Phosphate binding sites identification in protein structures

Nearly half of known protein structures interact with phosphate-containing ligands, such as nucleotides and other cofactors. Many methods have been developed for the identification of metal ions-binding sites and some for bigger ligands such as carbohydrates, but none is yet available for the prediction of phosphate-binding sites. Here we describe Pfinder, a method that predicts binding sites for phosphate groups, both in the form of ions or as parts of other non-peptide ligands, in proteins of known structure. Pfinder uses the Query3D local structural comparison algorithm to scan a protein structure for the presence of a number of structural motifs identified for their ability to bind the phosphate chemical group. Pfinder has been tested on a data set of 52 proteins for which both the apo and holo forms were available. We obtained at least one correct prediction in 63% of the holo structures and in 62% of the apo. The ability of Pfinder to recognize a phosphate-binding site in unbound protein structures makes it an ideal tool for functional annotation and for complementing docking and drug design methods. The Pfinder program is available at http://pdbfun.uniroma2.it/pfinder.     

Identification of protein interaction partners and protein-protein interaction sites

Rigid-body docking has become quite successful in predicting the correct conformations of binary protein complexes, at least when the constituent proteins do not undergo large conformational changes upon binding. However, determining whether two given proteins interact is a more difficult problem. Successful docking procedures often give equally good scores for proteins that do not interact experimentally. This is the case for the multiple minimization approach we use here. An analysis of the results where all proteins within a set are docked with all other proteins (complete cross-docking) shows that the predictions can be greatly improved if the location of the correct binding interface on each protein is known, since the experimental complexes are much more likely to bring these two interfaces into contact, at the same time as yielding good interaction energy scores. While various methods exist for identifying binding interfaces, it is shown that simply studying the interaction of all potential protein pairs within a data set can itself help to identify the correct interfaces.     

Molecular docking for substrate identification: the short-chain dehydrogenases/reductases

Protein ligand docking has recently been investigated as a tool for protein function identification, with some success in identifying both known and unknown substrates of proteins. However, identifying a protein's substrate when cross-docking a large number of enzymes and their cognate ligands remains a challenge. To explore a more limited yet practically important and timely problem in more detail, we have used docking for identifying the substrates of a single protein family with remarkable substrate diversity, the short-chain dehydrogenases/reductases.We examine different protocols for identifying candidate substrates for 27 short-chain dehydrogenase/reductase proteins of known catalytic function. We present the results of docking > 900 metabolites from the human metabolome to each of these proteins together with their known cognate substrates and products, and we investigate the ability of docking to (a) reproduce a viable binding mode for the substrate and (b) to rank the substrate highly amongst the dataset of other metabolites. In addition, we examine whether our docking results provide information about the nature of the substrate, based on the best-scoring metabolites in the dataset. We compare two different docking methods and two alternative scoring functions for one of the docking methods, and we attempt to rationalise both successes and failures.Finally, we introduce a new protocol, whereby we dock only a set of representative structures (medoids) to each of the proteins, in the hope of characterising each binding site in terms of its ligand preferences, with a reduced computational cost. We compare the results from this protocol with our original docking experiments, and we find that although the rank of the representatives correlates well with the mean rank of the clusters to which they belong, a simple structure-based clustering is too naïve for the purpose of substrate identification. Many clusters comprise ligands with widely varying affinities for the same protein; hence important candidates can be missed if a single representative is used.     

Prediction of Protein Binding to DNA in the Presence of Water-Mediated Hydrogen Bonds

We extend our previous analysis of binding specificity of DNA-protein complexes to complexes containing water-mediated bridges. Inclusion of water bridges between phosphate and base, phosphate and sugar, as well as proteins and DNA, improves the prediction of specificity; six data sets studied in this paper yield correct predictions for all base pairs that have two or more hydrogen-bonds. Beside massive computation, our approach relies highly on experimental data. After deriving protein structures from DNA-protein complexes in which coordinates were established by X-ray diffraction techniques, we analysed all possible DNA sequences to which these proteins might bind, ranking them in terms of Lennard-Jones potential for the optimal docking configuration. Our prediction algorithm rests on the following assumptions: (1) specificity comes mainly from direct hydrogen bonding; (2) electrostatic forces stabilise DNA-protein complexes and contribute only weakly to specificity since they occur at the charged phosphate groups; (3) Van der Waals forces and electrostatic interactions between positively charged groups on the protein and phosphates on DNA can be neglected as they contribute primarily to the free energy of stabilisation as opposed to specificity.     

