Classifying noisy protein sequence data: a case study of immunoglobulin light chains.
The classification of protein sequences obtained from patients with various immunoglobulin-related conformational diseases may provide insight into structural correlates of pathogenicity. However, clinical data are very sparse and, in the case of antibody-related proteins, the collected sequences have large variability with only a small subset of variations relevant to the protein pathogenicity (function). On this basis, these sequences represent a model system for development of strategies to recognize the small subset of function-determining variations among the much larger number of primary structure diversifications introduced during evolution. Under such conditions, most protein classification algorithms have limited accuracy. To address this problem, we propose a support vector machine (SVM)-based classifier that combines sequence and 3D structural averaging information. Each amino acid in the sequence is represented by a set of six physicochemical properties: hydrophobicity, hydrophilicity, volume, surface area, bulkiness and refractivity. Each position in the sequence is described by the properties of the amino acid at that position and the properties of its neighbors in 3D space or in the sequence. A structure template is selected to determine neighbors in 3D space and a window size is used to determine the neighbors in the sequence. The test data consist of 209 proteins of human antibody immunoglobulin light chains, each represented by aligned sequences of 120 amino acids. The methodology is applied to the classification of protein sequences collected from patients with and without amyloidosis, and indicates that the proposed modified classifiers are more robust to sequence variability than standard SVM classifiers, improving classification error between 5 and 25% and sensitivity between 9 and 17%. The classification results might also suggest possible mechanisms for the propensity of immunoglobulin light chains to amyloid formation.
Yu
Chenggang
C
Argonne National Laboratory 9700 S. Cass Avenue, Argonne, IL 60439, USA. cyu@bioanalysis.org
Zavaljevski
Nela
N
Stevens
Fred J
FJ
Yackovich
Kelly
K
Reifman
Jaques
J
D000465:Algorithms; D000596:Amino Acids / Q000737:chemistry; D019295:Computational Biology / Q000379:methods*; D030562:Databases, Protein; D006801:Humans; D021621:Imaging, Three-Dimensional; D007147:Immunoglobulin Light Chains / Q000737:chemistry*; D015233:Models, Statistical; D010363:Pattern Recognition, Automated; D011487:Protein Conformation; D011506:Proteins / Q000737:chemistry*; D012372:ROC Curve; D020539:Sequence Analysis, Protein / Q000379:methods*
D000596:Amino Acids; D007147:Immunoglobulin Light Chains; D011506:Proteins