TS-AMIR: A Topology String Alignment Method for Intensive Rapid Protein Structure Comparison

In structural biology, similarity analysis of protein structure is a crucial step in studying the relationship between proteins. Despite the considerable number of techniques that have been explored within the past two decades, the development of new alternative methods is still an active research area due to the need for high performance tools. Results: In this paper, we present TS-AMIR, a Topology String Alignment Method for Intensive Rapid comparison of protein structures. The proposed method works in two stages: In the first stage, the method generates a topology string based on the geometric details of secondary structure elements, and then, utilizes an n-gram modelling technique over entropy concept to capture similarities in these strings. This initial correspondence map between secondary structure elements is submitted to the second stage in order to obtain the alignment at the residue level. Applying the Kabsch method, a heuristic step-by-step algorithm is adopted in the second stage to align the residues, resulting in an optimal rotation matrix and minimized RMSD. The performance of the method was assessed in different information retrieval tests and the results were compared with those of CE and TM-align, representing two geometrical tools, and YAKUSA, 3D-BLAST and SARST as three representatives of linear encoding schemes. It is shown that the method obtains a high running speed similar to that of the linear encoding schemes. In addition, the method runs about 800 and 7200 times faster than TM-align and CE respectively, while maintaining a competitive accuracy with TM-align and CE. Conclusions: The experimental results demonstrate that linear encoding techniques are capable of reaching the same high degree of accuracy as that achieved by geometrical methods, while generally running hundreds of times faster than conventional programs.

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Human-pathogen Protein Interactions Illuminated With Bioinformatics

Previously blogging on the how protein interactions play significant roles in various aspects of the structural and functional organization of the cell, and their elucidation sheds light on the molecular mechanisms of biological processes. Researchers Find that a Small Molecule Can Activate an Important Cancer Suppressor Gene. Here is another study where researchers have provided the first global analysis of human proteins interacting with viral proteins and proteins in other pathogens. The scientists examined experimental data for 190 different pathogens that comprise 10,477 interactions between human and pathogen proteins. This approach provides a highly detailed network map of human proteins interfacing with proteins in different pathogens. The network of interactions reveals possible key intervention points for the future development of therapeutics against infectious diseases.

"Infectious diseases result in millions of deaths each year. Although much effort has been directed towards the study of how infection by a pathogen causes disease in humans. We have to leverage the opportunity to compare protein interactions between human and pathogen proteins, from 190 different pathogens to provide important insights into the strategies used by pathogens to infect human cells."

"Previous studies have suggested that protein interaction networks have topologies that are resilient to attacks on random nodes but are susceptible to targeted attacks, for example on hubs. Our results provide a striking example of how pathogens may have evolved the ability to exploit the structure of interactions between human proteins in order to promote infection. This global study also suggests that many viruses share similar strategies to control the human cell cycle, regulate programmed cell death, and transport viral genetic material across the nuclear membrane in the human cell." said one of the investigators

The researchers paid particular attention to two networks of human proteins -- proteins that interact with at least two viral pathogens and proteins that interact with at least two bacterial pathogens. Gene Ontology (GO) terms computed for both sets of proteins provided key information on the functions of the different proteins. Some of the striking findings of the study included a clear demonstration that pathogens preferentially interact with two classes of human proteins referred to as hubs and bottlenecks. Hubs are popular proteins that interact with many other proteins in the human protein interaction network.

Bottlenecks are proteins that lie on many of the shortest paths in the network. Pathogens appear to maximize their likelihood of success by targeting these high-impact, influential proteins during infection. In many cases, human proteins that mediate pathogen effects are proteins that are known to be involved in cancer pathways, for example, the transcription factor STAT1 or the tumor suppressor protein TP53. This finding suggests interesting parallels between pathogen infection and cancer and opens up future areas for research.

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