Tarsila G. Castro

The vast field of cosmetics is mainly explored through experimental methods, while computational tools find broader application in structuralbiology. The world of formulations remains relatively untouched or nondisclosed due to commercial interests. This work aims to better understand the applications of in silico methods in cosmetics research, at the molecular level, and how the recent work is advancing the state of the art. The integration of AI, molecular docking, and molecular dynamics simulations in cosmetics research enables the efficient discovery and optimization of ingredients, leading to innovative and effective cosmetic products. Importantly, the establishment of predictive computational protocols suitable for different cosmetic applications optimizes experimental processes and simplify testing, being economically important for the industry.

This review provides a fresh overview of non-canonical amino acids and their applications in the design of peptidomimetics. Non-canonical amino acids appear widely distributed in nature and are known to enhance the stability of specific secondary structures and/or biological function. Contrary to the ubiquitous DNA-encoded amino acids, the structure and function of these residues are not fully understood. Here, results from experimental and molecular modelling approaches are gathered to classify several classes of non-canonical amino acids according to their ability to induce specific secondary structures yielding different biological functions and improved stability. Regarding side-chain modifications, symmetrical and asymmetrical α,α-dialkyl glycines, Cα to Cα cyclized amino acids, proline analogues, β-substituted amino acids, and α,β-dehydro amino acids are some of the non-canonical representatives addressed. Backbone modifications were also examined, especially those that result in retro-inverso peptidomimetics and depsipeptides. All this knowledge has an important application in the field of peptidomimetics, which is in continuous progress and promises to deliver new biologically active molecules and new materials in the near future.

Self-assembled peptide-based hydrogels are archetypical nanostructured materials with a plethora of foreseeable applications in nanomedicine and as biomaterials. N-protected di- and tri-peptides are effective minimalist (molecular) hydrogelators. Independent variation of the capping group, peptide sequence and side chain modifications allows a wide chemical space to be explored and hydrogel properties to be tuned. In this work, we report the synthesis of a focused library of dehydrodipeptides N-protected with 1-naphthoyl and 2-naphthylacetyl groups. The 2-naphthylacetyl group was extensively reported for preparation of peptide-based self-assembled hydrogels, whereas the 1-naphthaloyl group was largely overlooked, owing presumably to the lack of a methylene linker between the naphthalene aromatic ring and the peptide backbone. Interestingly, dehydrodipeptides N-capped with the 1-naphthyl moiety afford stronger gels, at lower concentrations, than the 2-naphthylacetyl-capped dehydrodipeptides. Fluorescence and circular dichroism spectroscopy showed that the self-assembly of the dehydrodipeptides is driven by intermolecular aromatic π–π stacking interactions. Molecular dynamics simulations revealed that the 1-naphthoyl group allows higher order aromatic π–π stacking of the peptide molecules than the 2-naphthylacetyl group, together with hydrogen bonding of the peptide scaffold. The nanostructure of the gel networks was studied by TEM and STEM microscopy and was found to correlate well with the elasticity of the gels. This study contributes to understanding the interplay between peptide and capping group structure on the formation of self-assembled low-molecular-weight peptide hydrogels. Moreover, the results presented here add the 1-naphthoyl group to the palette of capping groups available for the preparation of efficacious low-molecular-weight peptide-based hydrogels.

The search for Golgi α-mannosidase II (GMII) potent and specific inhibitors has been a focus of many studies for the past three decades since this enzyme is a key target for cancer treatment. α-Mannosidases, such as those from Drosophila melanogaster or Jack bean, have been used as functional models of the human Golgi α-mannosidase II (hGMII) because mammalian mannosidases are difficult to purify and characterize experimentally. Meanwhile, computational studies have been seen as privileged tools able to explore assertive solutions to specific enzymes, providing molecular details of these macromolecules, their protonation states and their interactions. Thus, modelling techniques can successfully predict hGMII 3D structure with high confidence, speeding up the development of new hits. In this study, Drosophila melanogaster Golgi mannosidase II (dGMII) and a novel human model, developed in silico and equilibrated via molecular dynamics simulations, were both opposed for docking. Our findings highlight that the design of novel inhibitors should be carried out considering the human model’s characteristics and the enzyme operating pH. A reliable model is evidenced, showing a good correlation between K_i/IC₅₀ experimental data and theoretical ΔG_binding estimations in GMII, opening the possibility of optimizing the rational drug design of new derivatives. Communicated by Ramaswamy H. Sarma

The phosphatidylinositol-3 kinase (PI3K) pathway is one of the most frequently activated pathogenic signalling cascades in a wide variety of cancers. In the last 15 years, there has been an increase in the search for selective inhibitors of the four class I isoforms of PI3K, as they demonstrate better specificity and reduced toxicity in comparison to existing inhibitors. A ligand-based and target-based rational drug design strategy was employed to build a virtual library of 105 new compounds. Through this strategy, the four isoforms were compared regarding their activity pocket availability, amino acid sequences, and prone interactions. Additionally, a known active scaffold was used as a molecular base to design new derivatives. The virtual screening of the resultant library toward the four isoforms points to the obtention of 19 selective inhibitors for the PI3Kα and PI3Kγ targets. Three selective ligands, one for α-isoform and two for γ-isoform, present a ∆ (∆G_binding) equal or greater than 1.5 Kcal/mol and were identified as the most promising candidates. A principal component analysis was used to establish correlations between the affinity data and some of the physicochemical and structural properties of the ligands. The binding modes and interactions established by the selective ligands in the active centre of the α and γ isoforms of PI3K were also investigated. After modelling studies, a synthetic approach to generate selective ligands was developed and applied in synthesising a set of derivatives that were obtained in good to excellent yield.