High resolution crystal structure of Clostridium propionicum β‐alanyl‐CoA:ammonia lyase,a new member of the “hot dog fold” protein superfamily

Clostridium propionicum is the only organism known to ferment β‐alanine, a constituent of coenzyme A (CoA) and the phosphopantetheinyl prosthetic group of holo‐acyl carrier protein. The first step in the fermentation is a CoA‐transfer to β‐alanine. Subsequently, the resulting β‐alanyl‐CoA is deaminated by the enzyme β‐alanyl‐CoA:ammonia lyase (Acl) to reversibly form ammonia and acrylyl‐CoA. We have determined the crystal structure of Acl in its apo‐form at a resolution of 0.97 Å as well as in complex with CoA at a resolution of 1.59 Å. The structures reveal that the enyzme belongs to a superfamily of proteins exhibiting a so called “hot dog fold” which is characterized by a five‐stranded antiparallel β‐sheet with a long α‐helix packed against it. The functional unit of all “hot dog fold” proteins is a homodimer containing two equivalent substrate binding sites which are established by the dimer interface. In the case of Acl, three functional dimers combine to a homohexamer strongly resembling the homohexamer formed by YciA‐like acyl‐CoA thioesterases. Here, we propose an enzymatic mechanism based on the crystal structure of the Acl·CoA complex and molecular docking. Proteins 2014; 82:2041–2053. © 2014 Wiley Periodicals, Inc.     

Ab initio prediction of peptide-MHC binding geometry for diverse class I MHC allotypes

Since determining the crystallographic structure of all peptide-MHC complexes is infeasible, an accurate prediction of the conformation is a critical computational problem. These models can be useful for determining binding energetics, predicting the structures of specific ternary complexes with T-cell receptors, and designing new molecules interacting with these complexes. The main difficulties are (1) adequate sampling of the large number of conformational degrees of freedom for the flexible peptide, (2) predicting subtle changes in the MHC interface geometry upon binding, and (3) building models for numerous MHC allotypes without known structures. Whereas previous studies have approached the sampling problem by dividing the conformational variables into different sets and predicting them separately, we have refined the Biased-Probability Monte Carlo docking protocol in internal coordinates to optimize a physical energy function for all peptide variables simultaneously. We also imitated the induced fit by docking into a more permissive smooth grid representation of the MHC followed by refinement and reranking using an all-atom MHC model. Our method was tested by a comparison of the results of cross-docking 14 peptides into HLA-A*0201 and 9 peptides into H-2K(b) as well as docking peptides into homology models for five different HLA allotypes with a comprehensive set of experimental structures. The surprisingly accurate prediction (0.75 A backbone RMSD) for cross-docking of a highly flexible decapeptide, dissimilar to the original bound peptide, as well as docking predictions using homology models for two allotypes with low average backbone RMSDs of less than 1.0 A illustrate the method's effectiveness. Finally, energy terms calculated using the predicted structures were combined with supervised learning on a large data set to classify peptides as either HLA-A*0201 binders or nonbinders. In contrast with sequence-based prediction methods, this model was also able to predict the binding affinity for peptides to a different MHC allotype (H-2K(b)), not used for training, with comparable prediction accuracy.     

