An assessment of protein-ligand binding site polarizability

Electronic polarizability, an important physical property of biomolecules, is currently ignored in most biomolecular calculations. Yet, it is widely believed that polarization could account for a substantial fraction of the total nonbonded energy of a system. This belief is supported by studies of small complexes in vacuum. This perception is driving the development of a new class of polarizable force fields for biomolecular calculations. However, the quantification of this term for protein-ligand complexes has never been attempted. Here we explore the polarizable nature of protein-ligand complexes in order to evaluate the importance of this effect. We introduce two indexes describing the polarizability of protein binding sites. These we apply to a large range of pharmaceutically relevant complexes. We offer a recommendation of particular complexes as test systems with which to determine the effects of polarizability and as test cases with which to test the new generation of force fields. Additionally, we provide a tabulation of the amino acid composition of these binding sites and show that composition can be specific for certain classes of proteins. We also show that the relative abundance of some amino acids is different in binding sites than elsewhere in a protein's structure.     

MotifAdjuster: a tool for computational reassessment of transcription factor binding site annotations

Valuable binding-site annotation data are stored in databases. However, several types of errors can, and do, occur in the process of manually incorporating annotation data from the scientific literature into these databases. Here, we introduce MotifAdjuster , a tool that helps to detect these errors, and we demonstrate its efficacy on public data sets.     

A new test set for validating predictions of protein-ligand interaction

We present a large test set of protein-ligand complexes for the purpose of validating algorithms that rely on the prediction of protein-ligand interactions. The set consists of 305 complexes with protonation states assigned by manual inspection. The following checks have been carried out to identify unsuitable entries in this set: (1) assessing the involvement of crystallographically related protein units in ligand binding; (2) identification of bad clashes between protein side chains and ligand; and (3) assessment of structural errors, and/or inconsistency of ligand placement with crystal structure electron density. In addition, the set has been pruned to assure diversity in terms of protein-ligand structures, and subsets are supplied for different protein-structure resolution ranges. A classification of the set by protein type is available. As an illustration, validation results are shown for GOLD and SuperStar. GOLD is a program that performs flexible protein-ligand docking, and SuperStar is used for the prediction of favorable interaction sites in proteins. The new CCDC/Astex test set is freely available to the scientific community (http://www.ccdc.cam.ac.uk).     

CAFTAN: a tool for fast mapping, and quality assessment of cDNAs

Background  

The German cDNA Consortium has been cloning full length cDNAs and continued with their exploitation in protein localization experiments and cellular assays. However, the efficient use of large cDNA resources requires the development of strategies that are capable of a speedy selection of truly useful cDNAs from biological and experimental noise. To this end we have developed a new high-throughput analysis tool, CAFTAN, which simplifies these efforts and thus fills the gap between large-scale cDNA collections and their systematic annotation and application in functional genomics.     

The salt-dependence of a protein-ligand interaction: ion-protein binding energetics

Using the binding of a nucleotide inhibitor (guanosine-3'-monophosphate) to a ribonuclease (ribonuclease Sa) as a model system, we show that the salt-dependence of the interaction arises due to specific ion binding at the site of nucleotide binding. The presence of specific ion-protein binding is concluded from a combination of differential scanning calorimetry and NMR data. Isothermal titration calorimetry data are then fit to determine the energetic profile (enthalpy, entropy, and heat capacity) for both the ion-protein and nucleotide-protein interactions. The results provide insight into the energetics of charge-charge interactions, and have implications for the interpretation of an observed salt-dependence. Further, the presence of specific ion-binding leads to a system behavior as a function of temperature that is drastically different from that predicted from Poisson-Boltzmann calculations.     

Fundamental equation of thermodynamics for protein-ligand binding

For the internal energy and every thermodynamic potential that can be defined by a Legendre transform, there is a fundamental equation that contains all the thermodynamic information about a system. For a system involving the binding of molecular oxygen and hydrogen ions by a protein, fundamental equations are given for the Gibbs energy G, the transformed Gibbs energy G' at specified pH, and the further transformed Gibbs energy G" at specified pH and specified concentration of molecular oxygen. The Maxwell equations for these various Gibbs energies are important because they provide the connection with experimentally determined properties and increase our understanding of these properties. Measurements of the average number of oxygen molecules bound as a function of T, pH and concentration of molecular oxygen make it possible to calculate Delta(f)G"(o) of the reactant. Maxwell equations make it possible to calculate the average number of hydrogen ions bound, Delta(f)S"(o), Delta(f)H"(o) and their partial derivatives. These relations are illustrated with numerical calculations on a simple reaction system.     

