Protein surface conservation in binding sites

An algorithm is described which uses the conservation of the 3D structure of protein surfaces, as opposed to their sequences, to detect protein-protein binding sites. The protein in which protein-protein binding sites are sought is compared with structures of multiple structurally related proteins and the surface that is conserved at least once is considered to be a part of the binding site. The binding site predictions obtained in this way for a set of protein-protein complexes correspond well with the actual protein-protein binding sites. A comparison of this method with an algorithm using the support vector machine approach for predicting protein-protein binding sites shows structural conservation to be an important characteristic that distinguishes binding sites from the remainder of protein surfaces.     

SPILLO‐PBSS: Detecting hidden binding sites within protein 3D‐structures through a flexible structure‐based approach

The study reports a flexible structure‐based approach aimed at identifying binding sites within target proteins starting from a well‐defined reference binding site. The method, named SPILLO potential binding sites searcher (SPILLO‐PBSS), includes a suitably designed tolerance which allows an efficient recognition of the potential binding sites regardless of both involved residues and protein conformation. Hence, the proposed method overcomes the rigidity which affects the available approaches and which prevents a proper analysis of distorted binding sites. We apply SPILLO‐PBSS to several test cases, including the search for the guanosine diphosphate binding site in distorted H‐Ras proteins and the identification of acetylcholine binding proteins from among a library of heterogeneous resolved proteins. Tests are also performed to compare SPILLO‐PBSS with other related and available methods. The encouraging results confirm the notable potentialities of this approach and lay the foundation for its use to analyze and predict target proteins on a proteome‐wide scale. © 2014 Wiley Periodicals, Inc.     

Exploring the effectiveness of the TSR-based protein 3-D structural comparison method for protein clustering,and structural motif identification and discovery of protein kinases,hydrolases, and SARS-CoV-2’s protein via the application of amino acid grouping

Development of protein 3-D structural comparison methods is essential for understanding protein functions. Some amino acids share structural similarities while others vary considerably. These structures determine the chemical and physical properties of amino acids. Grouping amino acids with similar structures potentially improves the ability to identify structurally conserved regions and increases the global structural similarity between proteins. We systematically studied the effects of amino acid grouping on the numbers of Specific/specific, Common/common, and statistically different keys to achieve a better understanding of protein structure relations. Common keys represent substructures found in all types of proteins and Specific keys represent substructures exclusively belonging to a certain type of proteins in a data set. Our results show that applying amino acid grouping to the Triangular Spatial Relationship (TSR)-based method, while computing structural similarity among proteins, improves the accuracy of protein clustering in certain cases. In addition, applying amino acid grouping facilitates the process of identification or discovery of conserved structural motifs. The results from the principal component analysis (PCA) demonstrate that applying amino acid grouping captures slightly more structural variation than when amino acid grouping is not used, indicating that amino acid grouping reduces structure diversity as predicted. The TSR-based method uniquely identifies and discovers binding sites for drugs or interacting proteins. The binding sites of nsp16 of SARS-CoV-2, SARS-CoV and MERS-CoV that we have defined will aid future antiviral drug design for improving therapeutic outcome. This approach for incorporating the amino acid grouping feature into our structural comparison method is promising and provides a deeper insight into understanding of structural relations of proteins.     

A searchable database for comparing protein-ligand binding sites for the analysis of structure-function relationships

The rapid expansion of structural information for protein-ligand binding sites is potentially an important source of information in structure-based drug design and in understanding ligand cross reactivity and toxicity. We have developed a large database of ligand binding sites extracted automatically from the Protein Data Bank. This has been combined with a method for calculating binding site similarity based on geometric hashing to create a relational database for the retrieval of site similarity and binding site superposition. It contains an all-against-all comparison of binding sites and holds known protein-ligand binding sites, which are made accessible to data mining. Here we demonstrate its utility in two structure-based applications: in determining site similarity and in aiding the derivation of a receptor-based pharmacophore model. The database is available from http://www.bioinformatics.leeds.ac.uk/sb/.     

