Using amino acids co-occurrence matrices and explainability model to investigate patterns in dengue virus proteins

• Published in: BMC bioinformatics Vol. 23; no. 1; p. 80
• Main Authors: Souza, Leonardo R, Colonna, Juan G, Comodaro, Joseana M, Naveca, Felipe G
• Format: Journal Article
• Language: English
• Published: England BioMed Central Ltd 19-02-2022; BioMed Central; BMC
• Subjects: Amino Acid Sequence; Amino acids; Amino Acids - metabolism; Analysis; Control; Dengue - genetics; Dengue virus; Dengue Virus - genetics; Dengue viruses; Genetic markers; Genome, Viral; Hemorrhagic fever; Humans; Identification and classification; Machine learning; Methods; Prevention; Protein amino acid co-occurrence; Risk factors; Viral Nonstructural Proteins; Brazil; Dengue virus; Protein amino acid co-occurrence; Machine learning
• ISSN: 1471-2105; 1471-2105
• DOI: 10.1186/s12859-022-04597-y

Dengue is a common vector-borne disease in tropical countries caused by the Dengue virus. This virus may trigger a disease with several symptoms like fever, headache, nausea, vomiting, and muscle pain. Indeed, dengue illness may also present more severe and life-threatening conditions like hemorrhagic fever and dengue shock syndrome. The causes that lead hosts to develop severe infections are multifactorial and not fully understood. However, it is hypothesized that different viral genome signatures may partially contribute to the disease outcome. Therefore, it is plausible to suggest that deeper DENV genetic information analysis may bring new clues about genetic markers linked to severe illness.
Pattern recognition in very long protein sequences is a challenge. To overcome this difficulty, we map protein chains onto matrix data structures that reveal patterns and allow us to classify dengue proteins associated with severe illness outcomes in human hosts. Our analysis uses co-occurrence of amino acids to build the matrices and Random Forests to classify them. We then interpret the classification model using SHAP Values to identify which amino acid co-occurrences increase the likelihood of severe outcomes.
We trained ten binary classifiers, one for each dengue virus protein sequence. We assessed the classifier performance through five metrics: PR-AUC, ROC-AUC, F1-score, Precision and Recall. The highest score on all metrics corresponds to the protein E with a 95% confidence interval. We also compared the means of the classification metrics using the Tukey HSD statistical test. In four of five metrics, protein E was statistically different from proteins M, NS1, NS2A, NS2B, NS3, NS4A, NS4B and NS5, showing that E markers has a greater chance to be associated with severe dengue. Furthermore, the amino acid co-occurrence matrix highlight pairs of amino acids within Domain 1 of E protein that may be associated with the classification result.
We show the co-occurrence patterns of amino acids present in the protein sequences that most correlate with severe dengue. This evidence, used by the classification model and verified by statistical tests, mainly associates the E protein with the severe outcome of dengue in human hosts. In addition, we present information suggesting that patterns associated with such severe cases can be found mostly in Domain 1, inside protein E. Altogether, our results may aid in developing new treatments and being the target of debate on new theories regarding the infection caused by dengue in human hosts.