Cognate ligand domain mapping for enzymes

Here, we present an automatic assignment of potential cognate ligands to domains of enzymes in the CATH and SCOP protein domain classifications on the basis of structural data available in the wwPDB. This procedure involves two steps; firstly, we assign the binding of particular ligands to particular domains; secondly, we compare the chemical similarity of the PDB ligands to ligands in KEGG in order to assign cognate ligands. We find that use of the Enzyme Commission (EC) numbers is necessary to enable efficient and accurate cognate ligand assignment. The PROCOGNATE database currently has cognate ligand mapping for 3277 (4118) protein structures and 351 (302) superfamilies, as described by the CATH and (SCOP) databases, respectively. We find that just under half of all ligands are only and always bound by a single domain, with 16% bound by more than one domain and the remainder of the ligands showing a variety of binding modes. This finding has implications for domain recombination and the evolution of new protein functions. Domain architecture or context is also found to affect substrate specificity of particular domains, and we discuss example cases. The most popular PDB ligands are all found to be generic components of crystallisation buffers, highlighting the non-cognate ligand problem inherent in the PDB. In contrast, the most popular cognate ligands are all found to be universal cellular currencies of reducing power and energy such as NADH, FADH2 and ATP, respectively, reflecting the fact that the vast majority of enzymatic reactions utilise one of these popular co-factors. These ligands all share a common adenine ribonucleotide moiety, suggesting that many different domain superfamilies have converged to bind this chemical framework.     

Analysis of amino acid substitutions in AraC variants that respond to triacetic acid lactone

The Escherichia coli regulatory protein AraC regulates expression of ara genes in response to l ‐arabinose. In efforts to develop genetically encoded molecular reporters, we previously engineered an AraC variant that responds to the compound triacetic acid lactone (TAL). This variant (named “AraC‐TAL1”) was isolated by screening a library of AraC variants, in which five amino acid positions in the ligand‐binding pocket were simultaneously randomized. Screening was carried out through multiple rounds of alternating positive and negative fluorescence‐activated cell sorting. Here we show that changing the screening protocol results in the identification of different TAL‐responsive variants (nine new variants). Individual substituted residues within these variants were found to primarily act cooperatively toward the gene expression response. Finally, X‐ray diffraction was used to solve the crystal structure of the apo AraC‐TAL1 ligand‐binding domain. The resolved crystal structure confirms that this variant takes on a structure nearly identical to the apo wild‐type AraC ligand‐binding domain (root‐mean‐square deviation 0.93 Å), suggesting that AraC‐TAL1 behaves similar to wild‐type with regard to ligand recognition and gene regulation. Our results provide amino acid sequence–function data sets for training and validating AraC modeling studies, and contribute to our understanding of how to design new biosensors based on AraC.     

Contributions to the binding free energy of ligands to avidin and streptavidin

The free energy of binding of a ligand to a macromolecule is here formally decomposed into the (effective) energy of interaction, reorganization energy of the ligand and the macromolecule, conformational entropy change of the ligand and the macromolecule, and translational and rotational entropy loss of the ligand. Molecular dynamics simulations with implicit solvation are used to evaluate these contributions in the binding of biotin, biotin analogs, and two peptides to avidin and streptavidin. We find that the largest contribution opposing binding is the protein reorganization energy, which is calculated to be from 10 to 30 kcal/mol for the ligands considered here. The ligand reorganization energy is also significant for flexible ligands. The translational/rotational entropy is 4.5-6 kcal/mol at 1 M standard state and room temperature. The calculated binding free energies are in the correct range, but the large statistical uncertainty in the protein reorganization energy precludes precise predictions. For some complexes, the simulations show multiple binding modes, different from the one observed in the crystal structure. This finding is probably due to deficiencies in the force field but may also reflect considerable ligand flexibility.     

Osteoprotegerin: Multiple partners for multiple functions

Osteoprotegerin (OPG) is an essential secreted protein in bone turnover due to its role as a decoy receptor for the Receptor Activator of Nuclear Factor-kB ligand (RANKL) in the osteoclasts, thus inhibiting their differentiation. However, there are additional ligands of OPG that confer various biological functions. OPG can promote cell survival, cell proliferation and facilitates migration by binding TNF-related apoptosis inducing ligand (TRAIL), glycosaminoglycans or proteoglycans. A large number of in vitro, pre-clinical and clinical studies provide evidences of OPG involvement in vascular, bone, immune and tumor biology. This review describes an overview of the different OPG ligands regulating its biological functions.     