Interaction of c(60)-fullerene and carboxyfullerene with proteins: docking and binding site alignment

The unique properties of fullerenes have raised the interest of using them for biomedical applications. Within this framework, the interactions of fullerenes with proteins have been an exciting research target, yet little is known about how native proteins can bind fullerenes, and what is the nature of these interactions. Moreover, though some proteins have been shown to interact with fullerenes, up to date, no crystal structure of such complexes was obtained. Here we report docking studies aimed at examining the interactions of fullerene in two forms (C60 nonsubstituted fullerene and carboxyfullerene) with four proteins that are known to bind fullerene derivatives: HIV protease, fullerene-specific antibody, human serum albumin, and bovine serum albumin. Our work provides docking models with detailed binding pockets information, which closely match available experimental data. We further compare the predicted binding sites using a novel multiple binding site alignment method. A high similarity between the physicochemical properties and surface geometry was found for fullerene's binding sites of HIV protease and the human and bovine serum albumins.     

Lipid interaction networks of peripheral membrane proteins revealed by data-driven micelle docking

Many signaling and trafficking proteins contain modular domains that bind reversibly to cellular membranes. The structural basis of the intermolecular interactions which mediate these membrane-targeting events remains elusive since protein-membrane complexes are not directly accessible to standard structural biology techniques. Here we report a fast protein-micelle docking methodology that yields three-dimensional model structures of proteins inserted into micelles, revealing energetically favorable orientations, convergent insertion angles, and an array of protein-lipid interactions at atomic resolution. The method is applied to two peripheral membrane proteins, the early endosome antigen 1 (EEA1) FYVE (a zinc finger domain found in the proteins Fab1, YOTB/ZK632.12, Vac1, and EEA1) and Vam7p phagocyte oxidase homology domains, which are revealed to form extensive networks of interactions with multiple phospholipid headgroups and acyl chains. The resulting structural models explain extensive published mutagenesis data and reveal novel binding determinants. The docking restraints used here were based on NMR data, but can be derived from any technique that detects insertion of protein residues into a membrane, and can be applied to virtually any peripheral membrane protein or membrane-like structure.     

A mathematical model for the determination of steady-state cardiolipin remodeling mechanisms using lipidomic data

Technical advances in lipidomic analysis have generated tremendous amounts of quantitative lipid molecular species data, whose value has not been fully explored. We describe a novel computational method to infer mechanisms of de novo lipid synthesis and remodeling from lipidomic data. We focus on the mitochondrial-specific lipid cardiolipin (CL), a polyglycerol phospholipid with four acyl chains. The lengths and degree of unsaturation of these acyl chains vary across CL molecules, and regulation of these differences is important for mitochondrial energy metabolism. We developed a novel mathematical approach to determine mechanisms controlling the steady-state distribution of acyl chain combinations in CL . We analyzed mitochondrial lipids from 18 types of steady-state samples, each with at least 3 replicates, from mouse brain, heart, lung, liver, tumor cells, and tumors grown in vitro. Using a mathematical model for the CL remodeling mechanisms and a maximum likelihood approach to infer parameters, we found that for most samples the four chain positions have an independent and identical distribution, indicating they are remodeled by the same processes. Furthermore, for most brain samples and liver, the distribution of acyl chains is well-fit by a simple linear combination of the pools of acyl chains in phosphatidylcholine (PC), phosphatidylethanolamine (PE), and phosphatidylglycerol (PG). This suggests that headgroup chemistry is the key determinant of acyl donation into CL, with chain length/saturation less important. This canonical remodeling behavior appears damaged in some tumor samples, which display a consistent excess of CL molecules having particular masses. For heart and lung, the "proportional incorporation" assumption is not adequate to explain the CL distribution, suggesting additional acyl CoA-dependent remodeling that is chain-type specific. Our findings indicate that CL remodeling processes can be described by a small set of quantitative relationships, and that bioinformatic approaches can help determine these processes from high-throughput lipidomic data.     

