Creation of stable water-free antibody based protein liquids

Antibodies represent highly specific and high binding affinity biomolecular recognition elements for diagnostic assays, biosensors, and therapeutics, but are sensitive to denaturation and degradation. Consequently, the combination of existing in a hydrated state with a large and complex biomolecular structure results in loss of antibody-antigen binding, limited shelf-life, and decreased sensor response over time and under non-optimal conditions. The development and use of water-free protein liquids has led to stabilization of labile biomolecules, solvents for biotransformation reactions, and formation of new bio-composites with incompatible materials. Here, we exploit the polycationic nature of modified antibodies and their ability to form ion pairs for the conversion of primary Immunoglobulin G antibodies into stable protein liquids that retained more than 60% binding activity after repeated heating up to 125 °C, and demonstrate compatibility with thermoplastics.

Physics of Biomolecular Recognition and Conformational Dynamics

Biomolecular recognition usually leads to the formation of binding complexes, often accompanied by large-scale conformational changes. This process is fundamental to biological functions at the molecular and cellular levels. Uncovering the physical mechanisms of biomolecular recognition and quantifying the key biomolecular interactions are vital to understand these functions. The recently developed energy landscape theory has been successful in quantifying recognition processes and revealing the underlying mechanisms. Recent studies have shown that in addition to affinity, specificity is also crucial for biomolecular recognition. The proposed physical concept of intrinsic specificity based on the underlying energy landscape theory provides a practical way to quantify the specificity. Optimization of affinity and specificity can be adopted as a principle to guide the evolution and design of molecular recognition. This approach can also be used in practice for drug discovery using multidimensional screening to identify lead compounds. The energy landscape topography of molecular recognition is important for revealing the underlying flexible binding or binding-folding mechanisms. In this review, we first introduce the energy landscape theory for molecular recognition and then address four critical issues related to biomolecular recognition and conformational dynamics: (1) specificity quantification of molecular recognition; (2) evolution and design in molecular recognition; (3) flexible molecular recognition; (4) chromosome structural dynamics. The results described here and the discussions of the insights gained from the energy landscape topography can provide valuable guidance for further computational and experimental investigations of biomolecular recognition and conformational dynamics.

Ligand binding: evaluating the contribution of the water molecules network using the Fragment Molecular Orbital method

Water molecules play a crucial role in protein–ligand binding, and many tools exist that aim to predict the position and relative energies of these important, but challenging participants of biomolecular recognition. The available tools are, in general, capable of predicting the location of water molecules. However, predicting the effects of their displacement is still very challenging. In this work, a linear-scaling quantum mechanics-based approach was used to assess water network energetics and the changes in network stability upon ligand structural modifications. This approach offers a valuable way to improve understanding of SAR data and help guide compound design.

Biofunctionalisation of Gallium Arsenide with Neutravidin

<div>We report a study presenting a physicochemical surface characterisation of the GaAs surface along the functionalisation with a high-affinity bioconjugation pair widely explored in the life</div><div>sciences: biotin and neutravidin. Combined X-ray photoelectron spectroscopy (XPS), wettability measurements and spectroscopic ellipsometry were used for a reliable characterisation of the surface functionalisation process. The results suggest that a film with a thickness lower than 10nm was formed, with a neutravidin to biotin ratio of 1:25 on the GaAs surface. Reduction of non-specific binding of the protein to the surface was achieved by optimising the protein buffer and rinsing steps. This study shows the feasibility of using GaAs as a platform for specific biomolecular recognition, paving the way to a new generation of optoelectronic biosensors.</div>

Biofunctionalisation of Gallium Arsenide with Neutravidin

<div>We report a study presenting a physicochemical surface characterisation of the GaAs surface along the functionalisation with a high-affinity bioconjugation pair widely explored in the life</div><div>sciences: biotin and neutravidin. Combined X-ray photoelectron spectroscopy (XPS), wettability measurements and spectroscopic ellipsometry were used for a reliable characterisation of the surface functionalisation process. The results suggest that a film with a thickness lower than 10nm was formed, with a neutravidin to biotin ratio of 1:25 on the GaAs surface. Reduction of non-specific binding of the protein to the surface was achieved by optimising the protein buffer and rinsing steps. This study shows the feasibility of using GaAs as a platform for specific biomolecular recognition, paving the way to a new generation of optoelectronic biosensors.</div>

Cell penetration efficiency analysis of different atomic force microscopy nanoneedles into living cells

Over the last decade, nanoneedle-based systems have demonstrated to be extremely useful in cell biology. They can be used as nanotools for drug delivery, biosensing or biomolecular recognition inside cells; or they can be employed to select and sort in parallel a large number of living cells. When using these nanoprobes, the most important requirement is to minimize the cell damage, reducing the forces and indentation lengths needed to penetrate the cell membrane. This is normally achieved by reducing the diameter of the nanoneedles. However, several studies have shown that nanoneedles with a flat tip display lower penetration forces and indentation lengths. In this work, we have tested different nanoneedle shapes and diameters to reduce the force and the indentation length needed to penetrate the cell membrane, demonstrating that ultra-thin and sharp nanoprobes can further reduce them, consequently minimizing the cell damage.

DeepDTAF: a deep learning method to predict protein–ligand binding affinity

Biomolecular recognition between ligand and protein plays an essential role in drug discovery and development. However, it is extremely time and resource consuming to determine the protein–ligand binding affinity by experiments. At present, many computational methods have been proposed to predict binding affinity, most of which usually require protein 3D structures that are not often available. Therefore, new methods that can fully take advantage of sequence-level features are greatly needed to predict protein–ligand binding affinity and accelerate the drug discovery process. We developed a novel deep learning approach, named DeepDTAF, to predict the protein–ligand binding affinity. DeepDTAF was constructed by integrating local and global contextual features. More specifically, the protein-binding pocket, which possesses some special properties for directly binding the ligand, was firstly used as the local input feature for protein–ligand binding affinity prediction. Furthermore, dilated convolution was used to capture multiscale long-range interactions. We compared DeepDTAF with the recent state-of-art methods and analyzed the effectiveness of different parts of our model, the significant accuracy improvement showed that DeepDTAF was a reliable tool for affinity prediction. The resource codes and data are available at https: //github.com/KailiWang1/DeepDTAF.