Machine learning differentiates enzymatic and non-enzymatic metals in proteins

Metalloenzymes are 40% of all enzymes and can perform all seven classes of enzyme reactions. Because of the physicochemical similarities between the active sites of metalloenzymes and inactive metal binding sites, it is challenging to differentiate between them. Yet distinguishing these two classes is critical for the identification of both native and designed enzymes. Because of similarities between catalytic and non-catalytic metal binding sites, finding physicochemical features that distinguish these two types of metal sites can indicate aspects that are critical to enzyme function. In this work, we develop the largest structural dataset of enzymatic and non-enzymatic metalloprotein sites to date. We then use a decision-tree ensemble machine learning model to classify metals bound to proteins as enzymatic or non-enzymatic with 92.2% precision and 90.1% recall. Our model scores electrostatic and pocket lining features as more important than pocket volume, despite the fact that volume is the most quantitatively different feature between enzyme and non-enzymatic sites. Finally, we find our model has overall better performance in a side-to-side comparison against other methods that differentiate enzymatic from non-enzymatic sequences. We anticipate that our model’s ability to correctly identify which metal sites are responsible for enzymatic activity could enable identification of new enzymatic mechanisms and de novo enzyme design

Modulating Integrin αIIbβ3 Activity through Mutagenesis of Allosterically Regulated Intersubunit Contacts

This document is the Accepted Manuscript version of a Published Work that appeared in final form in Biochemistry, copyright © American Chemical Society after peer review and technical editing by the publisher. To access the final edited and published work see https://doi.org/10.1021/acs.biochem.9b00430.Integrin αIIbβ3, a transmembrane heterodimer, mediates platelet aggregation when it switches from an inactive to an active ligand-binding conformation following platelet stimulation. Central to regulating αIIbβ3 activity is the interaction between the αIIb and β3 extracellular stalks, which form a tight heterodimer in the inactive state and dissociate in the active state. Here, we demonstrate that alanine replacements of sensitive positions in the heterodimer stalk interface destabilize the inactive conformation sufficiently to cause constitutive αIIbβ3 activation. To determine the structural basis for this effect, we performed a structural bioinformatics analysis and found that perturbing intersubunit contacts with favorable interaction geometry through substitutions to alanine quantitatively accounted for the degree of constitutive αIIbβ3 activation. This mutational study directly assesses the relationship between favorable interaction geometry at mutation-sensitive positions and the functional activity of those mutants, giving rise to a simple model that highlights the importance of interaction geometry in contributing to the stability between protein–protein interactions.NIH P01 HL40387NIH R35 GM122603National Science Foundation 1709506National Science Foundation 165011

KIF-Key Interactions Finder : A program to identify the key molecular interactions that regulate protein conformational changes

Simulation datasets of proteins (e.g., those generated by molecular dynamics simulations) are filled with information about how a non-covalent interaction network within a protein regulates the conformation and, thus, function of the said protein. Most proteins contain thousands of non-covalent interactions, with most of these being largely irrelevant to any single conformational change. The ability to automatically process any protein simulation dataset to identify non-covalent interactions that are strongly associated with a single, defined conformational change would be a highly valuable tool for the community. Furthermore, the insights generated from this tool could be applied to basic research, in order to improve understanding of a mechanism of action, or for protein engineering, to identify candidate mutations to improve/alter the functionality of any given protein. The open-source Python package Key Interactions Finder (KIF) enables users to identify those non-covalent interactions that are strongly associated with any conformational change of interest for any protein simulated. KIF gives the user full control to define the conformational change of interest as either a continuous variable or categorical variable, and methods from statistics or machine learning can be applied to identify and rank the interactions and residues distributed throughout the protein, which are relevant to the conformational change. Finally, KIF has been applied to three diverse model systems (protein tyrosine phosphatase 1B, the PDZ3 domain, and the KE07 series of Kemp eliminases) in order to illustrate its power to identify key features that regulate functionally important conformational dynamics

KIF – Key Interactions Finder: A Program to Identify the Key Molecular Interactions that Regulate Protein Conformational Changes

Simulation datasets of proteins (e.g., those generated by molecular dynamics simulations) are filled with information about how the non-covalent interaction network within a protein regulates the conformation and thus function of said protein. Most proteins contain thousands of non-covalent interactions, with most of these being largely irrelevant to any single conformational change. The ability to automatically process any protein simulation dataset to identify the non-covalent interactions that are strongly associated with a single, defined conformational change would be a highly valuable tool for the community. Furthermore, the insights generated from this tool could be applied to both basic research, in order to improve understanding of a mechanism of action, or for protein engineering, to identify candidate mutations to improve/alter the functionality of any given protein. The open-source Python package Key Interactions Finder (KIF) enables users to identify those non-covalent interactions that are strongly associated with any conformational change of interest for any protein simulated. KIF gives the user full control to define the conformational change of interest as either a continuous or categorical variable, and methods from statistics or machine learning can be applied to identify and rank the interactions and residues distributed throughout the protein which are relevant to the conformational change. Finally, KIF has been applied to three diverse model systems (protein tyrosine phosphatase 1B, the PDZ3 domain, and the KE07 series of Kemp eliminases) in order to showcase its power to identify key features that regulate functionally important conformational dynamics

In Vivo Trp Scanning of the Small Multidrug Resistance Protein EmrE Confirms 3D Structure Models

The quaternary structure of the homodimeric small multidrug resistance protein EmrE has been studied intensely over the past decade. Structural models derived from both two- and three-dimensional crystals show EmrE as an anti-parallel homodimer. However, the resolution of the structures is rather low and their relevance for the in vivo situation has been questioned. Here, we have challenged the available structural models by a comprehensive in vivo Trp scanning of all four transmembrane helices in EmrE. The results are in close agreement with the degree of lipid exposure of individual residues predicted from coarse-grained molecular dynamics simulations of the anti-parallel dimeric structure obtained by X-ray crystallography, strongly suggesting that the X-ray structure provides a good representation of the active in vivo form of EmrE