Machine learning to determine optimal conditions for controlling the size of elastin-based particles

Elastin-like polypeptide-functionalized nanobody for column-free immunoaffinity purification of aflatoxin B1

A column-free immunoaffinity purification (CFIP) technique for sample preparation of aflatoxin B1 (AFB1) was developed using an AFB1-specific nanobody (named G8) and an elastin-like polypeptide (ELP). The reversible phase transition between liquid and solid in response to temperature changes was exhibited by the ELP which was derived from human elastin. The G8 was tagged with ELPs of various lengths (20, 40, 60, and 80 repeat units) at the C-terminus using recursive directional ligation (RDL). Coding sequences were then subcloned into pET30a at the multiple cloning sites. Bioactive recombinant proteins were produced by expressing them as inclusion bodies in Escherichia coli BL21 (DE3), then dissolved and refolded. Analysis by indirect competitive enzyme-linked immunosorbent assay (icELISA) and transition temperature (Tt) measurement confirmed that the refolded G8-ELPs preserved the ability to recognize AFB1 as well as phase transition when the temperature rose above Tt. To establish the optimal conditions for cleaning AFB1, the effects of various parameters on recovery were investigated. The recovery in ELISA tests was 95 ± 3.67% under the optimized CFIP workflow. Furthermore, the CFIP-prepared samples were applied for high-performance liquid chromatography (HPLC) detection. The recovery in the CFIP-HPLC test ranged from 54 ± 1.86% to 98 ± 3.58% for maize, rice, soy sauce, and vegetable oil samples. To the best of our knowledge, this is the first report combining the function of both nanobody and ELP to develop a cleanup technique for small molecules in a complex matrix. The CFIP for the sample pretreatment was easy to use and inexpensive. In contrast to conventional immunosensitivity materials, the reagent utilized in the CFIP was entirely biosynthesized without any chemical coupling reactions. This suggests that the nanobody-ELP may serve as a useful dual-functional reagent for the development of sample cleaning or purification methods.

Structural and physical basis for the elasticity of elastin

Elastin function is to endow vertebrate tissues with elasticity so that they can adapt to local mechanical constraints. The hydrophobicity and insolubility of the mature elastin polymer have hampered studies of its molecular organisation and structure-elasticity relationships. Nevertheless, a growing number of studies from a broad range of disciplines have provided invaluable insights, and several structural models of elastin have been proposed. However, many questions remain regarding how the primary sequence of elastin (and the soluble precursor tropoelastin) governs the molecular structure, its organisation into a polymeric network, and the mechanical properties of the resulting material. The elasticity of elastin is known to be largely entropic in origin, a property that is understood to arise from both its disordered molecular structure and its hydrophobic character. Despite a high degree of hydrophobicity, elastin does not form compact, water-excluding domains and remains highly disordered. However, elastin contains both stable and labile secondary structure elements. Current models of elastin structure and function are drawn from data collected on tropoelastin and on elastin-like peptides (ELPs) but at the tissue level, elasticity is only achieved after polymerisation of the mature elastin. In tissues, the reticulation of tropoelastin chains in water defines the polymer elastin that bears elasticity. Similarly, ELPs require polymerisation to become elastic. There is considerable interest in elastin especially in the biomaterials and cosmetic fields where ELPs are widely used. This review aims to provide an up-to-date survey of/perspective on current knowledge about the interplay between elastin structure, solvation, and entropic elasticity.

