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Proteins are large macromolecules that play critical roles in many cellular activities in living organisms. These include catalyzing metabolic reactions, mediating signal transduction, DNA replication, responding to stimuli, and transporting molecules, to name a few. Proteins perform their functions by interacting with other proteins and molecules. As a result, determining the nature of such interactions is critically important in many areas of biology and medicine. The primary structure of a protein refers to its specific sequence of amino acids, while the tertiary structure refers to its unique 3D shape, and the quaternary structure refers to the interaction of multiple protein subunits to form a larger, more complex structure. While the number of experimentally determined tertiary and quaternary structures are limited, databases of protein sequences continue to grow at an unprecedented rate, providing a wealth of information for training and improving sequence-based models. Recent developments in the sequence-based model using machine learning and deep learning has shown significant progress toward solving protein-related problems. Specifically, attention-based transformer models, a recent breakthrough in Natural Language Processing (NLP), has shown that large models trained on unlabeled data are able to learn powerful representations of protein sequences and can lead to significant improvements in understanding protein folding, function, and interactions, as well as in drug discovery and protein engineering. The research in this thesis has pursued two objectives using sequence-based modeling. The first is to use deep learning techniques based on NLP to address an important problem in cellular immune system studies, namely, predicting Major Histocompatibility Complex (MHC)-Peptide binding. The second is to improve the performance of the Cluspro docking server, a well-known protein-protein docking tool, in three ways: (i) integrating Cluspro with AlphaFold2, a well-known accurate protein structure predictor, for enhanced protein model docking, (ii) predicting distance maps to improve docking accuracy, and (iii) using regression techniques to rank protein clusters for better results.

Lung cancer is the third most common type of cancer and the leading cause of cancer death, in both men and women, and prognosis for lung carcinoma remains poor due to late diagnosis. While lung squamous cell carcinoma (LUSC) makes up 20-30% of all lung cancer cases, identification of genetic signatures and successful targeted therapies remain limited. An ongoing effort is being made to create an in vitro system for modeling the early stages of lung squamous carcinoma and premalignancy, which will ultimately serve as a model for drug discovery. A previous effort performed whole exome and targeted DNA sequencing to reveal the somatic mutations in endobronchial biopsies that harbored lung squamous premalignant histology. EP300 was identified as a candidate gene which may act as a driver for carcinogenesis, but remains understudied when compared to prominent oncogenic driver genes such TP53, NOTCH1, or NFE2L2 in LUSC. The p300 protein is a histone acetyltransferase that regulates gene expression by means of chromatin remodeling and has been implicated in various diseases, including cancer. My objective as part of my thesis was to first generate stable EP300 knockout (KO) clones from the NL20 bronchial epithelial cell line utilizing the CRISPR/Cas gene editing system. Using the NL20 clones and EP300 KO clones in the HBEC-3KT cell line generated in a previous effort, I then validated the knockouts at the DNA, RNA, and protein levels. Literature review was also conducted to identify possible cellular pathways that EP300 participates in and validate its role in those pathways by observing changes in downstream protein targets. Finally, I generated RNA sequencing data from the functionally validated clones to identify differentially expressed genes and cellular pathways perturbed by EP300 knockout. Through these efforts, I developed sets of gene signatures unique to each cell line and found that EP300 is associated with bronchial carcinogenesis progression and likely functions as an oncogene in LUSC.