Proteins are the fundamental building blocks of life, and the study of protein function is fundamental to fields such as biology, biochemistry, and medicine. To understand how a protein functions and how it interacts with other structures, it is vital to figure out its tertiary (three-dimensional) structure.
Traditionally, protein structures have been determined using onerous laboratory methods, including x-ray crystallography and nuclear magnetic resonance (NMR) spectroscopy. While these techniques are undeniably accurate, their cost and lack of scalability has led to significant bottlenecks in fields such as pharmaceutical development. Modern protein engineers need to determine large numbers of protein structures within days; not months or years. For this reason, computational methods of structure prediction are quickly becoming the preferred way to characterize protein structure.
Today, some software algorithms can predict the structure of a protein based on its amino acid sequence with accuracy rivaling these laboratory methods. With recent advances in computational biology, in silico prediction can be a fast, inexpensive, and reliable way to determine protein structures. Of the hundreds of prediction algorithms that have been objectively tested, the undisputed champion is AlphaFold 2, which was released in 2020. You can access AlphaFold 2 predictions online at the EMBL-EBI database, or you can run your own protein prediction using NovaFold AI, the first commercial software licensed to use AlphaFold 2.
Chapter 1 of this guide describes the steps involved in using software to determine tertiary protein structures. Chapter 2 discusses the advantages and challenges of using open source AlphaFold 2, while Chapter 3 shows how NovaFold AI can provide an easier way to use this powerful algorithm.