TemPred: A Novel Protein Template Search Engine to Improve Protein Structure Prediction

Among new protein structure predictors, the recently developed AlphaFold predictor relies on contact map in line with contact map potential based threading model that basically relies on fold recognition. In parallel, sequence similarity based homology model relies on homologue recognition. Both of these methods rely on sequence-structure or sequence-sequence similarity with protein with known structure in absence of which, as argued in the development of AlphaFold, the structure prediction becomes quite challenging. However, the term, known structure depends on the similarity method adopted to identify it, for example, through sequence match yielding homologue or sequence-structure match yielding a fold. Also, quite often, AlphaFold structures are found to be not acceptable by the structure evaluating gold standard parameters. In this context, this work utilized the concept of ordered local physicochemical property, ProtPCV by Pal et al (2020) providing a new similarity criteria to identify the template protein with known structure. Finally a template search engine, TemPred was developed using the ProtPCV similarity criteria. It was intriguing to find that quite often templates generated by TemPred were better than that produced by the conventional search engines. It pointed out the need of combined approach to get better structural model for a protein.

Automated summary

The AlphaFold predictor and the homology model both rely on proteins with known structure, but the structures predicted by AlphaFold are often not acceptable. This study used a new similarity criteria to identify template proteins and developed a template search engine which produced better results than traditional search engines. It highlights the need for a combined approach to obtain better protein structure models.