The 2.2 A structure of the rRNA methyltransferase ErmC' and its complexes with cofactor and cofactor analogs: implications for the reaction mechanism.

The rRNA methyltransferase ErmC' transfers methyl groups from S -adenosyl-l-methionine to atom N6 of an adenine base within the peptidyltransferase loop of 23 S rRNA, thus conferring antibiotic resistance against a number of macrolide antibiotics. The crystal structures of ErmC' and of its complexes with the cofactor S -adenosyl-l-methionine, the reaction product S-adenosyl-l-homocysteine and the methyltransferase inhibitor Sinefungin, respectively, show that the enzyme undergoes small conformational changes upon ligand binding. Overall, the ligand molecules bind to the protein in a similar mode as observed for other methyltransferases. Small differences between the binding of the amino acid parts of the different ligands are correlated with differences in their chemical structure. A model for the transition-state based on the atomic details of the active site is consistent with a one-step methyl-transfer mechanism and might serve as a first step towards the design of potent Erm inhibitors.     

A fragment library based on Gaussian mixtures predicting favorable molecular interactions

Here, a protein atom-ligand fragment interaction library is described. The library is based on experimentally solved structures of protein-ligand and protein-protein complexes deposited in the Protein Data Bank (PDB) and it is able to characterize binding sites given a ligand structure suitable for a protein. A set of 30 ligand fragment types were defined to include three or more atoms in order to unambiguously define a frame of reference for interactions of ligand atoms with their receptor proteins. Interactions between ligand fragments and 24 classes of protein target atoms plus a water oxygen atom were collected and segregated according to type. The spatial distributions of individual fragment - target atom pairs were visually inspected in order to obtain rough-grained constraints on the interaction volumes. Data fulfilling these constraints were given as input to an iterative expectation-maximization algorithm that produces as output maximum likelihood estimates of the parameters of the finite Gaussian mixture models. Concepts of statistical pattern recognition and the resulting mixture model densities are used (i) to predict the detailed interactions between Chlorella virus DNA ligase and the adenine ring of its ligand and (ii) to evaluate the "error" in prediction for both the training and validation sets of protein-ligand interaction found in the PDB. These analyses demonstrate that this approach can successfully narrow down the possibilities for both the interacting protein atom type and its location relative to a ligand fragment.     

Exhaustive comparison and classification of ligand‐binding surfaces in proteins

Many proteins function by interacting with other small molecules (ligands). Identification of ligand‐binding sites (LBS) in proteins can therefore help to infer their molecular functions. A comprehensive comparison among local structures of LBSs was previously performed, in order to understand their relationships and to classify their structural motifs. However, similar exhaustive comparison among local surfaces of LBSs (patches) has never been performed, due to computational complexity. To enhance our understanding of LBSs, it is worth performing such comparisons among patches and classifying them based on similarities of their surface configurations and electrostatic potentials. In this study, we first developed a rapid method to compare two patches. We then clustered patches corresponding to the same PDB chemical component identifier for a ligand, and selected a representative patch from each cluster. We subsequently exhaustively as compared the representative patches and clustered them using similarity score, PatSim. Finally, the resultant PatSim scores were compared with similarities of atomic structures of the LBSs and those of the ligand‐binding protein sequences and functions. Consequently, we classified the patches into ～2000 well‐characterized clusters. We found that about 63% of these clusters are used in identical protein folds, although about 25% of the clusters are conserved in distantly related proteins and even in proteins with cross‐fold similarity. Furthermore, we showed that patches with higher PatSim score have potential to be involved in similar biological processes.     

