PRIGSA2: Improved version of protein repeat identification by graph spectral analysis

Tandemly repeated structural motifs in proteins form highly stable structural folds and provide multiplebinding sites associated with diverse functional roles. The tertiary structure and function of these proteins aredetermined by the type and copy number of the repeating units. Each repeat type exhibits a unique pattern ofintra- and inter-repeat unit interactions that is well-captured by the topological features in the network representation of protein structures. Here we present an improved version of our graph based algorithm, PRIGSA,with structure-based validation and filtering steps incorporated for accurate detection of tandem structuralrepeats. The algorithm integrates available knowledge on repeat families with de novo prediction to detectrepeats in single monomer chains as well as in multimeric protein complexes. Three levels of performanceevaluation are presented: comparison with state-of-the-art algorithms on benchmark dataset of repeat and nonrepeat proteins, accuracy in the detection of members of 13 known repeat families reported in UniProt andexecution on the complete Protein Data Bank to show its ability to identify previously uncharacterizedproteins. A *3-fold increase in the coverage of the members of 13 known families and 3408 noveluncharacterized structural repeat proteins are identified on executing it on PDB. PRIGSA2 is available at http://bioinf.iiit.ac.in/PRIGSA2/.

Analysis of core–periphery organization in protein contact networks reveals groups of structurally and functionally critical residues.

The representation of proteins as networks of interacting amino acids, referred to as protein contact networks (PCN), and their subsequent analyses using graph theoretic tools, can provide novel insights into the key functional roles of specific groups of residues. We have characterized the networks corresponding to the native states of 66 proteins (belonging to different families) in terms of their core–periphery organization. The resulting hierarchical classification of the amino acid constituents of a protein arranges the residues into successive layers – having higher core order – with increasing connection density, ranging from a sparsely linked periphery to a densely intra-connected core (distinct from the earlier concept of protein core defined in terms of the three-dimensional geometry of the native state, which has least solvent accessibility). Our results show that residues in the inner cores are more conserved than those at the periphery. Underlining the functional importance of the network core, we see that the receptor sites for known ligand molecules of most proteins occur in the innermost core. Furthermore, the association of residues with structural pockets and cavities in binding or active sites increases with the core order. From mutation sensitivity analysis, we show that the probability of deleterious or intolerant mutations also increases with the core order. We also show that stabilization centre residues are in the innermost cores, suggesting that the network core is critically important in maintaining the structural stability of the protein. A publicly available Web resource for performing core–periphery analysis of any protein whose native state is known has been made available by us at http://www.imsc.res.in/ ~sitabhra/proteinKcore/index.html.

Effects of adaptive behavior on spreading dynamics of epidemics in structured populations / 军事医学

Human behavior significantly affects the spreading dynamics of infectious diseases in large populations .The study of the interplay between the adaptive behavior and the epidemic dynamics underlies the professional information release and psychological counseling mechanism and is conducive to disease control and social panic elimination .This paper aims at the investigation of the effect of adaptive behaviors on the spreading dynamics of epidemics in structured populations .We analyzed the empirical data on adaptive behavior from several large epidemics after the outbreak of SARS in 2003 and confirmed the induction effect of two main information sources on adaptive behavior , e.g., public available information and local perceived information .Then we investigated the effect of adaptive behavior on epidemic dynamics in a structured population based on two primary models , e.g., health-belief model and network model .The results showed that the individual adaptive behavior had significantly decreased the chance of infection and thus mitigated the epidemics . Human adaptive behavior has a significant effect on spreading dynamics of epidemics .An effective information release mechanism will induce human adaptive behavior and is thus conducive to control of epidemics .

A framework for computational epidemiology / 军事医学

Computational epidemiology is a fast-developing and interdisciplinary research area .Through comprehensive computation-analysis of multi-uncertain factors affecting the epidemic process , this method may add to our knowledge about epidemic patterns and help design reasonable response plans and emergency strategies .This article briefly summarizes the idea and theory of computational epidemiology based on related researches in the recent years , introduces the application of this method in case of smallpox bioterrorism and influenza pandemic , and predicts the development of this area .

Redes: um olhar sistêmico para a epidemiologia de doenças transmissíveis / Networks: epidemiology of transmissible diseases from a systemic perspective

A necessidade do contato físico entre pessoas (direta ou indiretamente) para a transmissão de agentes infecciosos trouxe para a epidemiologia, desde seus primórdios, a necessidade de compreender e descrever o processo de encontro entre pessoas. É neste espaço de encontros que a transmissão flui pela população e emerge, a nível sistêmico, na forma de epidemias. Durante todo o século XX, intenso esforço foi dedicado ao desvendamento dos fatores populacionais que favoreceriam ou não o surgimento de epidemias, sua temporalidade e espacialidade. Este caminho tem como inspiração inicial a física, com modelos de natureza quantitativa, nos quais a população é vista como um todo sem estrutura. Posteriormente, vê-se necessário incluir as estruturas sociais que compõem a população: grupos sociais, redes sociais, coesão social tornam-se conceitos de interesse no estudo quantitativo das epidemias. A integração com a sociologia torna-se óbvia, na medida em que seus conceitos interagem cada vez mais. É um breve olhar sobre esta jornada o objetivo deste ensaio.

The direct or indirect physical contact between human beings as a basic condition for the transmission of infectious diseases stimulated epidemiologists to put forth great efforts to understanding and describing the process of human contacts. It is through these contacts that disease spreads over populations and emerges, at the systemic level, in the form of epidemics. During the 20th century, many researchers dedicated themselves to revealing the population patterns that favor or not the emergence of epidemics and their temporal and spatial dynamics. The first insights came from population models adapted from the physical sciences, in which non-structured populations are considered. Later on it became clear that a more detailed description of social structures was required to correctly describe epidemic dynamics, and concepts such as social group, social network and social cohesion became important terms in the quantitative study of epidemics. The approximation between epidemiology and the social sciences turns obvious as their concepts are interacting more and more. To give a brief overview of this trajectory is the purpose of this article.