Luisa Di Paola

Shedding light on protein–ligand binding by graph theory: The topological nature of allostery

Allostery is a very important feature of proteins; we propose a mesoscopic approach to allosteric mechanisms elucidation, based on protein contact matrices. The application of graph theory methods to the characterization of the allosteric process and, more broadly, to obtain the conformational changes upon binding, reveals key features of the protein function. The proposed method highlights the leading role played by topological over geometrical changes in allosteric transitions. Topological invariants were able to discriminate between true allosteric motions and generic protein motions upon binding.

Proteins as Networks: A Mesoscopic Approach Using Haemoglobin Molecule as Case Study

Protein structures allow for a straightforward representation in terms of graph theory being the nodes the aminoacid residues and the edges the scoring of a spatial contact between the node pairs. Such a representation allows for a direct use in the realm of protein science of the vast repertoire of graph invariants developed in the analysis of complex networks. In this work we give a general overview of the protein as networks paradigm with a special emphasis on haemoglobin where the most important features of protein systems like allostery, protein-protein contacts and differential effect of mutations were demonstrated to be amenable to a graph theory oriented translation.

Auxin minimum triggers the developmental switch from cell division to cell differentiation in the Arabidopsis root

Significance The maintenance of boundaries between neighboring groups of distinct cell types is vital during development of multicellular organisms, as groups of cells with distinct functions must be kept physically separated to guarantee correct control of organ and body growth and function. In the Arabidopsis root, the transition zone is a developmental boundary in the meristem that separates dividing from differentiating cells. Here, we infer that a well-defined and tightly controlled minimum of the hormone auxin acts as a signal to establish the position of the transition zone by controlling the developmental switch from cell division to cell differentiation. We provide the mechanistic and genetic basis of how another hormone, cytokinin, controls and positions this auxin minimum, thus regulating root size. In multicellular organisms, a stringent control of the transition between cell division and differentiation is crucial for correct tissue and organ development. In the Arabidopsis root, the boundary between dividing and differentiating cells is positioned by the antagonistic interaction of the hormones auxin and cytokinin. Cytokinin affects polar auxin transport, but how this impacts the positional information required to establish this tissue boundary, is still unknown. By combining computational modeling with molecular genetics, we show that boundary formation is dependent on cytokinin’s control on auxin polar transport and degradation. The regulation of both processes shapes the auxin profile in a well-defined auxin minimum. This auxin minimum positions the boundary between dividing and differentiating cells, acting as a trigger for this developmental transition, thus controlling meristem size.

Structural and Functional Analysis of Hemoglobin and Serum Albumin Through Protein Long-Range Interaction Networks

Long-range contacts in protein structures were demonstrated to be predictive of different physiological properties of hemoglobin and albumin proteins. Complex networks based approach was demonstrated to highlight ba- sic principles of protein folding and activity. The presence of a natural scaling region ending at an approximate thresh- old of 120-150 residues shared by proteins of different size and quaternary structure was highlighted. This threshold is reminiscent of the typical size for a macromolecule to have a binding site sensible to environmental regulation.

P2X7 receptor antagonism: Implications in diabetic retinopathy

ABSTRACT Diabetic retinopathy (DR) is the most frequent complication of diabetes and one of leading causes of blindness worldwide. Early phases of DR are characterized by retinal pericyte loss mainly related to concurrent inflammatory process. Recently, an important link between P2X7 receptor (P2X7R) and inflammation has been demonstrated indicating this receptor as potential pharmacological target in DR. Here we first carried out an in silico molecular modeling study in order to characterize the allosteric pocket in P2X7R, and identify a suitable P2X7R antagonist through molecular docking. JNJ47965567 was identified as the hit compound in docking calculations, as well as for its absorption, distribution, metabolism and excretion (ADME) profile. As an in vitro model of early diabetic retinopathy, human retinal pericytes were exposed to high glucose (25 mM, 48 h) that caused a significant (p < 0.05) release of IL‐1&bgr; and LDH. The block of P2X7R by JNJ47965567 significantly (p < 0.05) reverted the damage elicited by high glucose, detected as IL‐1&bgr; and LDH release. Overall, our findings suggest that the P2X7R represents an attractive pharmacological target to manage the early phase of diabetic retinopathy, and the compound JNJ47965567 is a good template to discover other P2X7R selective antagonists.

GIANT: a cytoscape plugin for modular networks.

Network analysis provides deep insight into real complex systems. Revealing the link between topological and functional role of network elements can be crucial to understand the mechanisms underlying the system. Here we propose a Cytoscape plugin (GIANT) to perform network clustering and characterize nodes at the light of a modified Guimerà-Amaral cartography. This approach results into a vivid picture of the a topological/functional relationship at both local and global level. The plugin has been already approved and uploaded on the Cytoscape APP store.

