Biology subjects

Mansbach, R. A.

Publications and source records attributed to Mansbach, R. A..

4 recordsLinked to original sources

bioRxiv2024

In Silico Study of the Early Stages of Aggregation of β-Sheet Forming Antimicrobial Peptide GL13K

Antimicrobial peptides (AMPs) are of growing interest as potential candidates for antibiotics to which antimicrobial resistance increases slowly. In this article, we perform the first in silico study of the synthetic {beta} sheet-forming AMP GL13K. Through atomistic simulations of single and multipeptide systems under different charge conditions, we are able to shine a light on the short timescales of early aggregation. We find that isolated peptide conformations are primarily dictated by sequence rather than charge, whereas changing charge has a significant impact on the conformational free energy landscape of multi-peptide systems. We demonstrate that the lack of charge-charge repulsion is a sufficient minimal model for experimentally observed aggregation. Overall, our work explores the molecular biophysical underpinnings of the first stages of aggregation of a unique AMP, laying necessary groundwork for its further development as an antibiotic candidate.

biophysics↗

bioRxiv2022

Inferring pathways of oxidative folding from pre-folding free energy landscapes of disulfide-rich toxin

Short, cysteine-rich peptides can exist in stable or metastable structural ensembles due to the number of possible patterns of formation of their disulfide bonds. One interesting subset of this peptide group is the coonotoxins, which are produced by aquatic snails in the family Conidae. The {micro} conotoxins, which are antagonists and blockers of the voltage-gated sodium channel, exist in a folding spectrum: on one end of the spectrum are more hirudin-like folders, which form disulfide bonds and then reshuffle them, leading to an ensemble of kinetically trapped isomers-and on the other end are more BPTI-like folders-which form the native disulfide bonds one by one in a particular order, leading to a preponderance of conformations existing in a single stable state. In this article, we employ the composite diffusion map approach to study the unified free energy surface of pre-folding {micro}-conotoxin equilibrium. We identify the two most important nonlinear collective modes of the unified folding landscape and demonstrate that in the absence of their disulfides, the conotoxins can be thought of as largely disordered polymers. A small increase in the number of hydrophobic residues in the protein shifts the free energy landscape towards hydrophobically collapsed coil conformations responsible for cysteine proximity in hirudin-like folders, compared to semi-extended coil conformations with more distal cysteines in BPTI-like folders. Overall, this work sheds important light on the folding processes and free energy landscapes of cysteinerich peptides and demonstrates the extent to which sequence and length contribute to these landscapes.

biophysics↗

bioRxiv2020

The SARS-CoV-2 Spike Variant D614G Favors an Open Conformational State

The COVID-19 pandemic underwent a rapid transition with the emergence of a SARS-CoV-2 variant that carried the amino acid substitution D614G in the Spike protein that became globally prevalent. The G-form is both more infectious in vitro and associated with increased viral loads in infected people. To gain insight into the mechanism underlying these distinctive characteristics, we employed multiple replicas of microsecond all-atom simulations to probe the molecular-level impact of this substitution on Spikes closed and open states. The open state enables Spike interactions with its human cellular receptor, ACE2. Here we show that changes in the inter-protomer energetics due to the D614G substitution favor a higher population of infection-capable (open) states. The inter-protomer interactions between S1 and S2 subunits in the open state of the D-form are asymmetric. This asymmetry is resolved in the G-form due to the release of tensile hydrogen bonds resulting in an increased population of open conformations. Thus, the increased infectivity of the G-form is likely due to a higher rate of profitable binding encounters with the host receptor. It is also predicted to be more neutralization sensitive due to enhanced exposure of the receptor binding domain, a key target region for neutralizing antibodies.

biophysics↗

bioRxiv2019

A Graph-Directed Approach for Creation of a Homology Modeling Library: Application to Venom Structure Prediction

Many toxins are short, cysteine-rich peptides that are of great interest as novel therapeutic leads and of great concern as lethal biological agents due to their high affinity and specificity for various receptors involved in neuromuscular transmission. To perform initial candidate identification for design of a drug impacting a particular receptor or for threat assessment as a harmful toxin, one requires a set of candidate structures of reasonable accuracy with potential for interaction with the target receptor. In this article, we introduce a graph-based algorithm for identifying good extant template structures from a library of evolutionarily-related cysteine-containing sequences for structural determination of target sequences by homology modeling. We employ this approach to study the conotoxins, a set of toxin peptides produced by the family of aquatic cone snails. Currently, of the approximately six thousand known conotoxin sequences, only about three percent have experimentally characterized three-dimensional structures, leading to a serious bottleneck in identifying potential drug candidates. We demonstrate that the conotoxin template library generated by our approach may be employed to perform homology modeling and greatly increase the number of characterized conotoxin structures. We also show how our approach can guide experimental design by identifying and ranking sequences for structural characterization in a similar manner. Overall, we present and validate an approach for venom structure modeling and employ it to expand the library of extant conotoxin structures by almost 300% through homology modeling employing the template library determined in our approach.

bioinformatics↗