title: Conformational diversity analysis reveals three functional mechanisms in proteins.
abstract: Protein motions are a key feature to understand biological function. Recently, a large-scale analysis of protein conformational diversity showed a positively skewed distribution with a peak at 0.5 Å C-alpha root-mean-square-deviation (RMSD). To understand this distribution in terms of structure-function relationships, we studied a well curated and large dataset of ~5,000 proteins with experimentally determined conformational diversity. We searched for global behaviour patterns studying how structure-based features change among the available conformer population for each protein. This procedure allowed us to describe the RMSD distribution in terms of three main protein classes sharing given properties. The largest of these protein subsets (~60%), which we call "rigid" (average RMSD = 0.83 Å), has no disordered regions, shows low conformational diversity, the largest tunnels and smaller and buried cavities. The two additional subsets contain disordered regions, but with differential sequence composition and behaviour. Partially disordered proteins have on average 67% of their conformers with disordered regions, average RMSD = 1.1 Å, the highest number of hinges and the longest disordered regions. In contrast, malleable proteins have on average only 25% of disordered conformers and average RMSD = 1.3 Å, flexible cavities affected in size by the presence of disordered regions and show the highest diversity of cognate ligands. Proteins in each set are mostly non-homologous to each other, share no given fold class, nor functional similarity but do share features derived from their conformer population. These shared features could represent conformational mechanisms related with biological functions.
authors.affiliation: Departamento de Ciencia y Tecnología, Universidad Nacional de Quilmes (CONICET), Bernal, Buenos Aires, Argentina.
authors.forename: Alexander Miguel
authors.identifier: 0000-0003-0362-8218
authors.initials: AM
authors.lastname: Monzon
authors.affiliation: Bioinformatics Unit, Fundación Instituto Leloir (CONICET), Buenos Aires, Argentina.
authors.forename: Diego Javier
authors.identifier: 0000-0002-4254-9320
authors.initials: DJ
authors.lastname: Zea
authors.affiliation: Departamento de Ciencia y Tecnología, Universidad Nacional de Quilmes (CONICET), Bernal, Buenos Aires, Argentina.
authors.forename: María Silvina
authors.initials: MS
authors.lastname: Fornasari
authors.affiliation: Departamento de Ciencia y Tecnología, Universidad Nacional de Quilmes (CONICET), Bernal, Buenos Aires, Argentina.
authors.forename: Tadeo E
authors.identifier: 0000-0003-0017-4660
authors.initials: TE
authors.lastname: Saldaño
authors.affiliation: Departamento de Ciencia y Tecnología, Universidad Nacional de Quilmes (CONICET), Bernal, Buenos Aires, Argentina.
authors.forename: Sebastian
authors.identifier: 0000-0002-0916-5069
authors.initials: S
authors.lastname: Fernandez-Alberti
authors.affiliation: Department of Biomedical Sciences, University of Padua, Padua, Italy.
authors.forename: Silvio C E
authors.identifier: 0000-0003-4525-7793
authors.initials: SC
authors.lastname: Tosatto
authors.affiliation: Departamento de Ciencia y Tecnología, Universidad Nacional de Quilmes (CONICET), Bernal, Buenos Aires, Argentina.
authors.forename: Gustavo
authors.identifier: 0000-0001-7444-1624
authors.initials: G
authors.lastname: Parisi
mesh_terms: D008956:Models, Chemical; D015233:Models, Statistical; D056004:Molecular Dynamics Simulation; D011487:Protein Conformation; D011506:Proteins / Q000737:chemistry* / Q000648:ultrastructure*; D013329:Structure-Activity Relationship
chemical_list: D011506:Proteins