Electrostatic Interactions in Macromolecules: Theory and Applications

Sharp, Kim A.; Honig, Barry

doi:10.1146/annurev.bb.19.060190.001505

Cited by 1,334 publications

(1,101 citation statements)

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“…The receptor dehydration penalty was computed numerically with the DELPHI computer program Sharp & Honig, 1990) as the difference in the total electrostatic energy for the barnase charge distribution embedded in a low-dielectric region representing the bound and unbound state. The Poisson equation was solved (corresponding to zero ionic strength) with the low-dielectric (ein, = 4) region for the receptor defined as the spherical region for the complex with the spherical ligand region (and, for some computations, a tunnel leading from the spherical ligand to the exterior) subtracted.…”

Section: Methodsmentioning

confidence: 99%

“…To determine the total electrostatic contribution to binding, we computed the ligand dehydration penalty and ligand-receptor interaction energy analytically (because in each case the dielectric boundary was spherical) and computed the receptor dehydration penalty numerically with the DELPHI computer program Sharp & Honig, 1990). In each of the four cases, the overall electrostatic binding free energy was favorable, including the docking of the ligand with the total charge constrained to -le; this is in contrast to computations on many natural complexes, for which the electrostatic contribution to the binding free energy is unfavorable (Novomy & Sharp, 1992;Misra et al, 1994aMisra et al, , 1994bSharp, 1996;Shen & Wendoloski, 1996;Bruccoleri et al, 1997;Novotny et al, 1997 contributions to the electrostatic binding free energy for each liat least an atomic radius from the molecular surface.…”

Section: + Amentioning

confidence: 99%

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Computation of electrostatic complements to proteins: A case of charge stabilized binding

et al. 1998

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Recent evidence suggests that the net effect of electrostatics is generally to destabilize protein binding due to large desolvation penalties. A novel method for computing ligand-charge distributions that optimize the tradeoff between ligand desolvation penalty and favorable interactions with a binding site has been applied to a model for bamase. The result is a ligand-charge distribution with a favorable electrostatic contribution to binding due, in part, to ligand point charges whose direct interaction with the binding site is unfavorable, but which make strong intra-molecular interactions that are uncloaked on binding and thus act to lessen the ligand desolvation penalty.

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Section: Methodsmentioning

confidence: 99%

Section: + Amentioning

confidence: 99%

Computation of electrostatic complements to proteins: A case of charge stabilized binding

et al. 1998

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“…Each of the complexes was rigidly separated, and the electrostatic component of the solvation energy in both the bound and unbound states was computed using a finite-difference Poisson-Boltzmann (FDPB) approach, as implemented in a locally-modified version of the program DELPHI. (48)(49)(50)(51) Calculations were performed with the PARAM19 parameters using a two-stage focusing procedure (the molecule occupying first 23% then 92% of the grid) on a 129 × 129 × 129 grid. The hydrophobic contribution to the solvation free energy was estimated by a term proportional to the solvent accessible surface area, using a factor of 5 cal/mol/Å 2 .…”

Section: Protein Design Methodologymentioning

confidence: 99%

“…The survivors from the initial search (3678 sequences, 36780 structural states) were therefore re-evaluated using a more accurate, but relatively time-consuming, Poisson-Boltzmann/Surface Area (PBSA) treatment of solvation. (48)(49)(50)(51) The solvation energies of each complex structure (and of the isolated side-chain models used as a reference state) were computed and combined with in vacuo energies computed with the molecularmechanics (MM) force field. (40) This yielded a new energetically ranked list of all low energy sequences, with each sequence represented by up to ten low energy structures.…”

