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Disordered proline-rich motifs are common across the proteomes of many species and are often involved in protein-protein interactions. Proline is a unique amino acid due to the covalent bond between the backbone nitrogen and the proline side chain. The resulting five-membered ring allows proline to sample the cis state about its peptide bond, which other residues cannot do as readily. Because proline-rich disordered sequences exist as ensembles that likely include structures with the proline peptide bond in cis , a robust methodology to accurately account for these conformations in the overall ensemble is crucial. Observing the cis conformations of proline in a disordered sequence is challenging both experimentally and computationally. Nitrogen-hydrogen NMR spectroscopy cannot directly observe proline residues, which lack an amide bond, and computational methods struggle to overcome the large kinetic barrier between the cis and trans states, since isomerization usually occurs on the order of seconds. In the current work, Gaussian accelerated molecular dynamics was used to overcome this free energy barrier and simulate proline isomerization in a tetrapeptide (KPTP) and in the 12-residue proline-rich SH3 binding peptide, ArkA. We found that Gaussian accelerated molecular dynamics, when combined with a lowered peptide bond dihedral angle potential energy barrier (15 kcal/mol), allowed sufficient sampling of the proline cis and trans states on a microsecond timescale. All ArkA prolines spend a significant fraction of time in cis , leading to a more compact ensemble with less polyproline II helix structure than an ArkA ensemble with all peptide bonds in trans . The ensemble containing cis prolines also matches more closely to in vitro circular dichroism data than the all- trans ensemble. The ability of the ArkA prolines to isomerize likely affects the peptide’s ability to bind its partner SH3 domain, and should be studied further. This is the first molecular dynamics simulation study of proline isomerization in a biologically relevant proline-rich sequence that we know of, and a similar protocol could be applied to study multi-proline isomerization in other proline-containing proteins to improve conformational diversity and agreement with in vitro data. 

Charged residues on the surface of proteins are critical for both protein stability and interactions. However, many proteins contain binding regions with a high net charge that may destabilize the protein but are useful for binding to oppositely charged targets. We hypothesized that these domains would be marginally stable, as electrostatic repulsion would compete with favorable hydrophobic collapse during folding. Furthermore, by increasing the salt concentration, we predict that these protein folds would be stabilized by mimicking some of the favorable electrostatic interactions that take place during target binding. We varied the salt and urea concentrations to probe the contributions of electrostatic and hydrophobic interactions for the folding of the yeast SH3 domain found in Abp1p. The SH3 domain was significantly stabilized with increased salt concentrations due to Debye–Huckel screening and a nonspecific territorial ion‐binding effect. Molecular dynamics and NMR show that sodium ions interact with all 15 acidic residues but do little to change backbone dynamics or overall structure. Folding kinetics experiments show that the addition of urea or salt primarily affects the folding rate, indicating that almost all the hydrophobic collapse and electrostatic repulsion occur in the transition state. After the transition state formation, modest yet favorable short‐range salt bridges are formed along with hydrogen bonds, as the native state fully folds. Thus, hydrophobic collapse offsets electrostatic repulsion to ensure this highly charged binding domain can still fold and be ready to bind to its charged peptide targets, a property that is likely evolutionarily conserved over 1 billion years.

 

In this article, we provide advice and insights, based on our own experiences, for computational chemists who are beginning new tenure‐track positions at primarily undergraduate institutions. Each of us followed different routes to obtain our tenure‐track positions, but we all experienced similar challenges when getting started in our new position. In this article, we discuss our approaches to seven areas that we all found important for engaging undergraduate students in our computational chemistry research, including setting up computational resources, recruiting research students, training research students, designing student projects, managing the lab, mentoring students, and student conference participation.

 

The Molecular Sciences Software Institute (MolSSI) is an National Science Foundation (NSF) funded institute that focuses on improving software, education, and training in the computational molecular sciences. Through a collaboration with the Molecular Education and Research Consortium in Undergraduate computational chemistRY (MERCURY), the MolSSI has developed resources for undergraduate and other early career students to lay an educational foundation for the next generation of computational molecular scientists. The resources focus on introducing best practices in software engineering to students from the very start to make their software more useable, maintainable, and reproducible.