Comprehensive Analysis of the Distinct Nano Environments Characteristics Containing the Different Secondary Structure Elements: Α-Helices, Β-Sheets, and Turns
Authors: Mazoni Ivan , Salim José Augusto , de Moraes Fábio Rogério , Correa Jorge Luiz , Borro Luiz César , Neshich Goran 
Published: August 2024

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.