GAP: towards almost 100 percent prediction for β-strand-mediated aggregating peptides with distinct morphologies

MOTIVATION Distinguishing between amyloid fibril-forming and amorphous β-aggregating aggregation-prone regions (APRs) in proteins and peptides is crucial for designing novel biomaterials and improved aggregation inhibitors for biotechnological and therapeutic purposes. RESULTS Adjacent and alternate position residue pairs in hexapeptides show distinct preferences for occurrence in amyloid fibrils and amorphous β-aggregates. These observations were converted into energy potentials that were, in turn, machine learned. The resulting tool, called Generalized Aggregation Proneness (GAP), could successfully distinguish between amyloid fibril-forming and amorphous β-aggregating hexapeptides with almost 100 percent accuracies in validation tests performed using non-redundant datasets. CONCLUSION Accuracies of the predictions made by GAP are significantly improved compared with other methods capable of predicting either general β-aggregation or amyloid fibril-forming APRs. This work demonstrates that amino acid side chains play important roles in determining the morphological fate of β-mediated aggregates formed by short peptides. AVAILABILITY AND IMPLEMENTATION http://www.iitm.ac.in/bioinfo/GAP/.

[1]  Markus J Buehler,et al.  Nanomechanics of functional and pathological amyloid materials. , 2011, Nature nanotechnology.

[2]  Michael P Collins,et al.  The vasculitic neuropathies: an update. , 2012, Current opinion in neurology.

[3]  C. Dobson,et al.  Formation of mixed fibrils demonstrates the generic nature and potential utility of amyloid nanostructures , 2000 .

[4]  A. Miranker,et al.  A native to amyloidogenic transition regulated by a backbone trigger , 2006, Nature Structural &Molecular Biology.

[5]  M. Gromiha,et al.  Classification of transporters using efficient radial basis function networks with position‐specific scoring matrices and biochemical properties , 2010, Proteins.

[6]  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.

[7]  Exploring the sequence determinants of amyloid structure using position-specific scoring matrices , 2010, Nature Methods.

[8]  Bernhardt L Trout,et al.  Computational methods to predict therapeutic protein aggregation. , 2012, Methods in molecular biology.

[9]  Yu-Yen Ou,et al.  Identification of efflux proteins using efficient radial basis function networks with position‐specific scoring matrices and biochemical properties , 2013, Proteins.

[10]  M. Michael Gromiha,et al.  Protein Bioinformatics: From Sequence to Function , 2010 .

[11]  Bernhardt L Trout,et al.  Aggregation in protein-based biotherapeutics: computational studies and tools to identify aggregation-prone regions. , 2011, Journal of pharmaceutical sciences.

[12]  A. Cavalli,et al.  The role of aromaticity, exposed surface, and dipole moment in determining protein aggregation rates , 2004, Protein science : a publication of the Protein Society.

[13]  Ian H. Witten,et al.  Data mining: practical machine learning tools and techniques, 3rd Edition , 1999 .

[14]  Heather T. McFarlane,et al.  Atomic structures of amyloid cross-β spines reveal varied steric zippers , 2007, Nature.

[15]  David Eisenberg,et al.  The structural biology of protein aggregation diseases: Fundamental questions and some answers. , 2006, Accounts of chemical research.

[16]  Sandeep Kumar,et al.  Distinct position-specific sequence features of hexa-peptides that form amyloid-fibrils: application to discriminate between amyloid fibril and amorphous β-aggregate forming peptide sequences , 2013, BMC Bioinformatics.

[17]  Silvio C. E. Tosatto,et al.  The PASTA server for protein aggregation prediction. , 2007, Protein engineering, design & selection : PEDS.

[18]  Makiko Suwa,et al.  Influence of amino acid properties for discriminating outer membrane proteins at better accuracy. , 2006, Biochimica et biophysica acta.

[19]  Sandeep Kumar,et al.  Identification and Impact of Aggregation‐Prone Regions in Proteins and Therapeutic Monoclonal Antibodies , 2010 .

[20]  Thomas E. Creighton.  Proteins: Structure and Molecular Properties , 2013 .