Evolution of specific protein-protein interaction sites following gene duplication

Gene duplication is a common evolutionary process that leads to the expansion and functional diversification of protein subfamilies. The evolutionary events that cause paralogous proteins to bind different protein ligands (functionally diverged interfaces) are investigated and compared to paralogous proteins that bind the same protein ligand (functionally preserved interfaces). We find that functionally diverged interfaces possess more subfamily-specific residues than functionally preserved interfaces. These subfamily-specific residues are usually partially buried at the interface rim and achieve specific binding through optimized hydrogen bond geometries. In addition to optimized hydrogen bond geometries, side-chain modeling experiments suggest that steric effects are also important for binding specificity. Residues that are completely buried at the interface hub are also less conserved in functionally diverged interfaces than in functionally preserved interfaces. Consistent with this finding, hub residues contribute less to free energy of binding in functionally diverged interfaces than in functionally preserved interfaces. Therefore, we propose that protein binding is a delicate balance between binding affinity that primarily occurs at the interface hub and binding specificity that primarily occurs at the interface rim.     

Co-evolution of proteins with their interaction partners

The divergent evolution of proteins in cellular signaling pathways requires ligands and their receptors to co-evolve, creating new pathways when a new receptor is activated by a new ligand. However, information about the evolution of binding specificity in ligand-receptor systems is difficult to glean from sequences alone. We have used phosphoglycerate kinase (PGK), an enzyme that forms its active site between its two domains, to develop a standard for measuring the co-evolution of interacting proteins. The N-terminal and C-terminal domains of PGK form the active site at their interface and are covalently linked. Therefore, they must have co-evolved to preserve enzyme function. By building two phylogenetic trees from multiple sequence alignments of each of the two domains of PGK, we have calculated a correlation coefficient for the two trees that quantifies the co-evolution of the two domains. The correlation coefficient for the trees of the two domains of PGK is 0. 79, which establishes an upper bound for the co-evolution of a protein domain with its binding partner. The analysis is extended to ligands and their receptors, using the chemokines as a model. We show that the correlation between the chemokine ligand and receptor trees' distances is 0.57. The chemokine family of protein ligands and their G-protein coupled receptors have co-evolved so that each subgroup of chemokine ligands has a matching subgroup of chemokine receptors. The matching subfamilies of ligands and their receptors create a framework within which the ligands of orphan chemokine receptors can be more easily determined. This approach can be applied to a variety of ligand and receptor systems.     

Increased expression of death receptors 4 and 5 synergizes the apoptosis response to combined treatment with etoposide and TRAIL

Chemotherapeutic genotoxins induce apoptosis in epithelial-cell-derived cancer cells. The death receptor ligand TRAIL also induces apoptosis in epithelial-cell-derived cancer cells but generally fails to induce apoptosis in nontransformed cells. We show here that the treatment of four different epithelial cell lines with the topoisomerase II inhibitor etoposide in combination with TRAIL (tumor necrosis factor [TNF]-related apoptosis-inducing ligand) induces a synergistic apoptotic response. The mechanism of the synergistic effect results from the etoposide-mediated increase in the expression of the death receptors 4 (DR4) and 5 (DR5). Inhibition of NF-kappaB activation by expression of kinase-inactive MEK kinase 1(MEKK1) or dominant-negative IkappaB (DeltaIkappaB) blocked the increase in DR4 and DR5 expression following etoposide treatment. Addition of a soluble decoy DR4 fusion protein (DR4:Fc) to cell cultures reduced the amount of etoposide-induced apoptosis in a dose-dependent manner. The addition of a soluble TNF decoy receptor (TNFR:Fc) was without effect, demonstrating the specificity of DR4 binding ligands in the etoposide-induced apoptosis response. Thus, genotoxin treatment in combination with TRAIL is an effective inducer of epithelial-cell-derived tumor cell apoptosis relative to either treatment alone.     

Stability of Ligand-induced Protein Conformation Influences Affinity in Maltose-binding Protein

Our understanding of what determines ligand affinity of proteins is poor, even with high-resolution structures available. Both the non-covalent ligand–protein interactions and the relative free energies of available conformations contribute to the affinity of a protein for a ligand. Distant, non-binding site residues can influence the ligand affinity by altering the free energy difference between a ligand-free and ligand-bound conformation. Our hypothesis is that when different ligands induce distinct ligand-bound conformations, it should be possible to tweak their affinities by changing the free energies of the available conformations. We tested this idea for the maltose-binding protein (MBP) from Escherichia coli. We used single-molecule Förster resonance energy transfer (smFRET) to distinguish several unique ligand-bound conformations of MBP. We engineered mutations, distant from the binding site, to affect the stabilities of different ligand-bound conformations. We show that ligand affinity can indeed be altered in a conformation-dependent manner. Our studies provide a framework for the tuning of ligand affinity, apart from modifying binding site residues.     

