Abstract
Chronic lymphocytic leukemia (CLL) is the most common adult leukemia with still unclear etiology. Indications of antigenic pressure have been hinted, using sequence and structure-based reasoning. The accuracy of such approaches, and in particular of the ones derived from 3D models obtained from the patients’ antibody amino acid sequences, is intimately connected to both the reliability of the models and the quality of the methods used to compare and group them. The proposed work provides a sophisticated method for the classification of CLL patients based on clustering the amino acid sequences of the clonotypic B-cell receptor immunoglobulin, which is the ideal clone-specific marker, critical for clonal behavior and patient outcome. A novel CLL patient clustering method is hereby proposed, combining bioinformatics methods with the extraction of 3D object descriptors, used in machine learning applications. The proposed methodology achieved an efficient and highly informative grouping of CLL patients in accordance to their biological and clinical properties.
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References
Webb, B., Sali, A.: Comparative Protein Structure Modeling Using MODELLER. Current Protocols in Bioinformatics. John Wiley & Sons, Inc., (2002)
Skwark, M.J., et al.: Improved contact predictions using the recognition of protein like contact patterns. PLoS Comput. Biol. 10(11), e1003889 (2014)
Marcatili, P., et al.: Automated clustering analysis of immunoglobulin sequences in chronic lymphocytic leukemia based on 3D structural descriptors. Blood 128(22), 4365 (2016)
Marcatili, P., et al.: Antibody structural modeling with prediction of immunoglobulin structure (PIGS). Nat. Protoc. 9(12), 2771–2783 (2014)
Agathangelidis, A., et al.: Stereotyped B-cell receptors in one-third of chronic lymphocytic leukemia: a molecular classification with implications for targeted therapies. Blood 119(19), 4467 (2012)
Zhang, Y.: I-TASSER server for protein 3D structure prediction. BMC Bioinf. 9, 40 (2008)
Liao, S., Jain, A.K., Li, S.Z.: Partial face recognition: alignment-free approach. IEEE Trans. Pattern Anal. Mach. Intell. 35(5), 1193–1205 (2013)
Greene, L.H., et al.: The CATH domain structure database: new protocols and classification levels give a more comprehensive resource for exploring evolution. Nucleic Acids Res. 35(Database issue), D191–D197 (2007)
Andreeva, A., et al.: Data growth and its impact on the SCOP database: new developments. Nucleic Acids Res. 36(Database issue), D419–D425 (2008)
Holm, L., Sander, C.: The FSSP database of structurally aligned protein fold families. Nucleic Acids Res. 22(17), 3600–3609 (1994)
Finn, R.D., et al.: The Pfam protein families database: towards a more sustainable future. Nucleic Acids Res. 44(D1), D179–D185 (2016)
Blackburne, B.P., Whelan, S.: Class of multiple sequence alignment algorithm affects genomic analysis. Mol. Biol. Evol. 30(3), 642–653 (2013)
Larkin, M.A., et al.: Clustal W and Clustal X version 2.0. Bioinformatics 23(21), 2947–2948 (2007)
Edgar, R.C.: MUSCLE: a multiple sequence alignment method with reduced time and space complexity. BMC Bioinf. 5, 113 (2004)
Katoh, K., Standley, D.M.: MAFFT multiple sequence alignment software version 7: improvements in performance and usability. Mol. Biol. Evol. 30(4), 772–780 (2013)
Notredame, C., Higgins, D.G., Heringa, J.: T-Coffee: A novel method for fast and accurate multiple sequence alignment. J. Mol. Biol. 302(1), 205–217 (2000)
Ortiz, A.R., Strauss, C.E., Olmea, O.: MAMMOTH (matching molecular models obtained from theory): an automated method for model comparison. Protein Sci. 11(11), 2606–2621 (2002)
