An open-source automated PEG precipitation assay to measure the relative solubility of proteins with low material requirement

The solubility of proteins correlates with a variety of their properties, including function, production yield, pharmacokinetics, and formulation at high concentrations. High solubility is therefore a key requirement for the development of protein-based reagents for applications in life sciences, biotechnology, diagnostics, and therapeutics. Accurate solubility measurements, however, remain challenging and resource intensive, which limits their throughput and hence their applicability at the early stages of development pipelines, when long-lists of candidates are typically available in minute amounts. Here, we present an automated method based on the titration of a crowding agent (polyethylene glycol, PEG) to quantitatively assess relative solubility of proteins using about 200 µg of purified material. Our results demonstrate that this method is accurate and economical in material requirement and costs of reagents, which makes it suitable for high-throughput screening. This approach is freely-shared and based on a low cost, open-source liquid-handling robot. We anticipate that this method will facilitate the assessment of the developability of proteins and make it substantially more accessible.

Novel Bio-Based Amphiphilic Ionic Liquids for the Efficient Demulsification of Heavy Crude Oil Emulsions

In the last few decades, there has been an increasing trend for the usage of natural products and their derivatives as green and renewable oil-filed chemicals. Use of these compounds or their derivatives contributes to reducing the use of traditional chemicals, and enhances green chemistry principles. Curcumin (CRC) is one of the most popular natural products and is widely available. The green character, antioxidant action, and low cost of CRC prompt its use in several applications. In the present study, Curcumin was used to synthesize two new amphiphilic ionic liquids (AILs) by reacting with 1,3-propanesultone or bromoacetic acid to produce corresponding sulfonic and carboxylic acids, CRC-PS and CRC-BA, respectively. Following this, the formed CRC-PS and CRC-BA were allowed to react with 12-(2-hydroxyethyl)-15-(4-nonylphenoxy)-3,6,9-trioxa-12-azapentadecane-1,14-diol (HNTA) to form corresponding AILs, GCP-IL and GRB-IL, respectively. The chemical structures, surface tension, interfacial tension, and relative solubility number (RSN) of the synthesized AILs were investigated. The efficiency of GCP-IL and GRB-IL to demulsify water in heavy crude oil (W/O) emulsions was also investigated, where we observed that both GCP-IL and GRB-IL served as high-efficiency demulsifiers and the efficiency increased with a decreased ratio of water in W/O emulsion. Moreover, the data showed an increased efficiency of these AILs with an increased concentration. Among the two AILs, under testing conditions, GCP-IL exhibited a higher efficiency, shorter demulsification time, and cleaner demulsified water.

Effects of Deep Eutectic Solvents on H2SO4-catalyzed Alkylation: Combining Experiment and Molecular Dynamics Simulation

To enhance the catalytic performance of H2SO4-catalyzed alkylation, various catalytic additives have drawn considerable attention. Herein, the effects of deep eutectic solvents additives (DESs) on catalytic performance and interfacial properties of H2SO4 alkylation were systematically investigated using experimental methods and molecular dynamics (MD) simulation. Experimental results indicate that DESs additives with the optimal concentration about 1.0 wt% can efficiently improve C8 selectivity and research octane number (RON) of alkylate. However, DESs additives contribute less to the quality of alkylate at low temperature and to the lifetime of H2SO4. MD results reveal that the phenyl molecules of DESs additives play a major role in enhancing interfacial properties of H2SO4 alkylation, including enlargement of interfacial thickness, promotion of isobutane relative solubility and diffusion to butene, which is probably the main reason for the better quality of alkylate. This work gives a good guideline for the design of novel DESs for H2SO4 alkylation.

A Bayesian semi-parametric model for thermal proteome profiling

The thermal stability of proteins can be altered when they interact with small molecules, other biomolecules or are subject to post-translation modifications. Thus monitoring the thermal stability of proteins under various cellular perturbations can provide insights into protein function, as well as potentially determine drug targets and off-targets. Thermal proteome profiling is a highly multiplexed mass-spectrommetry method for monitoring the melting behaviour of thousands of proteins in a single experiment. In essence, thermal proteome profiling assumes that proteins denature upon heating and hence become insoluble. Thus, by tracking the relative solubility of proteins at sequentially increasing temperatures, one can report on the thermal stability of a protein. Standard thermodynamics predicts a sigmoidal relationship between temperature and relative solubility and this is the basis of current robust statistical procedures. However, current methods do not model deviations from this behaviour and they do not quantify uncertainty in the melting profiles. To overcome these challenges, we propose the application of Bayesian functional data analysis tools which allow complex temperature-solubility behaviours. Our methods have improved sensitivity over the state-of-the art, identify new drug-protein associations and have less restrictive assumptions than current approaches. Our methods allows for comprehensive analysis of proteins that deviate from the predicted sigmoid behaviour and we uncover potentially biphasic phenomena with a series of published datasets.

A Bayesian Semi-Parametric Model for Thermal Proteome Profiling

The thermal stability of proteins can be altered when they interact with small molecules. Thus monitoring the thermal stability of proteins under various cellular perturbations can provide insights into protein function, as well as potentially determine drug targets and off-targets. Thermal proteome profiling is a multiplexed mass-spectrommetry method for monitoring the melting behaviour of thousands of proteins in a single experiment. In essence, thermal proteome profiling assumes that proteins denature upon heating and hence become insoluble. Thus, by tracking the relative solubility of proteins at sequentially increasing temperatures, one can report on protein thermal stability. Standard thermodynamics predicts a sigmoidal relationship between temperature and relative solubility and this is the basis of current statistical procedures. However, current methods do not model deviations from this behaviour and they do not quantify uncertainty in the melting profiles. To overcome these challenges, we propose Bayesian functional data analysis tools which allow complex temperature-solubility behaviours. Our methods have improved sensitivity over the state-of-the art, identify new drug-protein associations and have less restrictive assumptions than current approaches. Our methods allows for comprehensive analysis of proteins that deviate from the predicted sigmoid behaviour and we uncover potentially biphasic phenomena with a series of published datasets.

A Bayesian semi-parametric model for thermal proteome profiling

The thermal stability of proteins can be altered when they interact with small molecules, other biomolecules or are subject to post-translation modifications. Thus monitoring the thermal stability of proteins under various cellular perturbations can provide insights into protein function, as well as potentially determine drug targets and off-targets. Thermal proteome profiling is a highly multiplexed mass-spectrommetry method for monitoring the melting behaviour of thousands of proteins in a single experiment. In essence, thermal proteome profiling assumes that proteins denature upon heating and hence become insoluble. Thus, by tracking the relative solubility of proteins at sequentially increasing temperatures, one can report on the thermal stability of a protein. Standard thermodynamics predicts a sigmoidal relationship between temperature and relative solubility and this is the basis of current robust statistical procedures. However, current methods do not model deviations from this behaviour and they do not quantify uncertainty in the melting profiles. To overcome these challenges, we propose the application of Bayesian functional data analysis tools which allow complex temperature-solubility behaviours. Our methods have improved sensitivity over the state-of-the art, identify new drug-protein associations and have less restrictive assumptions than current approaches. Our methods allows for comprehensive analysis of proteins that deviate from the predicted sigmoid behaviour and we uncover potentially biphasic phenomena with a series of published datasets.