Simple models for nonpolar solvation: Parameterization and testing

Michael, Eleni; Polydorides, Savvas; Simonson, Thomas; Archontis, Georgios

doi:10.1002/jcc.24910

Cited by 21 publications

(33 citation statements)

References 75 publications

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“…Here, we used a carefully-parameterized Generalized Born variant [33], a physically-plausible value of the protein dielectric constant and an "FDB" computational scheme that maintains the many-body nature of the GB model. The simpler, NEA scheme gave somewhat poorer results, similar to another recent study [35]. For nonpolar contributions to solvation, we compared a Surface Area (SA) treatment and a Lazaridis-Karplus (LK) treatment, which gave similar results.…”

Section: Discussionsupporting

confidence: 68%

“…The MM term used the Amber ff99SB force field [30,32]. The SA term was described earlier [33][34][35]. The LK term and its parameterization were described earlier [35].…”

Section: Energy Function and Matrixmentioning

confidence: 99%

“…The SA term was described earlier [33][34][35]. The LK term and its parameterization were described earlier [35]. The GB term corresponds to a variant very similar to the one used in Amber, detailed in previous articles [33,36,37].…”

Section: Energy Function and Matrixmentioning

confidence: 99%

“…These were kept up to date over the course of the MC simulation, so the GB interaction could be deduced with little additional calculation [37,39]. The solvent dielectric constant was 80; the protein one was 4.0 with the GBSA variants and 6.8 with GBLK [35]. Each solvent model is referred to by its GB variant and nonpolar term; for example, the FDBLK model combines FDB with LK.…”

Section: Energy Function and Matrixmentioning

confidence: 99%

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Adaptive landscape flattening allows the design of both enzyme: Substrate binding and catalytic power

et al. 2020

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Designed enzymes are of fundamental and technological interest. Experimental directed evolution still has significant limitations, and computational approaches are a complementary route. A designed enzyme should satisfy multiple criteria: stability, substrate binding, transition state binding. Such multi-objective design is computationally challenging. Two recent studies used adaptive importance sampling Monte Carlo to redesign proteins for ligand binding. By first flattening the energy landscape of the apo protein, they obtained positive design for the bound state and negative design for the unbound. We have now extended the method to design an enzyme for specific transition state binding, i.e., for its catalytic power. We considered methionyl-tRNA synthetase (MetRS), which attaches methionine (Met) to its cognate tRNA, establishing codon identity. Previously, MetRS and other synthetases have been redesigned by experimental directed evolution to accept noncanonical amino acids as substrates, leading to genetic code expansion. Here, we have redesigned MetRS computationally to bind several ligands: the Met analog azidonorleucine, methionyl-adenylate (MetAMP), and the activated ligands that form the transition state for MetAMP production. Enzyme mutants known to have azidonorleucine activity were recovered by the design calculations, and 17 mutants predicted to bind MetAMP were characterized experimentally and all found to be active. Mutants predicted to have low activation free energies for MetAMP production were found to be active and the predicted reaction rates agreed well with the experimental values. We suggest the present method should become the paradigm for computational enzyme design.

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Section: Discussionsupporting

confidence: 68%

“…The MM term used the Amber ff99SB force field [30,32]. The SA term was described earlier [33][34][35]. The LK term and its parameterization were described earlier [35].…”

Section: Energy Function and Matrixmentioning

confidence: 99%

Section: Energy Function and Matrixmentioning

confidence: 99%

Section: Energy Function and Matrixmentioning

confidence: 99%

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Adaptive landscape flattening allows the design of both enzyme: Substrate binding and catalytic power

et al. 2020

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“…The term E SA X takes into account the tendency of solute atoms to be exposed or hidden from solvent via a surface area (SA) term. The term E DI X incorporates solute-water attractive dispersion interactions (33)(34)(35)(36).…”

Section: Estimation Of Binding Affinitiesmentioning

confidence: 99%

Computational optimization of the SARS-CoV-2 receptor-binding-motif affinity for human ACE2

Polydorides

Archontis

2020

Preprint

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The coronavirus SARS-CoV-2, that is responsible for the COVID-19 pandemic, and the closely related SARS-CoV coronavirus enter cells by binding at the human angiotensin converting enzyme 2 (hACE2). The stronger hACE2 affinity of SARS-CoV-2 has been connected with its higher infectivity. In this work, we study hACE2 complexes with the receptor binding domains (RBDs) of the human SARS-CoV-2 and human SARS-CoV viruses, using all-atom molecular dynamics (MD) simulations and Computational Protein Design (CPD) with a physics-based energy function. The MD simulations identify charge-modifying substitutions between the CoV-2 and CoV RBDs, which either increase or decrease the hACE2 affinity of the SARS-CoV-2 RBD. The combined effect of these mutations is small, and the relative affinity is mainly determined by substitutions at residues in contact with hACE2. Many of these findings are in line and interpret recent experiments. Our CPD calculations redesign positions 455, 493, 494 and 501 of the SARS-CoV-2 RBM, which contact hACE2 in the complex and are important for ACE2 recognition. Sampling is enhanced by an adaptive importance sampling Monte Carlo method. Sequences with increased affinity replace CoV-2 glutamine by a negative residue at position 493, and serine by nonpolar, aromatic or a threonine at position 494. Substitutions at positions positions 455 and 501 have a smaller effect on affinity. Substitutions suggested by our design are seen in viral sequences encountered in other species, including bat and pangolin. Our results might be used to identify potential virus strains with higher human infectivity and assist in the design of peptide-based or peptidomimetic compounds with the potential to inhibit SARS-CoV-2 binding at hACE2.

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Combining the polarizable Drude force field with a continuum electrostatic Poisson–Boltzmann implicit solvation model

et al. 2018

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In this work, we have combined the polarizable force field based on the classical Drude oscillator with a continuum Poisson-Boltzmann/solvent-accessible surface area (PB/SASA) model. In practice, the positions of the Drude particles experiencing the solvent reaction field arising from the fixed charges and induced polarization of the solute must be optimized in a self-consistent manner. Here, we parameterized the model to reproduce experimental solvation free energies of a set of small molecules. The model reproduces well-experimental solvation free energies of 70 molecules, yielding a root mean square difference of 0.8 kcal/mol versus 2.5 kcal/mol for the CHARMM36 additive force field. The polarization work associated with the solute transfer from the gas-phase to the polar solvent, a term neglected in the framework of additive force fields, was found to make a large contribution to the total solvation free energy, comparable to the polar solute-solvent solvation contribution. The Drude PB/SASA also reproduces well the electronic polarization from the explicit solvent simulations of a small protein, BPTI. Model validation was based on comparisons with the experimental relative binding free energies of 371 single alanine mutations. With the Drude PB/SASA model the root mean square deviation between the predicted and experimental relative binding free energies is 3.35 kcal/mol, lower than 5.11 kcal/mol computed with the CHARMM36 additive force field. Overall, the results indicate that the main limitation of the Drude PB/SASA model is the inability of the SASA term to accurately capture non-polar solvation effects. © 2018 Wiley Periodicals, Inc.

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Simple models for nonpolar solvation: Parameterization and testing

Cited by 21 publications

References 75 publications

Adaptive landscape flattening allows the design of both enzyme: Substrate binding and catalytic power

Adaptive landscape flattening allows the design of both enzyme: Substrate binding and catalytic power

Computational optimization of the SARS-CoV-2 receptor-binding-motif affinity for human ACE2

Combining the polarizable Drude force field with a continuum electrostatic Poisson–Boltzmann implicit solvation model

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