Novel dynamic residue network analysis approaches to study homodimeric allosteric modulation in SARS-CoV-2 Mpro and in its evolutionary mutations

The rational search for allosteric modulators and the allosteric mechanisms of these modulators in the presence of evolutionary mutations, including resistant ones, is a relatively unexplored field. Here, we established novel in silico approaches and applied to SARS-CoV-2 main protease (Mpro). First, we identified six potential allosteric modulators (SANC00302, SANC00303, SANC00467, SANC00468, SANC00469, SANC00630) from the South African Natural Compounds Database (SANCDB) bound to the allosteric pocket of Mpro that we determined in our previous work. We also checked the stability of these compounds against Mpro of laboratory strain HCoV-OC43 and identified differences due to residue changes between the two proteins. Next, we focused on understanding the allosteric effects of these modulators on each protomer of the reference Mpro protein, while incorporating the symmetry problem in the functional homodimer. In general, asymmetric behavior of multimeric proteins is not commonly considered in computational analysis. We introduced a novel combinatorial approach and dynamic residue network (DRN) analysis algorithms to examine patterns of change and conservation of critical nodes, according to five independent criteria of network centrality (betweenness centrality (BC), closeness centrality (CC), degree centrality (DC), eigencentrality (EC) and katz centrality (KC)). The relationships and effectiveness of each metric in characterizing allosteric behavior were also investigated. We observed highly conserved network hubs for each averaged DRN metric on the basis of their existence in both protomers in the absence and presence of all ligands, and we called them persistent hubs (residues 17, 111, 112 and 128 for averaged BC; 6, 7, 113, 114, 115, 124, 125, 126, 127 and 128 for averaged CC; 36, 91, 146, 150 and 206 for averaged DC; 7, 115 and 125 for EC; 36, 125 and 146 for KC). We also detected ligand specific signal changes some of which were in or around functional residues (i.e. chameleon switch PHE140). Using EC persistent hubs and ligand introduced hubs we identified a residue communication path between allosteric binding site and catalytic site. Finally, we examined the effects of the mutations on the behavior of the protein in the presence of selected potential allosteric modulators and investigated the ligand stability. The hit compounds showed various levels of stability in the presence of SARS-CoV-2 Mpro mutations, being most stable in A173V, N274D and R279C, and least stable in R60C, N151D V157I, C160S and A255V. SANC00468 was the most stable compound in the 43 mutant protein systems. We further used DRN metric analysis to define cold spots as being those regions that are least impacted, or not impacted, by mutations. One crucial outcome of this study was to show that EC centrality hubs form an allosteric communication path between the allosteric ligand binding site to the active site going through the interface residues of Domain I and II; and this path was either weakened or lost in the presence of some of the mutations. Overall, the results of this study revealed crucial aspects that need to be considered in drug discovery in COVID-19 specifically and in general for rational computational drug design purposes.

An Axiom System for Feedback Centralities

In recent years, the axiomatic approach to centrality measures has attracted attention in the literature. However, most papers propose a collection of axioms dedicated to one or two considered centrality measures. In result, it is hard to capture the differences and similarities between various measures. In this paper, we propose an axiom system for four classic feedback centralities: Eigenvector centrality, Katz centrality, Katz prestige and PageRank. We prove that each of these four centrality measures can be uniquely characterized with a subset of our axioms. Our system is the first one in the literature that considers all four feedback centralities.

SAKE

Katz centrality is a fundamental concept to measure the influence of a vertex in a social network. However, existing approaches to calculating Katz centrality in a large-scale network are unpractical and computationally expensive. In this article, we propose a novel method to estimate Katz centrality based on graph sampling techniques, which object to achieve comparable estimation accuracy of the state-of-the-arts with much lower computational complexity. Specifically, we develop a Horvitz–Thompson estimate for Katz centrality by using a multi-round sampling approach and deriving an unbiased mean value estimator. We further propose SAKE , a <underline>S</underline> ampling-based <underline>A</underline> lgorithm for fast <underline>K</underline> atz centrality <underline>E</underline> stimation. We prove that the estimator calculated by SAKE is probabilistically guaranteed to be within an additive error from the exact value. Extensive evaluation experiments based on four real-world networks show that the proposed algorithm can estimate Katz centralities for partial vertices with low sampling rate, low computation time, and it works well in identifying high influence vertices in social networks.

Trilateral Spearman Katz Centrality Based Least Angle Regression for Influential Node Tracing in Social Network

With the epidemic growth of online social networks (OSNs), a large scale research on information dissemination in OSNs has been made an appearance in contemporary years. One of the essential researches is influence maximization (IM). Most research adopts community structure, greedy stage, and centrality measures, to identify the influence node set. However, the time consumed in analyzing the influence node set for edge server placement, service migration and service recommendation is ignored in terms of propagation delay. Considering the above analysis, we concentrate on the issue of time-sensitive influence maximization and maximize the targeted influence spread. To solve the problem, we propose a method called, Trilateral Spearman Katz Centrality-based Least Angle Regression (TSKC-LAR) for influential node tracing in social network is proposed. Besides, two algorithms are used in our work to find the influential node in social network with maximum influence spread and minimal time, namely Trilateral Statistical Node Extraction algorithm and Katz Centrality Least Angle Influence Node Tracing algorithm, respectively. Extensive experiments on The Telecom dataset demonstrate the efficiency and influence performance of the proposed algorithms on evaluation metrics, namely, sensitivity, specificity, accuracy, time and influence spread

Student Participation in Online Content-Related Discussion and Its Relation to Students’ Background Knowledge

This paper presents two novel network methods developed for education research. These methods were used to investigate online discussions and the structure of students’ background knowledge in a blended university course for pre-service teachers (n = 11). Consequently, these measures were used for correlation analysis. The social network analysis of the online discussions was based on network roles defined using triadic motifs instead of more commonly used centrality measures. The network analysis of the background knowledge is based on the Katz centrality measure and Jaccard similarity. The results reveal that both measures have characteristic features that are typical for each student. These features, however, are not correlated when student participation is controlled for. The results show that the structure and extension of a student’s background knowledge does not explain their activity and role in online discussions. The limitations and implications of the developed methods and results are discussed.

Multi-Documents Extractive Text Summarization using Node Centrality

The advancement of technologies produce vast amount of data over the internet. The massive amount of information flooded in the webpages become more difficult to extract the meaningful insights. Social media websites are playing major role in publishing news events on the similar topic with different contents. Extracting the hidden information from the multiple webpages are tedious job for researchers and industrialists. This paper mainly focuses on gathering information from multiple webpages and to produce summary from those contents under similar topic. Multi-document extractive summarization has been developed using the graph based text summarization method. Proposed method builds a graph between the multi-documents using the Katz centrality of nodes. The performance of proposed GeSUM (Graph based Extractive Summarization) is evaluated with the ROUGE metrics.