Exploring Protein Kinase Conformation Using Swarm-Enhanced Sampling Molecular Dynamics

Atzori, Alessio; Bruce, Neil J.; Burusco, Kepa K.; Wroblowski, Berthold; Bonnet, Pascal; Bryce, Richard A.

doi:10.1021/ci5003334

Cited by 14 publications

(17 citation statements)

References 52 publications

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“…44 Specialized methods to overcome energetic barriers might remedy this. 13, 45, 46 For the purpose of PF prediction the weighing of contributions from MD simulations of various known stable conformations might be sufficient and should be explored in the future.…”

Section: Discussionmentioning

confidence: 99%

Estimation of Hydrogen-Exchange Protection Factors from MD Simulation Based on Amide Hydrogen Bonding Analysis

Park

Venable

Steckler

et al. 2015

J. Chem. Inf. Model.

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Hydrogen exchange (HX) studies have provided critical insight into our understanding of protein folding, structure and dynamics. More recently, Hydrogen Exchange Mass Spectrometry (HX-MS) has become a widely applicable tool for HX studies. The interpretation of the wealth of data generated by HX-MS experiments as well as other HX methods would greatly benefit from the availability of exchange predictions derived from structures or models for comparison with experiment. Most reported computational HX modeling studies have employed solvent-accessible-surface-area based metrics in attempts to interpret HX data on the basis of structures or models. In this study, a computational HX-MS prediction method based on classification of the amide hydrogen bonding modes mimicking the local unfolding model is demonstrated. Analysis of the NH bonding configurations from Molecular Dynamics (MD) simulation snapshots is used to determine partitioning over bonded and non-bonded NH states and is directly mapped into a protection factor (PF) using a logistics growth function. Predicted PFs are then used for calculating deuteration values of peptides and compared with experimental data. Hydrogen exchange MS data for Fatty acid synthase thioesterase (FAS-TE) collected for a range of pHs and temperatures was used for detailed evaluation of the approach. High correlation between prediction and experiment for observable fragment peptides is observed in the FAS-TE and additional benchmarking systems that included various apo/holo proteins for which literature data were available. In addition, it is shown that HX modeling can improve experimental resolution through decomposition of in-exchange curves into rate classes, which correlate with prediction from MD. Successful rate class decompositions provide further evidence that the presented approach captures the underlying physical processes correctly at the single residue level. This assessment is further strengthened in a comparison of residue resolved protection factor predictions for staphylococcal nuclease with NMR data, which was also used to compare prediction performance with other algorithms described in the literature. The demonstrated transferable and scalable MD based HX prediction approach adds significantly to the available tools for HX-MS data interpretation based on available structures and models.

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Section: Discussionmentioning

confidence: 99%

Estimation of Hydrogen-Exchange Protection Factors from MD Simulation Based on Amide Hydrogen Bonding Analysis

Park

Venable

Steckler

et al. 2015

J. Chem. Inf. Model.

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“…A range of sampling methodologies have been proposed; for example, approaches based on TI include the independent trajectory TI (IT-TI) method,8 enhancing sampling by coupling to accelerated molecular dynamics (aMD)9 or replica-exchange-based methods such as RETI 10. Recently, we have explored a multi-copy molecular dynamics method designed to improve conformational exploration of rugged energy landscapes 11. A development out of the SWARM-MD approach of Huber and van Gunsteren,12,13 we term this method swarm-enhanced sampling molecular dynamics (sesMD) 11.…”

Section: Introductionmentioning

confidence: 99%

“…[10] Recently,w ehave explored am ulti-copym olecular dynamics methodd esignedt oi mprove conformational exploration of rugged energy landscapes. [11] Ad evelopment out of the SWARM-MD approach of Huber and van Gunsteren, [12,13] we term this method swarm-enhanced sampling molecular dynamics (sesMD). [11] The sesMDmethod links multiple simulation replicas into as warm, using attractive and repulsive pair potentials acting on dihedrala ngles to promote barrierc rossing into alternative energy minima.…”

