structure and sequence analysis , 2000, Nucleic Acids Res..

[4]  P. Lansbury,et al.  Models of amyloid seeding in Alzheimer's disease and scrapie: mechanistic truths and physiological consequences of the time-dependent solubility of amyloid proteins. , 1997, Annual review of biochemistry.

[5]  Elena Orlova,et al.  Cryo‐electron microscopy structure of an SH3 amyloid fibril and model of the molecular packing , 1999, The EMBO journal.

[6]  William J Welsh,et al.  Detecting hidden sequence propensity for amyloid fibril formation , 2004, Protein science : a publication of the Protein Society.

[7]  B. Ahrén,et al.  Islet amyloid and type 2 diabetes mellitus. , 2000, The New England journal of medicine.

[8]  S. Marqusee,et al.  A rapid test for identification of autonomous folding units in proteins. , 2000, Journal of molecular biology.

[9]  D. Baker,et al.  Contact order, transition state placement and the refolding rates of single domain proteins. , 1998, Journal of molecular biology.

[10]  S Rackovsky On the nature of the protein folding code. , 1993, Proceedings of the National Academy of Sciences of the United States of America.

[11]  S. Sudarsanam,et al.  Structural diversity of sequentially identical subsequences of proteins: Identical octapeptides can have different conformations , 1998, Proteins.

[12]  E. Nevo,et al.  Adaptive role of increased frequency of polypurine tracts in mRNA sequences of thermophilic prokaryotes. , 2004, Proceedings of the National Academy of Sciences of the United States of America.

[13]  W. Kabsch,et al.  Dictionary of protein secondary structure: Pattern recognition of hydrogen‐bonded and geometrical features , 1983, Biopolymers.

[14]  K. Ikeda,et al.  Free‐energy landscape of a chameleon sequence in explicit water and its inherent α/β bifacial property , 2003 .

[15]  James C. Sacchettini,et al.  Therapeutic strategies for human amyloid diseases , 2002, Nature Reviews Drug Discovery.

[16]  Igor V. Tetko,et al.  Application of a Pruning Algorithm To Optimize Artificial Neural Networks for Pharmaceutical Fingerprinting , 1998, J. Chem. Inf. Comput. Sci..

[17]  Zhi-Xin Wang,et al.  What Is the Minimum Number of Residues to Determine the Secondary Structural State? , 1999, Journal of protein chemistry.

[18]  Pierre Baldi,et al.  Improving the prediction of protein secondary structure in three and eight classes using recurrent neural networks and profiles , 2002, Proteins.

[19]  C. Dobson,et al.  Rationalization of the effects of mutations on peptide andprotein aggregation rates , 2003, Nature.

[20]  Sharon Gilead,et al.  Identification and characterization of a novel molecular-recognition and self-assembly domain within the islet amyloid polypeptide. , 2002, Journal of molecular biology.

[21]  Ehud Gazit,et al.  A possible role for π‐stacking in the self‐assembly of amyloid fibrils , 2002, FASEB journal : official publication of the Federation of American Societies for Experimental Biology.

[22]  Christopher M. Dobson,et al.  Kinetic partitioning of protein folding and aggregation , 2002, Nature Structural Biology.

[23]  C M Dobson,et al.  Designing conditions for in vitro formation of amyloid protofilaments and fibrils. , 1999, Proceedings of the National Academy of Sciences of the United States of America.

[24]  D Baker,et al.  Global properties of the mapping between local amino acid sequence and local structure in proteins. , 1996, Proceedings of the National Academy of Sciences of the United States of America.