Zemla, A.: LGA: a method for finding 3D similarities in protein structures. Nucleic Acids Res. 31(13), 3370–3374 (2003)
Kolodny, R., Koehl, P., Levitt, M.: Comprehensive evaluation of protein structure alignment methods: scoring by geometric measures. J. Mol. Biol. 346(4), 1173–1188 (2005)
Herbert, A., Sternberg, M.J.E.: MaxCluster–a tool for protein structure comparison and clustering (2014). http://www.sbg.bio.ic.ac.uk/~maxcluster
Aung, Z., Tan, K.-L.: MatAlign: precise protein structure comparison by matrix alignment. J. Bioinf. Comput. Biol. 04(06), 1197–1216 (2006)
MartĂnez, L., Andreani, R., MartĂnez, J.M.: Convergent algorithms for protein structural alignment. BMC Bioinf. 8(1), 306 (2007)
Krissinel, E., Henrick, K.: Secondary-structure matching (SSM), a new tool for fast protein structure alignment in three dimensions. Acta Crystallographica Sect. D 60(12 Part 1), 2256–2268 (2004)
Pandit, S.B., Skolnick, J.: Fr-TM-align: a new protein structural alignment method based on fragment alignments and the TM-score. BMC Bioinf. 9, 531 (2008)
Rusu, R.B., Blodow, N., Beetz, M.: Fast point feature histograms (FPFH) for 3D registration. In: IEEE International Conference on Robotics and Automation, ICRA 2009 (2009)
Ryslik, G., Yuwei C., and Hongyu Z. SpacePAC: identifying mutational clusters in 3D protein space using simulation (2013)
Messih, M.A., et al.: Improving the accuracy of the structure prediction of the third hypervariable loop of the heavy chains of antibodies. Bioinformatics 30(19), 2733–2740 (2014)
Zhang, Y., Skolnick, J.: TM-align: a protein structure alignment algorithm based on the TM-score. Nucleic acids res. 33(7), 2302–2309 (2005)
Zhang, Y., Skolnick, J.: Scoring function for automated assessment of protein structure template quality. Proteins: Struct., Funct., Bioinf. 57(4), 702–710 (2004)
Louchet, H., Kuzmin, K., Richter, A.: Frequency, phase, and polarization-tracking algorithms for arbitrary four-dimensional signal constellations. In: SPIE OPTO, International Society for Optics and Photonics, pp. 900907–900907 (2013)
Rusu, R.B.: Semantic 3D object maps for everyday manipulation in human living environments. KI - Künstliche Intelligenz 24(4), 345–348 (2010)
Tran, T.N., Drab, K., Daszykowski, M.: Revised DBSCAN algorithm to cluster data with dense adjacent clusters. Chemometr. Intell. Lab. Syst. 120, 92–96 (2013)
Celebi, M.E., Kingravi, H.A., Vela, P.A.: A comparative study of efficient initialization methods for the k-means clustering algorithm. Expert Syst. Appl. 40(1), 200–210 (2013)
Meila, M., Heckerman, D.: An experimental comparison of several clustering and initialization methods. arXiv preprint arXiv:1301.7401 (2013)
Henikoff, S., Henikoff, J.G.: Amino acid substitution matrices from protein blocks. PNAS 89(22), 10915–10919 (1992). PMC: 50453, PMID: 1438297
Meyer, M.J., et al.: mutation3D: cancer gene prediction through atomic clustering of coding variants in the structural proteome. Hum. Mutat. 37(5), 447–456 (2016)
Wohlkinger, W., Vincze, M.: Ensemble of shape functions for 3D object classification. In: 2011 IEEE International Conference on Robotics and Biomimetics (ROBIO), pp. 2987–2992. IEEE, December 2011
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Mochament, K. et al. (2017). 3D Protein-Structure-Oriented Discovery of Clinical Relation Across Chronic Lymphocytic Leukemia Patients. In: Rojas, I., Ortuño, F. (eds) Bioinformatics and Biomedical Engineering. IWBBIO 2017. Lecture Notes in Computer Science(), vol 10209. Springer, Cham. https://doi.org/10.1007/978-3-319-56154-7_14
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DOI: https://doi.org/10.1007/978-3-319-56154-7_14
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