Section: Computational Sequence Selectionmentioning

confidence: 99%

Rational Design of New Binding Specificity by Simultaneous Mutagenesis of Calmodulin and a Target Peptide

et al. 2006

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Calcium-saturated calmodulin (CaM) binds and influences the activity of a varied collection of target proteins in most cells. This promiscuity underlies CaM's role as a shared participant in calciumdependent signal transduction pathways, but imposes a handicap on popular CaM-based calcium biosensors, which display an undesired tendency to cross-react with cellular proteins. Designed CaM/ target pairs that retain high affinity for one another, but lack affinity for wild-type CaM and its natural interaction partners, would therefore be useful as sensor components, and possibly also as elements of "synthetic" cellular signaling networks. Here we have adopted a rational approach to creating suitably modified CaM/target complexes by using computational design methods to guide parallel site-directed mutagenesis of both binding partners. A hierarchical design procedure was applied to suggest a small number of complementary mutations on CaM and on a peptide ligand derived from skeletal muscle light chain kinase (M13). Experimental analysis showed that the procedure was successful in identifying CaM and M13 mutants with novel specificity for one another. Importantly, the designed complexes retained affinity comparable to the wild-type CaM/M13 complex. These results represent a step toward the creation of CaM and M13 derivatives with specificity fully orthogonal to the wild-type proteins, and show that qualitatively accurate predictions may be obtained from computational methods applied simultaneously to two proteins involved in multiple linked binding equilibria. Figure 1A). (2,3) Calcium-loaded CaM binds to isolated target sequences with affinity comparable to its complexes with the intact proteins, and with dissociation constants often in the nanomolar range. With few exceptions, CaM ligands tend to be highly basic, presenting lysine and arginine residues that form salt-bridge networks with negatively-charged and polar amino-acid side chains bordering the binding cleft on CaM ( Figure 1B). In addition, many CaM targets appear to be anchored by a key pair of bulky groups spaced apart by 2.5 or 3.5 helical turns. (4) Deletion or mutation of these anchor residues dramatically reduces CaMbinding affinity. (5,6) Considerable interest in CaM/peptide interactions has revolved around potential applications in biotechnology. CaM-affinity chromatography (7) The ability to understand and control determinants of binding specificity at the CaM/target interface is important both for biotechnological applications and for appreciation of how CaM transduces metabolic signals via multiple protein interaction networks. Many studies have probed the contributions of both the charged and hydrophobic anchor residues to CaM binding affinity, (5,6,(19)(20)(21)(22)) but relatively few have sought to manipulate specificity through purposeful mutagenesis of one or both binding partners. A step in this direction was taken by Mayo and colleagues,(23,24) and involved the integration of computational and experimental methods to bias CaM spe...

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“…Alternatively, treating the water as a continuum medium with macroscopic properties, such as a bulk dielectric constant, allows a faster computation of the electrostatic and hydrodynamic effects. [1][2][3] Methods for evaluating the electrostatic potential in continuum solvation models include the generalized Born method, 4,5 the effective charge method of ref. 6, and various numerical methods for direct solution of the Poisson-Boltzmann equation.…”

Section: Introductionmentioning

confidence: 99%

Boundary element solution of the linear Poisson–Boltzmann equation and a multipole method for the rapid calculation of forces on macromolecules in solution

Bordner

Huber

2003

J Comput Chem

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Abstract:The Poisson-Boltzmann equation is widely used to describe the electrostatic potential of molecules in an ionic solution that is treated as a continuous dielectric medium. The linearized form of this equation, applicable to many biologic macromolecules, may be solved using the boundary element method. A single-layer formulation of the boundary element method, which yields simpler integral equations than the direct formulations previously discussed in the literature, is given. It is shown that the electrostatic force and torque on a molecule may be calculated using its boundary element representation and also the polarization charge for two rigid molecules may be rapidly calculated using a noniterative scheme. An algorithm based on a fast adaptive multipole method is introduced to further increase the speed of the calculation. This method is particularly suited for Brownian dynamics or molecular dynamics simulations of large molecules, in which the electrostatic forces must be calculated for many different relative positions and orientations of the molecules. It has been implemented as a set of programs in Cϩϩ, which are used to study the accuracy and speed of this method for two actin monomers.

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Electrostatic Interactions in Macromolecules: Theory and Applications

Cited by 1,334 publications

References 1 publication

Computation of electrostatic complements to proteins: A case of charge stabilized binding

Computation of electrostatic complements to proteins: A case of charge stabilized binding

Rational Design of New Binding Specificity by Simultaneous Mutagenesis of Calmodulin and a Target Peptide

Boundary element solution of the linear Poisson–Boltzmann equation and a multipole method for the rapid calculation of forces on macromolecules in solution

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