CHARMM36: An Improved Force Field for Folded and Intrinsically Disordered Proteins

Huang, Jing; Rauscher, Sarah; Nawrocki, Grzegorz; Ran, Ting; Feig, Michael; Groot, Bert L. de; Grubmüller, Helmut; MacKerell, Alexander D.

doi:10.1016/j.bpj.2016.11.971

Cited by 91 publications

(94 citation statements)

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“…PyMol (The PyMOL Molecular Graphics System, Version 2.0 Schrödinger, LLC.) NAMD 2.12 (Phillips et al, 2005) was used for simulating this system, utilising the CHARMM36 force field (Huang et al, 2017) in a Langevin temperature and pressure controlled (NPT @ 310K) ensemble with periodic boundary conditions and particle-mesh Ewald electrostatics. A NAMD topology of the dimer was generated using the psfgen plugin of VMD 1.9.3 (Humphrey et al, 1996).…”

Section: Structural Modellingmentioning

confidence: 99%

“…The full system comprised 123,533 atoms. NAMD 2.12 (Phillips et al, 2005) was used for simulating this system, utilising the CHARMM36 force field (Huang et al, 2017) in a Langevin temperature and pressure controlled (NPT @ 310K) ensemble with periodic boundary conditions and particle-mesh Ewald electrostatics. Following 10,000 energy minimisation steps (2 fs/step) to remove van der Waals clashes within the system, a production run of 10 ns was performed.…”

Section: Structural Modellingmentioning

confidence: 99%

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Intrinsic lipid binding activity of ATG 16L1 supports efficient membrane anchoring and autophagy

et al. 2019

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Membrane targeting of autophagy‐related complexes is an important step that regulates their activities and prevents their aberrant engagement on non‐autophagic membranes. ATG16L1 is a core autophagy protein implicated at distinct phases of autophagosome biogenesis. In this study, we dissected the recruitment of ATG16L1 to the pre‐autophagosomal structure (PAS) and showed that it requires sequences within its coiled‐coil domain (CCD) dispensable for homodimerisation. Structural and mutational analyses identified conserved residues within the CCD of ATG16L1 that mediate direct binding to phosphoinositides, including phosphatidylinositol 3‐phosphate (PI3P). Mutating putative lipid binding residues abrogated the localisation of ATG16L1 to the PAS and inhibited LC3 lipidation. On the other hand, enhancing lipid binding of ATG16L1 by mutating negatively charged residues adjacent to the lipid binding motif also resulted in autophagy inhibition, suggesting that regulated recruitment of ATG16L1 to the PAS is required for its autophagic activity. Overall, our findings indicate that ATG16L1 harbours an intrinsic ability to bind lipids that plays an essential role during LC3 lipidation and autophagosome maturation.

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Section: Structural Modellingmentioning

confidence: 99%

Section: Structural Modellingmentioning

confidence: 99%

Intrinsic lipid binding activity of ATG 16L1 supports efficient membrane anchoring and autophagy

et al. 2019

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“…A number of research groups set out to modify the existing FF parameters such that they capture the structural diversity and flexibility of IDPs, producing less folded and more expanded IDP conformations. These force field modifications include strengthening the water-protein London dispersion interactions, 28,30 refining the protein backbone parameters to create more expanded structures or reducing the tendency for certain ordered conforma-tions, 31 and/or altering the salt-bridge interactions. 31 In a recent effort, D. E. Shaw Research used AMBER99SB*-ILDN 23,24 combined with TIP4P-D water 28 as a starting point and reparametrized torsion parameters and the protein-water van der Waals (vdW) interaction terms with the aim to develop a FF that provides an accurate model for both folded proteins and IDPs.…”

Section: Introductionmentioning

confidence: 99%

Thermodynamics and kinetics of the amyloid-β peptide revealed by Markov state models based on MD data in agreement with experiment

Paul

Samantray

Anteghini

et al. 2020

Preprint

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The amlyoid-β peptide (Aβ) is closely linked to the development of Alzheimer’s disease. Molecular dynamics (MD) simulations have become an indispensable tool for studying the behavior of this peptide at the (sub)molecular level, thereby providing insight into the molecular basis of Alzheimer’s disease. General key aspects of MD simulations are the force field used for modeling the peptide or protein and its environment, which is important for accurate modeling of the system of interest, and the length of the simulations, which determines whether or not equilibrium is reached. In this study we address these points by analyzing 30-µs MD simulations acquired for Aβ40 using seven different force fields. We assess the convergence of these simulations based on the convergence of various structural properties and of NMR and fluorescence spectroscopic observables. Moreover, we calculate Markov state models for each of the seven MD simulations, which provide an unprecedented view of the thermodynamics and kinetics of the amyloid-β peptide. This further allows us to provide answers for pertinent questions, like: Which force fields are suitable for modeling Aβ? (a99SB-UCB and a99SB-ILDN/TIP4P-D); What does Aβ peptide really look like? (mostly extended and disordered) and; How long does it take MD simulations of Aβ to attain equilibrium? (20–30 µs). We believe the analyses presented in this study will provide a useful reference guide for important questions relating to the structure and dynamics of Aβin particular, and by extension other similar disordered peptides.

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“…The following references are cited in the supporting information: Macke & Case (1998); Phillips et al (2005); Huang et al (2017); Jorgensen et al (1983); Humphrey et al (1996); Essmann et al (1995).…”

Section: Related Literaturementioning

confidence: 99%

Machine learning deciphers structural features of RNA duplexes measured with solution X-ray scattering

Chen

Pollack

2020

Int Union Crystallogr J

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Macromolecular structures can be determined from solution X-ray scattering. Small-angle X-ray scattering (SAXS) provides global structural information on length scales of 10s to 100s of Ångstroms, and many algorithms are available to convert SAXS data into low-resolution structural envelopes. Extension of measurements to wider scattering angles (WAXS or wide-angle X-ray scattering) can sharpen the resolution to below 10 Å, filling in structural details that can be critical for biological function. These WAXS profiles are especially challenging to interpret because of the significant contribution of solvent in addition to solute on these smaller length scales. Based on training with molecular dynamics generated models, the application of extreme gradient boosting (XGBoost) is discussed, which is a supervised machine learning (ML) approach to interpret features in solution scattering profiles. These ML methods are applied to predict key structural parameters of double-stranded ribonucleic acid (dsRNA) duplexes. Duplex conformations vary with salt and sequence and directly impact the foldability of functional RNA molecules. The strong structural periodicities in these duplexes yield scattering profiles with rich sets of features at intermediate-to-wide scattering angles. In the ML models, these profiles are treated as 1D images or features. These ML models identify specific scattering angles, or regions of scattering angles, which correspond with and successfully predict distinct structural parameters. Thus, this work demonstrates that ML strategies can integrate theoretical molecular models with experimental solution scattering data, providing a new framework for extracting highly relevant structural information from solution experiments on biological macromolecules.

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CHARMM36: An Improved Force Field for Folded and Intrinsically Disordered Proteins

Cited by 91 publications

References 0 publications

Intrinsic lipid binding activity of ATG 16L1 supports efficient membrane anchoring and autophagy

Intrinsic lipid binding activity of ATG 16L1 supports efficient membrane anchoring and autophagy

Thermodynamics and kinetics of the amyloid-β peptide revealed by Markov state models based on MD data in agreement with experiment

Machine learning deciphers structural features of RNA duplexes measured with solution X-ray scattering

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