Combinations of turns in proteins

A Perspective on the (Rise and Fall of) Protein β-Turns

The β-turn is the third defined secondary structure after the α-helix and the β-sheet. The β-turns were described more than 50 years ago and account for more than 20% of protein residues. Nonetheless, they are often overlooked or even misunderstood. This poor knowledge of these local protein conformations is due to various factors, causes that I discuss here. For example, confusion still exists about the assignment of these local protein structures, their overlaps with other structures, the potential absence of a stabilizing hydrogen bond, the numerous types of β-turns and the software's difficulty in assigning or visualizing them. I also propose some ideas to potentially/partially remedy this and present why β-turns can still be helpful, even in the AlphaFold 2 era.

Improving Protein Gamma-Turn Prediction Using Inception Capsule Networks

Protein gamma-turn prediction is useful in protein function studies and experimental design. Several methods for gamma-turn prediction have been developed, but the results were unsatisfactory with Matthew correlation coefficients (MCC) around 0.2–0.4. Hence, it is worthwhile exploring new methods for the prediction. A cutting-edge deep neural network, named Capsule Network (CapsuleNet), provides a new opportunity for gamma-turn prediction. Even when the number of input samples is relatively small, the capsules from CapsuleNet are effective to extract high-level features for classification tasks. Here, we propose a deep inception capsule network for gamma-turn prediction. Its performance on the gamma-turn benchmark GT320 achieved an MCC of 0.45, which significantly outperformed the previous best method with an MCC of 0.38. This is the first gamma-turn prediction method utilizing deep neural networks. Also, to our knowledge, it is the first published bioinformatics application utilizing capsule network, which will provide a useful example for the community. Executable and source code can be download at http://dslsrv8.cs.missouri.edu/~cf797/MUFoldGammaTurn/download.html.

Unifying the microscopic picture of His-containing turns: from gas phase model peptides to crystallized proteins

The presence in crystallized proteins of a local anchoring between the side chain of a His residue, located in the central position of a γ- or β-turn, and its local main chain environment, is assessed by the comparison of protein structures with relevant isolated model peptides.

Extension of the classical classification of β-turns

The functional properties of a protein primarily depend on its three-dimensional (3D) structure. These properties have classically been assigned, visualized and analysed on the basis of protein secondary structures. The β-turn is the third most important secondary structure after helices and β-strands. β-turns have been classified according to the values of the dihedral angles φ and ψ of the central residue. Conventionally, eight different types of β-turns have been defined, whereas those that cannot be defined are classified as type IV β-turns. This classification remains the most widely used. Nonetheless, the miscellaneous type IV β-turns represent 1/3^rd of β-turn residues. An unsupervised specific clustering approach was designed to search for recurrent new turns in the type IV category. The classical rules of β-turn type assignment were central to the approach. The four most frequently occurring clusters defined the new β-turn types. Unexpectedly, these types, designated IV₁, IV₂, IV₃ and IV₄, represent half of the type IV β-turns and occur more frequently than many of the previously established types. These types show convincing particularities, in terms of both structures and sequences that allow for the classical β-turn classification to be extended for the first time in 25 years.

Molecular Rotors as Simple Models to Study Amide NH−Aromatic Interactions and Their Role in the Folding of Peptide-like Structures

The conformational behavior of designed macrocyclic naphthalenophanes (1a,b and 2a,b) derived from amino acids (Phe and Val) has been used for studying NH...pi interactions. The cycles having 16- and 17-membered rings showed a dynamic process within the NMR time scale, produced by the flipping of the aromatic naphthalene moiety with respect to the macrocyclic main plane. We used the temperature dependence of 1H NMR to obtain activation parameters of the energetic barrier for the process (variable temperature NMR and line shape analysis). The rate of the movement clearly depends on the macrocyclic ring size and, more interestingly, on the nature of the peptidomimetic side chain, the energetic barrier being higher for the compounds bearing aromatic side chains. A largely negative entropic contribution to the free energy of activation was observed, with clear differences due to the side chain nature. Molecular modeling studies suggest that the aromatic rings interact with intramolecularly H-bonded amide NH groups, protecting them from solvation and thus leading to a larger unfavorable activation entropy. This NH...pi interaction has been exploited for the preparation of new systems (1c and meso-1b) with designed conformational preferences, in which aromatic rings tend to fold over amide NH groups. Thus, these minimalistic molecular rotors have served us as simple model systems for the study of NH...pi interactions and their implication in the folding of peptide-like molecules.

ProRegIn: A regularity index for the selection of native-like tertiary structures of proteins

Automated protein tertiary structure prediction from sequence information alone remains an elusive goal to computational prescriptions. Dividing the problem into three stages viz. secondary structure prediction, generation of plausible main chain loop dihedrals and side chain dihedral optimization, considerable progress has been achieved in our laboratory (http://www.scfbio-iitd.res.in/bhageerath/index.jsp) and elsewhere for proteins with less than 100 amino acids. As a part of our on-going efforts in this direction and to facilitate tertiary structure selection/rejection in containing the combinatorial explosion of trial structures for a specified amino acid sequence, we describe here a web-enabled tool ProRegIn (Protein Regularity Index) developed based on the regularity in the Phi, Psi dihedral angles of the amino acids that constitute loop regions. We have analysed the dihedrals in loop regions in a non-redundant dataset of 7351 proteins drawn from the Protein Data Bank and categorized them as helix-like or sheet-like (regular) or irregular. We noticed that the regularity thus defined exceeds 86% for Phi barring glycine and 70% for Psi for all the amino acid side chains including glycine, compelling us to reexamine the conventional view that loops are irregular regions structurally. The regularity index is presented here as a simple tool that finds its application in protein structure analysis as a discriminatory scoring function for rapid screening before the more compute intensive atomic level energy calculations could be undertaken. The tool is made freely accessible over the internet at www.scfbio-iitd.res.in/software/proregin.jsp.

PSSARD: protein sequence-structure analysis relational database

We have implemented a relational database comprising a representative dataset of amino acid sequences and their associated secondary structure. The representative amino acid sequences were selected according to the PDB_SELECT program by choosing proteins corresponding to protein crystal structure data deposited in the protein data bank that share less than 25% overall pair-wise sequence identity. The secondary structure was extracted from the protein data bank website. The information content in the database includes the protein description, PDB code, crystal structure resolution, total number of amino acid residues in the protein chain, amino acid sequence, secondary structure conformation and its summary. The database is freely accessible from the website mentioned below and is useful to query on any of the above fields. The database is particularly useful to quickly retrieve amino acid sequences that are compatible to any super-secondary structure conformation from several proteins simultaneously.

Characterization of the nonregular regions of proteins by a contortion index

Nonstructured regions in proteins that provide the link between two regular structured regions play a significant role in maintaining the scaffold of the protein. Not only do they act as connectors between two regular secondary structural elements of proteins but they also provide the necessary turn or reversal in the polypeptide chain. This incorporates flexibility in the structure. Thus an understanding of the structural aspects of the nonregular regions is necessary to have a better insight into these features. We can assume the nonregular region to be a contorted polypeptide segment tethered by regular secondary structured regions at both ends. To describe the undulating nature of the nonregular regions, we introduce a parameter called the "contortion index." This index describes how tortuously the region is organized. Our analysis shows that the contortion index is related to other physicochemical parameters and can be used to characterize the nonregular regions of proteins.