The roles of contact residue disorder and domain composition in characterizing protein-ligand binding specificity and promiscuity

Most protein chains interact with only one ligand but a small number of protein chains can bind several ligands, and many examples are available in the protein-ligand complex database of PDB. Among these proteins, some show preferences for the ligands or types of ligands they bind; however, so far we have only poor understanding of what determines protein-ligand binding and its specificity. Here we investigate the structural and functional properties of proteins in protein-ligand complexes. Analysis of the protein-ligand complex dataset from the PDB structure database reveals that proteins with more interactions have more disordered contact residues. Those proteins containing few disordered contact residues that bind multiple ligands have a tendency to consist of several domains. Analysis of physicochemical properties of hub contact residues binding multiple ligands indicates that they are enriched for hydrophilic, charged, polar and His-Asp catalytic triad residues. Finally, in order to differentiate proteins binding different classes of ligands, we mapped the three most prominent classes of ligands onto different superfamily domains. Our results demonstrate that contact residue disorder and ordered multiple domains are complementary factors that play a crucial role in determining ligand binding specificity and promiscuity.     

TICO: a tool for improving predictions of prokaryotic translation initiation sites

SUMMARY: We provide the tool 'TICO' (Translation Initiation site COrrection) for improving the results of conventional gene finders for prokaryotic genomes with regard to exact localization of the translation initiation site (TIS). At the current state TICO provides an interface for direct post processing of the predictions obtained from the widely used program GLIMMER. Our program is based on a clustering algorithm for completely unsupervised scoring of potential TIS locations. AVAILABILITY: Our tool can be freely accessed through a web interface at http://tico.gobics.de/ CONTACT: maike@gobics.de     

Modulating LOV domain photodynamics with a residue alteration outside the chromophore binding site

Phototropins, a class of light-activated protein kinases, are essential for several blue light responses in plants and algae, including phototropism. These proteins contain two internal light, oxygen, and voltage sensitive (LOV) domains, which bind flavin chromophores and undergo a reversible photochemical formation of a cysteinyl-flavin adduct as part of the light sensing process. While the photodynamic properties of such photosensory domains are dictated by interactions between the chromophore and surrounding protein, more distant residues can play a significant role as well. Here we explore the role of the Phe434 residue in the photosensory response of the second LOV domain of Avena sativa phototropin 1 (AsLOV2), a model photochemical system for these LOV domains. Phe434 is more than 6 ? from the FMN chromophore in AsLOV2; nevertheless, an F434Y point mutation is likely to change several structural features of the chromophore binding site, as we demonstrate using molecular dynamics simulations. Transient absorption signals spanning 15 decades in time were compared for wild-type AsLOV2 and the F434Y mutant, showing that the latter has significantly altered photodynamics, including (i) a faster intersystem crossing leading to triplet formation on a nanosecond time scale, (ii) biphasic formation of adduct-state kinetics on the microsecond time scale, and (iii) greatly accelerated ground-state recovery kinetics on a second time scale. We present mechanistic models that link these spectroscopic differences to changes in the configuration of the critical cysteine residue and in the chromophore's accessibility to solvent and oxygen according to MD trajectories and purging experiments. Taken together, these results demonstrate the importance of residues outside the chromophore-binding pocket in modulating LOV domain photodynamics.     

Scoring optimisation of unbound protein-protein docking including protein binding site predictions

The prediction of the structure of the protein-protein complex is of great importance to better understand molecular recognition processes. During systematic protein-protein docking, the surface of a protein molecule is scanned for putative binding sites of a partner protein. The possibility to include external data based on either experiments or bioinformatic predictions on putative binding sites during docking has been systematically explored. The external data were included during docking with a coarse-grained protein model and on the basis of force field weights to bias the docking search towards a predicted or known binding region. The approach was tested on a large set of protein partners in unbound conformations. The significant improvement of the docking performance was found if reliable data on the native binding sites were available. This was possible even if data for single key amino acids at a binding interface are included. In case of binding site predictions with limited accuracy, only modest improvement compared with unbiased docking was found. The optimisation of the protocol to bias the search towards predicted binding sites was found to further improve the docking performance resulting in approximately 40% acceptable solutions within the top 10 docking predictions compared with 22% in case of unbiased docking of unbound protein structures.     

Identification of lysine residue 199 of human serum albumin as a binding site for benzylpenicilloyl groups

Cyanogen bromide and tryptic fragments of penicilloylated serum albumin from penicillin-treated patients were separated by HPLC. Determinations of benzylpenicilloyl (BPO) were performed on the different fractions. A BPO containing peptide was identified by its amino acid sequence and the bound BPO group was located on the lysine residue 199.     

Tryptophan residue at the heparin binding site in antithrombin III

Chemical modifications have demonstrated that the ultraviolet difference spectrum produced when heparin interacts with antithrombin III is due primarily to changes in the tryptophan environment. This is based on the observation that this spectrum could be abolished by treatment of antithrombin III with dimethyl (2-hydroxy-5-nitrobenzyl) sulfonium bromide but not with tetranitromethane. The tryptophan-modified antithrombin III is still capable of binding to thrombin even when it has lost 85% of heparin cofactor activity. A marked decrease in reactivity of tryptophan residues is observed when modification is carried out in the presence of heparin. Evidence is presented that tryptophan is in the heparin binding site.     