SuperStar: improved knowledge-based interaction fields for protein binding sites

SuperStar is an empirical method for identifying interaction sites in proteins, based entirely on experimental information about non-bonded interactions occurring in small-molecule crystal structures, taken from the IsoStar database. We describe recent modifications and additions to SuperStar, validating the results on a test set of 122 X-ray structures of protein-ligand complexes. In this validation, propensity maps are generated for all the binding sites of these proteins, using four different probes: a charged NH(+)(3) nitrogen atom, a carbonyl oxygen atom, a hydroxyl oxygen atom and a methyl carbon atom. Next, the maps are compared with the experimentally observed positions of ligand atoms of these types. A peak-searching algorithm is introduced that highlights potential interaction hot spots. For the three hydrogen-bonding probes - NH(+)(3) nitrogen atom, carbonyl oxygen atom and hydroxyl oxygen atom - the average distance from the ligand atom to the nearest SuperStar peak is 1.0-1.2 A (0.8-1.0 A for solvent-inaccessible ligand atoms). For the methyl carbon atom probe, this distance is about 1.5 A, probably because interactions to methyl groups are much less directional.The most important addition to SuperStar is the enabling of propensity maps around metal centres - Ca(2+), Mg(2+) and Zn(2+) - in protein binding sites. The results are validated on a test set of 24 protein-ligand complexes that have a metal ion in their binding site. Coordination geometries are derived automatically, using only the protein atoms that coordinate to the metal ion. The correct coordination geometry is derived in approximately 75 % of the cases. If the derived geometry is assumed during the SuperStar calculation, the average distance from a ligand atom coordinating to the metal ion to the nearest peak in the propensity map for an oxygen probe is 0.87(7) A. If the correct coordination geometry is imposed, this distance reduces to 0.59(7)A. This indicates that the SuperStar predictions around metal-binding sites are at least as good as those around other protein groups. Using clustering techniques, a non-redundant set of probes is selected from the set of probes available in the IsoStar database. The performance in SuperStar of all these probes is tested on the test set of protein-ligand complexes. With the exception of the "ether oxygen" probe and the "any NH(+)" probe, all new probes perform as well as the four probes introduced first.     

Molecular docking using surface complementarity

A method is described to dock a ligand into a binding site in a protein on the basis of the complementarity of the inter-molecular atomic contacts. Docking is performed by maximization of a complementarity function that is dependent on atomic contact surface area and the chemical properties of the contacting atoms. The generality and simplicity of the complementarity function ensure that a wide range of chemical structures can be handled. The ligand and the protein are treated as rigid bodies, but displacement of a small number of residues lining the ligand binding site can be taken into account. The method can assist in the design of improved ligands by indicating what changes in complementarity may occur as a result of the substitution of an atom in the ligand. The capabilities of the method are demonstrated by application to 14 protein–ligand complexes of known crystal structure. © 1996 Wiley Liss, Inc.     

Towards predicting Ca2+‐binding sites with different coordination numbers in proteins with atomic resolution

Ca²⁺‐binding sites in proteins exhibit a wide range of polygonal geometries that directly relate to an equally‐diverse set of biological functions. Although the highly‐conserved EF‐Hand motif has been studied extensively, non‐EF‐Hand sites exhibit much more structural diversity which has inhibited efforts to determine the precise location of Ca²⁺‐binding sites, especially for sites with few coordinating ligands. Previously, we established an algorithm capable of predicting Ca²⁺‐binding sites using graph theory to identify oxygen clusters comprised of four atoms lying on a sphere of specified radius, the center of which was the predicted calcium position. Here we describe a new algorithm, MUG (MUltiple Geometries), which predicts Ca²⁺‐binding sites in proteins with atomic resolution. After first identifying all the possible oxygen clusters by finding maximal cliques, a calcium center (CC) for each cluster, corresponding to the potential Ca²⁺ position, is located to maximally regularize the structure of the (cluster, CC) pair. The structure is then inspected by geometric filters. An unqualified (cluster, CC) pair is further handled by recursively removing oxygen atoms and relocating the CC until its structure is either qualified or contains fewer than four ligand atoms. Ligand coordination is then determined for qualified structures. This algorithm, which predicts both Ca²⁺ positions and ligand groups, has been shown to successfully predict over 90% of the documented Ca²⁺‐binding sites in three datasets of highly‐diversified protein structures with 0.22 to 0.49 Å accuracy. All multiple‐binding sites (i.e. sites with a single ligand atom associated with multiple calcium ions) were predicted, as were half of the low‐coordination sites (i.e. sites with less than four protein ligand atoms) and 14/16 cofactor‐coordinating sites. Additionally, this algorithm has the flexibility to incorporate surface water molecules and protein cofactors to further improve the prediction for low‐coordination and cofactor‐coordinating Ca²⁺‐binding sites. Proteins 2009. © 2008 Wiley‐Liss, Inc.     

