Computational analysis of transition temperatures (Tts) of proteins fused to elastin-like polypeptide (ELP): deep fake evaluation of proteins, linkers, and trailers features

The phase transition temperature (Tt) prediction of the Elastin-like polypeptides (ELPs) is not trivial because it is related to complex sets of variables such as composition, sequence length, hydrophobic characterization, hydrophilic characterization, the sequence order in the fused proteins, linkers and trailer constructs. In this paper, two unique quantitative models are presented for the prediction of the Tt of a family of ELPs that could be fused to different proteins, linkers, and trailers. The lack of need to use multiple software, peptide information, such as PDB file, as well as knowing the second and third structures of proteins are the advantages of this model besides its high accuracy and speed. One of our models could predict the Tt values of the fused ELPs by entering the protein, linker, and trailer features with R2=99%. Also, another model is able to predict the Tt value by entering the fused protein feature with R2=96%. For more reliability, our method is enriched by Artificial Intelligence (AI) to generate similar proteins. In this regard, Generative Adversarial Network (GAN) is our AI method to create fake proteins and similar values. The experimental results show that our strategy for prediction of Tt is reliable in large data. Author SummaryThe application of Elastin-like polypeptides (ELPs) as a protein tag is now developed in a variety of biotechnology aspects especially in proteins purification and drug delivery. ELPs application as a protein tag is owed to retain the phase transition behavior when ELPs fused to other proteins, linkers and tags. ELPs undergo the phase transition behavior by changing from soluble phase to insoluble phase above its inverse transition temperature (Tt) within a short time span. This biophysical behavior is usually reversible at the temperature below the Tt. There are few reports for evaluation of the Tt of ELPs types by using the dissimilar equations and algorithms. Our current predictions are the most accurate calculations presented so far by using the protein, linker and trailer effects and the results were evaluated in accordance with the available experimental data. Furthermore, our results also show that our strategy for prediction of Tt is reliable in large data.