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Article

A New Look at the Structures of Old Sepsis Actors by Exploratory Data Analysis Tools

1
SMBNOS—Università degli Studi di Bari, 70124 Bari, Italy
2
Ionian Department, Microbiology and Virology Lab Unit, University Hospital of Bari, Università degli Studi di Bari, 70124 Bari, Italy
*
Authors to whom correspondence should be addressed.
Received: 26 September 2019 / Revised: 2 November 2019 / Accepted: 6 November 2019 / Published: 14 November 2019
(This article belongs to the Special Issue Sepsis: Pathophysiology, Diagnosis and Therapy)
Sepsis is a life-threatening condition that accounts for numerous deaths worldwide, usually complications of common community infections (i.e., pneumonia, etc), or infections acquired during the hospital stay. Sepsis and septic shock, its most severe evolution, involve the whole organism, recruiting and producing a lot of molecules, mostly proteins. Proteins are dynamic entities, and a large number of techniques and studies have been devoted to elucidating the relationship between the conformations adopted by proteins and what is their function. Although molecular dynamics has a key role in understanding these relationships, the number of protein structures available in the databases is so high that it is currently possible to build data sets obtained from experimentally determined structures. Techniques for dimensionality reduction and clustering can be applied in exploratory data analysis in order to obtain information on the function of these molecules, and this may be very useful in immunology to better understand the structure-activity relationship of the numerous proteins involved in host defense, moreover in septic patients. The large number of degrees of freedom that characterize the biomolecules requires special techniques which are able to analyze this kind of data sets (with a small number of entries respect to the number of degrees of freedom). In this work we analyzed the ability of two different types of algorithms to provide information on the structures present in three data sets built using the experimental structures of allosteric proteins involved in sepsis. The results obtained by means of a principal component analysis algorithm and those obtained by a random projection algorithm are largely comparable, proving the effectiveness of random projection methods in structural bioinformatics. The usefulness of random projection in exploratory data analysis is discussed, including validation of the obtained clusters. We have chosen these proteins because of their involvement in sepsis and septic shock, aimed to highlight the potentiality of bioinformatics to point out new diagnostic and prognostic tools for the patients. View Full-Text
Keywords: Sepsis; allosteric; albumin; cyclooxygenase; hemoglobin; PCA; random projection; pathophisiology; bioinformatics tools; clinical chemistry Sepsis; allosteric; albumin; cyclooxygenase; hemoglobin; PCA; random projection; pathophisiology; bioinformatics tools; clinical chemistry
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MDPI and ACS Style

Gnoni, A.; De Nitto, E.; Scacco, S.; Santacroce, L.; Palese, L.L. A New Look at the Structures of Old Sepsis Actors by Exploratory Data Analysis Tools. Antibiotics 2019, 8, 225. https://0-doi-org.brum.beds.ac.uk/10.3390/antibiotics8040225

AMA Style

Gnoni A, De Nitto E, Scacco S, Santacroce L, Palese LL. A New Look at the Structures of Old Sepsis Actors by Exploratory Data Analysis Tools. Antibiotics. 2019; 8(4):225. https://0-doi-org.brum.beds.ac.uk/10.3390/antibiotics8040225

Chicago/Turabian Style

Gnoni, Antonio, Emanuele De Nitto, Salvatore Scacco, Luigi Santacroce, and Luigi L. Palese 2019. "A New Look at the Structures of Old Sepsis Actors by Exploratory Data Analysis Tools" Antibiotics 8, no. 4: 225. https://0-doi-org.brum.beds.ac.uk/10.3390/antibiotics8040225

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