Structure-Based Identification and Design of Angiotensin Converting Enzyme-Inhibitory Peptides from Whey Proteins.

Besides their nutritional value, whey protein (WP) peptides are food components retaining important pharmacological properties for controlling hypertension. We herein report how the use of complementary experimental and theoretical investigations allowed the identification of novel angiotensin converting enzyme inhibitory (ACEI) peptides obtained from a WP hydrolysate and addressed the rational design of even shorter sequences based on molecular pruning. Thus, after bromelain digestion followed by a 5 kDa cutoff ultrafiltration, WP hydrolysate with ACEI activity was fractioned by RP-HPLC; 2 out of 23 collected fractions retained ACEI activity and were analyzed by liquid chromatography-tandem mass spectrometry (LC-MS/MS). In the face of 128 identified peptides, molecular docking was carried out to prioritize peptides and to rationally guide the design of novel shorter and bioactive sequences. Therefore, 11 peptides, consisting of 3-6 amino acids and with molecular weights in the range from 399 to 674 Da, were rationally designed and then purchased to determine the IC50 value. This approach allowed the identification of two novel peptides: MHI and IAEK with IC50 ACEI values equal to 11.59 and 25.08 µM, respectively. Interestingly, we also confirmed the well-known ACEI IPAVF with an IC50 equal to 9.09 µM. In light of these results, this integrated approach could pave the way for high-throughput screening and identification of new peptides in dairy products. In addition, the herein proposed ACEI peptides could be exploited for novel applications both for food production and pharmaceuticals.

FAD/NADH Dependent Oxidoreductases: From Different Amino Acid Sequences to Similar Protein Shapes for Playing an Ancient Function.

Flavoprotein oxidoreductases are members of a large protein family of specialized dehydrogenases, which include type II NADH dehydrogenase, pyridine nucleotide-disulphide oxidoreductases, ferredoxin-NAD+ reductases, NADH oxidases, and NADH peroxidases, playing a crucial role in the metabolism of several prokaryotes and eukaryotes. Although several studies have been performed on single members or protein subgroups of flavoprotein oxidoreductases, a comprehensive analysis on structure-function relationships among the different members and subgroups of this great dehydrogenase family is still missing. Here, we present a structural comparative analysis showing that the investigated flavoprotein oxidoreductases have a highly similar overall structure, although the investigated dehydrogenases are quite different in functional annotations and global amino acid composition. The different functional annotation is ascribed to their participation in species-specific metabolic pathways based on the same biochemical reaction, i.e., the oxidation of specific cofactors, like NADH and FADH2. Notably, the performed comparative analysis sheds light on conserved sequence features that reflect very similar oxidation mechanisms, conserved among flavoprotein oxidoreductases belonging to phylogenetically distant species, as the bacterial type II NADH dehydrogenases and the mammalian apoptosis-inducing factor protein, until now retained as unique protein entities in Bacteria/Fungi or Animals, respectively. Furthermore, the presented computational analyses will allow consideration of FAD/NADH oxidoreductases as a possible target of new small molecules to be used as modulators of mitochondrial respiration for patients affected by rare diseases or cancer showing mitochondrial dysfunction, or antibiotics for treating bacterial/fungal/protista infections.

Accelerating Drug Discovery by Early Protein Drug Target Prediction Based on a Multi-Fingerprint Similarity Search.

In this continuing work, we have updated our recently proposed Multi-fingerprint Similarity Search algorithm (MuSSel) by enabling the generation of dominant ionized species at a physiological pH and the exploration of a larger data domain, which included more than half a million high-quality small molecules extracted from the latest release of ChEMBL (version 24.1, at the time of writing). Provided with a high biological assay confidence score, these selected compounds explored up to 2822 protein drug targets. To improve the data accuracy, samples marked as prodrugs or with equivocal biological annotations were not considered. Notably, MuSSel performances were overall improved by using an object-relational database management system based on PostgreSQL. In order to challenge the real effectiveness of MuSSel in predicting relevant therapeutic drug targets, we analyzed a pool of 36 external bioactive compounds published in the Journal of Medicinal Chemistry from October to December 2018. This study demonstrates that the use of highly curated chemical and biological experimental data on one side, and a powerful multi-fingerprint search algorithm on the other, can be of the utmost importance in addressing the fate of newly conceived small molecules, by strongly reducing the attrition of early phases of drug discovery programs.

A New Approach for Drug Target and Bioactivity Prediction: The Multifingerprint Similarity Search Algorithm (MuSSeL).

We present MuSSeL, a multifingerprint similarity search algorithm, able to predict putative drug targets for a given query small molecule as well as to return a quantitative assessment of its bioactivity in terms of Ki or IC50 values. Predictions are automatically made exploiting a large collection of high quality experimental bioactivity data available from ChEMBL (version 22.1) combining, in a consensus-like approach, predictions resulting from a similarity search performed using 13 different fingerprint definitions. Importantly, the herein proposed algorithm is also effective in detecting and handling activity cliffs. A calibration set including small molecules present in the last updated version of ChEMBL (version 23) was employed to properly tune the algorithm parameters. Three randomly built external sets were instead challenged for model performances. The potential use of MuSSeL was also challenged by a prospective exercise for the prediction of five bioactive compounds taken from articles published in the Journal of Medicinal Chemistry just few months ago. The paper emphasizes the importance of implementing multifingerprint consensus strategies to increase the confidence in prediction of similarity search algorithms and provides a fast and easy-to-run tool for drug target and bioactivity prediction.