Histone modifiers at the crossroads of oncolytic and oncogenic viruses.

Cancer is a disease caused by loss of regulatory processes that control the cell cycle, resulting in increased proliferation. The loss of control can deregulate both tumor suppressors and oncogenes. Apart from cell intrinsic gene mutations and environmental factors, infection by cancer-causing viruses also induces changes that lead to malignant transformation. This can be caused by both expression of oncogenic viral proteins and also by changes in cellular genes and proteins that affect the epigenome. Thus, these epigenetic modifiers are good therapeutic targets, and several epigenetic inhibitors are approved for the treatment of different cancers. In addition to small molecule drugs, biological therapies, such as antibodies and viral therapies, are also increasingly being used to treat cancer. An HSV-1-derived oncolytic virus is currently approved by the US FDA and the European Medicines Agency. Similarly, an adenovirus-based therapeutic is approved for use in China for some cancer types. Because viruses can affect cellular epigenetics, the interaction of epigenome-targeting drugs with oncogenic and oncolytic viruses is a highly significant area of investigation. Here, we will review the current knowledge about the impact of using epigenetic drugs in tumors positive for oncogenic viruses or as therapeutic combinations with oncolytic viruses.

Local Similarity Matrix for Cysteine Disulfide Connectivity Prediction from Protein Sequences.

Accurately predicting three dimensional protein structures from sequences would present us with targets for drugs via molecular dynamics that would treat cancer, viral infections, and neurological diseases. These treatments would have a far reaching impact to our economy, quality of life, and society. The goal of this research was to build a data mining framework to predict cysteine connectivity in proteins from the sequence and oxidation state of cysteines. Accurately predicting the cysteine bonding configuration improves the TM-Score, a quantitative measurement of protein structure prediction accuracy. We provided state of the art Qp and Qc on the PDBCYS and IVD-54 Datasets. Furthermore, we have produced a Local Similarity Matrix that compares favorably to the default PSSMs generated from PSI-Blast in a statistically significant way. Our Qp for SP39, PDBCYS, and IVD-54 were 90.6, 80.6, and 68.5, respectively.

Residue Adjacency Matrix Based Feature Engineering for Predicting Cysteine Reactivity in Proteins.

Free radicals that form from reactive species of nitrogen and oxygen can react dangerously with cellular components and are involved with the pathogenesis of diabetes, cancer, Parkinson's, and heart disease. Cysteine amino acids, due to their reactive nature, are prone to oxidation by these free radicals. Determining which cysteines oxidize within proteins is crucial to our understanding of these chronic diseases. Wet lab techniques, like differential alkylation, to determine which cysteines oxidize are often expensive and time-consuming. We utilize machine learning as a fast and inexpensive approach to identifying cysteines with oxidative capabilities. We created the original features RAMmod and RAMseq for use in classification. We also incorporated well-known features such as PROPKA, SASA, PSS, and PSSM. Our algorithm requires only the protein sequence to operate; however, we do use template matching by MODELLER to acquire 3D coordinates for additional feature extraction. There was a mean improvement of RAM over N6C by 22.04% MCC. It was statistically significant with a p-value of 0.015. RAM provided a significant increase over PSSM with a p-value of 0.040 and an average 70.09% improvement MCC.