Comprehensive Analysis of the Distinct Nano Environments Characteristics Containing the Different Secondary Structure Elements: α-Helices, β-Sheets, and Turns

Background: This work is the third part of our initiative to fully describe the internal protein nano environments (NEs) for the three existing types of secondary structure elements (SSE). In our previous work, the NE of both the α-helix and the β-sheet were analyzed. The focus of this and previous research is improving our understanding of the SSEs: α-helices, β-sheets and turns, within protein structures. We found that the structural similarities between turns and α-helices are very high and turns may be considered as the “incomplete” initiation of α-helices. The knowledge we were able to compile during this work might be essential for predicting a tertiary structure of proteins, with higher precision and subsequently being in a more favourable position with regard to designing drugs for certain protein structure/function related diseases. Considering that the formation of secondary structure elements is a crucial step in the general folding of protein 3D structure, an important contribution of this work is augmenting the efficiency of estimating if modelled structures, for example, are predicting SSEs in full agreement to necessary/required/ sufficient values of respective SSEs nanoenvironment descriptors. During exactly that modelling phase, preceding the more precise final 3D protein structure construction, our Dictionary of Most Relevant Nanoenvironment Descriptors is able to answer some fundamental questions regarding SSE correctness with regard to nanoenvironment characteristics found to be generally required. Expanding this vision, our current work is part of an effort by our laboratory to create a “dictionary of internal protein nanoenvironments” - DIPN. The ten most studied internal protein nanoenvironments are described in DIPN in physicochemical and structural terms, and this knowledge is now available to be used to aid drug design - probably the most important area of application for the results we are presenting here. Methods: In the current paper, STING´s database of physical-chemical and structural descriptors was used to gather the necessary information to characterize the NE of loops, or, as they are often called, turns. Given that approximately 20% of all protein-type residues form turns, research in this field is essential, and analysis of the obtained results will further contribute to our comprehension of how proteins fold. In addition, the results in this paper will contribute to the better training of algorithms that evaluate the degree of overall protein structure quality and, consequently, structure prediction. This is currently very important given we are witnessing a revolution in algorithms employing artificial intelligence for protein structure prediction. Powered by the STING’s database (wide-ranging protein structure information source), statistical testing was used to retrieve a set of descriptors that fully delineate the NE of turns. By collecting such data, it is then possible to list the variances with respect to the NE of α-helices and β-sheets and, by doing so, establish the most relevant NE descriptors (MRND) for each of the three SSEs. Results: The results show that the α-helical and β-sheet Nes, as well as the amino acid residue composition, all behave in a similar fashion as a “key and lock” system. In other words, it is necessary for a set of specific descriptors to assume respective specific values (within the bounds of a very definite value region) to construct the specific secondary structure element NE at a certain protein location. Conclusion: Consequently, there is a set of descriptors that act together and are required to satisfy specific conditions for secondary structure element occurrences. The very same requirement, we found, occurs in the case of turns.