The key role of atom types, reference states, and interaction cutoff radii in the knowledge-based method: new variational approach

We present a variational method to derive knowledge-based potentials. The method is based on an optimization procedure of objective variables: atom types, reference states, and interaction cutoff radii. We suggest and apply new unsymmetrical reference states. The cutoff radii and atom types are optimized to improve docking accuracy of the corresponding potentials. The atom types are varied along an atom type tree, with 6 root and 49 top atom types, and the set of 18 optimal atom types is obtained. We demonstrate strong dependence between the choice of atom types and the docking accuracy of the potentials derived with these atom types. The averaged root-mean square deviations (RMSDs) of the ligand docked positions relative to the experimentally determined positions decrease when the elements C, N, O are split into the optimal types.     

A tool for calculating binding-site residues on proteins from PDB structures

Background  

In the research on protein functional sites, researchers often need to identify binding-site residues on a protein. A commonly used strategy is to find a complex structure from the Protein Data Bank (PDB) that consists of the protein of interest and its interacting partner(s) and calculate binding-site residues based on the complex structure. However, since a protein may participate in multiple interactions, the binding-site residues calculated based on one complex structure usually do not reveal all binding sites on a protein. Thus, this requires researchers to find all PDB complexes that contain the protein of interest and combine the binding-site information gleaned from them. This process is very time-consuming. Especially, combing binding-site information obtained from different PDB structures requires tedious work to align protein sequences. The process becomes overwhelmingly difficult when researchers have a large set of proteins to analyze, which is usually the case in practice.     

Fragment-based modeling of NAD binding to the catalytic subunits of diphtheria and pertussis toxins

We describe a novel application of a fragment-based ligand docking technique; similar methods are commonly applied to the de novo design of ligands for target protein binding sites. We have used several new flexible docking and superposition tools, as well as a more conventional rigid-body (fragment) docking method, to examine NAD binding to the catalytic subunits of diphtheria (DT) and pertussis (PT) toxins, and to propose a model of the NAD–PT complex. Docking simulations with the rigid NAD fragments adenine and nicotinamide revealed that the low-energy dockings clustered in three distinct sites on the two proteins. Two of the sites were common to both fragments and were related to the structure of NAD bound to DT in an obvious way; however, the adenine subsite of PT was shifted relative to that of DT. We chose adenine/nicotinamide pairs of PT dockings from these clusters and flexibly superimposed NAD onto these pairs. A Monte Carlo–based flexible docking procedure and energy minimization were used to refine the modeled NAD–PT complexes. The modeled complex accounts for the sequence and structural similarities between PT and DT and is consistent with many results that suggest the catalytic importance of certain residues. A possible functional role for the structural difference between the two complexes is discussed. Proteins 31:282–298, 1998. © 1998 Wiley-Liss, Inc.     

A mathematical model for dynamics of CD40 clustering

Ligand bound-receptors in a signalosome complex trigger signals to determine cellular functions. Upon ligand binding, the ligand–receptor complexes form clusters on cell membrane. Guided by the previous experimental reports on the cluster formation of CD40, a trans membrane receptor for CD40-ligand, we built a minimal model of the receptor cluster formation. In this model, we studied co-operative and non-co-operative clustering of a maximum of four CD40 molecules assuming a positive mediator of clustering such as cholesterol to be present in both cases. We observed that co-operative interactions between CD40 molecules resulted in more of the largest CD40 clusters than that observed with the non-co-operatively interacting CD40 molecules. We performed global sensitivity analysis on the model parameters and the analyses suggested that cholesterol influenced only the initial stage of the co-operatively clustering CD40 molecules but it affects both the initial and the final stages in case of the non-co-operatively clustering CD40 molecules. Robustness analyses revealed that in both co-operative and non-co-operative interactions, the higher order clusters beyond a critical size are more robust with respect to alterations in the environmental parameters including the cholesterol. Thus, the role of co-operative and non-co-operative interactions in environment-influenced receptor clustering is reported for the first time.     

