Sharif Digital Repository

Regarding the importance of biological systems in daily life and the complexity of these systems, this project is concerned with this problem and especially with applications of chemometrics in proteomics. In this respect, specific importance of peptides has been taken into account in the process of construction of especial and necessary proteins for human body. Due to the risks involved in some experimental investigations, it is quite preferable to utilize modeling approaches using different sets of data. Achieving a number of specific descriptors, a powerful can be established. This model could be quiet comprehensive for the prediction of the electrophoretic mobility of peptides. This... 

Today, almost all the country's needs in the field of industrial enzymes are met through imports. In this regard, much attention has been paid to the industrial production of various enzymes in the country. The production of recombinant proteins through strain engineering is one of the most important ways to meet this demand due to its many benefits. One of the most important microorganisms that is widely used in the enzyme industry is Bacillus subtilis. Due to its thick peptidoglycan wall, this gram-positive bacterium hardly accepts engineered carriers for genetic modification as hosts. The aim of this study was to evaluate the permeability of the wall and cell membrane of Bacillus subtilis... 

The Major Histocompatibility Complex (MHC) Class I molecules play a crucial role in the immune system. These molecules present peptides derived from intracellular proteins on the cell surface to be recognized by T cells. This process is vital for identifying and eliminating cancerous or infected cells. In cancer therapy, particularly in the development of personalized vaccines, accurately selecting peptides that can effectively bind to MHC Class I and stimulate a strong immune response is a significant challenge. This research introduces an innovative neural network model that utilizes a cross-encoder architecture and leverages a pre-trained model to simultaneously process peptide and MHC... 

The human leukocyte antigen protein, commonly known as HLA, has the ability to present small protein fragments called peptides on the surface of cells, whether they originate from within the cell or externally. The binding of these peptides to HLA receptors is a crucial step that triggers an immune response. By estimating the affinity between peptides and HLA class I, we can identify novel antigens that have the potential to be targeted by cancer therapeutic vaccines. Computational methods that predict the binding affinity between peptides and HLA receptors have the potential to expedite the design process of cancer vaccines. Currently, most computational methods exclusively rely on... 

The Major Histocompatibility Complex (MHC) binds to the derived peptides from pathogens to present them to killer T cells on the cell surface. Developing computational methods for accurate, fast, and explainable peptide-MHC binding prediction can facilitate immunotherapies and vaccine development. Various deep learning-based methods rely on feature extraction from the peptide and MHC sequences separately and ignore their valuable binding information. This paper develops a capsule neural network-based method to efficiently capture and model the peptide-MHC complex features to predict the peptide- MHC class I binding. Various evaluations over multiple datasets using popular performance metrics...