54 research outputs found
Protein multi-scale organization through graph partitioning and robustness analysis: Application to the myosin-myosin light chain interaction
Despite the recognized importance of the multi-scale spatio-temporal
organization of proteins, most computational tools can only access a limited
spectrum of time and spatial scales, thereby ignoring the effects on protein
behavior of the intricate coupling between the different scales. Starting from
a physico-chemical atomistic network of interactions that encodes the structure
of the protein, we introduce a methodology based on multi-scale graph
partitioning that can uncover partitions and levels of organization of proteins
that span the whole range of scales, revealing biological features occurring at
different levels of organization and tracking their effect across scales.
Additionally, we introduce a measure of robustness to quantify the relevance of
the partitions through the generation of biochemically-motivated surrogate
random graph models. We apply the method to four distinct conformations of
myosin tail interacting protein, a protein from the molecular motor of the
malaria parasite, and study properties that have been experimentally addressed
such as the closing mechanism, the presence of conserved clusters, and the
identification through computational mutational analysis of key residues for
binding.Comment: 13 pages, 7 Postscript figure
Community Structure Characterization
This entry discusses the problem of describing some communities identified in
a complex network of interest, in a way allowing to interpret them. We suppose
the community structure has already been detected through one of the many
methods proposed in the literature. The question is then to know how to extract
valuable information from this first result, in order to allow human
interpretation. This requires subsequent processing, which we describe in the
rest of this entry
Community evolution in patent networks: technological change and network dynamics
When studying patent data as a way to understand innovation and technological change, the conventional indicators might fall short, and categorizing technologies based on the existing classification systems used by patent authorities could cause inaccuracy and misclassification, as shown in literature. Gao et al. (International Workshop on Complex Networks and their Applications, 2017) have established a method to analyze patent classes of similar technologies as network communities. In this paper, we adopt the stabilized Louvain method for network community detection to improve consistency and stability. Incorporating the overlapping community mapping algorithm, we also develop a new method to identify the central nodes based on the temporal evolution of the network structure and track the changes of communities over time. A case study of Germany’s patent data is used to demonstrate and verify the application of the method and the results. Compared to the non-network metrics and conventional network measures, we offer a heuristic approach with a dynamic view and more stable results
Community evolution in patent networks: technological change and network dynamics
When studying patent data as a way to understand innovation and technological change, the conventional indicators might fall short, and categorizing technologies based on the existing classification systems used by patent authorities could cause inaccuracy and misclassification, as shown in literature. Gao et al. (International Workshop on Complex Networks and their Applications, 2017) have established a method to analyze patent classes of similar technologies as network communities. In this paper, we adopt the stabilized Louvain method for network community detection to improve consistency and stability. Incorporating the overlapping community mapping algorithm, we also develop a new method to identify the central nodes based on the temporal evolution of the network structure and track the changes of communities over time. A case study of Germany’s patent data is used to demonstrate and verify the application of the method and the results. Compared to the non-network metrics and conventional network measures, we offer a heuristic approach with a dynamic view and more stable results
Mechanism of Human Papillomavirus Binding to Human Spermatozoa and Fertilizing Ability of Infected Spermatozoa
Human papillomaviruses (HPVs) are agents of the most common sexually transmitted diseases in females and males. Precise data about the presence, mechanism of infection and clinical significance of HPV in the male reproductive tract and especially in sperm are not available. Here we show that HPV can infect human sperm, it localizes at the equatorial region of sperm head through interaction between the HPV capsid protein L1 and syndecan-1. Sperm transfected with HPV E6/E7 genes and sperm exposed to HPV L1 capsid protein are capable to penetrate the oocyte and transfer the virus into oocytes, in which viral genes are then activated and transcribed. These data show that sperm might function as vectors for HPV transfer into the oocytes, and open new perspectives on the role of HPV infection in males and are particularly intriguing in relation to assisted reproduction techniques
- …