MEnTaT: A machine-learning approach for the identification of mutations to increase protein stability.

Enhancing protein thermal stability is important for biomedical and industrial applications as well as in the research laboratory. Here, we describe a simple machine-learning method which identifies amino acid substitutions that contribute to thermal stability based on comparison of the amino acid sequences of homologous proteins derived from bacteria that grow at different temperatures. A key feature of the method is that it compares the sequences based not simply on the amino acid identity, but rather on the structural and physicochemical properties of the side chain.

Structural Insights into the Substrate Range of a Bacterial Monoamine Oxidase.

Monoamine oxidases (MAOs) play a key role in the breakdown of primary and secondary amines. In eukaryotic organisms, these enzymes are vital to the regulation of monoamine neurotransmitters and the degradation of dietary monoamines. MAOs have also been identified in prokaryotic species, although their role in these organisms is not well understood.

Structure-Guided Insight into the Specificity and Mechanism of a Parasitic Nucleoside Hydrolase.

is the causative parasitic protozoan of the disease trichomoniasis, the most prevalent, nonviral sexually transmitted disease in the world. is a parasite that scavenges nucleosides from the host organism via catalysis by nucleoside hydrolase (NH) enzymes to yield purine and pyrimidine bases. One of the four NH enzymes identified within the genome of displays unique specificity toward purine nucleosides, adenosine and guanosine, but not inosine, and atypically shares greater sequence similarity to the pyrimidine hydrolases.

Recapitulating the Binding Affinity of Nrf2 for KEAP1 in a Cyclic Heptapeptide, Guided by NMR, X-ray Crystallography, and Machine Learning.

Macrocycles, including macrocyclic peptides, have shown promise for targeting challenging protein-protein interactions (PPIs). One PPI of high interest is between Kelch-like ECH-Associated Protein-1 (KEAP1) and Nuclear Factor (Erythroid-derived 2)-like 2 (Nrf2). Guided by X-ray crystallography, NMR, modeling, and machine learning, we show that the full 20 nM binding affinity of Nrf2 for KEAP1 can be recapitulated in a cyclic 7-mer peptide, c[()-β-homoAla-DPETGE].

Interaction Energetics and Druggability of the Protein-Protein Interaction between Kelch-like ECH-Associated Protein 1 (KEAP1) and Nuclear Factor Erythroid 2 Like 2 (Nrf2).

Development of small molecule inhibitors of protein-protein interactions (PPIs) is hampered by our poor understanding of the druggability of PPI target sites. Here, we describe the combined application of alanine-scanning mutagenesis, fragment screening, and FTMap computational hot spot mapping to evaluate the energetics and druggability of the highly charged PPI interface between Kelch-like ECH-associated protein 1 (KEAP1) and nuclear factor erythroid 2 like 2 (Nrf2), an important drug target. FTMap identifies four binding energy hot spots at the active site.