![]() SS prediction as the imbalanced classification problem should not be judged by the commonly used Q3/Q8 metrics. Apart from input features calculation, the top models usually need extensive computational resources for training and prediction and are barely possible to run on a regular PC. Currently, most of the top methods use evolutionary-based input features produced by PSSM and HHblits software, although quite recently the embeddings-the new description of protein sequences generated by language models (LM) have appeared that could be leveraged as input features. As the experimental methods are expensive and sometimes impossible, many SS predictors, mainly based on different machine learning methods have been proposed for many years. Note that because of processes such as the post-translational modifications to proteins we still need protein sequencing and I believe that we currently rely too heavily on DNA sequencing.The prediction of protein secondary structures is a crucial and significant step for ab initio tertiary structure prediction which delivers the information about proteins activity and functions. This is because it is now much easier to sequence DNA. Instead, since it has been worked out (mostly) how DNA codes for protein, we usually infer the protein sequence from the DNA sequence. However, it is now relatively rare to directly determine protein sequence! The very first protein sequence (bovine insulin) was determined by Fredrick Sanger in 1951-2 (note that this was more than a decade before the first nucleotide sequence). ![]() There are many different techniques for directly determining protein sequences - this wikipedia article is a decent introduction: There are also methods that have been developed to remove amino acids one at a time.īy combining theses techniques it is possible to directly determine protein sequences. ![]() This is a great question, but actually quite complicated so I'm not going to try to give a complete answer - I have given some useful links below if you wish to learn more.Įach amino acid has unique chemical properties that can be used to tell them apart. ![]()
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