| chou_fasman {proteinKit} | R Documentation |
It was developed by Peter Y. Chou and Gerald D. Fasman in the 1970s. The method is based on the observation <br /> that certain amino acids have a propensity to form specific types of secondary structures, such as alpha-helices, <br /> beta-sheets, and turns.<br /> <br /> Here's a brief overview of how the Chou-Fasman method works:<br /> <br /> 1. Amino Acid Propensities: Each amino acid is assigned a set of probability values that reflect its <br /> tendency to be found in alpha-helices, beta-sheets, and turns. These values are derived from statistical <br /> analysis of known protein structures.<br /> 2. Sliding Window Technique: A sliding window of typically 7 to 9 amino acids is moved along the protein <br /> sequence. At each position, the average propensity for each type of secondary structure is calculated <br /> for the amino acids within the window.<br /> 3. Thresholds and Rules: The method uses predefined thresholds and rules to identify regions of the <br /> protein sequence that are likely to form alpha-helices or beta-sheets based on the calculated propensities. <br /> For example, a region with a high average propensity for alpha-helix and meeting certain criteria <br /> might be predicted to form an alpha-helix.<br /> 4. Secondary Structure Prediction: The method predicts the secondary structure by identifying contiguous <br /> regions of the sequence that exceed the thresholds for helix or sheet formation. It also takes into <br /> account the likelihood of turns, which are important for the overall folding of the protein.<br /> 5. Refinement: The initial predictions are often refined using additional rules and considerations, such <br /> as the tendency of certain amino acids to stabilize or destabilize specific structures, and the overall <br /> composition of the protein.<br /> <br /> The Chou-Fasman method was one of the first widely used techniques for predicting protein secondary structure<br /> and played a significant role in the field of structural bioinformatics. However, it has largely been superseded<br /> by more accurate methods, such as those based on machine learning and neural networks, which can take into<br /> account more complex patterns and interactions within protein sequences.<br /> <br /> Despite its limitations, the Chou-Fasman method remains a historical milestone in the understanding of <br /> protein structure and the development of computational methods for predicting it. It also serves as a <br /> foundational concept for those learning about protein structure prediction and bioinformatics.
chou_fasman(prot,
polyaa = FALSE); print(chou_fasman("AAABAAGKKKJLLMMMMMM"));