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

AAontology: An Ontology of Amino Acid Scales for Interpretable Machine Learning

Amino acid scales are crucial for protein prediction tasks, many of them being curated in the AAindex database. Despite various clustering attempts to organize them and to better understand their relationships, these approaches lack the fine-grained classification necessary for satisfactory interpretability in many protein prediction problems. To address this issue, we developed AAontology-a two-level classification for 586 amino acid scales (mainly from AAindex) together with an in-depth analysis of their relations-using bag-of-word-based classification, clustering, and manual refinement over multiple iterations. AAontology organizes physicochemical scales into 8 categories and 67 subcategories, enhancing the interpretability of scale-based machine learning methods in protein bioinformatics. Thereby it enables researchers to gain a deeper biological insight. We anticipate that AAontology will be a building block to link amino acid properties with protein function and dysfunctions as well as aid informed decision-making in mutation analysis or protein drug design.

Geometric descriptors for beta turns

Beta turns, in which the protein backbone abruptly changes direction over four amino acid residues, are the most common type of protein secondary structure after alpha helices and beta sheets and play key structural and functional roles. Previous work has produced classification systems for turn geometry at multiple levels of precision, but these operate in backbone dihedral-angle (Ramachandran) space, and the absence of a local Euclidean-space coordinate system and structural alignment for turns, or of any systematic Euclidean-space characterization of turn backbone shape, presents challenges for the visualization, comparison and analysis of the wide range of turn conformations and the design of turns and the structures that incorporate them. This work derives a turn-local coordinate system that implicitly aligns turns, together with a set of geometric descriptors that characterize the bulk BB shapes of turns and describe modes of structural variation not explicitly captured by existing systems. These modes are shown to be meaningful by the demonstration of clear relationships between descriptor values and the electrostatic energy of the beta-turn H-bond, the overrepresentations of key side-chain motifs, and the structural contexts of turns. Geometric turn descriptors complement Ramachandran-space classifications, and they can be used to select turn structures for compatibility with particular side-chain interactions or contexts. Potential applications include protein design and other tasks in which an enhanced Euclidean-space characterization of turns may improve understanding or performance. The web-based tools ExploreTurns, MapTurns, and ProfileTurn, available at www.betaturn.com, incorporate turn-local coordinates and turn descriptors and demonstrate their utility.

Peptide-Mediated Nanocarriers for Targeted Drug Delivery: Developments and Strategies

Effective drug delivery is essential for cancer treatment. Drug delivery systems, which can be tailored to targeted transport and integrated tumor therapy, are vital in improving the efficiency of cancer treatment. Peptides play a significant role in various biological and physiological functions and offer high design flexibility, excellent biocompatibility, adjustable morphology, and biodegradability, making them promising candidates for drug delivery. This paper reviews peptide-mediated drug delivery systems, focusing on self-assembled peptides and peptide-drug conjugates. It discusses the mechanisms and structural control of self-assembled peptides, the varieties and roles of peptide-drug conjugates, and strategies to augment peptide stability. The review concludes by addressing challenges and future directions.

Emerging role of carbonyl-carbonyl interactions in the classification of beta turns

Carbonyl-carbonyl interactions in peptides and proteins attracted considerable interest in recent years. Here, we report a survey of carbonyl-carbonyl interactions in cyclic peptides, depsipeptides, peptoids and discuss the relationship between backbone torsion angles and CO∙∙∙CO distances. In general, φ values in the range between -40° and -90° and between 40° and 90° correspond to CO∙∙∙CO distances below 3.22 Å. By extending the analysis of carbonyl-carbonyl interactions in different types of beta turns in proteins, we also highlight the role of direct or reciprocal carbonyl-carbonyl interactions in stabilizing the beta turn conformation for each specific type. We confirmed the new type II beta turn, detected by Dunbrack and coworkers, and named Pa, and detect the presence of a direct carbonyl-carbonyl interaction between the second and third residues of the turn. We also evidenced the existence of another new type II beta turn, named pA (following Dunbrack's notation), which represents the alternative conformation of Pa with opposite φ and ψ values and is characterized by a direct carbonyl-carbonyl interaction between the second and third residues of the turn. Finally, we show that the occurrence of CO∙∙∙CO interactions could be also advocated to explain from a chemical point of view the diversity of turn types.

β-Turn Induction by a Diastereopure Azepane-Derived Quaternary Amino Acid

β-Turns are one of the most common secondary structures found in proteins. In the interest of developing novel β-turn inducers, a diastereopure azepane-derived quaternary amino acid has been incorporated into a library of simplified tetrapeptide models in order to assess the effect of the azepane position and peptide sequence on the stabilization of β-turns. The conformational analysis of these peptides by molecular modeling, NMR spectroscopy, and X-ray crystallography showed that this azepane amino acid is an effective β-turn inducer when incorporated at the i + 1 position. Moreover, the analysis of the supramolecular self-assembly of one of the β-turn-containing peptide models in the solid state reveals that it forms a supramolecular helical arrangement while maintaining the β-turn structure. The results here presented provide the basis for the use of this azepane quaternary amino acid as a strong β-turn inducer in the search for novel peptide-based bioactive molecules, catalysts, and biomaterials.

Progress of the "Molecular Informatics" Section in 2022

This is the first Editorial of the "Molecular Informatics" Section (MIS) of the International Journal of Molecular Sciences (IJMS), which was created towards the end of 2018 (the first article was submitted on 27 September 2018) and has experienced significant growth from 2018 to now [...].

An agnostic analysis of the human AlphaFold2 proteome using local protein conformations

Knowledge of the 3D structure of proteins is a valuable asset for understanding their precise biological mechanisms. However, the cost of production of 3D structures and experimental difficulties limit their obtaining. The proposal of 3D structural models is consequently an appealing alternative. The release of the AlphaFold Deep Learning approach has revolutionized the field. The recent near-complete human proteome proposal makes it possible to analyse large amounts of data and evaluate the results of the approach in greater depth. The 3D human proteome was thus analysed in light of the classic secondary structures, and many less-used protein local conformations (PolyProline II helices, type of γ-turns, of β-turns and of β-bulges, curvature of the helices, and a structural alphabet). Without questioning the global quality of the approach, this analysis highlights certain local conformations, which maybe poorly predicted and they could therefore be better addressed.