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Prof. Dr. Paola Posocco

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Department of Engineering and Architecture, University of Trieste, Via A. Valerio 6/1, 34127 Trieste, Italy
Interests: self-assembling; amphiphiles; surfaces and interfaces; nanostructures; molecular recognition; adapative and stimuli-responsive materials; many-scale molecular models

Special Issue Information

Dear Colleagues,

Self-assembling is a fascinating phenomenon observed in nearly all life processes. It relies on precise non-covalent interactions that act on extraordinarily diverse scales of time, size, and complexity while preserving an exceptional level of specificity. This has motivated researchers from several areas of nanobiotechnology to mimic and engineer assemblies. Most often, their design takes its inspiration or is informed by the features of naturally occurring assemblies, and this work has promoted the growth of predictive computational models enabling the design of novel supramolecular materials.

Achieving full control over self-assembled complexes with target architectures and specific functionalities is still one of the open challenges in the field. Success requires the strict integration of carefully designed experiments with improved or novel computational approaches able to treat the complexity and multiple length scale features of the forces driving the self-assembling process.

The focus of this Special Issue is to cover the recent progress made in modeling smart materials via self-assembly, with a focus on amphiphiles, stimuli-responsive copolymers, functionalized inorganic nanoparticles and their assemblies, supramolecular polymers, as well as self-assembling peptides. It will offer an overview of the state of the art, challenges and opportunities to help move from observation to predictive design.

Prof. Dr. Paola Posocco
Guest Editor

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Keywords

Molecular self-assembling
Amphiphiles
Hairy inorganic nanoparticles
Self-assembling peptides
Stimuli-responsive copolymers
Supramolecular polymers
Molecular machines
Computational molecular methods

Published Papers (2 papers)

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Research

18 pages, 4141 KiB

Open AccessArticle

Computational Evolution of Beta-2-Microglobulin Binding Peptides for Nanopatterned Surface Sensors

by Abimbola Feyisara Adedeji Olulana, Miguel A. Soler, Martina Lotteri, Hendrik Vondracek, Loredana Casalis, Daniela Marasco, Matteo Castronovo and Sara Fortuna

Int. J. Mol. Sci. 2021, 22(2), 812; https://0-doi-org.brum.beds.ac.uk/10.3390/ijms22020812 - 15 Jan 2021

Cited by 8 | Viewed by 2663

Abstract

The bottom-up design of smart nanodevices largely depends on the accuracy by which each of the inherent nanometric components can be functionally designed with predictive methods. Here, we present a rationally designed, self-assembled nanochip capable of capturing a target protein by means of pre-selected binding sites. The sensing elements comprise computationally evolved peptides, designed to target an arbitrarily selected binding site on the surface of beta-2-Microglobulin (β2m), a globular protein that lacks well-defined pockets. The nanopatterned surface was generated by an atomic force microscopy (AFM)-based, tip force-driven nanolithography technique termed nanografting to construct laterally confined self-assembled nanopatches of single stranded (ss)DNA. These were subsequently associated with an ssDNA–peptide conjugate by means of DNA-directed immobilization, therefore allowing control of the peptide’s spatial orientation. We characterized the sensitivity of such peptide-containing systems against β2m in solution by means of AFM-based differential topographic imaging and surface plasmon resonance (SPR) spectroscopy. Our results show that the confined peptides are capable of specifically capturing β2m from the surface–liquid interface with micromolar affinity, hence providing a viable proof-of-concept for our approach to peptide design. Full article

(This article belongs to the Special Issue Smart Materials from Molecular Self-Assembling at Chemical Resolution: What Can Be Learned?)

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14 pages, 3068 KiB

Open AccessArticle

Sequence-Dependent Nanofiber Structures of Phenylalanine and Isoleucine Tripeptides

by Qinsi Xiong, Ziye Liu and Wei Han

Int. J. Mol. Sci. 2020, 21(22), 8431; https://0-doi-org.brum.beds.ac.uk/10.3390/ijms21228431 - 10 Nov 2020

Cited by 4 | Viewed by 2378

Abstract

The molecular design of short peptides to achieve a tailor-made functional architecture has attracted attention during the past decade but remains challenging as a result of insufficient understanding of the relationship between peptide sequence and assembled supramolecular structures. We report a hybrid-resolution model to computationally explore the sequence–structure relationship of self-assembly for tripeptides containing only phenylalanine and isoleucine. We found that all these tripeptides have a tendency to assemble into nanofibers composed of laterally associated filaments. Molecular arrangements within the assemblies are diverse and vary depending on the sequences. This structural diversity originates from (1) distinct conformations of peptide building blocks that lead to different surface geometries of the filaments and (2) unique sidechain arrangements at the filament interfaces for each sequence. Many conformations are available for tripeptides in solution, but only an extended β-strand and another resembling a right-handed turn are observed in assemblies. It was found that the sequence dependence of these conformations and the packing of resulting filaments are determined by multiple competing noncovalent forces, with hydrophobic interactions involving Phe being particularly important. The sequence pattern for each type of assembly conformation and packing has been identified. These results highlight the importance of the interplay between conformation, molecular packing, and sequences for determining detailed nanostructures of peptides and provide a detailed insight to support a more precise design of peptide-based nanomaterials. Full article

(This article belongs to the Special Issue Smart Materials from Molecular Self-Assembling at Chemical Resolution: What Can Be Learned?)

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