A Statistical Journey through the Topological Determinants of the ß2 Adrenergic Receptor Dynamics.

Activation of G-protein-coupled receptors (GPCRs) is mediated by molecular switches throughout the transmembrane region of the receptor. In this work, we continued along the path of a previous computational study wherein energy transport in the ß2 Adrenergic Receptor (ß2-AR) was examined and allosteric switches were identified in the molecular structure through the reorganization of energy transport networks during activation. In this work, we further investigated the allosteric properties of ß2-AR, using Protein Contact Networks (PCNs). In this paper, we report an extensive statistical analysis of the topological and structural properties of ß2-AR along its molecular dynamics trajectory to identify the activation pattern of this molecular system. The results show a distinct character to the activation that both helps to understand the allosteric switching previously identified and confirms the relevance of the network formalism to uncover relevant functional features of protein molecules.

The Discovery of a Putative Allosteric Site in the SARS-CoV-2 Spike Protein Using an Integrated Structural/Dynamic Approach.

SARS-CoV-2 has caused the largest pandemic of the twenty-first century (COVID-19), threatening the life and economy of all countries in the world. The identification of novel therapies and vaccines that can mitigate or control this global health threat is among the most important challenges facing biomedical sciences. To construct a long-term strategy to fight both SARS-CoV-2 and other possible future threats from coronaviruses, it is critical to understand the molecular mechanisms underlying the virus action. The viral entry and associated infectivity stems from the formation of the SARS-CoV-2 spike protein complex with angiotensin-converting enzyme 2 (ACE2). The detection of putative allosteric sites on the viral spike protein molecule can be used to elucidate the molecular pathways that can be targeted with allosteric drugs to weaken the spike-ACE2 interaction and, thus, reduce viral infectivity. In this study, we present the results of the application of different computational methods aimed at detecting allosteric sites on the SARS-CoV-2 spike protein. The adopted tools consisted of the protein contact networks (PCNs), SEPAS (Affinity by Flexibility), and perturbation response scanning (PRS) based on elastic network modes. All of these methods were applied to the ACE2 complex with both the SARS-CoV2 and SARS-CoV spike proteins. All of the adopted analyses converged toward a specific region (allosteric modulation region [AMR]), present in both complexes and predicted to act as an allosteric site modulating the binding of the spike protein with ACE2. Preliminary results on hepcidin (a molecule with strong structural and sequence with AMR) indicated an inhibitory effect on the binding affinity of the spike protein toward the ACE2 protein.

Modelling and Recognition of Protein Contact Networks by Multiple Kernel Learning and Dissimilarity Representations.

Multiple kernel learning is a paradigm which employs a properly constructed chain of kernel functions able to simultaneously analyse different data or different representations of the same data. In this paper, we propose an hybrid classification system based on a linear combination of multiple kernels defined over multiple dissimilarity spaces. The core of the training procedure is the joint optimisation of kernel weights and representatives selection in the dissimilarity spaces. This equips the system with a two-fold knowledge discovery phase: by analysing the weights, it is possible to check which representations are more suitable for solving the classification problem, whereas the pivotal patterns selected as representatives can give further insights on the modelled system, possibly with the help of field-experts. The proposed classification system is tested on real proteomic data in order to predict proteins' functional role starting from their folded structure: specifically, a set of eight representations are drawn from the graph-based protein folded description. The proposed multiple kernel-based system has also been benchmarked against a clustering-based classification system also able to exploit multiple dissimilarities simultaneously. Computational results show remarkable classification capabilities and the knowledge discovery analysis is in line with current biological knowledge, suggesting the reliability of the proposed system.

GH32 family activity: a topological approach through protein contact networks.

