Comparing pairwise‐additive and many‐body generalized Born models for acid/base calculations and protein design

Villa, F.; Mignon, David; Polydorides, Savvas; Simonson, Thomas

doi:10.1002/jcc.24898

Cited by 19 publications

(37 citation statements)

References 107 publications

(243 reference statements)

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“…Transition state modeling was done simply, by combining two X-ray structures and running a standard quantum chemistry protocol for atomic charges, consistent with the usual Amber force field [32]. Several variants of the implicit solvent model were compared; somewhat better results were obtained using a sophisticated GB approach (FDB variant [37]) that captures the many-body character of polar solvation. All the procedures were carried out with the Proteus software, which is freely available to academics (https://proteus.polytechnique.fr).…”

Section: Discussionmentioning

confidence: 99%

“…For the SLL variant, the predicted rotamers for binding site residues were in good agreement with the X-ray structure ( Supplementary Material). The FDBSA solvent model gave similar results, while NEASA was slightly poorer (not shown), possibly due to its simpler GB treatment [37]. We also searched for MetRS variants that maximized the AnL binding specificity, relative to Met.…”

Section: Designing Metrs To Bind Azidonorleucinementioning

confidence: 96%

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

Opuu

Nigro

Schmitt

et al. 2019

Preprint

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Designed enzymes are of fundamental and technological interest. Experimental directed evolution still has significant limitations, and computational approaches are complementary. 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 extend the method to the design of an enzyme for specific transition state binding, i.e., for catalytic power. We consider methionyl-tRNA synthetase (MetRS), which attaches methionine (Met) to its cognate tRNA, helping establishing codon identity. MetRS and other synthetases have been extensively redesigned by experimental directed evolution to accept noncanonical amino acids as substrates, leading to genetic code expansion. We 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, and mutants predicted to bind MetAMP were characterized experimentally and 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 expect the present method will become the paradigm for computational enzyme design.

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Section: Discussionmentioning

confidence: 99%

Section: Designing Metrs To Bind Azidonorleucinementioning

confidence: 96%

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

Opuu

Nigro

Schmitt

et al. 2019

Preprint

Self Cite

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“…In contrast, preserving the many-body solvation effects was shown recently to give improved accuracy for side chain pK a 's. 47 It also led to increased similarity between CPD sequences and natural sequences of several PDZ proteins. 47 Therefore, for the present CASK redesign, we applied the newer, many-body FDB model and obtained improved results.…”

Section: Discussionmentioning

confidence: 99%

“…They were obtained using a new apo CASK PDZ domain structure (PDB code 6NH9) as template and the more rigorous FDB electrostatics model. 47 The expression yields in E. coli were improved over the NEA Tiam1 designs, though not to the level typically seen with native PDZ domains. In contrast to the NEA Tiam1 designs, CD spectra of the FDB designs were similar to native PDZ domains, suggesting that these designs were structured (Fig 4).…”

Section: Experimental Characterization Of Selected Sequencesmentioning

confidence: 98%

A physics-based energy function allows the computational redesign of a PDZ domain

Opuu

Sun

Hou

et al. 2019

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A powerful approach to understand protein structure and evolution is to perform computer simulations that mimic aspects of evolution. In particular, structure-based computational protein design (CPD) can address the inverse folding problem, exploring a large space of amino acid sequences and selecting ones predicted to adopt a given fold. Previously, CPD has been used to entirely redesign several proteins: all or most of the protein sequence was allowed to mutate freely; among sampled sequences, those with low computed folding energy were selected, and a few percent of them did indeed adopt the correct fold. Those studies used an energy function that was partly or largely knowledge-based, with several empirical terms. Here, we show that a PDZ domain can be entirely redesigned using a "physics-based" energy function that combines standard molecular mechanics and a recent, continuum electrostatic solvent model. Many thousands of sequences were generated by Monte Carlo simulation. Among the lowest-energy sequences, three were chosen for experimental testing. All three could be overexpressed and had native-like circular dichroism and 1D-NMR spectra. Two exhibited an increase in their thermal denaturation curves when a peptide ligand was present, indicating binding and suggesting correctly folded proteins. Evidently, the physical principles that govern molecular mechanics and continuum electrostatics are sufficient to perform whole-protein redesign. This is encouraging, since these methods provide physical insights, can be systematically improved, and are transferable to other biopolymers and ligands of medical or technological interest.2

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“…Because consideration of the effects of explicit water molecules is difficult, protein design scoring functions usually rely upon an implicit solvation model such as the Lazaridis – Karplus ( EEF 1) [138], Generalized Born [139], or Poisson-Boltzmann methods [140,141]. Implicit models treat water molecules as a continuum, approximating the energy of solvation as a linear function of the accessible surface area.…”

Section: Challenges In Automated Protein Designmentioning

confidence: 99%

Recent advances in automated protein design and its future challenges

Setiawan

Brender

Zhang

2018

Expert Opinion on Drug Discovery

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Introduction Protein function is determined by protein structure which is in turn determined by the corresponding protein sequence. If the rules that cause a protein to adopt a particular structure are understood, it should be possible to refine or even redefine the function of a protein by working backwards from the desired structure to the sequence. Automated protein design attempts to calculate the effects of mutations computationally with the goal of more radical or complex transformations than are accessible by experimental techniques. Areas covered The authors give a brief overview of the recent methodological advances in computer-aided protein design, showing how methodological choices affect final design and how automated protein design can be used to address problems considered beyond traditional protein engineering, including the creation of novel protein scaffolds for drug development. Also, the authors address specifically the future challenges in the development of automated protein design. Expert opinion Automated protein design holds potential as a protein engineering technique, particularly in cases where screening by combinatorial mutagenesis is problematic. Considering solubility and immunogenicity issues, automated protein design is initially more likely to make an impact as a research tool for exploring basic biology in drug discovery than in the design of protein biologics.

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Comparing pairwise‐additive and many‐body generalized Born models for acid/base calculations and protein design

Cited by 19 publications

References 107 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

A physics-based energy function allows the computational redesign of a PDZ domain

Recent advances in automated protein design and its future challenges

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