Gerrit Vriend

Positioning hydrogen atoms by optimizing hydrogen-bond networks in protein structures

A method is presented that positions polar hydrogen atoms in protein structures by optimizing the total hydrogen bond energy. For this goal, an empirical hydrogen bond force field was derived from small molecule crystal structures. Bifurcated hydrogen bonds are taken into account. The procedure also predicts ionization states of His, Asp, and Glu residues. During optimization, sidechain conformations of His, Gln, and Asn residues are allowed to change their last χ angle by 180° to compensate for crystallographic misassignments. Crystal structure symmetry is taken into account where appropriate. The results can have significant implications for molecular dynamics simulations, protein engineering, and docking studies. The largest impact, however, is in protein structure verification: over 85% of protein structures tested can be improved by using our procedure. Proteins 26:363–376

PredictProtein—an open resource for online prediction of protein structural and functional features

PredictProtein is a meta-service for sequence analysis that has been predicting structural and functional features of proteins since 1992. Queried with a protein sequence it returns: multiple sequence alignments, predicted aspects of structure (secondary structure, solvent accessibility, transmembrane helices (TMSEG) and strands, coiled-coil regions, disulfide bonds and disordered regions) and function. The service incorporates analysis methods for the identification of functional regions (ConSurf), homology-based inference of Gene Ontology terms (metastudent), comprehensive subcellular localization prediction (LocTree3), protein–protein binding sites (ISIS2), protein–polynucleotide binding sites (SomeNA) and predictions of the effect of point mutations (non-synonymous SNPs) on protein function (SNAP2). Our goal has always been to develop a system optimized to meet the demands of experimentalists not highly experienced in bioinformatics. To this end, the PredictProtein results are presented as both text and a series of intuitive, interactive and visually appealing figures. The web server and sources are available at http://ppopen.rostlab.org.

Optimizing the hydrogen-bond network in Poisson-Boltzmann equation-based pK(a) calculations.

pKa calculation methods that are based on finite difference solutions to the Poisson–Boltzmann equation (FDPB) require that energy calculations be performed for a large number of different protonation states of the protein. Normally, the differences between these protonation states are modeled by changing the charges on a few atoms, sometimes the differences are modeled by adding or removing hydrogens, and in a few cases the positions of these hydrogens are optimized locally. We present an FDPB‐based pKa calculation method in which the hydrogen‐bond network is globally optimized for every single protonation state used. This global optimization gives a significant improvement in the accuracy of calculated pKa values, especially for buried residues. It is also shown that large errors in calculated pKa values are often due to structural artifacts induced by crystal packing. Optimization of the force fields and parameters used in pKa calculations should therefore be performed with X‐ray structures that are corrected for crystal artifacts. Proteins 2001;43:403–412.

Synergism with the Coactivator OBF-1 (OCA-B, BOB-1) Is Mediated by a Specific POU Dimer Configuration

POU domain proteins contain a bipartite DNA binding domain divided by a flexible linker that enables them to adopt various monomer configurations on DNA. The versatility of POU protein operation is additionally conferred at the dimerization level. The POU dimer formed on the PORE (ATTTGAAATGCAAAT) can recruit the transcriptional coactivator OBF-1, whereas POU dimers formed on the consensus MORE (ATGCATATGCAT) or on MOREs from immunoglobulin heavy chain promoters (AT[G/A][C/A]ATATGCAA) fail to interact. An interaction with OBF-1 is precluded since the same Oct-1 residues that form the MORE dimerization interface are also used for OBF-1/Oct-1 interactions on the PORE. Our findings provide a paradigm of how specific POU dimer assemblies can differentially recruit a coregulatory activity with distinct transcriptional readouts.

Objectively judging the quality of a protein structure from a Ramachandran plot

MOTIVATIONnStatistical methods that compare observed and expected distributions of experimental observables provide powerful tools for the quality control of protein structures. The distribution of backbone dihedral angles (Ramachandran plot) has often been used for such quality control, but without a firm statistical foundation.nnnRESULTSnA new and-simple method is presented for judging the quality of a protein structure based on the distribution of backbone dihedral angles. Inputs to the method are 60 torsion angle distributions extracted from protein structures solved at high resolution; one for each combination of residue type and tri-state secondary structure. Output for a protein is a Ramachandran Z-score, expressing the quality of the Ramachandran plot relative to current state-of-the-art structures.

Receptors coupling to G proteins: is there a signal behind the sequence?