Mapping the ligand binding site at protein side-chains in protein-ligand complexes through NOE difference spectroscopy

This report describes a novel NMR approach for mapping the interaction surface between an unlabeled ligand and a ¹³C,¹⁵N-labeled protein. The method relies on the spin inversion properties of the dipolar relaxation pathways and records the differential relaxation of two spin modes, where ligand and protein ¹H magnetizations are aligned either in a parallel or anti-parallel manner. Selective inversion of protein protons is achieved in a straightforward manner by exploiting the one-bond heteronuclear scalar couplings (¹J_CH, ¹J_NH). Suppression of indirect relaxation pathways mediated by bulk water or rapidly exchanging protons is achieved by selective inversion of the water signal in the middle of the NOESY mixing period. The method does not require deuteration of the protein or well separated spectral regions for the protein and the ligand, respectively. Additionally, in contrast to previous methods, the new experiment identifies side-chain enzyme ligand interactions along the intermolecular binding interface. The method is demonstrated with an application to the B₁₂-binding subunit of glutamate mutase from Clostridium tetanomorphum for which NMR chemical shift changes upon B₁₂-nucleotide loop binding and a high-resolution solution structure are available.     

WaterScore: a novel method for distinguishing between bound and displaceable water molecules in the crystal structure of the binding site of protein-ligand complexes

We have performed a multivariate logistic regression analysis to establish a statistical correlation between the structural properties of water molecules in the binding site of a free protein crystal structure, with the probability of observing the water molecules in the same location in the crystal structure of the ligand-complexed form. The temperature B-factor, the solvent-contact surface area, the total hydrogen bond energy and the number of protein-water contacts were found to discriminate between bound and displaceable water molecules in the best regression functions obtained. These functions may be used to identify those bound water molecules that should be included in structure-based drug design and ligand docking algorithms.FIGURE The binding site ( thin sticks) of penicillopepsin (3app) with its crystallographically determined water molecules ( spheres) and superimposed ligand (in thick sticks, from complexed structure 1ppk). Water molecules sterically displaced by the ligand upon complexation are shown in cyan. Bound water molecules are shown in blue. Displaced water molecules are shown in yellow. Water molecules removed from the analysis due to a lack of hydrogen bonds to the protein are shown in white. WaterScore correctly predicted waters in blue as Probability=1 to remain bound and waters in yellow as Probability<1x10(-20) to remain bound.     

Four-body atomic potential for modeling protein-ligand binding affinity: application to enzyme-inhibitor binding energy prediction

Background

Models that are capable of reliably predicting binding affinities for protein-ligand complexes play an important role the field of structure-guided drug design.

Methods

Here, we begin by applying the computational geometry technique of Delaunay tessellation to each set of atomic coordinates for over 1400 diverse macromolecular structures, for the purpose of deriving a four-body statistical potential that serves as a topological scoring function. Next, we identify a second, independent set of three hundred protein-ligand complexes, having both high-resolution structures and known dissociation constants. Two-thirds of these complexes are randomly selected to train a predictive model of binding affinity as follows: two tessellations are generated in each case, one for the entire complex and another strictly for the isolated protein without its bound ligand, and a topological score is computed for each tessellation with the four-body potential. Predicted protein-ligand binding affinity is then based on an empirically derived linear function of the difference between both topological scores, one that appropriately scales the value of this difference.

Results

A comparison between experimental and calculated binding affinity values over the two hundred complexes reveals a Pearson's correlation coefficient of r = 0.79 with a standard error of SE = 1.98 kcal/mol. To validate the method, we similarly generated two tessellations for each of the remaining protein-ligand complexes, computed their topological scores and the difference between the two scores for each complex, and applied the previously derived linear transformation of this topological score difference to predict binding affinities. For these one hundred complexes, we again observe a correlation of r = 0.79 (SE = 1.93 kcal/mol) between known and calculated binding affinities. Applying our model to an independent test set of high-resolution structures for three hundred diverse enzyme-inhibitor complexes, each with an experimentally known inhibition constant, also yields a correlation of r = 0.79 (SE = 2.39 kcal/mol) between experimental and calculated binding energies.

Conclusions

Lastly, we generate predictions with our model on a diverse test set of one hundred protein-ligand complexes previously used to benchmark 15 related methods, and our correlation of r = 0.66 between the calculated and experimental binding energies for this dataset exceeds those of the other approaches. Compared with these related prediction methods, our approach stands out based on salient features that include the reliability of our model, combined with the rapidity of the generated predictions, which are less than one second for an average sized complex.