Biology subjects

Kruse, H.

Publications and source records attributed to Kruse, H..

6 recordsLinked to original sources

bioRxiv2024

AQuaRef: Machine learning accelerated quantum refinement of protein structures

Cryo-EM and X-ray crystallography provide crucial experimental data for obtaining atomic-detail models of biomacromolecules. Refining these models relies on library-based stereochemical restraints, which, in addition to being limited to known chemical entities, do not include meaningful noncovalent interactions relying solely on nonbonded repulsions. Quantum mechanical (QM) calculations could alleviate these issues but are too expensive for large molecules. We present a novel AI-enabled Quantum Refinement (AQuaRef) based on AIMNet2 neural network potential mimicking QM at substantially lower computational costs. By refining 41 cryo-EM and 30 X-ray structures, we show that this approach yields atomic models with superior geometric quality compared to standard techniques, while maintaining an equal or better fit to experimental data. Notably, AQuaRef aids in determining proton positions, as illustrated in the challenging case of short hydrogen bonds in the parkinsonism-associated human protein DJ-1 and its bacterial homolog YajL.

biochemistry↗

bioRxiv2022

Optimal clustering for quantum refinement of biomolecular structures: Q|R#4

Quantum refinement (Q|R) of crystallographic or cryo-EM derived structures of biomolecules within the Q|R project aims at using ab initio computations instead of library-based chemical restraints. An atomic model refinement requires the calculation of the gradient of the objective function. While it is not a computational bottleneck in classic refinement it is a roadblock if the objective function requires ab initio calculations. A solution to this problem adopted in Q|R is to divide the molecular system into manageable parts and do computations for these parts rather than using the whole macromolecule. This work focuses on the validation and optimization of the automatic divide-and-conquer procedure developed within the Q|R project. Also, we propose an atomic gradient error score that can be easily examined with common molecular visualization programs. While the tool is designed to work within the Q|R setting the error score can be adapted to similar fragmentation methods. The gradient testing tool presented here allows a priori determination of the computationally efficient strategy given available resources for the potentially time-expensive refinement process. The procedure is illustrated using a peptide and small protein models considering different quantum mechanical (QM) methodologies from Hartree-Fock, including basis set and dispersion corrections, to the modern semi-empirical method from the GFN-xTB family. The results obtained provide some general recommendations for the reliable and effective quantum refinement of larger peptides and proteins.

biochemistry↗

bioRxiv2020

Phosphorothioate substitutions in RNA structure studied by molecular dynamics simulations, QM/MM calculations and NMR experiments

Phosphorothioates (PTs) are important chemical modifications of the RNA backbone where a single non-bridging oxygen of the phosphate is replaced with a sulphur atom. PT can stabilize RNAs by protecting them from hydrolysis and is commonly used as tool to explore their function. It is, however, unclear what basic physical effects PT has on RNA stability and electronic structure. Here, we present Molecular Dynamics (MD) simulations, quantum mechanical (QM) calculations, and NMR spectroscopy measurements, exploring the effects of PT modifications in the structural context of the Neomycin-sensing riboswitch (NSR). The NSR is the smallest biologically functional riboswitch with a well-defined structure stabilized by a U-turn motif. Three of the signature interactions of the U-turn; an H-bond, an anion-{pi} interaction and a potassium binding site; are formed by RNA phosphates, making the NSR an ideal model for studying how PT affects RNA structure and dynamics. By comparing with high-level QM calculations, we reveal the distinct physical properties of the individual interactions facilitated by the PT. The sulphur substitution, besides weakening the direct H-bond interaction, reduces the directionality of H-bonding while increasing its dispersion and induction components. It also reduces the induction and increases dispersion component of the anion-{pi} stacking. The sulphur force-field parameters commonly employed in the literature do not reflect these distinctions, leading to unsatisfactory description of PT in simulations of the NSR. We show that it is not possible to accurately describe the PT interactions using one universal set of van der Waals sulphur parameters and provide suggestions for improving the force-field performance.

