AlphaFold and its Role in Predicting Structure of Cancer-Related Mutant Proteins

Cancer is one of the most complex and challenging diseases of the ۲۱st century, rooted in complex genetic and molecular changes. Recent advances in molecular biology and genomics have shown that a major part of cancer progression and resistance to treatment is due to mutations that alter the structure and function of proteins. In this regard, a detailed understanding of the three-dimensional structure of cancer-related proteins plays a key role in diagnosis, prognosis prediction, and design of targeted therapies. However, traditional experimental methods such as X-ray crystallography and cryo-electron microscopy are time-consuming, expensive, and in many cases limited. The emergence of AlphaFold AI as an advanced deep learning model for predicting the three-dimensional structure of proteins is a milestone in structural biology. AlphaFold has been able to predict the structure of thousands of human proteins, including proteins involved in cancer, with an accuracy close to laboratory methods. This development has opened up new opportunities to analyze the effect of cancer mutations on protein structure, identify new drug targets, and develop personalized therapies. The aim of this systematic review is to investigate the role of AlphaFold in predicting the structure of cancer-related mutant proteins and its applications in cancer diagnosis, prognosis, and treatment. In this study, articles published in reputable scientific databases were reviewed and the available evidence on the accuracy, limitations, and capabilities of AlphaFold in the field of molecular oncology was analyzed. A special focus was placed on key proteins such as p۵۳, BRCA۱/۲, EGFR, and KRAS, whose mutations play a decisive role in the occurrence and progression of various types of cancer. The results of this systematic review show that the use of AlphaFold not only enables structural analysis of mutant proteins, but also can accelerate the process of targeted drug design and identification of novel drug binding sites. Also, the combination of AlphaFold predicted data with clinical and genomic data paves the way for the development of precision and personalized medicine in cancer treatment. Despite these advantages, challenges such as the prediction of protein complex structures and molecular dynamics remain, which are discussed in this article.

کلیدواژه ها:

AlphaFold-Based ، Cancer ، Prediction ، Treatment