Statistical Analysis of Protein Sequences: A Coevolutionary Study of Molecular Chaperones

Recent advances in DNA sequencing technologies led to the accumulation of enormous quantities of genetic information available in public databases. This rapid growth of available biological datasets calls for quantitative analysis tools and concomitantly opens the doors for new analysis paradigms. Particularly, the analysis of correlated mutations and their structural interpretation have witnessed a second youth in the last years. A natural formulation for such approaches is provided by the statistical physics of disordered systems. This thesis is articulated around different projects aimed at studying particular biological systems of interests, the Hsp70 molecular chaperones, through the lens provided by methods rooted in statistical physics. In a first project, we focus on correlated mutations within the Hsp70 family. Our analysis reveals the existence of a biologically important macro-molecular arrangement of these chaperones and we investigate its phylogenetic origin. A second project investigates the interactions between the Hsp70 chaperones and one of their main co-chaperones, J-proteins. Through the combined use of coevolutionary analysis and molecular simulations at both coarse-grained and atomistic levels, we construct a structural and dynamical model of this interaction which rationalizes previous experimental evidence. In a subsequent study, we specifically focus on the J-protein co-chaperones. Through phylogenetic and coevolutionary methods, we investigate the origin of recently discovered interactions which form the basis of the disaggregation machinery in higher eukaryotes. Finally, in a fourth project, we shift our attention to the analysis of proteins involved in the iron-sulfur cluster assembly pathway. Analysis of residue coevolution in the different proteins composing this pathway reveals multiple structural insights at several scales.

Studying membrane-protein interplay by modeling realistic conditions

Martina Audagnotto

Realistic models of cellular environments have long captured the imagination of biologists and physical scientists. More than 20 years ago, Goodshellâs inspiring rendering of cellular environments provided the foundation for the idea of capturing the full complexity of biological cells. Since then, increasingly realistic simulations of subcellular models have appeared, showing that we are rapidly moving towards comprehensive, as well as physically and biologically accurate cellular models. The major challenge in developing such models is the necessity to cover a wide range of scales. Indeed, atomistic details of molecular processes are on the length scale of 0.1 nm, while cellular dimensions range from 300 nm (for the smallest bacterial cells) up to 100 ÎŒm (for large eukaryotic cells). The biological range also encompasses the internal dynamics of individual molecules (from ns to ms) as well as entire biological processes (ms to hours). Although an atomistic representation of an entire cell is conceivable, the cost for achieving biologically significant timescales with most advanced computers is very high or nearly impossible currently. Therefore, the combination of in silico approaches with in vivo and in vitro experimental data, allows gaining further insights into the biophysics of the cell. In this thesis, a multi-scale approach was applied to investigate the interplay between proteins and their respective biological environments. In particular, two specific systems were the objects of my Ph.D. project: (i) the amyloid precursor protein (APP) and its interactions at the synaptic plasma membrane, and (ii) the human acyl-protein thioesterase I (hAPT1) and its role in protein depalmitoylation cycle across the Golgi and plasma membranes. Combining atomistic and coarse-grained molecular dynamics (MD) simulations, the stability of the dimeric conformation of APP in a realistic model of the synaptic plasma membrane was investigated. Within this approach, the stable dimeric conformation of the APP as a function of lipid membrane composition was identified, highlighting the important role of lipids unsaturation in protein stability and function. In detail, this indicated not only the preferential dimerization motif of the protein, but also the possible recruiting mode of cholesterol, which has previously been experimentally observed to correlate with Alzheimerâs disease. Furthermore, the role of the synaptic plasma membrane and its ability to form raft microdomains was explored for the interactions of APP with Î³-secretase, which specific catalytic cleavage is responsible for the production of physiological or toxic amyloid Î² peptides, responsible for the Alzheimerâs disease onset. This same multi-scale approach was applied to understand the role of S-acylation in the trafficking and function of hAPT1. In particular, the combination of experimental techniques (i.e., cell biology, biochemistry, microscopy and structural biology) with molecular modeling and simulation disclosed the substrate/enzyme interaction mode, the enzyme/membrane interface, and the role for hAPT1 for a key post-translational modification as S-palmitoylation. Based on our findings, we provided for the first time an atomistic representation of the hAPT1 enzyme mechanism, suggesting that hAPT1 is active in the monomeric form. In conclusion, these studies demonstrate once more the importance of modeling the molecular details of the biological environment at the most accu

