Proposta di ricerca:
Biological activity of protein molecules depends on their structure and internal dynamics, including
collective slow protein motions resulting from structural fluctuations. Normal mode analysis represents a
powerful and cheap way to study such motions, but they cannot catch phenomena out of equilibrium, such
as movements of protein fragments induced by the presence of ligands, or triggered by signal transduction
processes. These phenomena can be simulated by molecular dynamics (MD), however robust and efficient
tools are required for postprocessing the huge amount of data generated. In this context, Dr. Caliandro
with his coworkers developed a new tool, based on internal descriptors (backbone dihedral angles) [1]. It
overcomes the limitations of the existing ones and proved very effective in identifying hinge motions and
hot spot residues [1,2,3]. It has been also used to explain flexibility features observed in intrinsic disordered
proteins upon ligand binding [4]. On the other hand, Dr. Lesyng with his coworkers developed a
multivariate autoregressive model using selected degrees of freedom, which was able to capture causal
relations in molecular structural changes, giving valuable information to interpret the mechanisms of
functioning of biomolecular systems [5,6]. Also a web service capable to indicate dynamic proteins domains
from MD simulation data is being currently developed [7]. Within this project we plan to combine the
mentioned above approaches, based on directional statistics of dihedral descriptors with the dynamic
domain identification and Granger-type causality analyses, to achieve a deeper understanding of the
dynamics of complex biomolecules, and characterizing the out-of-equilibrium functional movements of
protein domains. As a further step, we plan to develop a comprehensive tool which would be able to
process MD data or ensemble of experimentally determined protein conformations to identify dynamic
domains, residues which serve as hinge points, and interfacial regions in proteins. The use of internal
coordinates should allow differentiating motions consisting in fluctuations around a given configuration
from those representing a transition between different configurations (in this case the system experiences
a conformational change). New algorithms for clustering protein conformations sampled during MD could
be consequently envisaged. They will be based on the identification of dynamic domains [7] and on recent
algorithms for structural comparison [8]. Classification of protein conformations based on their internal
dynamics will be used to facilitate the protein crystal structure solution. The more frequent method for
protein crystal solution (68% of existing structures) is molecular replacement (MR), where the correct
position of a rigid model of an homologous protein in the crystal cell is searched by fitting diffraction
intensities. Novel and more efficient MR procedures have been recently developed [9,10], which still
require small deviations between model and target structures. Crystallographic methods developed by the
Dr. Caliandro group will be combined to computational modelling skills and algorithms developed by the
Dr. Lesyng group to push forward actual limits of MR procedures, according to the following strategy: 1)
generate a structural model by homology modelling, by using the target sequence 2) execute molecular
dynamics simulations on the generated model 3) a) identify the dynamic domains of the model, by using
tools developed within this project b) sample their more frequent conformations and discard very flexible
linkers among domains 4) fit the dynamic domains in their selected conformations as rigid bodies in the
crystal cell - in fact, existing MR procedures are able to fit several different models in the unit cell, provided
they are large enough to produce a sensible effect on the used cost functions. This procedure aims at
introducing flexibility of the search model in the MR procedure, to account for structural differences with
the target structure. At different stages of the pipeline the information on the mutual orientation of alphahelices will be used as constraint. It is supplied by a recent algorithm which identifies alpha-helix directly
from diffraction patterns [11]. References: [1] R. Caliandro, G. Rossetti, P. Carloni "Local fluctuations and
conformational transitions in proteins", J. Chem. Theory Comput., 2012 8 4775; [2] G. Rossetti, X.J. Cong, R.
Caliandro et al. "Common Structural Traits across Pathogenic Mutants of the Human Prion Protein and
Their Implications for Familial Prion Diseases", J. Mol. Biol. 2011 411 700; [3] R. Caliandro, G. Nico, G. Tiscia
et al., "Structural analysis of protein Z gene variants in patients with foetal losses", Thromb. Haemost. 2013
110 534; [4] D. Dibenedetto, G. Rossetti, R. Caliandro et al. "A molecular dynamics simulation-based
interpretation of nuclear magnetic resonance multidimensional heteronuclear spectra of alphasynuclein•dopamine adducts", Biochem. 2013 52 6672; [5] A. Gorecki, J. Trylska, B. Lesyng "Causal relations
in molecular dynamics from the multi-variate autoregressive model", Europhys. Lett., 2006 75 503; [6] P.
Daniluk, M. DziubiDski, B. Lesyng et al. "From experimental, structural probability distributions to the
theoretical causality analysis of molecular changes", CAMES, 2012 19 257; [7] M. Dziubinski, P. Daniluk, B.
Lesyng "ResiCon: a web server for the identification of dynamic domains, hinges and interfacial regions in
proteins", in preparation; [8] P. Daniluk, B. Lesyng "A novel method to compare protein structures using
local descriptors", BMC Bioinform. 2011 12 344; [9] R. Caliandro, B. Carrozzini, G.L. Cascarano et al.
"Molecular replacement: the probabilistic approach of the program REMO09 and its applications" Acta
Cryst. 2009 A65 512; [10] B. Carrozzini, G.L. Cascarano, G. Comunale et al. "The use of VLD (vive la
difference) in the molecular-replacement approach: a pipeline", Acta Cryst. 2013 D69, 1038; [11] R.
Caliandro, D. Dibenedetto, G.L. Cascarano et al. "Automatic alpha-helix identification in Patterson maps",
Acta Cryst. 2012 D68, 1;