Methods for the prediction of protein-ligand binding sites for structure-based drug design and virtual ligand screening

Structure Based Drug Design (SBDD) is a computational approach to lead discovery that uses the three-dimensional structure of a protein to fit drug-like molecules into a ligand binding site to modulate function. Identifying the location of the binding site is therefore a vital first step in this process, restricting the search space for SBDD or virtual screening studies. The detection and characterisation of functional sites on proteins has increasingly become an area of interest. Structural genomics projects are increasingly yielding protein structures with unknown functions and binding sites. Binding site prediction was pioneered by pocket detection, since the binding site is often found in the largest pocket. More recent methods involve phylogenetic analysis, identifying structural similarity with proteins of known function and identifying regions on the protein surface with a potential for high binding affinity. Binding site prediction has been used in several SBDD projects and has been incorporated into several docking tools. We discuss different methods of ligand binding site prediction, their strengths and weaknesses, and how they have been used in SBDD.     

The tethered agonist approach to mapping ion channel proteins--toward a structural model for the agonist binding site of the nicotinic acetylcholine receptor

BACKGROUND: The integral membrane proteins of neurons and other excitable cells are generally resistant to high resolution structural tools. Structure-function studies, especially those enhanced by the nonsense suppression methodology for unnatural amino acid incorporation, constitute one of the most powerful probes of ion channels and related structures. The nonsense suppression methodology can also be used to incorporate functional side chains designed to deliver novel structural probes to membrane proteins. In this vein, we sought to generalize a potentially powerful tool - the tethered agonist approach - for mapping the agonist binding site of ligand-gated ion channels. RESULTS: Using the in vivo nonsense suppression method for unnatural amino acid incorporation, a series of tethered quaternary ammonium derivatives of tyrosine have been incorporated into the nicotinic acetylcholine receptor. At three sites a constitutively active receptor results, but the pattern of activation as a function of chain length is different. At position alpha149, there is a clear preference for a three-carbon tether, while at position alpha93 tethers of 2-5 carbons are comparably effective. At position gamma55/delta57 all tethers except the shortest one can activate the receptor. Based on these and other data, a model for the receptor binding site can be developed by analogy to the acetylcholine esterase crystal structure. CONCLUSION: Through the use of nonsense suppression techniques, the tethered agonist approach has been made into a general tool for probing receptor structures. When applied to the nicotinic receptor, the method places new restrictions on developing models for the agonist binding site.     

Site of metabolism prediction on cytochrome P450 2C9: a knowledge-based docking approach

A novel structure-based approach for site of metabolism prediction has been developed. This knowledge-based method consists of three steps: (1) generation of possible metabolites, (2) docking the predicted metabolites to the CYP binding site and (3) selection of the most probable metabolites based on their complementarity to the binding site. As a proof of concept we evaluated our method by using MetabolExpert for metabolite generation and Glide for docking into the binding site of the CYP2C9 crystal structure. Our method could identify the correct metabolite among the three best-ranked compounds in 69% of the cases. The predictive power of our knowledge-based method was compared to that achieved by substrate docking and two alternative literature approaches.     

Individually double minimum-distance definition of protein–RNA binding residues and application to structure-based prediction

Identifying protein–RNA binding residues is essential for understanding the mechanism of protein–RNA interactions. So far, rigid distance thresholds are commonly used to define protein–RNA binding residues. However, after investigating 182 non-redundant protein–RNA complexes, we find that it would be unsuitable for a certain amount of complexes since the distances between proteins and RNAs vary widely. In this work, a novel definition method was proposed based on a flexible distance cutoff. This method can fully consider the individual differences among complexes by setting a variable tolerance limit of protein–RNA interactions, i.e. the double minimum-distance by which different distance thresholds are achieved for different complexes. In order to validate our method, a comprehensive comparison between our flexible method and traditional rigid methods was implemented in terms of interface structure, amino acid composition, interface area and interaction force, etc. The results indicate that this method is more reasonable because it incorporates the specificity of different complexes by extracting the important residues lost by rigid distance methods and discarding some redundant residues. Finally, to further test our double minimum-distance definition strategy, we developed a classifier to predict those binding sites derived from our new method by using structural features and a random forest machine learning algorithm. The model achieved a satisfactory prediction performance and the accuracy on independent data sets reaches to 85.0%. To the best of our knowledge, it is the first prediction model to define positive and negative samples using a flexible cutoff. So the comparison analysis and modeling results have demonstrated that our method would be a very promising strategy for more precisely defining protein–RNA binding sites.     