Multiple binding modes for palmitate to barley lipid transfer protein facilitated by the presence of proline 12

Molecular dynamics simulations have been used to characterise the binding of the fatty acid ligand palmitate in the barley lipid transfer protein 1 (LTP) internal cavity. Two different palmitate binding modes (1 and 2), with similar protein–ligand interaction energies, have been identified using a variety of simulation strategies. These strategies include applying experimental protein–ligand atom–atom distance restraints during the simulation, or protonating the palmitate ligand, or using the vacuum GROMOS 54B7 force‐field parameter set for the ligand during the initial stages of the simulations. In both the binding modes identified the palmitate carboxylate head group hydrogen bonds with main chain amide groups in helix A, residues 4 to 19, of the protein. In binding mode 1 the hydrogen bonds are to Lys 11, Cys 13, and Leu 14 and in binding mode 2 to Thr 15, Tyr 16, Val 17, Ser 24 and also to the OH of Thr 15. In both cases palmitate binding exploits irregularity of the intrahelical hydrogen‐bonding pattern in helix A of barley LTP due to the presence of Pro 12. Simulations of two variants of barley LTP, namely the single mutant Pro12Val and the double mutant Pro12Val Pro70Val, show that Pro 12 is required for persistent palmitate binding in the LTP cavity. Overall, the work identifies key MD simulation approaches for characterizing the details of protein–ligand interactions in complexes where NMR data provide insufficient restraints.     

Automatic identification and representation of protein binding sites for molecular docking.

Molecular docking is a popular way to screen for novel drug compounds. The method involves aligning small molecules to a protein structure and estimating their binding affinity. To do this rapidly for tens of thousands of molecules requires an effective representation of the binding region of the target protein. This paper presents an algorithm for representing a protein's binding site in a way that is specifically suited to molecular docking applications. Initially the protein's surface is coated with a collection of molecular fragments that could potentially interact with the protein. Each fragment, or probe, serves as a potential alignment point for atoms in a ligand, and is scored to represent that probe's affinity for the protein. Probes are then clustered by accumulating their affinities, where high affinity clusters are identified as being the "stickiest" portions of the protein surface. The stickiest cluster is used as a computational binding "pocket" for docking. This method of site identification was tested on a number of ligand-protein complexes; in each case the pocket constructed by the algorithm coincided with the known ligand binding site. Successful docking experiments demonstrated the effectiveness of the probe representation.     

Novel statistical-thermodynamic methods to predict protein-ligand binding positions using probability distribution functions

We present two novel methods to predict native protein-ligand binding positions. Both methods identify the native binding position as the most probable position corresponding to a maximum of a probability distribution function (PDF) of possible binding positions in a protein active site. Possible binding positions are the origins of clusters composed, on the basis of root-mean square deviations (RMSD), from the multiple ligand positions determined by a docking algorithm. The difference between the methods lies in the ways the PDF is derived. To validate the suggested methods, we compare the averaged RMSD of the predicted ligand docked positions relative to the experimentally determined positions for a set of 135 PDB protein-ligand complexes. We demonstrate that the suggested methods improve docking accuracy by as much as 21-24% in comparison with a method that simply identifies the binding position as the energy top-scored ligand position.     

The second and fourth cluster of class A cysteine-rich repeats of the low density lipoprotein receptor-related protein share ligand-binding properties.

The low density lipoprotein receptor-related protein (LRP) is a multifunctional endocytic cell-surface receptor that binds and internalizes a diverse array of ligands. The receptor contains four putative ligand-binding domains, generally referred to as clusters I, II, III, and IV. In this study, soluble recombinant receptor fragments, representing each of the four individual clusters, were used to map the binding sites of a set of structurally and functionally distinct ligands. Using surface plasmon resonance, we studied the binding of these fragments to methylamine-activated alpha(2)-macroglobulin, pro-urokinase-type plasminogen activator, tissue-type plasminogen activator (t-PA), plasminogen activator inhibitor-1, t-PA.plasminogen activator inhibitor-1 complexes, lipoprotein lipase, apolipoprotein E, tissue factor pathway inhibitor, lactoferrin, the light chain of blood coagulation factor VIII, and the intracellular chaperone receptor-associated protein (RAP). No binding of the cluster I fragment to any of the tested ligands was observed. The cluster III fragment only bound to the anti-LRP monoclonal antibody alpha(2)MRalpha3 and weakly to RAP. Except for t-PA, we found that each of the ligands tested binds both to cluster II and to cluster IV. The affinity rate constants of ligand binding to clusters II and IV and to LRP were measured, showing that clusters II and IV display only minor differences in ligand-binding kinetics. Furthermore, we demonstrate that the subdomains C3-C7 of cluster II are essential for binding of ligands and that this segment partially overlaps with a RAP-binding site on cluster II. Finally, we show that one RAP molecule can bind to different clusters simultaneously, supporting a model in which RAP binding to LRP induces a conformational change in the receptor that is incompatible with ligand binding.     