EPFL2017

Complex networks

Cécile Caretta Cartozo

The last decade has witnessed a fundamental change in the role of network theory. Side by side with the increasing relevance of interdisciplinarity, this ensemble of mathematical tools and methods has known such a success and such a wide range of different applications to transform it in the proper scientific field of complex networks that groups researchers from different communities. Two different approaches can be distinguished. The first one is descriptive, tightly related to the experimental world. Reality is permeated with complex systems that lend themselves naturally to a network representation. The amount of data on natural, social and artificial systems is constantly increasing, and network theory provides a powerful framework for the analysis of such large datasets. The second is theoretical, aimed at producing models and analytical solutions for the reproduction and understanding of real networks properties. The results presented in this Thesis reflect the interdisciplinarity of the field of complex networks as well as its twofold nature. The first part of the manuscript describes new methods for the study of gene expression and protein interactions. Biological systems are highly complex and sensitive to the environment. Experimental data are affected by errors and poor reproducibility. We propose a method for the comparative analysis of similar datasets. We present an application to three independent studies on gene expression in the cell cycle of the fission yeast that show very poor agreement. In an attempt to reconcile them, we define a periodic genes network in which each node represents a gene identified as periodically regulated in the cell cycle and an edge between two nodes is drawn according to the phase difference between the expression peaks of the corresponding genes. The analysis of the topological structure of the network reveals a universal picture that overcomes the discrepancies and that is strongly related to the biological structure of the cell cycle and to the regulation of its progression. The method is able to group genes that are co-expressed during the cell cycle and to identify putative regulators of the transition from one phase to the next. Proteins are the fundamental product of gene expression. They are characterized by a modular structure comprising independent domains that act as mediators of protein interactions and are common to evolutionary related proteins. We propose the study of domain-centered interaction networks as a complementary tool for the understanding of protein interactions. We explore their topological structure looking for stable similarity groups and we investigate domain-peptide correlations that could be used as patterns for the prediction of the properties and functions to unknown proteins. The second part of this Thesis presents an exact theoretical solution for the navigability of small world networks with an inverse power-law distribution of the length of the connections. Optimal navigability is crucial for real networks involving transportation or communication processes (Internet, electric power grid, neural networks). Most of these systems are small worlds, characterized by a surprisingly small distance between any two nodes that grows only logarithmically with the size. How they are able to exploit this property for optimized performances without a complete knowledge of the structure is still an open question. It has been shown that a small world can arise from a proper compromise between local and long-range connections. In the specific case in which the length of the connections is power-law distributed and a greedy algorithm is used to forward a message from a source to a target, we obtained an original exact solution for the behavior of the expected delivery time in any dimension. We discussed the resulting scenario for the navigability and the implication of different structural parameters.

EPFL2009

The conformational flexibility of the carboxy terminal residues 105-114 is a key modulator of the catalytic activity and stability of macrophage migration inhibitory factor

Michele Cascella, Farah El Turk, Hilal Lashuel, Hajer Ouertatani-Sakouhi, Ursula Röthlisberger, Magdalena Zweckstetter

Macrophage migration inhibitory factor (MIF) is a multifunctional protein and a major mediator of innate immunity. Although X-ray crystallography revealed that MIF exists as a homotrimer, its oligomerization state in vivo and the factors governing its oligomerization and stability remain poorly understood. The C-terminal region of MIF is highly conserved and participates in several intramolecular interactions that suggest a role in modulating the stability and biochemical activity of MIF. To determine the importance of these interactions, point mutations (A48P, L46A), insertions (P107) at the monomer-monomer interfaces, and C-terminal deletion (Delta 110-114NSTFA and Delta 105-114NVGWNNSTFA) variants were designed and their structural properties, thermodynamic stability, oligomerization state, catalytic activity and receptor binding were characterized using a battery of biophysical methods. The C-terminal deletion mutants DeltaC5 huMIF 1-109 and DeltaC10 huMIF 1-104 were enzymatically inactive and thermodynamically less stable than wild type MIF. Analytical ultracentrifugation studies demonstrate that both C-terminal mutants sediment as trimers and exhibit similar binding to CD74 as the wild type protein. Disrupting the conformation of the C-terminal region 105-114 and increasing its conformational flexibility through the insertion of a proline residue at position 107 was sufficient to reproduce the structural, biochemical and thermodynamic properties of the deletion mutants. P107 MIF forms an enzymatically inactive trimer and exhibits reduced thermodynamic stability relative to the wild type protein. To provide a rationale for the changes induced by these mutations at the molecular level, we also performed molecular dynamics simulations on these mutants in comparison to the wild type MIF. Together, our studies demonstrate that intersubunit interactions involving the C-terminal region 105-114, including a salt-bridge interaction between Arg73 of one monomer and the carboxy terminus of a neighboring monomer, play critical roles in modulating tertiary structure stabilization, enzymatic activity, and thermodynamic stability of MIF, but not its oligomerization state and receptor binding properties. Our results suggest that targeting the C-terminal region could provide new strategies for allosteric modulation of MIF enzymatic activity and the development of novel inhibitors of MIF tautomerase activity.

American Chemical Society2008