biophysics↗

bioRxiv2020

UNCG RNA tetraloop as a formidable force-field challenge for MD simulations

Explicit solvent atomistic molecular dynamics (MD) simulations represent an established technique to study structural dynamics of RNA molecules and an important complement for diverse experimental methods. However, performance of molecular mechanical (MM) force fields (ffs) remains far from satisfactory even after decades of development, as apparent from a problematic structural description of some important RNA motifs. Actually, some of the smallest RNA molecules belong to the most challenging systems for MD simulations and, among them, the UNCG tetraloop is saliently difficult. We report a detailed analysis of UNCG MD simulations, depicting the sequence of events leading to the loss of the UNCG native state during MD simulations. We identify molecular interactions, backbone conformations and substates that are involved in the process. The total amount of MD simulation data analyzed in this work is close to 1.3 millisecond. Then, we unravel specific ff deficiencies using diverse quantum mechanical/molecular mechanical (QM/MM) and QM calculations. Comparison between the MM and QM methods shows discrepancies in the description of the 5-flanking phosphate moiety and both signature sugar-base interactions. Our work indicates that poor behavior of the UNCG tetraloop in simulations is a complex issue that cannot be attributed to one dominant and straightforwardly correctable factor. Instead, there is a concerted effect of multiple ff inaccuracies that are coupled and amplifying each other. We attempted to improve the simulation behavior by some carefully-tailored interventions but the results are still far from satisfactory, underlying the difficulties in development of accurate nucleic acids ffs.

biophysics↗

bioRxiv2020

Real-space quantum-based refinement for cryo-EM: Q|R#3

Electron cryo-microscopy (cryo-EM) is fast becoming a major competitor to X-ray crystallography especially for large structures that are difficult or impossible to crystallize. While recent spectacular technology improvements are leading to significantly higher resolution of three-dimensional reconstructions, the average quality of cryo-EM maps is still on the low-resolution end of the range compared to crystallography. A long-standing challenge for atomic model refinement has been the production of stereochemically meaningful models for this resolution regime. Here we demonstrate how including accurate model geometry restraints derived from ab initio quantum-chemical calculations (HF-D3/6-31G) can improve the refinements of an example structure (chain A of 3j63). The robustness of the procedure is tested for additional structures with up to 7k atoms (3a5x, and chain C of 5fn5) by means of the less expensive semi-empirical (GFN1-xTB) model. Necessary algorithms enabling real-space quantum refinement are implemented in the latest version of qr.refine and are described herein. SynopsisThe implementation of quantum-based real-space refinement in qr.refine is described.

bioinformatics↗

bioRxiv2019

Including Crystallographic Symmetry in Quantum-based Refinement: Q|R#2

Three-dimensional structure models refined using low-resolution data from crystallographic or electron cryo-microscopy experiments can benefit from high quality restraints derived from quantum chemical methods. However, non-periodic atom-centered quantum chemistry codes do not inherently account for nearest neighbor interactions of crystallographic symmetry related copies in a satisfactory way. Herein, we have included these nearest neighbor effects in our model by expanding to a super-cell, and then truncating the super-cell to only include residues from neighboring cells that are interacting with the asymmetric unit. In this way our fragmentation approach can adequately and efficiently include the nearest neighbor effects. We have shown previously that a moderately sized X-ray structure can be treated with quantum methods if a fragmentation approach was applied. In this study, we partition a target protein (4gif) into a number of large fragments. The use of large fragments (typically hundreds of atoms) is tractable when a GPU based package such as TeraChem is employed or cheaper (semi-empirical) methods are used. We run the QM calculations at the HF-D3/6-31G level. We compare and contrast the models refined using a recently developed semi-empirical method (GFN2-xTB). To validate the refinement procedure for a non-P1 structure, we use a standard set of crystallographic metrics. We show the robustness of our implementation by refining 13 additional protein models across multiple space-groups and present the summary of the refinement metrics.\n\nSynopsisC-terminal coiled-coil domain of transient receptor potential channel TRPP3 in the P321 space group (PDB code: 4gif) is re-refined with restraints from quantum chemistry using Hartree-Fock theory.

bioinformatics↗