Outer membrane proteins (OMPs) are embedded in the outer membrane of Gram-negative bacteria, mitochondria, and chloroplasts. The cellular location and functional diversity of OMPs makes them an important protein class. Researches on prediction of OMPs by bioinformatics methods can bring helpful methodologies for identifying OMPs from genomic sequences and for the successful prediction of their secondary and tertiary structures. In this paper, three feature classes were calculated from protein sequences: amino acid compositions, dipeptide compositions and weighted amino acid index correlation coefficients. Then, three feature classes were combined and inputted into a support vector machine (SVM) based predictor to identify OMPs from other folding types of proteins. The results of discrimination using several combined features including four amino acid index categories were calculated, and the influence on discrimination accuracy using different correlation coefficients with different orders and weights was discussed. In cross-validated tests and independent tests for identifying OMPs from a dataset of 1087 proteins belonging to all different types of globular and membrane proteins, the method using combined features obtains an overall accuracy of 96.96% and 97.33% respectively. And these results outperform that of other methods in the literature. Using this method, high specificities are shown from the results of identifying OMPs in five bacterial genomes, and over 99% OMPs with known three-dimensional structures in the PDB database are correctly discriminated. These results indicate that the method is a powerful tool for OMPs discrimination in genomes.
Algorithms ; Amino Acids ; chemistry ; Bacterial Outer Membrane Proteins ; chemistry ; genetics ; Computational Biology ; methods ; Discriminant Analysis ; Genome, Bacterial ; genetics ; Gram-Negative Bacteria ; genetics ; Models, Statistical ; Protein Structure, Secondary ; Protein Structure, Tertiary

Induction of cancer cell ferroptosis has been proposed as a potential treatment in several cancer types. Tumor-associated macrophages (TAMs) play a key role in promoting tumor malignant progression and therapy resistance. However, the roles and mechanisms of TAMs in regulating tumor ferroptosis is still unexplored and remains enigmatic. This study shows ferroptosis inducers has shown therapeutic outcomes in cervical cancer in vitro and in vivo. TAMs have been found to suppress cervical cancer cells ferroptosis. Mechanistically, macrophage-derived miRNA-660-5p packaged into exosomes are transported into cancer cells. In cancer cells, miRNA-660-5p attenuates ALOX15 expression to inhibit ferroptosis. Moreover, the upregulation of miRNA-660-5p in macrophages depends on autocrine IL4/IL13-activated STAT6 pathway. Importantly, in clinical cervical cancer cases, ALOX15 is negatively associated with macrophages infiltration, which also raises the possibility that macrophages reduce ALOX15 levels in cervical cancer. Moreover, both univariate and multivariate Cox analyses show ALOX15 expression is independent prognostic factor and positively associated with good prognosis in cervical cancer. Altogether, this study reveals the potential utility of targeting TAMs in ferroptosis-based treatment and ALOX15 as prognosis indicators for cervical cancer.