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Protein structures are often represented as seen in crystals as (i) rigid macromolecules (ii) with helices, sheets and coils. However, both definitions are partial because (i) proteins are highly dynamic macromolecules and (ii) the description of protein structures could be more precise. With regard to these two points, we analyzed and quantified the stability of helices by considering α-helices as well as 310- and π-helices. Molecular dynamic (MD) simulations were performed on a large set of 169 representative protein domains. The local protein conformations were followed during each simulation and analyzed. The classical flexibility index (B-factor) was confronted with the MD root mean square flexibility (RMSF) index. Helical regions were classified according to their level of helicity from high to none. For the first time, a precise quantification showed the percentage of rigid and flexible helices that underlie unexpected behaviors. Only 76.4% of the residues associated with α-helices retain the conformation, while this tendency drops to 40.5% for 310-helices and is never observed for π-helices. α-helix residues that do not remain as an α-helix have a higher tendency to assume β-turn conformations than 310- or π-helices. The 310-helices that switch to the α-helix conformation have a higher B-factor and RMSF values than the average 310-helix but are associated with a lower accessibility. Rare π-helices assume a β-turn, bend and coil conformations, but not α- or 310-helices. The view on π-helices drastically changes with the new DSSP (Dictionary of Secondary Structure of Proteins) assignment approach, leading to behavior similar to 310-helices, thus underlining the importance of secondary structure assignment methods.

V H H, i.e., VH domains of camelid single-chain antibodies, are very promising therapeutic agents due to their significant physicochemical advantages compared to classical mammalian antibodies. The number of experimentally solved V H H structures has significantly improved recently, which is of great help, because it offers the ability to directly work on 3D structures to humanise or improve them. Unfortunately, most V H Hs do not have 3D structures. Thus, it is essential to find alternative ways to get structural information. The methods of structure prediction from the primary amino acid sequence appear essential to bypass this limitation. This review presents the most extensive overview of structure prediction methods applied for the 3D modelling of a given V H H sequence (a total of 21). Besides the historical overview, it aims at showing how model software programs have been shaping the structural predictions of V H Hs. A brief explanation of each methodology is supplied, and pertinent examples of their usage are provided. Finally, we present a structure prediction case study of a recently solved V H H structure. According to some recent studies and the present analysis, AlphaFold 2 and NanoNet appear to be the best tools to predict a structural model of V H H from its sequence.

About half of the globular proteins are composed of regular secondary structures, α-helices, and β-sheets, while the rest are constituted of irregular secondary structures, such as turns or coil conformations. Other regular secondary structures are often ignored, despite their importance in biological processes. Among such structures, the polyproline II helix (PPII) has interesting behaviours. PPIIs are not usually associated with conventional stabilizing interactions, and recent studies have observed that PPIIs are more frequent than anticipated. In addition, it is suggested that they may have an important functional role, particularly in protein-protein or protein-nucleic acid interactions and recognition. Residues associated with PPII conformations represent nearly 5% of the total residues, but the lack of PPII assignment approaches prevents their systematic analysis. This short review will present current knowledge and recent research in PPII area. In a first step, the different methodologies able to assign PPII are presented. In the second step, recent studies that have shown new perspectives in PPII analysis in terms of structure and function are underlined with three cases: (1) PPII in protein structures. For instance, the first crystal structure of an oligoproline adopting an all-trans polyproline II (PPII) helix had been presented; (2) the involvement of PPII in different diseases and drug designs; and (3) an interesting extension of PPII study in the protein dynamics. For instance, PPIIs are often linked to disorder region analysis and the precise analysis of a potential PPII helix in hypogonadism shows unanticipated PPII formations in the patient mutation, while it is not observed in the wild-type form of KISSR1 protein.

Protein structures are valuable tools to understand protein function. Nonetheless, proteins are often considered as rigid macromolecules while their structures exhibit specific flexibility, which is essential to complete their functions. Analyses of protein structures and dynamics are often performed with a simplified three-state description, i.e., the classical secondary structures. More precise and complete description of protein backbone conformation can be obtained using libraries of small protein fragments that are able to approximate every part of protein structures. These libraries, called structural alphabets (SAs), have been widely used in structure analysis field, from definition of ligand binding sites to superimposition of protein structures. SAs are also well suited to analyze the dynamics of protein structures. Here, we review innovative approaches that investigate protein flexibility based on SAs description. Coupled to various sources of experimental data (e.g., B-factor) and computational methodology (e.g., Molecular Dynamic simulation), SAs turn out to be powerful tools to analyze protein dynamics, e.g., to examine allosteric mechanisms in large set of structures in complexes, to identify order/disorder transition. SAs were also shown to be quite efficient to predict protein flexibility from amino-acid sequence. Finally, in this review, we exemplify the interest of SAs for studying flexibility with different cases of proteins implicated in pathologies and diseases.