Molecular features of interaction between VEGFA and anti-angiogenic drugs used in retinal diseases: a computational approach.

Anti-angiogenic agents are biological drugs used for treatment of retinal neovascular degenerative diseases. In this study, we aimed at in silico analysis of interaction of vascular endothelial growth factor A (VEGFA), the main mediator of angiogenesis, with binding domains of anti-angiogenic agents used for treatment of retinal diseases, such as ranibizumab, bevacizumab and aflibercept. The analysis of anti-VEGF/VEGFA complexes was carried out by means of protein-protein docking and molecular dynamics (MD) coupled to molecular mechanics-Poisson Boltzmann Surface Area (MM-PBSA) calculation. Molecular dynamics simulation was further analyzed by protein contact networks. Rough energetic evaluation with protein-protein docking scores revealed that aflibercept/VEGFA complex was characterized by electrostatic stabilization, whereas ranibizumab and bevacizumab complexes were stabilized by Van der Waals (VdW) energy term; these results were confirmed by MM-PBSA. Comparison of MM-PBSA predicted energy terms with experimental binding parameters reported in literature indicated that the high association rate (Kon) of aflibercept to VEGFA was consistent with high stabilizing electrostatic energy. On the other hand, the relatively low experimental dissociation rate (Koff) of ranibizumab may be attributed to lower conformational fluctuations of the ranibizumab/VEGFA complex, higher number of contacts and hydrogen bonds in comparison to bevacizumab and aflibercept. Thus, the anti-angiogenic agents have been found to be considerably different both in terms of molecular interactions and stabilizing energy. Characterization of such features can improve the design of novel biological drugs potentially useful in clinical practice.

Assessing protein resilience via a complex network approach

In recent years the topological study of proteins is gaining momentum rapidly, and several studies are providing more and more insights on the structural and dynamical properties of proteins by exploiting topological indexes based on Complex Network Theory. To this end the amino acid residues play the role of graph vertices, while non-covalent contacts are the arcs. Topological structure of proteins can be imagined as resulting by folding a thread of pearls (primary sequence of aminoacids) in which amino acid (nodes) relatively distant along the sequence come into contact thanks to the folding process. The result is a configuration sharing some properties with Complex Networks. In this work we derive insights on the resilience of protein contact networks by evaluating the degradation in the size of the giant component with respect to iterated node removal. Specifically, several strategies based on topological indicators (e.g., removing nodes in descending order of clustering coefficient) are exploited, considering the human serum albumin as case study. The analysis of progressive giant component desegregation offered some interesting hints about protein folding principles and suggested some strategies to locate the amino acids most relevant for stability of the studied molecule.

Observer-based techniques for the identification and analysis of avascular tumor growth

Cancer represents one of the most challenging issues for the biomedical research, due its large impact on the public health state. For this reason, many mathematical methods have been proposed to forecast the time evolution of cancer size and invasion. In this paper, we study how to apply the Gompertzs model to describe the growth of an avascular tumor in a realistic setting. To this aim, we introduce mathematical techniques to discretize the model, an important requirement when discrete-time measurements are available. Additionally, we describe observed-based techniques, borrowed from the field of automation theory, as a tool to estimate the model unknown parameters. This identification approach is a promising alternative to traditional statistical methods, and it can be easily extended to other models of cancer growth as well as to the evaluation of not measurable variables, on the basis of the available measurements. We show an application of this method to the analysis of solid tumor growth and parameters estimation in presence of a chemotherapy agent.

Characterization of Protein–Protein Interfaces through a Protein Contact Network Approach

Anthrax toxin comprises three different proteins, jointly acting to exert toxic activity: a non-toxic protective agent (PA), toxic edema factor (EF), and lethal factor (LF). Binding of PA to anthrax receptors promotes oligomerization of PA, binding of EF and LF, and then endocytosis of the complex. Homomeric forms of PA, complexes of PA bound to LF and to the endogenous receptor capillary morphogenesis gene 2 (CMG2) were analyzed. In this work, we characterized protein–protein interfaces (PPIs) and identified key residues at PPIs of complexes, by means of a protein contact network (PCN) approach. Flexibility and global and local topological properties of each PCN were computed. The vulnerability of each PCN was calculated using different node removal strategies, with reference to specific PCN topological descriptors, such as participation coefficient, contact order, and degree. The participation coefficient P, the topological descriptor of the node’s ability to intervene in protein inter-module communication, was the key descriptor of PCN vulnerability of all structures. High P residues were localized both at PPIs and other regions of complexes, so that we argued an allosteric mechanism in protein–protein interactions. The identification of residues, with key role in the stability of PPIs, has a huge potential in the development of new drugs, which would be designed to target not only PPIs but also residues localized in allosteric regions of supramolecular complexes.