Structural models of protein-DNA complexes based on interface prediction and docking

Protein-DNA interactions are the physical basis of gene expression and DNA modification. Structural models that reveal these interactions are essential for their understanding. As only a limited number of structures for protein-DNA complexes have been determined by experimental methods, computation methods provide a potential way to fill the need. We have developed the DISPLAR method to predict DNA binding sites on proteins. Predicted binding sites have been used to assist the building of structural models by docking, either by guiding the docking or by selecting near-native candidates from the docked poses. Here we applied the DISPLAR method to predict the DNA binding sites for 20 DNA-binding proteins, which have had their DNA binding sites characterized by NMR chemical shift perturbation. For two of these proteins, the structures of their complexes with DNA have also been determined. With the help of the DISPLAR predictions, we built structural models for these two complexes. Evaluations of both the DNA binding sites for 20 proteins and the structural models of the two protein-DNA complexes against experimental results demonstrate the significant promise of our model-building approach.     

Characterization of platinum anticancer drug protein-binding sites using a top-down mass spectrometric approach

A proof-of-principle study on the application of a top-down electrospray ionization Fourier transform ion cyclotron resonance mass spectrometric approach for characterization of the primary binding sites of the platinum anticancer agents cisplatin, transplatin, and oxaliplatin on ubiquitin is presented. Through employment of different fragmentation techniques, the binding sites of cisplatin and oxaliplatin were found at N-terminal methionine-containing ubiquitin fragments, while transplatin was observed to be attached to 19Pro-Ser-Asp-Thr-Ile-Glu24. The binding to proteins is of particular relevance for the mode of action of metallodrugs with regard to (de)activation, transport, excretion, etc. To the best of our knowledge, this is the first top-down mass spectrometric study on the protein binding site characterization of transition-metal anticancer agents and demonstrates the potential of the applied technique for investigating metal drug-protein interactions.     

A pose prediction approach based on ligand 3D shape similarity

Molecular docking predicts the best pose of a ligand in the target protein binding site by sampling and scoring numerous conformations and orientations of the ligand. Failures in pose prediction are often due to either insufficient sampling or scoring function errors. To improve the accuracy of pose prediction by tackling the sampling problem, we have developed a method of pose prediction using shape similarity. It first places a ligand conformation of the highest 3D shape similarity with known crystal structure ligands into protein binding site and then refines the pose by repacking the side-chains and performing energy minimization with a Monte Carlo algorithm. We have assessed our method utilizing CSARdock 2012 and 2014 benchmark exercise datasets consisting of co-crystal structures from eight proteins. Our results revealed that ligand 3D shape similarity could substitute conformational and orientational sampling if at least one suitable co-crystal structure is available. Our method identified poses within 2 Å RMSD as the top-ranking pose for 85.7 % of the test cases. The median RMSD for our pose prediction method was found to be 0.81 Å and was better than methods performing extensive conformational and orientational sampling within target protein binding sites. Furthermore, our method was better than similar methods utilizing ligand 3D shape similarity for pose prediction.     

Protein-protein binding-sites prediction by protein surface structure conservation

A new algorithm to predict protein-protein binding sites using conservation of both protein surface structure and physical-chemical properties in structurally similar proteins is developed. Binding-site residues in proteins are known to be more conserved than the rest of the surface, and finding local surface similarities by comparing a protein to its structural neighbors can potentially reveal the location of binding sites on this protein. This approach, which has previously been used to predict binding sites for small ligands, is now extended to predict protein-protein binding sites. Examples of binding-site predictions for a set of proteins, which have previously been studied for sequence conservation in protein-protein interfaces, are given. The predicted binding sites and the actual binding sites are in good agreement. Our algorithm for finding conserved surface structures in a set of similar proteins is a useful tool for the prediction of protein-protein binding sites.     

MCSS-based predictions of RNA binding sites

The diversity of RNA tertiary structures provides the basis for specific recognition by proteins or small molecules. To investigate the structural basis and the energetics which control RNA-ligand interactions, favorable RNA binding sites are identified using the MCSS method, which has been employed previously only for protein receptors. Two different RNAs for which the structures have been determined by NMR spectroscopy were examined: two structures of the TAR RNA which contains an arginine binding site, and the structure of the 16S rRNA which contains an aminoglycoside binding site (paromomycin). In accord with the MCSS methodology, the functional groups representing the entire ligand or only part of it (one residue in the case of the aminoglycosides) are first replicated and distributed with random positions and orientations around the target and then energy minimized in the force field of the target RNA. The Coulombic term and the dielectric constant of the force field are adjusted to approximate the effects of solvent-screening and counterions. Optimal force field parameters are determined to reproduce the binding mode of arginine to the TAR RNA. The more favorable binding sites for each residue of the aminoglycoside ligands are then calculated and compared with the binding sites observed experimentally. The predictability of the method is evaluated and refinements are proposed to improve its accuracy. Received: 24 April 1998 / Accepted: 4 August 1998 / Published online: 7 December 1998     