A theoretical evaluation of the effect of netropsin binding on the reactivity of DNA towards alkylating agents.

The effect of netropsin binding on the electrostatic potential of DNA reactive sites is presented. Calculations are performed for atoms N7 and O6 of guanine, N3 and N7 of adenine of model, 25 base pair long, DNA-netropsin complexes. An important weakening of the potential is found spreading along all the oligonucleotide chain studied. The results are discussed in connection with the inhibitory effect of a related ligand, distamycin A, on DNA methylation.     

Cluster N1 of complex I from Yarrowia lipolytica studied by pulsed EPR spectroscopy

After reduction with nicotinamide adenine dinucleotide (NADH), NADH:ubiquinone oxidoreductase (complex I) of the strictly aerobic yeast Yarrowia lipolytica shows clear signals from five different paramagnetic iron-sulfur (FeS) clusters (N1-N5) which can be detected using electron paramagnetic resonance (EPR) spectroscopy. The ligand environment and the assignment of several FeS clusters to specific binding motifs found in several subunits of the complex are still under debate. In order to characterize the hyperfine interaction of the surrounding nuclei with FeS cluster N1, one- and two-dimensional electron spin echo envelope modulation experiments were performed at a temperature of 30 K. At this temperature only cluster N1 contributes to the overall signal in a pulsed EPR experiment. The hyperfine and quadrupole tensors of a nitrogen nucleus and the isotropic and dipolar hyperfine couplings of two sets of protons could be determined by numerical simulation of the one- and two-dimensional spectra. The values obtained are in perfect agreement with a ferredoxin-like binding structure by four cysteine amino acid residues and allow the assignment of the nitrogen couplings to a backbone nitrogen nucleus and the proton couplings to the beta-protons of the bound cysteine residues.     

Study of ATP binding in the active site of Na,K-ATPase as probed by ultraviolet resonance Raman spectroscopy

The ultraviolet resonance Raman (UV RR) spectra of functional ATP/membrane-bound Na⁺K⁺-ATPase complexes have been obtained. The substrate binding in the enzyme active site has been shown to be accompanied with significant changes in the electronic vibrational structure of the adenine ring. From the spectral analysis of ATP, 8-Br-ATP and 6-NHMe-adenine at various pH values the conclusion was made that N1 and the NH₂, group and, probably, N7 of the substrate adenine part, interact with the protein surroundings via hydrogen bonds.     

Sequence-dependent binding of metal ion to DNA oligomeres. A comparison of molecular electrostatic potentials with NMR data

Experimentally observed sequence-selective binding of metal ion to DNA oligonucleotides have been compared with variations of electrostatic potential (EP) along the helix. Calculations of EP have been performed for three atomic models of the oligonucleotide duplex [d(CGCGAATTCGCG)2] using several variants of EP calculations, including a solution of non-linear Poisson-Boltzmann equation (NPBE). N7 atom of guanine adjacent to adenine base was identified as a region with the most negative electrostatic potential in the major groove. The EP value for the Me ion binding site surpasses the value for N7 of other guanines by 10-26% depending on particular duplex conformation. Qualitatively, the sequence dependent variations of EP near guanine N7 atoms are in agreement with the sequence-selective behavior of Mn(II) and Zn(II) ions as revealed by NMR experiments. But the difference in EP between the two most negative regions near guanine N7 atoms does not exceed 1.25 kT/e. Simple model suggests that metal ions are capable to form ion-hydrate complexes with G-Pu steps of DNA duplex. These complexes are formed via one Me...G and five Me...water coordination bonds with water molecules hydrogen bonded to two adjacent purine bases in the same chain. We suppose that such a stereospecific structural possibility is the main factor which control the sequence-selectivity in the metal ion binding. A combination of both mechanisms allows to explain sequence specific Mn(II) and Zn(II) binding to a set of oligonucleotides.