Targeting cysteine rich C1 domain of Scaffold protein Kinase Suppressor of Ras (KSR) with anthocyanidins and flavonoids – a binding affinity characterization study

Kinase Suppressor of Ras (KSR) is a molecular scaffold that interacts with the core kinase components of the ERK cascade, Raf, MEK, ERK to provide spatial and temporal regulation of Ras-dependent ERK cascade signaling. Interruption of this mechanism can have a high influence in inhibiting the downstream signaling of the mutated tyrosine kinase receptor kinase upon ligand binding. Still none of the studies targeted to prevent the binding of Raf, MEK binding on kinase suppressor of RAS. In that perspective the cysteine rich C1 domain of scaffold proteins kinase suppressor of Ras-1 was targeted rather than its ATP binding site with small ligand molecules like flavones and anthocyanidins and analyzed through insilico docking studies. The binding energy evaluation shows the importance of hydroxyl groups at various positions on the flavone and anthocyanidin nucleus. Over all binding interaction shows these ligands occupied the potential sites of cysteine rich C1 domain of scaffold protein KSR.     

Optimized distance‐dependent atom‐pair‐based potential DOOP for protein structure prediction

The DOcking decoy‐based Optimized Potential (DOOP) energy function for protein structure prediction is based on empirical distance‐dependent atom‐pair interactions. To optimize the atom‐pair interactions, native protein structures are decomposed into polypeptide chain segments that correspond to structural motives involving complete secondary structure elements. They constitute near native ligand–receptor systems (or just pairs). Thus, a total of 8609 ligand–receptor systems were prepared from 954 selected proteins. For each of these hypothetical ligand–receptor systems, 1000 evenly sampled docking decoys with 0–10 Å interface root‐mean‐square‐deviation (iRMSD) were generated with a method used before for protein–protein docking. A neural network‐based optimization method was applied to derive the optimized energy parameters using these decoys so that the energy function mimics the funnel‐like energy landscape for the interaction between these hypothetical ligand–receptor systems. Thus, our method hierarchically models the overall funnel‐like energy landscape of native protein structures. The resulting energy function was tested on several commonly used decoy sets for native protein structure recognition and compared with other statistical potentials. In combination with a torsion potential term which describes the local conformational preference, the atom‐pair‐based potential outperforms other reported statistical energy functions in correct ranking of native protein structures for a variety of decoy sets. This is especially the case for the most challenging ROSETTA decoy set, although it does not take into account side chain orientation‐dependence explicitly. The DOOP energy function for protein structure prediction, the underlying database of protein structures with hypothetical ligand–receptor systems and their decoys are freely available at http://agknapp.chemie.fu‐berlin.de/doop/ . Proteins 2015; 83:881–890. © 2015 Wiley Periodicals, Inc.     

A proximal pair of positive charges provides the dominant ligand-binding contribution to complement-like domains from the LRP (low-density lipoprotein receptor-related protein)

The LRP (low-density lipoprotein receptor-related protein) can bind a wide range of structurally diverse ligands to regions composed of clusters of ~40 residue Ca2+-dependent, disulfide-rich, CRs (complement-like repeats). Whereas lysine residues from the ligands have been implicated in binding, there has been no quantification of the energetic contributions of such interactions and hence of their relative importance in overall affinity, or of the ability of arginine or histidine residues to bind. We have used four representative CR domains from the principal ligand-binding cluster of LRP to determine the energetics of interaction with well-defined small ligands that include methyl esters of lysine, arginine, histidine and aspartate, as well as N-terminally blocked lysine methyl ester. We found that not only lysine but also arginine and histidine bound well, and when present with an additional proximal positive charge, accounted for about half of the total binding energy of a protein ligand such as PAI-1 (plasminogen activator inhibitor-1). Two such sets of interactions, one to each of two CR domains could thus account for almost all of the necessary binding energy of a real ligand such as PAI-1. For the CR domains, a central aspartate residue in the sequence DxDxD tightens the Kd by ~20-fold, whereas DxDDD is no more effective. Together these findings establish the rules for determining the binding specificity of protein ligands to LRP and to other LDLR (low-density lipoprotein receptor) family members.     

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.