Upon the binding of their ligands, G protein‐coupled receptors couple to the heterotrimeric G proteins to transduce a signal. One receptor family may couple to a single G protein subtype and another family to several ones. Is there a signal in the receptor sequence that can give an indication of the G protein subtype selectivity? We used a sequence analysis method on biogenic amine and adenosine receptors and concluded that a weak signal can be detected in receptor families where specialization for coupling to a given G protein occurred during a recent divergent evolutionary process. Proteins 2000;41:448–459.

A sequence and structural study of transmembrane helices.

A comparison is made between the distribution of residue preferences, three dimensional nearest neighbour contacts, preferred rotamers, helix-helix crossover angles and peptide bond angles in three sets of proteins: a non-redundant set of accurately determined globular protein structures, a set of four-helix bundle structures and a set of membrane protein structures. Residue preferences for the latter two sets may reflect overall helix stabilising propensities but may also highlight differences arising out of the contrasting nature of the solvent environments in these two cases. The results bear out the expectation that there may be differences between residue type preferences in membrane proteins and in water soluble globular proteins. For example, the β-branched residue types valine and isoleucine are considerably more frequently encountered in membrane helices. Likewise, glycine and proline, residue types normally associated with `helix-breaking propensity are found to be relatively more common in membrane helices. Three dimensional nearest neighbour contacts along the helix, preferred rotamers, and peptide bond angles are very similar in the three sets of proteins as far as can be ascertained within the limits of the relatively low resolution of the membrane proteins dataset. Crossing angles for helices in the membrane protein set resemble the four helix bundle set more than the general non-redundant set, but in contrast to both sets they have smaller crossing angles consistent with the dual requirements for the helices to form a compact structure while having to span the membrane. In addition to the pairwise packing of helices we investigate their global packing and consider the question of helix supercoiling in helix bundle proteins.

Analysis of the black-eyed pea trypsin and chymotrypsin inhibitor-α-chymotrypsin complex

The black‐eyed pea trypsin and chymotrypsin inhibitor (BTCI) is a member of the Bowman‐Birk protease inhibitor (BBI) family. The three‐dimensional model of the BTCI‐chymotrypsin complex was built based on the homology to Bowman‐Birk inhibitors with known structures. An extensive theoretical and experimental study of these known structures has been performed. The model confirms the ideas about Bowman‐Birk inhibitor structure‐function relations and agrees well with our experimental data (circular dichroism, IR and light scattering). The electrostatic potentials at the enzyme‐inhibitor contact surface reveal a pattern of complementary electrostatic potentials from which mutations can be inferred that could give these inhibitors an altered specificity.

Improving the thermostability of the neutral protease of Bacillus stearothermophilus by replacing a buried asparagine by leucine

Amino acids buried in the hydrophobic interior of a protein with polar side chain atoms, which are not involved in hydrogen bonding or electrostatic interactions, have an adverse effect on protein stability. Replacing such residues by hydrophobic ones may render a protein more stable. Asparagine 241, which is buried in the neutral protease of Bacillus stearothermophilus, was replaced by leucine by side‐directed mutagenesis. This mutation increased the stability of the protein by 0.7 ± 0.1 degree.

Validation of nuclear magnetic resonance structures of proteins and nucleic acids: Hydrogen geometry and nomenclature

A statistical analysis is reported of 1,200 of the 1,404 nuclear magnetic resonance (NMR)‐derived protein and nucleic acid structures deposited in the Protein Data Bank (PDB) before 1999. Excluded from this analysis were the entries not yet fully validated by the PDB and the more than 100 entries that contained < 95% of the expected hydrogens. The aim was to assess the geometry of the hydrogens in the remaining structures and to provide a check on their nomenclature. Deviations in bond lengths, bond angles, improper dihedral angles, and planarity with respect to estimated values were checked. More than 100 entries showed anomalous protonation states for some of their amino acids. Approximately 250,000 (1.7%) atom names differed from the consensus PDB nomenclature. Most of the inconsistencies are due to swapped prochiral labeling. Large deviations from the expected geometry exist for a considerable number of entries, many of which are average structures. The most common causes for these deviations seem to be poor minimization of average structures and an improper balance between force‐field constraints for experimental and holonomic data. Some specific geometric outliers are related to the refinement programs used. A number of recommendations for biomolecular databases, modeling programs, and authors submitting biomolecular structures are given. Proteins 1999;37:404–416. ©1999 Wiley‐Liss, Inc.