Molecular simulations minimally restrained by experimental data

BICePs v2.0: Software for Ensemble Reweighting Using Bayesian Inference of Conformational Populations

Bayesian Inference of Conformational Populations (BICePs) version 2.0 (v2.0) is a free, open-source Python package that reweights theoretical predictions of conformational state populations using sparse and/or noisy experimental measurements. In this article, we describe the implementation and usage of the latest version of BICePs (v2.0), a powerful, user-friendly and extensible package which makes several improvements upon the previous version. The algorithm now supports many experimental NMR observables (NOE distances, chemical shifts, J-coupling constants, and hydrogen-deuterium exchange protection factors), and enables convenient data preparation and processing. BICePs v2.0 can perform automatic analysis of the sampled posterior, including visualization, and evaluation of statistical significance and sampling convergence. We provide specific coding examples for these topics, and present a detailed example illustrating how to use BICePs v2.0 to reweight a theoretical ensemble using experimental measurements.

Incorporating NOE-Derived Distances in Conformer Generation of Cyclic Peptides with Distance Geometry

Nuclear magnetic resonance (NMR) data from NOESY (nuclear Overhauser enhancement spectroscopy) and ROESY (rotating frame Overhauser enhancement spectroscopy) experiments can easily be combined with distance geometry (DG) based conformer generators by modifying the molecular distance bounds matrix. In this work, we extend the modern DG based conformer generator ETKDG, which has been shown to reproduce experimental crystal structures from small molecules to large macrocycles well, to include NOE-derived interproton distances. In noeETKDG, the experimentally derived interproton distances are incorporated into the distance bounds matrix as loose upper (or lower) bounds to generate large conformer sets. Various subselection techniques can subsequently be applied to yield a conformer bundle that best reproduces the NOE data. The approach is benchmarked using a set of 24 (mostly) cyclic peptides for which NOE-derived distances as well as reference solution structures obtained by other software are available. With respect to other packages currently available, the advantages of noeETKDG are its speed and that no prior force-field parametrization is required, which is especially useful for peptides with unnatural amino acids. The resulting conformer bundles can be further processed with the use of structural refinement techniques to improve the modeling of the intramolecular nonbonded interactions. The noeETKDG code is released as a fully open-source software package available at www.github.com/rinikerlab/customETKDG.

Reconciling Simulations and Experiments With BICePs: A Review

Bayesian Inference of Conformational Populations (BICePs) is an algorithm developed to reconcile simulated ensembles with sparse experimental measurements. The Bayesian framework of BICePs enables population reweighting as a post-simulation processing step, with several advantages over existing methods, including the proper use of reference potentials, and the estimation of a Bayes factor-like quantity called the BICePs score for model selection. Here, we summarize the theory underlying this method in context with related algorithms, review the history of BICePs applications to date, and discuss current shortcomings along with future plans for improvement.

Algal Toxin Goniodomin A Binds Potassium Ion Selectively to Yield a Conformationally Altered Complex with Potential Biological Consequences

The marine toxin goniodomin A (GDA) is a polycyclic macrolide containing a spiroacetal and three cyclic ethers as part of the macrocycle backbone. GDA is produced by three species of the Alexandrium genus of dinoflagellates, blooms of which are associated with "red tides", which are widely dispersed and can cause significant harm to marine life. The toxicity of GDA has been attributed to stabilization of the filamentous form of the actin group of structural proteins, but the structural basis for its binding is not known. Japanese workers, capitalizing on the assumed rigidity of the heavily substituted macrolide ring, assigned the relative configuration and conformation by relying on NMR coupling constants and NOEs; the absolute configuration was assigned by degradation to a fragment that was compared with synthetic material. We have confirmed the absolute structure and broad features of the conformation by X-ray crystallography but have found GDA to complex with alkali metal ions in spite of two of the heterocyclic rings facing outward. Such an arrangement would have been expected to impair the ability of GDA to form a crown-ether-type multidentate complex. GDA shows preference for K⁺, Rb⁺, and Cs⁺ over Li⁺ and Na⁺ in determinations of relative affinities by TLC on metal-ion-impregnated silica gel plates and by electrospray mass spectrometry. NMR studies employing the K⁺ complex of GDA, formed from potassium tetrakis[pentafluorophenyl]borate (KBArF₂₀), reveal a major alteration of the conformation of the macrolide ring. These observations argue against the prior assumption of rigidity of the ring. Alterations in chemical shifts, coupling constants, and NOEs indicate the involvement of most of the molecule other than ring F. Molecular mechanics simulations suggest K⁺ forms a heptacoordinate complex involving O_A, O_B, O_C, O_D, O_E, and the C-26 and C-27 hydroxy groups. We speculate that complexation of K⁺ with GDA electrostatically stabilizes the complex of GDA with filamentous actin in marine animals due to the protein being negatively charged at physiological pH. GDA may also cause potassium leakage through cell membranes. This study provides insight into the structural features and chemistry of GDA that may be responsible for significant ecological damage associated with the GDA-producing algal blooms.