Optimal design of protein docking potentials: efficiency and limitations

Protein-protein docking is a challenging computational problem in functional genomics, particularly when one or both proteins undergo conformational change(s) upon binding. The major challenge is to define scoring function soft enough to tolerate these changes and specific enough to distinguish between near-native and "misdocked" conformations. Using a linear programming technique, we derived protein docking potentials (PDPs) that comply with this requirement. We considered a set of 63 nonredundant complexes to this aim, and generated 400,000 putative docked complexes (decoys) based on shape complementarity criterion for each complex. The PDPs were required to yield for the native (correctly docked) structure a potential energy lower than those of all the nonnative (misdocked) structures. The energy constraints applied to all complexes led to ca. 25 million inequalities, the simultaneous solution of which yielded an optimal set of PDPs that discriminated the correctly docked (up to 4.0 A root-mean-square deviation from known complex structure) structure among the 85 top-ranking (0.02%) decoys in 59/63 examined bound-bound cases. The high performance of the potentials was further verified in jackknife tests and by ranking putative docked conformation submitted to CAPRI. In addition to their utility in identifying correctly folded complexes, the PDPs reveal biologically meaningful features that distinguish docking potentials from folding potentials.     

Positively-Charged Semi-Tunnel Is a Structural and Surface Characteristic of Polyphosphate-Binding Proteins: An In-Silico Study

Phosphate is essential for all major life processes, especially energy metabolism and signal transduction. A linear phosphate polymer, polyphosphate (polyP), linked by high-energy phosphoanhydride bonds, can interact with various proteins, playing important roles as an energy source and regulatory factor. However, polyP-binding structures are largely unknown. Here we proposed a putative polyP binding site, a positively-charged semi-tunnel (PCST), identified by surface electrostatics analyses in polyP kinases (PPKs) and many other polyP-related proteins. We found that the PCSTs in varied proteins were folded in different secondary structure compositions. Molecular docking calculations revealed a significant value for binding affinity to polyP in PCST-containing proteins. Utilizing the PCST identified in the β subunit of PPK3, we predicted the potential polyP-binding domain of PPK3. The discovery of this feature facilitates future searches for polyP-binding proteins and discovery of the mechanisms for polyP-binding activities. This should greatly enhance the understanding of the many physiological functions of protein-bound polyP and the involvement of polyP and polyP-binding proteins in various human diseases.     

Evaluation of the FLEXX incremental construction algorithm for protein-ligand docking

We report on a test of FLEXX, a fully automatic docking tool for flexible ligands, on a highly diverse data set of 200 protein-ligand complexes from the Protein Data Bank. In total 46.5% of the complexes of the data set can be reproduced by a FLEXX docking solution at rank 1 with an rms deviation (RMSD) from the observed structure of less than 2 A. This rate rises to 70% if one looks at the entire generated solution set. FLEXX produces reliable results for ligands with up to 15 components which can be docked in 80% of the cases with acceptable accuracy. Ligands with more than 15 components tend to generate wrong solutions more often. The average runtime of FLEXX on this test set is 93 seconds per complex on a SUN Ultra-30 workstation. In addition, we report on "cross-docking" experiments, in which several receptor structures of complexes with identical proteins have been used for docking all cocrystallized ligands of these complexes. In most cases, these experiments show that FLEXX can acceptably dock a ligand into a foreign receptor structure. Finally we report on screening runs of ligands out of a library with 556 entries against ten different proteins. In eight cases FLEXX is able to find the original inhibitor within the top 7% of the total library.     

Accuracy of functional surfaces on comparatively modeled protein structures

Identification and characterization of protein functional surfaces are important for predicting protein function, understanding enzyme mechanism, and docking small compounds to proteins. As the rapid speed of accumulation of protein sequence information far exceeds that of structures, constructing accurate models of protein functional surfaces and identify their key elements become increasingly important. A promising approach is to build comparative models from sequences using known structural templates such as those obtained from structural genome projects. Here we assess how well this approach works in modeling binding surfaces. By systematically building three-dimensional comparative models of proteins using Modeller, we determine how well functional surfaces can be accurately reproduced. We use an alpha shape based pocket algorithm to compute all pockets on the modeled structures, and conduct a large-scale computation of similarity measurements (pocket RMSD and fraction of functional atoms captured) for 26,590 modeled enzyme protein structures. Overall, we find that when the sequence fragment of the binding surfaces has more than 45% identity to that of the template protein, the modeled surfaces have on average an RMSD of 0.5 Å, and contain 48% or more of the binding surface atoms, with nearly all of the important atoms in the signatures of binding pockets captured.     

