Prediction of protein-protein interaction types using machine learning approaches

Prediction and analysis of protein-protein interactions (PPIs) is an important problem in life science research because of the fundamental roles of PPIs in many biological processes in living cells. One of the important problems surrounding PPIs is the identification and prediction of different types of complexes, which are characterized by properties such as type and numbers of proteins that interact, stability of the proteins, and also duration of the interactions. This thesis focuses on studying the temporal and stability aspects of the PPIs mostly using structural data. We have addressed the problem of predicting obligate and non-obligate protein complexes, as well as those aspects related to transient versus permanent because of the importance of non-obligate and transient complexes as therapeutic targets for drug discovery and development. We have presented a computational model to predict-protein interaction types using our proposed physicochemical features of desolvation and electrostatic energies and also structural and sequence domain-based features. To achieve a comprehensive comparison and demonstrate the strength of our proposed features to predict PPI types, we have also computed a wide range of previously used properties for prediction including physical features of interface area, chemical features of hydrophobicity and amino acid composition, physicochemical features of solvent-accessible surface area (SASA) and atomic contact vectors (ACV). After extracting the main features of the complexes, a variety of machine learning approaches have been used to predict PPI types. The prediction is performed via several state-of-the-art classification techniques, including linear dimensionality reduction (LDR), support vector machine (SVM), naive Bayes (NB) and k-nearest neighbor (k-NN). Moreover, several feature selection algorithms including gain ratio (GR), information gain (IG), chi-square (Chi2) and minimum redundancy maximum relevance (mRMR) are applied on the available datasets to obtain more discriminative and relevant properties to distinguish between these two types of complexes Our computational results on different datasets confirm that using our proposed physicochemical features of desolvation and electrostatic energies lead to significant improvements on prediction performance. Moreover, using structural and sequence domains of CATH and Pfam and doing biological analysis help us to achieve a better insight on obligate and non-obligate complexes and their interactions

Mass Spectrometric Proteomics

As suggested by the title of this Special Issue, liquid chromatography-mass spectrometry plays a pivotal role in the field of proteomics. Indeed, the research and review articles  published in the Issue clearly evidence how the data produced by this sophisticated methodology may promote impressive advancements in this area. From among the topics discussed in the Issue, a few point to the development of  new procedures for the  optimization of the experimental conditions that should be applied  for the identification of proteins present in complex mixtures.  Other applications  described in these articles show  the huge potential of  these strategies in the protein profiling of organs and  range from  to the study of post-translational tissue modifications to the investigation of the molecular mechanisms behind human disorders and the identification of potential biomarkers of these diseases

Abstracts from the 11th European Conference on Fungal Genetics

Programs and Abstracts from the 11th European Conference on Fungal Genetic

The effect of green extraction method, subcritical, water extraction, on selected biological activities of Chaga mushroom

Inonotus obliquus, or Chaga, is a medicinal mushroom with a long history of folk medicinal use and growing popularity worldwide. It cannot be cultivated so novel, greener extraction methods are needed to satisfy the market and obtain high-quality supplements. In this study, we evaluated the biological activity (antimicrobial and antioxidant) of Mongolian (IM) and Serbian (IS) Chaga extracts obtained by subcritical water extraction. They were also screened for general chemical composition. Both IM and IS, have high phenolic content. HPLC analysis revealed that extracts are especially rich in chlorogenic acid, followed by catechins, p-coumaric and cinnamic acids. ABTS assay confirmed the remarkable scavenging ability of extracts, reaching 98% when a concentration of 2 mg/mL was tested. The antimicrobial potential was examined by microdilution method using Gram-positive and (Enterococcus faecalis and Staphylococcus aureus), Gram-negative bacteria (Escherichia coli and Actinetobacteria baumannii). According to the obtained minimal inhibitory concentration (MIC) for E. faecalis S. aureus (MIC -1.25 mg / mL), E. coli, and A. baumannii (MIC -2.5 mg / mL) both extracts were shown to have the same effect on the tested bacteria. Hereby, we demonstrated the high biological potential of extracts obtained by subcritical water extraction

Towards an integrated understanding of low-dose chemical exposures in the development of human cancer

Both genetic and environmental factors can play a role in an individual’s cancer susceptibility, and lifestyle-related factors have been a primary focus of our prevention efforts for several decades. However, advances in our understanding of cancer causation have resulted in additional concerns being raised about exposures to chronic, low-level exposures to combinations of chemicals. In this project, a large multinational task force comprised of twelve teams was organized to review 11 hallmark phenotypes of cancer and identify priority target sites for disruption in each area. Prototypical chemical disruptors for all targets were then identified, and dose-response information was gathered. Evidence of low-dose effects for each chemical was noted and cross-hallmark effects for all targets and chemicals were documented. In total, 85 examples of chemicals were reviewed for actions on key pathways/mechanisms related to carcinogenesis. Only 15% (13/85) were found to have evidence of a dose-response threshold, whereas 59% (50/85) exerted low-dose effects. No dose-response information was found for the remaining 26% (22/85). This analysis reveals that every day exposures to individual (non-carcinogenic) chemicals that act on a range of mechanisms, pathways, and systems could conspire to instigate environmental carcinogenesis. Additional research on carcinogenesis is needed and the carcinogenic potential of low-dose exposures to mixtures of chemical that act selectively to enable these hallmark phenotypes also needs to be explored. Current models of risk assessment will also need to be revisited as they are not at aligned with our current understanding of cancer biology