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H W van Vlijmen

Publications and source records attributed to H W van Vlijmen.

7 recordsLinked to original sources
PubMed1998

Improving the accuracy of protein pKa calculations: conformational averaging versus the average structure.

Several methods for including the conformational flexibility of proteins in the calculation of titration curves are compared. The methods use the linearized Poisson-Boltzmann equation to calculate the electrostatic free energies of solvation and are applied to bovine pancreatic trypsin inhibitor (BPTI) and hen egg-white lysozyme (HEWL). An ensemble of conformations is generated by a molecular dynamics simulation of the proteins with explicit solvent. The average titration curve of the ensemble is calculated in three different ways: an average structure is used for the pKa calculation; the electrostatic interaction free energies are averaged and used for the pKa calculation; and the titration curve for each structure is calculated and the curves are averaged. The three averaging methods give very similar results and improve the pKa values to approximately the same degree. This suggests, in contrast to implications from other work, that the observed improvement of pKa values in the present studies is due not to averaging over an ensemble of structures, but rather to the generation of a single properly averaged structure for the pKa calculation.

Aprotinin↗
PubMed1998

Titration calculations of foot-and-mouth disease virus capsids and their stabilities as a function of pH.

Foot-and-mouth disease virus (FMDV), a non-enveloped picornavirus, is sensitive to acidic conditions. At pH values below 7 the icosahedral virus capsid, formed from 60 copies of a protomer containing four polypeptides (VP1 to 4), dissociates into 12 pentamers, releasing the viral RNA. Evidence suggests that this acid lability may assist FMDV cell entry via an endosomal pathway. Calculations of titration curves and pH-stability profiles are presented for three different strains of FMDV, O1BFS, A10(61) and A22 Iraq, and compared with experimental data for complete virions and empty capsids (which lack RNA). The finite difference Poisson-Boltzmann method was used for the calculation of electrostatic free energies with the solvent treated as a dielectric continuum. The inter-pentamer interface in the virus is formed by two protomers related by 2-fold icosahedral symmetry. As a simple model for inter-pentamer interactions, a dimer and two separate protomers were compared. The association free energy was computed by integrating the difference between the titration curves of the two species. The calculations reproduced the observed decrease in capsid stability at acidic pH but not the difference in pH sensitivities of the two type A viruses. It is shown that only residues within 15 A of the interface play a significant role in determining acid lability. For the experimentally studied pH range (5 to 7.6), histidine residues were found to dominate the pH-dependence of the stability. Two histidine residues in VP3, H142 and H145, are shown to have the greatest effect by virtue of their interactions with many polar residues across the inter-pentamer interface; the interaction of H142 with an alpha-helix in the opposite pentamer contributes only a small proportion of the destabilization energy.

Aphthovirus↗
PubMed1997

PDB-based protein loop prediction: parameters for selection and methods for optimization.

An approach to loop prediction that starts with a database search is presented and analyzed. To obtain meaningful statistics, 130 loops from 21 proteins were studied. The correlation between the internal conformation of the loop and the conformation of the neighboring stem residues was examined. Distances between C(alpha) and C(beta) of the immediate neighbor residues at each end select template loops as well as more complex (e.g. three residues on either side) matching criteria. To have a high probability that the best possible loop candidate in the database is included in the set, relatively large cutoffs for matching the interatomic distances of the stem residues have to be used in the template loop selection procedure; for loops of length 5, this results in an average of 1000 loops and for loops of length 9, the number is about 1500. The required number increases only slowly with loop length, in contrast to the exponential time increase involved in direct searches of the conformational space. The best loops among the large number of candidates can be determined by ranking them with the standard CHARMM non-bonded energy function (without electrostatics) applied to the backbone and C(beta) atoms. The same representation (backbone plus C(beta)) can be used to optimize the loop orientations relative to the rest of the protein by constrained energy minimization. Target loops that have many non-bonded contacts with the protein yield better results so that analysis of the non-bonded contacts of the selected template loops is useful in determining the expected accuracy of a prediction. The method for loop selection and optimization predicted eight (out of 18) loops of up to nine residues to an RMSD better than 1.07 A relative to the crystal structure; for 17 of the 18 loops, one of the three lowest energy template loops had an RMSD of less than 1.79 A. The prediction of antibody loops from a database search is more effective than that for non-antibody loops. Provided that they belong to one of the canonical classes, very similar antibody loops are certain to exist in the database. Superposition of the stem residues for antibody loops also results in a better orientation than with arbitrary target loops because the neighboring residues tend to have a more similar beta-strand structure. Two H3 loops (for which no canonical structures have been proposed) were predicted with reasonable accuracy (RMSD of 0.49 A and 1.07 A) even though no corresponding antibody loops were in the database.

