Dataset Information

Dopamine Self-Polymerization as a Simple and Powerful Tool to Modulate the Viscoelastic Mechanical Properties of Peptide-Based Gels.

ABSTRACT: Dopamine is a small versatile molecule used for various biotechnological and biomedical applications. This neurotransmitter, in addition to its biological role, can undergo oxidative self-polymerization to yield polydopamine, a robust universal coating material. Herein, we harness dopamine self-polymerization to modulate the viscoelastic mechanical properties of peptide-based gels, expanding their ever-growing application potential. By combining rapid peptide assembly with slower dopamine auto-polymerization, a double network gel is formed, where the fibrillar peptide gel network serves as a scaffold for polydopamine deposition, allowing polydopamine to interpenetrate the gel network as well as establishing crosslinks within the matrix. We have shown that triggering the assembly of a lysine-rich peptide gelator in the presence of dopamine can increase the mechanical rigidity of the resultant gel by a factor of 90 in some cases, while retaining the gel's shear thin-recovery behavior. We further investigate how factors such as polymerization time, dopamine concentration and peptide concentration alter the mechanical properties of the resultant gel. The hybrid peptide-dopamine gel systems were characterized using rheological measurements, circular dichroism spectroscopy and transmission electron microscopy. Overall, triggering peptide gelation in the presence of dopamine represents a simple yet powerful approach to modulate the viscoelastic mechanical properties of peptide-based gels.

SUBMITTER: Fichman G

PROVIDER: S-EPMC7961423 | biostudies-literature |

REPOSITORIES: biostudies-literature

ACCESS DATA

Similar Datasets

Project description:BackgroundModern biomedical research is data-driven and relies heavily on the re-use and sharing of data. Biomedical data, however, is subject to strict data protection requirements. Due to the complexity of the data required and the scale of data use, obtaining informed consent is often infeasible. Other methods, such as anonymization or federation, in turn have their own limitations. Secure multi-party computation (SMPC) is a cryptographic technology for distributed calculations, which brings formally provable security and privacy guarantees and can be used to implement a wide-range of analytical approaches. As a relatively new technology, SMPC is still rarely used in real-world biomedical data sharing activities due to several barriers, including its technical complexity and lack of usability.ResultsTo overcome these barriers, we have developed the tool EasySMPC, which is implemented in Java as a cross-platform, stand-alone desktop application provided as open-source software. The tool makes use of the SMPC method Arithmetic Secret Sharing, which allows to securely sum up pre-defined sets of variables among different parties in two rounds of communication (input sharing and output reconstruction) and integrates this method into a graphical user interface. No additional software services need to be set up or configured, as EasySMPC uses the most widespread digital communication channel available: e-mails. No cryptographic keys need to be exchanged between the parties and e-mails are exchanged automatically by the software. To demonstrate the practicability of our solution, we evaluated its performance in a wide range of data sharing scenarios. The results of our evaluation show that our approach is scalable (summing up 10,000 variables between 20 parties takes less than 300 s) and that the number of participants is the essential factor.ConclusionsWe have developed an easy-to-use "no-code solution" for performing secure joint calculations on biomedical data using SMPC protocols, which is suitable for use by scientists without IT expertise and which has no special infrastructure requirements. We believe that innovative approaches to data sharing with SMPC are needed to foster the translation of complex protocols into practice.

Dataset Information

Dopamine Self-Polymerization as a Simple and Powerful Tool to Modulate the Viscoelastic Mechanical Properties of Peptide-Based Gels.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets