Structural and Dynamic Determinants of Molecular Recognition in Bile Acid-Binding Proteins

Disorders in bile acid transport and metabolism have been related to a number of metabolic disease states, atherosclerosis, type-II diabetes, and cancer. Bile acid-binding proteins (BABPs), a subfamily of intracellular lipid-binding proteins (iLBPs), have a key role in the cellular trafficking and metabolic targeting of bile salts. Within the family of iLBPs, BABPs exhibit unique binding properties including positive binding cooperativity and site-selectivity, which in different tissues and organisms appears to be tailored to the local bile salt pool. Structural and biophysical studies of the past two decades have shed light on the mechanism of bile salt binding at the atomic level, providing us with a mechanistic picture of ligand entry and release, and the communication between the binding sites. In this review, we discuss the emerging view of bile salt recognition in intestinal- and liver-BABPs, with examples from both mammalian and non-mammalian species. The structural and dynamic determinants of the BABP-bile–salt interaction reviewed herein set the basis for the design and development of drug candidates targeting the transcellular traffic of bile salts in enterocytes and hepatocytes.

Machine learning screening of bile acid-binding peptides in a peptide database derived from food proteins

Bioactive peptides (BPs) are protein fragments that exhibit a wide variety of physicochemical properties, such as basic, acidic, hydrophobic, and hydrophilic properties; thus, they have the potential to interact with a variety of biomolecules, whereas neither carbohydrates nor fatty acids have such diverse properties. Therefore, BP is considered to be a new generation of biologically active regulators. Recently, some BPs that have shown positive benefits in humans have been screened from edible proteins. In the present study, a new BP screening method was developed using BIOPEP-UWM and machine learning. Training data were initially obtained using high-throughput techniques, and positive and negative datasets were generated. The predictive model was generated by calculating the explanatory variables of the peptides. To understand both site-specific and global characteristics, amino acid features (for site-specific characteristics) and peptide global features (for global characteristics) were generated. The constructed models were applied to the peptide database generated using BIOPEP-UWM, and bioactivity was predicted to explore candidate bile acid-binding peptides. Using this strategy, seven novel bile acid-binding peptides (VFWM, QRIFW, RVWVQ, LIRYTK, NGDEPL, PTFTRKL, and KISQRYQ) were identified. Our novel screening method can be easily applied to industrial applications using whole edible proteins. The proposed approach would be useful for identifying bile acid-binding peptides, as well as other BPs, as long as a large amount of training data can be obtained.

Validation of Recombinant Chicken Liver Bile Acid Binding Protein as a Tool for Cholic Acid Hosting

Bile acids (BAs) are hydroxylated steroids derived from cholesterol that act at the intestinal level to facilitate the absorption of several nutrients and also play a role as signaling molecules. In the liver of various vertebrates, the trafficking of BAs is mediated by bile acid-binding proteins (L-BABPs). The ability to host hydrophobic or amphipathic molecules makes BABPs suitable for the distribution of a variety of physiological and exogenous substances. Thus, BABPs have been proposed as drug carriers, and more recently, they have also been employed to develop innovative nanotechnology and biotechnology systems. Here, we report an efficient protocol for the production, purification, and crystallization of chicken liver BABP (cL-BABP). By means of target expression as His6-tag cL-BABP, we obtained a large amount of pure and homogeneous proteins through a simple purification procedure relying on affinity chromatography. The recombinant cL-BABP showed a raised propensity to crystallize, allowing us to obtain its structure at high resolution and, in turn, assess the structural conservation of the recombinant cL-BABP with respect to the liver-extracted protein. The results support the use of recombinant cL-BABP for the development of drug carriers, nanotechnologies, and innovative synthetic photoswitch systems.

Machine Learning Screening of Bile Acid-Binding Peptides in APeptide Database Derived From Food Proteins

In the present study, a new bioactive peptide screening method was developed using BIOPEP-UWM and machine learning. Training data was initially obtained using high-throughput techniques, and positive and negative datasets were generated. The predictive model was generated by calculating the explanatory variables of the peptides. To understand both site-specific and global characteristics, amino acid features (for site-specific characteristics), and peptide features (for global characteristics) were generated. The constructed models were applied to the peptide database generated using BIOPEP-UWM, and bioactivity was predicted to explore candidate bile acid-binding peptides. Using this strategy, seven novel bile acid-binding peptides (VFWM, QRIFW, RVWVQ, LIRYTK, NGDEPL, PTFTRKL, and KISQRYQ) were identified. Our novel screening method could be easily applied to industrial applications using whole edible proteins. The proposed approach would be useful for identifying bile acid-binding peptides, as well as other bioactive peptides, as long as a large amount of training data can be obtained.