Section: Introductionmentioning

confidence: 99%

“…[11] Ad evelopment out of the SWARM-MD approach of Huber and van Gunsteren, [12,13] we term this method swarm-enhanced sampling molecular dynamics (sesMD). [11] The sesMDmethod links multiple simulation replicas into as warm, using attractive and repulsive pair potentials acting on dihedrala ngles to promote barrierc rossing into alternative energy minima. Application of sesMD has led to enhanced sampling of the conformationso fs mall-molecule systemsa nd ap rotein kinase, [11] as swarm replicas cooperatively sample ag reater volume of phase space by steering each other across potential energy barriers.…”

Section: Introductionmentioning

confidence: 99%

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Free Energy Calculations using a Swarm‐Enhanced Sampling Molecular Dynamics Approach

et al. 2015

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Free energy simulations are an established computational tool in modelling chemical change in the condensed phase. However, sampling of kinetically distinct substates remains a challenge to these approaches. As a route to addressing this, we link the methods of thermodynamic integration (TI) and swarm-enhanced sampling molecular dynamics (sesMD), where simulation replicas interact cooperatively to aid transitions over energy barriers. We illustrate the approach by using alchemical alkane transformations in solution, comparing them with the multiple independent trajectory TI (IT-TI) method. Free energy changes for transitions computed by using IT-TI grew increasingly inaccurate as the intramolecular barrier was heightened. By contrast, swarm-enhanced sampling TI (sesTI) calculations showed clear improvements in sampling efficiency, leading to more accurate computed free energy differences, even in the case of the highest barrier height. The sesTI approach, therefore, has potential in addressing chemical change in systems where conformations exist in slow exchange.

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“…23,[28][29][30][31][32][33][34] Here, we map out the conformational landscape of Lewis oligosaccharides in aqueous solution, introducing and applying a computationally efficient variant of our earlier biased MD method. [35][36] We term this variant multi-dimensional swarmenhanced sampling molecular dynamics (msesMD). We first assess the ability of msesMD to explore sLe a conformations in explicit aqueous solvent, comparing with multi-microsecond unbiased MD simulations, umbrella sampling and accelerated molecular dynamics (aMD).…”

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confidence: 99%

Identification of Rare Lewis Oligosaccharide Conformers in Aqueous Solution Using Enhanced Sampling Molecular Dynamics

et al. 2018

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Determining the conformations accessible to carbohydrate ligands in aqueous solution is important for understanding their biological action. In this work, we evaluate the conformational free-energy surfaces of Lewis oligosaccharides in explicit aqueous solvent using a multidimensional variant of the swarm-enhanced sampling molecular dynamics (msesMD) method; we compare with multi-microsecond unbiased MD simulations, umbrella sampling, and accelerated MD approaches. For the sialyl Lewis A tetrasaccharide, msesMD simulations in aqueous solution predict conformer landscapes in general agreement with the other biased methods and with triplicate unbiased 10 μs trajectories; these simulations find a predominance of closed conformer and a range of low-occupancy open forms. The msesMD simulations also suggest closed-to-open transitions in the tetrasaccharide are facilitated by changes in ring puckering of its GlcNAc residue away from the C form, in line with previous work. For sialyl Lewis X tetrasaccharide, msesMD simulations predict a minor population of an open form in solution corresponding to a rare lectin-bound pose observed crystallographically. Overall, from comparison with biased MD calculations, we find that triplicate 10 μs unbiased MD simulations may not be enough to fully sample glycan conformations in aqueous solution. However, the computational efficiency and intuitive approach of the msesMD method suggest potential for its application in glycomics as a tool for analysis of oligosaccharide conformation.

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Exploring Protein Kinase Conformation Using Swarm-Enhanced Sampling Molecular Dynamics

Cited by 14 publications

References 52 publications

Estimation of Hydrogen-Exchange Protection Factors from MD Simulation Based on Amide Hydrogen Bonding Analysis

Estimation of Hydrogen-Exchange Protection Factors from MD Simulation Based on Amide Hydrogen Bonding Analysis

Free Energy Calculations using a Swarm‐Enhanced Sampling Molecular Dynamics Approach

Identification of Rare Lewis Oligosaccharide Conformers in Aqueous Solution Using Enhanced Sampling Molecular Dynamics

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