Proteins as Sponges: A Statistical Journey along Protein Structure Organization Principles

The analysis of a large database of protein structures by means of topological and shape indexes inspired by complex network and fractal analysis shed light on some organizational principles of proteins. Proteins appear much more similar to "fractal" sponges than to closely packed spheres, casting doubts on the tenability of the hydrophobic core concept. Principal component analysis highlighted three main order parameters shaping the protein universe: (1) "size", with the consequent generation of progressively less dense and more empty structures at an increasing number of residues, (2) "microscopic structuring", linked to the existence of a spectrum going from the prevalence of heterologous (different hydrophobicity) to the prevalence of homologous (similar hydrophobicity) contacts, and (3) "fractal shape", an organizing protein data set along a continuum going from approximately linear to very intermingled structures. Perhaps the time has come for seriously taking into consideration the real relevance of time-honored principles like the hydrophobic core and hydrophobic effect.

[1]  宁北芳,et al.  疟原虫var基因转换速率变化导致抗原变异[英]／Paul H, Robert P, Christodoulou Z, et al//Proc Natl Acad Sci U S A , 2005 .

[2]  William L. Jorgensen,et al.  Journal of Chemical Information and Modeling , 2005, J. Chem. Inf. Model..

[3]  Olav H.J. Christie,et al.  Introduction to multivariate methodology, an alternative way? , 1995 .

[4]  M Michael Gromiha,et al.  Inter-residue interactions in protein folding and stability. , 2004, Progress in biophysics and molecular biology.

[5]  K. Dill.  Dominant forces in protein folding. , 1990, Biochemistry.

[6]  Bernd Hamann,et al.  Visual Analysis of Biomolecular Surfaces , 2008, Visualization in Medicine and Life Sciences.

[7]  Gil Amitai,et al.  Network analysis of protein structures identifies functional residues. , 2004, Journal of molecular biology.

[8]  A Giuliani,et al.  Elucidating protein secondary structures using alpha‐carbon recurrence quantifications , 2001, Proteins.

[9]  R. Preisendorfer,et al.  Principal Component Analysis in Meteorology and Oceanography , 1988 .

[10]  S. Vishveshwara,et al.  Identification of side-chain clusters in protein structures by a graph spectral method. , 1999, Journal of molecular biology.

[11]  D. Leitner,et al.  Mass fractal dimension and the compactness of proteins. , 2005, Physical review. E, Statistical, nonlinear, and soft matter physics.

[12]  Alessandro Giuliani,et al.  A topologically related singularity suggests a maximum preferred size for protein domains , 2006, Proteins.

[13]  M. Newman,et al.  Finding community structure in networks using the eigenvectors of matrices. , 2006, Physical review. E, Statistical, nonlinear, and soft matter physics.

[14]  Masaru Tomita,et al.  Implications from a Network-Based Topological Analysis of Ubiquitin Unfolding Simulations , 2008, PloS one.

[15]  Alessandro Giuliani,et al.  Proteins as Networks: A Mesoscopic Approach Using Haemoglobin Molecule as Case Study , 2009 .

[16]  Barbara J. Wold,et al.  Mining gene expression data by interpreting principal components , 2006, BMC Bioinformatics.

[17]  G. Arteca,et al.  Overcrossing spectra of protein backbones: Characterization of three‐dimensional molecular shape and global structural homologies , 1993, Biopolymers.

[18]  I. Ghosh,et al.  Revisiting the Myths of Protein Interior: Studying Proteins with Mass-Fractal Hydrophobicity-Fractal and Polarizability-Fractal Dimensions , 2009, PloS one.

[19]  Masaru Tomita,et al.  Network scaling invariants help to elucidate basic topological principles of proteins. , 2007, Journal of proteome research.

[20]  S Vajda,et al.  Empirical potentials and functions for protein folding and binding. , 1997, Current opinion in structural biology.

[21]  C A Heckman.  Geometrical constraints on the shape of cultured cells. , 1990, Cytometry.

[22]  Alfredo Colosimo,et al.  On the constructive role of noise in spatial systems , 1998 .

[23]  Frances H Arnold,et al.  Structural determinants of the rate of protein evolution in yeast. , 2006, Molecular biology and evolution.

[24]  M. Newman,et al.  Nonequilibrium phase transition in the coevolution of networks and opinions. , 2006, Physical review. E, Statistical, nonlinear, and soft matter physics.

[25]  D. Avnir,et al.  Steady-state diffusion and reactions in catalytic fractal porous media , 1991 .

[26]  Kazuaki Sakoda,et al.  Localization of electromagnetic waves in three-dimensional fractal cavities. , 2004, Physical review letters.

[27]  Alessandro Giuliani,et al.  Looking for an Unambiguous Geometrical Definition of Organic Series from 3-D Molecular Similarity Indices , 2003, J. Chem. Inf. Comput. Sci..

[28]  A J Hopfinger,et al.  Theory and application of molecular potential energy fields in molecular shape analysis: a quantitative structure--activity relationship study of 2,4-diamino-5-benzylpyrimidines as dihydrofolate reductase inhibitors. , 1983, Journal of medicinal chemistry.

[29]  Ned S Wingreen,et al.  Flexibility of β‐sheets: Principal component analysis of database protein structures , 2004, Proteins.

[30]  Alfredo Colosimo,et al.  Structure-Related Statistical Singularities along Protein Sequences: A Correlation Study , 2005, J. Chem. Inf. Model..

[31]  Donald A. Jackson.  STOPPING RULES IN PRINCIPAL COMPONENTS ANALYSIS: A COMPARISON OF HEURISTICAL AND STATISTICAL APPROACHES' , 1993 .

[32]  Albert-László Barabási,et al.  Distribution of node characteristics in complex networks , 2007, Proceedings of the National Academy of Sciences.