Catalytic hyperbranched polymers as enzyme mimics; exploiting the principles of encapsulation and supramolecular chemistry

Nature uses the principles of encapsulation and supramolecular chemistry to bind and orientate substrates within active catalytic sites. Over the years, synthetic chemistry has generated a number of small molecule active site mimics capable of catalysing reactions involving bound substrates. Another approach uses larger molecules that better represent an enzymes globular structure. These molecules mimic an enzymes structure by incorporating binding/catalytic sites within the globular structure of the polymer. As such, the electronic and steric properties around the binding/catalytic site(s) can be controlled and fine-tuned. One class of polymer that is particularly adept at mimicking the globular structure of enzymes are dendritic polymers. This review will concentrate on the use of hyperbranched polymers as synthetic enzyme mimics.     

Identification of ligand binding sites on proteins using a multi-scale approach

Identification of a ligand binding site on a protein is pivotal to drug discovery. To date, no reliable and computationally feasible general approach to this problem has been published. Here we present an automated efficient method for determining binding sites on proteins for potential ligands without any a priori knowledge. Our method is based upon the multiscale concept where we deal with a hierarchy of models generated using a k-means clustering algorithm for the potential ligand. This is done in a simple approach whereby a potential ligand is represented by a growing number of feature points. At each increasing level of detail, a pruning of potential binding site is performed. A nonbonding energy function is used to score the interactions between molecules at each step. The technique was successfully employed to seven protein-ligand complexes. In the current paper we show that the algorithm considerably reduces the computational effort required to solve this problem. This approach offers real opportunities for exploiting the large number of structures that will evolve from structural genomics.     

Spectroscopic identification of different types of copper centers generated in synthetic four-helix bundle proteins

Using a combined rational-combinatorial approach, stable copper binding sites were implemented in template-assembled synthetic four-helix bundle proteins constructed by three different helices with only 16 amino acid residues. These peptides include two histidines and one cysteine at positions appropriate for coordinating a copper ion. Sequence variations of the helices were made in the second coordination shell or even more remote from the copper binding site (i) to increase the overall stability of the metalloproteins and (ii) to fine-tune the structure and properties of the copper center. As a result, ca. 90% of the 180 proteins that were synthesized were capable to bind copper with a substantially higher specificity than those obtained in the first design cycle (Schnepf, R.; Horth, P.; Bill, E.; Wieghardt, K.; Hildebrandt, P.; Haehnel, W. J. Am. Chem. Soc. 2001, 123, 2186-2195). Furthermore, the stabilities of the copper protein complexes were increased by up to 2 orders of magnitude and thus allowed a UV-vis absorption, resonance Raman, electron paramagnetic resonance, and (magnetic) circular dichroism spectroscopic identification and characterization of three different types of copper binding sites. It could be shown that particularly steric perturbations in the vicinity of the His(2)Cys ligand set control the formation of either a tetragonal (type II) or a tetrahedral (type I) copper binding site. With the introduction of two methionine residues above the histidine ligands, a mixed-valent dinuclear copper binding site was generated with spectroscopic properties that are very similar to those of Cu(A) sites in natural proteins. The results of the present study demonstrate for the first time that structurally different metal binding sites can be formed and stabilized in four-helix bundle proteins.     

Electrospray ionization mass spectrometry as a tool for determination of drug binding sites to human serum albumin by noncovalent interaction

Most proteins in blood plasma bind ligands. Human serum albumin (HSA) is the main transport protein with a very high capacity for binding of endogenous and exogenous compounds in plasma. Many pharmacokinetic properties of a drug depend on the level of binding to plasma proteins. This work reports studies of noncovalent interactions by means of nanoelectrospray ionization mass spectrometry (nanoESI-MS) for determination of the specific binding of selected drug candidates to HSA. Warfarin, iopanoic acid and digitoxin were chosen as site-specific probes that bind to the main sites of HSA. Two drug candidates and two known binders to HSA were analyzed using a competitive approach. The drugs were incubated with the target protein followed by addition of site-specific probes, one at a time. The drug candidates showed predominant affinity to site I (warfarin site). Naproxen and glyburide showed affinity to both sites I and II. The advantages of nanoESI-MS for these studies are the sensitivity, the absence of labeled molecules and the short method development time.