KEY MESSAGE: The application of Protein Contact Networks methodology allowed to highlight a novel response of border region between the two domains to substrate binding. Glycoside hydrolases (GH) are enzymes that mainly hydrolyze the glycosidic bond between two carbohydrates or a carbohydrate and a non-carbohydrate moiety. These enzymes are involved in many fundamental and diverse biological processes in plants. We have focused on the GH32 family, including enzymes very similar in both sequence and structure, each having however clear specificities of substrate preferences and kinetic properties. Structural and topological differences among proteins of the GH32 family have been here identified by means of an emerging approach (Protein Contact network, PCN) based on the formalization of 3D structures as contact networks among amino-acid residues. The PCN approach proved successful in both reconstructing the already known functional domains and in identifying the structural counterpart of the properties of GH32 enzymes, which remain uncertain, like their allosteric character. The main outcome of the study was the discovery of the activation upon binding of the border (cleft) region between the two domains. This reveals the allosteric nature of the enzymatic activity for all the analyzed forms in the GH32 family, a character yet to be highlighted in biochemical studies. Furthermore, we have been able to recognize a topological signature (graph energy) of the different affinity of the enzymes towards small and large substrates.

Head or tail? A molecular dynamics approach to the complex structure of TNF-associated factor TRAF2.

Tumor necrosis factor receptor-associated factor proteins (TRAFs) are trimeric proteins that play a fundamental role in signaling, acting as intermediaries between the tumor necrosis factor (TNF) receptors and the proteins that transmit the downstream signal. The monomeric subunits of all the TRAF family members share a common tridimensional structure: a C-terminal globular domain and a long coiled-coil tail characterizing the N-terminal section. In this study, the dependence of the TRAF2 dynamics on the length of its tail was analyzed in silico. In particular, we used the available crystallographic structure of a C-terminal fragment of TRAF2 (168 out of 501 a.a.), TRAF2-C, and that of a longer construct, addressed as TRAF2-plus, that we have re-constructed using the AlphaFold2 code. The results indicate that the longer N-terminal tail of TRAF2-plus has a strong influence on the dynamics of the globular regions in the protein C-terminal head. In fact, the quaternary interactions among the TRAF2-C subunits change asymmetrically in time, while the movements of TRAF2-plus monomers are rather limited and more ordered than those of the shorter construct. Such findings shed a new light on the dynamics of TRAF subunits and on the protein mechanism in vivo, since TRAF monomer-trimer equilibrium is crucial for several reasons (receptor recognition, membrane binding, hetero-oligomerization).

Disclosing Allostery Through Protein Contact Networks.

Proteins are located in the twilight zone between chemistry and biology, where a peculiar kind of complexity starts. Proteins are the smallest 'devices' showing a sensible adaptation to their environment by the production of appropriate behavior when facing a specific stimulus. This fact qualifies (from the 'effector' side) proteins as nanomachines working as catalysts, motors, or switches. However (from the sensor side), the need to single out the 'specific stimulus' out of thermal noise qualifies proteins as information processing devices. Allostery corresponds to the modification of the configuration (in a broad sense) of the protein molecule in response to a specific stimulus in a non-strictly local way, thereby connecting the sensor and effector sides of the nanomachine. This is why the 'disclosing' of allostery phenomenon is at the very heart of protein function; in this chapter, we will demonstrate how a network-based representation of protein structure in terms of nodes (aminoacid residues) and edges (effective contacts between residues) is the natural language for getting rid of allosteric phenomena and, more in general, of protein structure/function relationships.

Complex network analysis of thermostable mutants of Bacillus subtilis Lipase A.

Three-dimensional structures of proteins that regulate their functions can be modelled using complex network based approaches for understanding the structure-function relationship. The six mutants of the protein Lipase A from Bacillus subtilis, harbouring 2 to 12 mutations, retain their function at higher temperatures with negligible variation in their overall three-dimensional crystallographic structures. This enhanced thermostability of the mutants questions the structure-function paradigm. In this paper, a coarse-grained complex network approach is used to elucidate the structural basis of enhanced thermostability in the mutant proteins, by uncovering small but significant local changes distributed throughout the structure, rendering stability to the mutants at higher temperatures. Community structure analysis of the six mutant protein networks uncovers the specific reorganisations among the nodes/residues that occur, in absence of overall structural variations, which induce enhanced rigidity underlying the increased thermostability. This study offers a novel and significant application of complex network analysis that proposes to be useful in the understanding and designing of thermostable proteins.

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.