Conotoxin Prediction: New Features to Increase Prediction Accuracy.

Conotoxins are toxic, disulfide-bond-rich peptides from cone snail venom that target a wide range of receptors and ion channels with multiple pathophysiological effects. Conotoxins have extraordinary potential for medical therapeutics that include cancer, microbial infections, epilepsy, autoimmune diseases, neurological conditions, and cardiovascular disorders. Despite the potential for these compounds in novel therapeutic treatment development, the process of identifying and characterizing the toxicities of conotoxins is difficult, costly, and time-consuming. This challenge requires a series of diverse, complex, and labor-intensive biological, toxicological, and analytical techniques for effective characterization. While recent attempts, using machine learning based solely on primary amino acid sequences to predict biological toxins (e.g., conotoxins and animal venoms), have improved toxin identification, these methods are limited due to peptide conformational flexibility and the high frequency of cysteines present in toxin sequences. This results in an enumerable set of disulfide-bridged foldamers with different conformations of the same primary amino acid sequence that affect function and toxicity levels. Consequently, a given peptide may be toxic when its cysteine residues form a particular disulfide-bond pattern, while alternative bonding patterns (isoforms) or its reduced form (free cysteines with no disulfide bridges) may have little or no toxicological effects. Similarly, the same disulfide-bond pattern may be possible for other peptide sequences and result in different conformations that all exhibit varying toxicities to the same receptor or to different receptors. We present here new features, when combined with primary sequence features to train machine learning algorithms to predict conotoxins, that significantly increase prediction accuracy.

Integrative structural studies of the SARS-CoV-2 spike protein during the fusion process (2022).

SARS-CoV-2 is the virus responsible for the COVID-19 pandemic and catastrophic, worldwide health and economic impacts. The spike protein on the viral surface is responsible for viral entry into the host cell. The binding of spike protein to the host cell receptor ACE2 is the first step leading to fusion of the host and viral membranes. Despite the vast amount of structure data that has been generated for the spike protein of SARS-CoV-2, many of the detailed structures of the spike protein in different stages of the fusion pathway are unknown, leaving a wealth of potential drug-target space unexplored. The atomic-scale structure of the complete S2 segment, as well as the complete fusion intermediate are also unknown and represent major gaps in our knowledge of the infectious pathway of SAR-CoV-2. The conformational changes of the spike protein during this process are similarly not well understood. Here we present structures of the spike protein at different stages of the fusion process. With the transitions being a necessary step before the receptor binding, we propose sites along the transition pathways as potential targets for drug development.

Conformational control via sequence for a heteropeptoid in water: coupled NMR and Rosetta modelling.

We report a critical advance in the generation and characterization of peptoid hetero-oligomers. A library of sub-monomers with amine and carboxylate side-chains are combined in different sequences using microwave-assisted synthesis. Their sequence-structure propensity is confirmed by circular dichroism, and conformer subtypes are enumerated by NMR. Biasing the ψ-angle backbone to trans (180°) in Monte Carlo modelling favors i to i + 3 naphthyl-naphthyl stacking, and matches experimental ensemble distributions. Taken together, high-yield synthesis of heterooligomers and NMR with structure prediction enables rapid determination of sequences that induce secondary structural propensities for predictive design of hydrophilic peptidomimetic foldamers and their future libraries.

Modeling the Influenza A NP-vRNA-Polymerase Complex in Atomic Detail.

Seasonal flu is an acute respiratory disease that exacts a massive toll on human populations, healthcare systems and economies. The disease is caused by an enveloped Influenza virus containing eight ribonucleoprotein (RNP) complexes. Each RNP incorporates multiple copies of nucleoprotein (NP), a fragment of the viral genome (vRNA), and a viral RNA-dependent RNA polymerase (POL), and is responsible for packaging the viral genome and performing critical functions including replication and transcription. A complete model of an Influenza RNP in atomic detail can elucidate the structural basis for viral genome functions, and identify potential targets for viral therapeutics. In this work we construct a model of a complete Influenza A RNP complex in atomic detail using multiple sources of structural and sequence information and a series of homology-modeling techniques, including a motif-matching fragment assembly method. Our final model provides a rationale for experimentally-observed changes to viral polymerase activity in numerous mutational assays. Further, our model reveals specific interactions between the three primary structural components of the RNP, including potential targets for blocking POL-binding to the NP-vRNA complex. The methods developed in this work open the possibility of elucidating other functionally-relevant atomic-scale interactions in additional RNP structures and other biomolecular complexes.

Equilibrium Denaturation and Preferential Interactions of an RNA Tetraloop with Urea.

Urea is an important organic cosolute with implications in maintaining osmotic stress in cells and differentially stabilizing ensembles of folded biomolecules. We report an equilibrium study of urea-induced denaturation of a hyperstable RNA tetraloop through unbiased replica exchange molecular dynamics. We find that, in addition to destabilizing the folded state, urea smooths the RNA free energy landscape by destabilizing specific configurations, and forming favorable interactions with RNA nucleobases. A linear concentration-dependence of the free energy (m-value) is observed, in agreement with the results of other RNA hairpins and proteins. Additionally, analysis of the hydrogen-bonding and stacking interactions within RNA primarily show temperature-dependence, while interactions between RNA and urea primarily show concentration-dependence. Our findings provide valuable insight into the effects of urea on RNA folding and describe the thermodynamics of a basic RNA hairpin as a function of solution chemistry.

Free-energy landscape of a hyperstable RNA tetraloop.

We report the characterization of the energy landscape and the folding/unfolding thermodynamics of a hyperstable RNA tetraloop obtained through high-performance molecular dynamics simulations at microsecond timescales. Sampling of the configurational landscape is conducted using temperature replica exchange molecular dynamics over three isochores at high, ambient, and negative pressures to determine the thermodynamic stability and the free-energy landscape of the tetraloop. The simulations reveal reversible folding/unfolding transitions of the tetraloop into the canonical A-RNA conformation and the presence of two alternative configurations, including a left-handed Z-RNA conformation and a compact purine Triplet. Increasing hydrostatic pressure shows a stabilizing effect on the A-RNA conformation and a destabilization of the left-handed Z-RNA. Our results provide a comprehensive description of the folded free-energy landscape of a hyperstable RNA tetraloop and highlight the significant advances of all-atom molecular dynamics in describing the unbiased folding of a simple RNA secondary structure motif.