Machine learning approaches for biomolecular, biophysical, and biomaterials research

A fluent conversation with a virtual assistant, person-tailored news feeds, and deep-fake images created within seconds-all those things that have been unthinkable for a long time are now a part of our everyday lives. What these examples have in common is that they are realized by different means of machine learning (ML), a technology that has fundamentally changed many aspects of the modern world. The possibility to process enormous amount of data in multi-hierarchical, digital constructs has paved the way not only for creating intelligent systems but also for obtaining surprising new insight into many scientific problems. However, in the different areas of biosciences, which typically rely heavily on the collection of time-consuming experimental data, applying ML methods is a bit more challenging: Here, difficulties can arise from small datasets and the inherent, broad variability, and complexity associated with studying biological objects and phenomena. In this Review, we give an overview of commonly used ML algorithms (which are often referred to as "machines") and learning strategies as well as their applications in different bio-disciplines such as molecular biology, drug development, biophysics, and biomaterials science. We highlight how selected research questions from those fields were successfully translated into machine readable formats, discuss typical problems that can arise in this context, and provide an overview of how to resolve those encountered difficulties.

Adaptive Recombinant Nanoworms from Genetically Encodable Star Amphiphiles

Recombinant nanoworms are promising candidates for materials and biomedical applications ranging from the templated synthesis of nanomaterials to multivalent display of bioactive peptides and targeted delivery of theranostic agents. However, molecular design principles to synthesize these assemblies (which are thermodynamically favorable only in a narrow region of the phase diagram) remain unclear. To advance the identification of design principles for the programmable assembly of proteins into well-defined nanoworms and to broaden their stability regimes, we were inspired by the ability of topologically engineered synthetic macromolecules to acess rare mesophases. To test this design principle in biomacromolecular assemblies, we used post-translational modifications (PTMs) to generate lipidated proteins with precise topological and compositional asymmetry. Using an integrated experimental and computational approach, we show that the material properties (thermoresponse and nanoscale assembly) of these hybrid amphiphiles are modulated by their amphiphilic architecture. Importantly, we demonstrate that the judicious choice of amphiphilic architecture can be used to program the assembly of proteins into adaptive nanoworms, which undergo a morphological transition (sphere-to-nanoworms) in response to temperature stimuli.

Molecular bases for temperature sensitivity in supramolecular assemblies and their applications as thermoresponsive soft materials

Thermoresponsive supramolecular assemblies have been extensively explored in diverse formats, from injectable hydrogels to nanoscale carriers, for a variety of applications including drug delivery, tissue engineering and thermo-controlled catalysis. Understanding the molecular bases behind thermal sensitivity of materials is fundamentally important for the rational design of assemblies with optimal combination of properties and predictable tunability for specific applications. In this review, we summarize the recent advances in this area with a specific focus on the parameters and factors that influence thermoresponsive properties of soft materials. We summarize and analyze the effects of structures and architectures of molecules, hydrophilic and lipophilic balance, concentration, components and external additives upon the thermoresponsiveness of the corresponding molecular assemblies.

Manipulating the solution environment to control the surface roughness of elastin-based polymer coatings

Elastin-like polypeptides (ELP) have been used as a genetically-engineered, biocompatible substitute for elastin. Cell culture coatings prepared using ELP conjugated to low molecular weight polyethyleneimine (PEI) entices cells to form three-dimensional cellular aggregates that mimic their in vivo counterparts. This study seeks to control the deposition of the ELP and ELP-PEI molecules to control the roughness of the final coatings. The two polymers were coated onto three different substrates (glass, polystyrene, tissue-culture polystyrene) and the solution environment was altered by changing the polymer concentration (0.5, 1.0, 1.5 mg/mL) and/or salt concentration (None, 0.2 M phosphate buffered saline) for a total of 36 conditions. Atomic force microscopy (AFM) was used to measure the average roughness (R_a) of the samples and found that ELP coated samples had a higher R_a than their ELP-PEI counterparts. The coatings were tested for stability by performing cell culture media changes every three days for 11 days. AFM showed that the average roughness of the tested samples increased with each media change. To address this, the surfaces were crosslinked using hexamethyl diisocyanate, which minimized the change in surface roughness even when subjected to an intense sonication process. This study provides parameters to achieve elastin-based coatings with controlled roughness that can be used to support stable, long-term in vitro cell culture.