[21]  Salvador Ventura,et al.  AGGRESCAN: method, application, and perspectives for drug design. , 2012, Methods in molecular biology.

[22]  David S. Goodsell,et al.  The RCSB Protein Data Bank: new resources for research and education , 2012, Nucleic Acids Res..

[23]  C. Cotman,et al.  Structure‐Activity Analyses of β‐Amyloid Peptides: Contributions of the β25–35 Region to Aggregation and Neurotoxicity , 1995 .

[24]  Anthony J Turner,et al.  Journal of Neurochemistry special issue on Alzheimer’s disease: ‘amyloid cascade hypothesis – 20 years on’ , 2012, Journal of neurochemistry.

[25]  Stavros J. Hamodrakas,et al.  A Consensus Method for the Prediction of ‘Aggregation-Prone’ Peptides in Globular Proteins , 2013, PloS one.

[26]  L. Serrano,et al.  Prediction of sequence-dependent and mutational effects on the aggregation of peptides and proteins , 2004, Nature Biotechnology.

[27]  Devadasan Velmurugan,et al.  How do thermophilic proteins resist aggregation? , 2012, Proteins.

[28]  Ronald Wetzel,et al.  A serendipitous survey of prediction algorithms for amyloidogenicity. , 2013, Biopolymers.

[29]  W. Qi,et al.  Physicochemical strategies for inhibition of amyloid fibril formation: an overview of recent advances. , 2012, Current medicinal chemistry.

[30]  อนิรุธ สืบสิงห์,et al.  Data Mining Practical Machine Learning Tools and Techniques , 2014 .

[31]  David Eisenberg,et al.  Atomic structures of amyloid cross-beta spines reveal varied steric zippers. , 2007, Nature.

[32]  Susan C Shin,et al.  Amyloid neuropathies. , 2012, The Mount Sinai journal of medicine, New York.

[33]  Matteo Ramazzotti,et al.  Prediction of amyloid aggregation in vivo , 2011, EMBO reports.

[34]  Ehud Gazit,et al.  Amyloids: not only pathological agents but also ordered nanomaterials. , 2008, Angewandte Chemie.

[35]  C. Cotman,et al.  Structure-activity analyses of beta-amyloid peptides: contributions of the beta 25-35 region to aggregation and neurotoxicity. , 1995, Journal of neurochemistry.

[36]  Francesc X. Avilés,et al.  AGGRESCAN: a server for the prediction and evaluation of "hot spots" of aggregation in polypeptides , 2007, BMC Bioinform..

[37]  T. Creighton.  Proteins: Structures and Molecular Properties , 1986 .

[38]  L. Tjernberg,et al.  Charge Attraction and β Propensity Are Necessary for Amyloid Fibril Formation from Tetrapeptides* , 2002, The Journal of Biological Chemistry.

[39]  Michele Vendruscolo,et al.  Prediction of "aggregation-prone" and "aggregation-susceptible" regions in proteins associated with neurodegenerative diseases. , 2005, Journal of molecular biology.

[40]  Sandeep Kumar,et al.  On the Role of Aggregation Prone Regions in Protein Evolution, Stability, and Enzymatic Catalysis: Insights from Diverse Analyses , 2013, PLoS Comput. Biol..

[41]  András Kocsor,et al.  ROC analysis: applications to the classification of biological sequences and 3D structures , 2008, Briefings Bioinform..

[42]  Andreas Hoenger,et al.  Identification of a novel ‘aggregation‐prone’/‘amyloidogenic determinant’ peptide in the sequence of the highly amyloidogenic human calcitonin , 2013, FEBS letters.

[43]  Shandar Ahmad,et al.  Application of residue distribution along the sequence for discriminating outer membrane proteins , 2005, Comput. Biol. Chem..

[44]  J Q Trojanowski,et al.  A Hydrophobic Stretch of 12 Amino Acid Residues in the Middle of α-Synuclein Is Essential for Filament Assembly* , 2001, The Journal of Biological Chemistry.

[45]  C. Dobson.  Protein misfolding, evolution and disease. , 1999, Trends in biochemical sciences.