MDTraj: A Modern Open Library for the Analysis of Molecular Dynamics Trajectories

Accurately predicting protein-ligand binding affinities and binding modes is a major goal in computational chemistry, but even the prediction of ligand binding modes in proteins poses major challenges. Here, we focus on solving the binding mode prediction problem for rigid fragments. That is, we focus on computing the dominant placement, conformation, and orientations of a relatively rigid, fragment-like ligand in a receptor, and the populations of the multiple binding modes which may be relevant. This problem is important in its own right, but is even more timely given the recent success of alchemical free energy calculations. Alchemical calculations are increasingly used to predict binding free energies of ligands to receptors. However, the accuracy of these calculations is dependent on proper sampling of the relevant ligand binding modes. Unfortunately, ligand binding modes may often be uncertain, hard to predict, and/or slow to interconvert on simulation timescales, so proper sampling with current techniques can require prohibitively long simulations. We need new methods which dramatically improve sampling of ligand binding modes. Here, we develop and apply a nonequilibrium candidate Monte Carlo (NCMC) method to improve sampling of ligand binding modes. In this technique, the ligand is rotated and subsequently allowed to relax in its new position through alchemical perturbation before accepting or rejecting the rotation and relaxation as a nonequilibrium Monte Carlo move. When applied to a T4 lysozyme model binding system, this NCMC method shows over two orders of magnitude improvement in binding mode sampling efficiency compared to a brute force molecular dynamics simulation. This is a first step towards applying this methodology to pharmaceutically-relevant binding of fragments and, eventually, drug-like molecules. We are making this approach available via our new Binding Modes of Ligands using Enhanced Sampling (BLUES) package which is freely available on GitHub.

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“…88 Finally, the interactions are subsequently turned back on via a reverse of the original procedure.…”

Section: Theory and Computational Methodsmentioning

confidence: 99%

Binding Modes of Ligands Using Enhanced Sampling (BLUES): Rapid Decorrelation of Ligand Binding Modes via Nonequilibrium Candidate Monte Carlo

et al. 2018

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“…We used MDTraj (version 1.7.2) 45 in combination with in-house Python scripts to calculate distances and dihedral angles.…”

Section: Methodsmentioning

confidence: 99%

Exploring Substrate Binding in the Extracellular Vestibule of MhsT by Atomistic Simulations and Markov Models

Abramyan

Quick

Xue

et al. 2018

J. Chem. Inf. Model.

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Neurotransmitter:sodium symporters (NSS) terminate neurotransmission through Na+-driven reuptake of cognate neurotransmitters. Crystallographically, whereas both substrates and inhibitors have been found to bind in the central binding (S1) site of NSS, inhibitors were found to bind to a second binding (S2) site in the extracellular vestibule (EV) of transporters for leucine (LeuT) and serotonin. Based on computational and experimental studies, we proposed that substrates bind to the S2 site of LeuT as well, and that substrate binding to the S2 site is essential for Na+-coupled symport. Recent binding experiments show that substrate (L-Trp) binding in the S2 site of MhsT, another bacterial NSS, is also central to the allosteric transport mechanism. Here, we used extensive molecular dynamics simulations combined with Markov state model analysis to investigate the interaction of L-Trp with the EV of MhsT, and identified potential binding poses of L-Trp as well as induced conformational changes in the EV. Our computational findings were validated by experimental mutagenesis studies, and shed light on the ligand binding characteristics of the EV of NSS, which may facilitate development of allosteric ligands targeting NSS.

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“…We used the MDTraj 43 library to process individual structure and molecular dynamics coordinates in Python. 44 To select frames for a given shadow, we stored data in NumPy arrays and processed them using the NumPy library.…”

Section: Methodsmentioning

confidence: 99%

All-Atom Molecular Dynamics Reveals Mechanism of Zinc Complexation with Therapeutic F10

2016

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Advancing the use of therapeutic nucleic acids requires understanding the chemical and structural properties that allow these polymers to promote the death of malignant cells. Here we explore Zn2+ complexation by the fluoropyrimidine polymer F10, which has strong activities in multiple preclinical models of cancer. Delivery of fluoropyrimidine FdUMP in the 10-residue polymer F10 rather than the nucleobase (5-fluorouracil) allows consideration of metal ion binding effects on drug delivery. The differences in metal ion interactions with fluoropyrimidine compared to normal DNA results in conformation changes that affect protein binding, cell uptake, and codelivery of metals such as zinc, and the cytoxicity thereof. Microsecond-time-scale, all-atom simulations of F10 predict that zinc selectively stabilizes the polymer via interactions with backbone phosphate groups and suggest a mechanism of complexation for the zinc-base interactions shown in previous experimental work. The positive zinc ions are attracted to the negatively charged phosphate groups. Once the Zn2+ ions are near F10, they cause the base’s N3 nitrogen to deprotonate. Subsequently, magnesium atoms displace zinc from their interactions with phosphate, freeing the zinc ions to interact with the FdU bases by forming weak interactions with the O4 oxygen and the fluorine attached to C5. These interactions of magnesium with phosphate groups and zinc with nucleobases agree with previous experimental results and are seen in MD simulations only when magnesium is introduced after N3 deprotonation, indicating a specific order of metal binding events. Additionally, we predict interactions between zinc and F10’s O2 atoms, which were not previously observed. By comparison to 10mers of polyU and polydT, we also predict that the presence of fluorine increases the binding affinity of zinc to F10 relative to analogous strands of RNA and DNA consisting of only native nucleotides.

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MDTraj: A Modern Open Library for the Analysis of Molecular Dynamics Trajectories

Cited by 1,781 publications

References 34 publications

Binding Modes of Ligands Using Enhanced Sampling (BLUES): Rapid Decorrelation of Ligand Binding Modes via Nonequilibrium Candidate Monte Carlo

Binding Modes of Ligands Using Enhanced Sampling (BLUES): Rapid Decorrelation of Ligand Binding Modes via Nonequilibrium Candidate Monte Carlo

Exploring Substrate Binding in the Extracellular Vestibule of MhsT by Atomistic Simulations and Markov Models

All-Atom Molecular Dynamics Reveals Mechanism of Zinc Complexation with Therapeutic F10

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