Modeling Molecular Recognition: Theory and Application

Mardis, Kristy L.; Luo, Ray; David, Laurent; Potter, Michael J.; Glemza, Amy Jo; Payne, Greg; Gilson, Michael K.

doi:10.1080/07391102.2000.10506608

Cited by 6 publications

(4 citation statements)

References 40 publications

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“…The PB solvent in its current form has been demonstrated to be reliable in reproducing the energetics and conformations as compared with explicit solvent simulations and experimental measurements for a wide range of systems,59–61 although the computation involved is usually inefficient. We have significantly improved the numerical stability and efficiency of a finite‐difference PB (FDPB) solvent for biomolecule simulations,39, 40 so that this model can be directly applied to calculations involving large number of conformations as in protein structure prediction.…”

Section: Methodsmentioning

confidence: 99%

Physical scoring function based on AMBER force field and Poisson–Boltzmann implicit solvent for protein structure prediction

Hsieh

Luo

2004

Proteins

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A well-behaved physics-based all-atom scoring function for protein structure prediction is analyzed with several widely used all-atom decoy sets. The scoring function, termed AMBER/Poisson-Boltzmann (PB), is based on a refined AMBER force field for intramolecular interactions and an efficient PB model for solvation interactions. Testing on the chosen decoy sets shows that the scoring function, which is designed to consider detailed chemical environments, is able to consistently discriminate all 62 native crystal structures after considering the heteroatom groups, disulfide bonds, and crystal packing effects that are not included in the decoy structures. When NMR structures are considered in the testing, the scoring function is able to discriminate 8 out of 10 targets. In the more challenging test of selecting near-native structures, the scoring function also performs very well: for the majority of the targets studied, the scoring function is able to select decoys that are close to the corresponding native structures as evaluated by ranking numbers and backbone Calpha root mean square deviations. Various important components of the scoring function are also studied to understand their discriminative contributions toward the rankings of native and near-native structures. It is found that neither the nonpolar solvation energy as modeled by the surface area model nor a higher protein dielectric constant improves its discriminative power. The terms remaining to be improved are related to 1-4 interactions. The most troublesome term is found to be the large and highly fluctuating 1-4 electrostatics term, not the dihedral-angle term. These data support ongoing efforts in the community to develop protein structure prediction methods with physics-based potentials that are competitive with knowledge-based potentials.

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Section: Methodsmentioning

confidence: 99%

Physical scoring function based on AMBER force field and Poisson–Boltzmann implicit solvent for protein structure prediction

Hsieh

Luo

2004

Proteins

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“…We have recently developed an efficient method for the direct calculation of free energies and binding affinities , that has given promising results for a series of simple systems. − This method is applied here to seven synthetic adenine receptors that were developed to explore nucleic acid base-pairing in water. , The receptors are equipped with imide moieties that can form hydrogen bonds with adenine and with a variety of different aromatic groups that permit stacking (Figure ). The calculations yield standard free energies of binding (absolute binding free energies), permitting direct comparison with measured binding affinities.…”

Section: Introductionmentioning

confidence: 99%

Synthetic Adenine Receptors: Direct Calculation of Binding Affinity and Entropy

Luo

Gilson

2000

J. Am. Chem. Soc.

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A novel method for calculating binding free energies is applied to a series of water-soluble adenine receptors that have been characterized experimentally. The calculations use a predominant states method, "Mining Minima", to identify and account for the low-energy conformations of the free and bound species. The CHARMM force field is used to estimate potential energies, and an adjusted form of the generalized Born/ surface area model is used to estimate solvation energies as a function of conformation. The computed binding free energies agree with experiment to within 2.9 kJ/mol (0.7 kcal/mol) and reproduce observed trends across the series of receptors. Preorganization of two rotatable bonds enhances the calculated affinity of one receptor/ adenine complex by -2.5 kJ/mol (-0.6 kcal/mol), and the change in translational/rotational entropy (-T∆S°t rans/rot ) is 30 kJ/mol (7 kcal/mol). The concept of the translational/rotational entropy change upon binding in the present model is compared with others previously presented in the literature.

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“…5,6 The complex multi-step process of molecular recognition requires a balance of entropic and enthalpic components. 7,8 Enthalpic and entropic contributions to binding vary between the different steps from unbound to the fully bound state. Electrostatics are a critical component of the enthalpic contributions, dominating and guiding the recognition process, especially when the binding partners are still distant from one another.…”

Section: Introductionmentioning

confidence: 99%

PEP-Patch: Electrostatics in Protein-Protein Recognition, Specificity and Antibody Developability

Waibl

Pomarici

Hoerschinger

et al. 2023

Preprint

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The electrostatic properties of proteins arise from the number and distribution of polar and charged residues. Due to their long-ranged nature, electrostatic interactions in proteins play a critical role in numerous processes, such as molecular recognition, protein solubility, viscosity, and antibody developability. Thus, characterizing and quantifying electrostatic properties of a protein is a pre-requisite for understanding these processes. Here, we present PEP-Patch, a tool to visualize and quantify the electrostatic potential on the protein surface and showcase its applicability to elucidate protease substrate specificity, antibody-antigen recognition and predict heparin column retention times of antibodies as an indicator of pharmacokinetics.

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Modeling Molecular Recognition: Theory and Application

Cited by 6 publications

References 40 publications

Physical scoring function based on AMBER force field and Poisson–Boltzmann implicit solvent for protein structure prediction

Physical scoring function based on AMBER force field and Poisson–Boltzmann implicit solvent for protein structure prediction

Synthetic Adenine Receptors: Direct Calculation of Binding Affinity and Entropy

PEP-Patch: Electrostatics in Protein-Protein Recognition, Specificity and Antibody Developability

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