Algorithms↗
PubMed1990

A model for the antagonist binding site on the adenosine A1 receptor, based on steric, electrostatic, and hydrophobic properties.

With the aid of molecular modeling, both adenosine and adenosine A1 receptor antagonists belonging to various chemical classes were compared with respect to their minimum-energy conformations and molecular electrostatic potentials, as computed by the semiempirical molecular orbital program MOPAC. Distinct steric and electrostatic similarities between adenosine and the prototypic adenosine antagonist theophylline are evident when both compounds are superimposed, with theophylline in a so-called flipped orientation. Similar patterns were found for all other A1 antagonists investigated in this study. A model for the antagonist binding site on the adenosine A1 receptor, based on steric, electrostatic, and hydrophobic properties contributing to potency, is proposed.

Adenosine↗
PubMed1989

Molecular modeling of a putative antagonist binding site on helix III of the beta-adrenoceptor.

In recent biochemical studies it was demonstrated that residue Asp113 of the beta-adrenoceptor (beta-AR) is an indispensable amino acid for the binding of beta-AR antagonists. Earlier fluorescence studies showed that a tryptophan-rich region of the beta-AR is involved in the binding of propranolol, the prototype beta-AR antagonist. Bearing these two biochemical findings in mind, we explored the beta-AR part containing Asp113, for an energetically favorable antagonist binding site. This was done by performing molecular docking studies with the antagonist propranolol and a specific beta-AR peptide which included, besides Asp113, two possibly relevant tryptophan residues. In the docking calculations, the propranolol molecule was allowed to vary all its internal torsional angles. The receptor peptide was kept in an alpha-helix conformation, while side chains relevant to ligand binding were flexible to enable optimal adaptations to the ligand's binding conformation. By means of force-field calculations the total energy was minimized, consisting of the intramolecular energies of both ligand and receptor peptide, and the intermolecular energy. We found an antagonist binding site, consisting of amino acids Asp113 and Trp109, which enabled energetically favorable interactions with the receptor-binding groups of propranolol. According to these results, binding involves three main interaction points: (i) a reinforced ionic bond; (ii) a hydrogen bond; and (iii) a hydrophobic/charge transfer interaction. The deduced binding site shows a difference in affinity between the levo- and dextrorotatory isomers of propranolol caused by a difference in ability to form a hydrogen bond, which is in conformity with the experimentally observed stereoselectivity. Moreover, it also provides an explanation for the beta 1-selectivity of p-phenyl substituted phenoxypropanolamines like betaxolol. The p-phenyl substituent of betaxolol was shown to be sterically hindered upon binding to the beta 2-AR peptide, whereas this hindrance is very likely to be much less with the beta 1-AR peptide. Finally, the proposed antagonist binding site is discussed in the light of some recent biochemical findings and theories.

Adrenergic beta-Antagonists↗
PubMed1988

A molecular graphics study exploring a putative ligand binding site of the beta-adrenoceptor.

The recent elucidation of the primary structure of the cell membrane-bound beta-adrenoceptor has prompted us to explore putative ligand binding sites on this physiologically important receptor. By minimizing the energies of the 'prototype' ligand propranolol, (part of) the receptor and the proposed ligand-receptor complex with the aid of force field and quantum chemical calculations, we identified amino acid residue Trp313 as a highly probable candidate for interaction with the aromatic moiety of propranolol. The charge distribution on the indole nucleus of another beta-blocker, pindolol, with higher affinity for the beta-adrenoceptor, enables an even stronger interaction with the tryptophan residue. The carboxylic amino acid residue Glu306, located near the extracellular space of the cell membrane, interacts favorably with the positively charged nitrogen atom in the aliphatic side chain of the ligands. Finally, this putative model is discussed in the light of recent findings in mutagenesis studies, and compared to other ideas with respect to ligand-receptor interactions.

Amino Acid Sequence↗