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.     

Improving accuracy and efficiency of blind protein-ligand docking by focusing on predicted binding sites

The use of predicted binding sites (binding sites calculated from the protein structure alone) is evaluated here as a tool to focus the docking of small molecule ligands into protein structures, simulating cases where the real binding sites are unknown. The resulting approach consists of a few independent docking runs carried out on small boxes, centered on the predicted binding sites, as opposed to one larger blind docking run that covers the complete protein structure. The focused and blind approaches were compared using a set of 77 known protein-ligand complexes and 19 ligand-free structures. The focused approach is shown to: (1) identify the correct binding site more frequently than blind docking; (2) produce more accurate docking poses for the ligand; (3) require less computational time. Additionally, the results show that very few real binding sites are missed in spite of focusing on only three predicted binding sites per target protein. Overall the results indicate that, by improving the sampling in regions that are likely to correspond to binding sites, the focused docking approach increases accuracy and efficiency of protein ligand docking for those cases where the ligand-binding site is unknown. This is especially relevant in applications such as reverse virtual screening and structure-based functional annotation of proteins.     

Sequence Alignment Reveals Possible MAPK Docking Motifs on HIV Proteins

Over the course of HIV infection, virus replication is facilitated by the phosphorylation of HIV proteins by human ERK1 and ERK2 mitogen-activated protein kinases (MAPKs). MAPKs are known to phosphorylate their substrates by first binding with them at a docking site. Docking site interactions could be viable drug targets because the sequences guiding them are more specific than phosphorylation consensus sites. In this study we use multiple bioinformatics tools to discover candidate MAPK docking site motifs on HIV proteins known to be phosphorylated by MAPKs, and we discuss the possibility of targeting docking sites with drugs. Using sequence alignments of HIV proteins of different subtypes, we show that MAPK docking patterns previously described for human proteins appear on the HIV matrix, Tat, and Vif proteins in a strain dependent manner, but are absent from HIV Rev and appear on all HIV Nef strains. We revise the regular expressions of previously annotated MAPK docking patterns in order to provide a subtype independent motif that annotates all HIV proteins. One revision is based on a documented human variant of one of the substrate docking motifs, and the other reduces the number of required basic amino acids in the standard docking motifs from two to one. The proposed patterns are shown to be consistent with in silico docking between ERK1 and the HIV matrix protein. The motif usage on HIV proteins is sufficiently different from human proteins in amino acid sequence similarity to allow for HIV specific targeting using small-molecule drugs.     

Discovery of binding proteins for a protein target using protein–protein docking‐based virtual screening

Target structure‐based virtual screening, which employs protein‐small molecule docking to identify potential ligands, has been widely used in small‐molecule drug discovery. In the present study, we used a protein–protein docking program to identify proteins that bind to a specific target protein. In the testing phase, an all‐to‐all protein–protein docking run on a large dataset was performed. The three‐dimensional rigid docking program SDOCK was used to examine protein–protein docking on all protein pairs in the dataset. Both the binding affinity and features of the binding energy landscape were considered in the scoring function in order to distinguish positive binding pairs from negative binding pairs. Thus, the lowest docking score, the average Z‐score, and convergency of the low‐score solutions were incorporated in the analysis. The hybrid scoring function was optimized in the all‐to‐all docking test. The docking method and the hybrid scoring function were then used to screen for proteins that bind to tumor necrosis factor‐α (TNFα), which is a well‐known therapeutic target for rheumatoid arthritis and other autoimmune diseases. A protein library containing 677 proteins was used for the screen. Proteins with scores among the top 20% were further examined. Sixteen proteins from the top‐ranking 67 proteins were selected for experimental study. Two of these proteins showed significant binding to TNFα in an in vitro binding study. The results of the present study demonstrate the power and potential application of protein–protein docking for the discovery of novel binding proteins for specific protein targets. Proteins 2014; 82:2472–2482. © 2014 Wiley Periodicals, Inc.