Variation of protein binding cavity volume and ligand volume in protein–ligand complexes

We have systematically analyzed the variation of protein binding cavity volume of 200 protein–ligand complexes belonging to eight protein families. Wide variation in protein binding cavity volume for the same protein is observed on binding different ligands. Analysis of individual protein families shows high correlation between atom–atom interactions in binding site and ligand volume. This study implies the significance of protein flexibility in docking small molecule inhibitors on the basis of protein binding cavity volume with respect to ligand volume.     

Influence of quantum dots on the aromaticity of thiosalicylic acid

When ligands are coordinated to quantum dots (QDs), the ring current of the ligand strongly influences the applications of the QDs, for example in solar cell technology. The Raman spectrum of the ligand can be used to probe and identify ions or measure ion concentrations. Here, we investigated, using a theoretical method, the aromaticities and Raman spectra of CdTe, CdSe, and CdS QDs coordinated with thiosalicylic acid ligands. We found that the aromaticity of the benzene ring in free thiosalicylic acid increased when it was used as a QD ligand. The ring currents of the benzene rings in the CdTe–ligand, CdSe–ligand, and CdS–ligand systems were stronger than the ring current of the benzene ring in free thiosalicylic acid; in other words, the QDs influence the ring current—they enhance the electron transfer rate of the benzene ring. We also discovered that the CdTe–ligand and CdSe–ligand systems have stronger ring currents than the CdS–ligand system. The high electronegativity and vacant d orbital of the sulfur atom influence the ring current of the ligand in the CdS–ligand system. Further, the Raman spectrum of free thiosalicylic acid was different from the spectra of the ligands in the QD–ligand systems: the Raman spectra of COO^? in each QD–ligand system was enhanced compared with that of the COO^? in free thiosalicylic acid.

Recovering the true targets of specific ligands by virtual screening of the protein data bank

The Protein Data Bank (PDB) has been processed to extract a screening protein library (sc-PDB) of 2148 entries. A knowledge-based detection algorithm has been applied to 18,000 PDB files to find regular expressions corresponding to either protein, ions, co-factors, solvent, or ligand atoms. The sc-PDB database comprises high-resolution X-ray structures of proteins for which (i) a well-defined active site exists, (ii) the bound-ligand is a small molecular weight molecule. The database has been screened by an inverse docking tool derived from the GOLD program to recover the known target of four unrelated ligands. Both the database and the inverse screening procedures are accurate enough to rank the true target of the four investigated ligands among the top 1% scorers, with 70-100 fold enrichment with respect to random screening. Applying the proposed screening procedure to a small-sized generic ligand was much less accurate suggesting that inverse screening shall be reserved to rather selective compounds.     

Determination of Amino Acid Residues Responsible for Specific Interaction of Protein Kinases with Small Molecule Inhibitors

Identifying amino acid positions that determine the specific interaction of proteins with small molecule ligands, is required for search of pharmaceutical targets, drug design, and solution of other biotechnology problems. We studied applicability of an original method SPrOS (specificity projection on sequence) developed to recognize functionally significant positions in amino acid sequences. The method allows residues specific to functional subgroups to be determined within the protein family based on their local surroundings in amino acid sequences. The efficiency of the method has been estimated on the protein kinase family. The residues associated with the protein specificity to inhibitors have been predicted. The results have been verified using 3D structures of protein–ligand complexes. Three small molecule inhibitors have been tested. Residues predicted with SPrOS either in contacted the inhibitor or influenced the conformation of the ligand–binding area. Excluding close homologues from the studied set makes it possible to decrease the number of difficult to interpret positions. The expediency of this procedure was determined by the relationship between an inhibitory spectrum and phylogenic partition. Thus, the method efficiency has been confirmed by matching the prediction results with the protein 3D structures.     

fconv: Format conversion, manipulation and feature computation of molecular data

fconv is a program intended for parsing and manipulating multiple aspects and properties of molecular data. Up to now, it has been developed and extensively tested for 3 years. It has become a very robust and comprehensive tool involved in a broad range of computational workflows that are currently applied in our drug design environment. Typical tasks are as follows: conversion and error correction of formats such as PDB(QT), MOL2, SDF, DLG and CIF; extracting ligands from PDB as MOL2; automatic or ligand-based cavity detection; rmsd calculation and clustering; substructure searches; alignment and structural superposition; building of crystal packings; adding hydrogens; calculation of various properties like the number of rotatable bonds; molecular weights or vdW volumes. The atom type classification is based on a consistent assignment of internal atom types, which are by far more differentiated compared with e.g. Sybyl atom types. Apart from the predefined mapping of these types onto Sybyl types, the user is able to assign own mappings by providing modified template files, thus allowing for tailor-made atom type sets. AVAILABILITY: fconv is free software available under GNU General Public License. C++ sources and precompiled executables for LINUX/UNIX, Mac OS and Windows, as well as tutorials are available on http://www.agklebe.de.     

Computational design of protein-small molecule interfaces

The computational design of proteins that bind small molecule ligands is one of the unsolved challenges in protein engineering. It is complicated by the relatively small size of the ligand which limits the number of intermolecular interactions. Furthermore, near-perfect geometries between interacting partners are required to achieve high binding affinities. For apolar, rigid small molecules the interactions are dominated by short-range van der Waals forces. As the number of polar groups in the ligand increases, hydrogen bonds, salt bridges, cation–π, and π–π interactions gain importance. These partial covalent interactions are longer ranged, and additionally, their strength depends on the environment (e.g. solvent exposure). To assess the current state of protein-small molecule interface design, we benchmark the popular computer algorithm Rosetta on a diverse set of 43 protein–ligand complexes. On average, we achieve sequence recoveries in the binding site of 59% when the ligand is allowed limited reorientation, and 48% when the ligand is allowed full reorientation. When simulating the redesign of a protein binding site, sequence recovery among residues that contribute most to binding was 52% when slight ligand reorientation was allowed, and 27% when full ligand reorientation was allowed. As expected, sequence recovery correlates with ligand displacement.     

Determining binding sites of polycyclic aromatic small molecule-based amyloid-beta peptide aggregation modulators using sequence-specific antibodies

Numerous aromatic small molecule modulators of amyloid-beta peptide (Aβ) monomer aggregation and neurotoxicity have been identified with the ultimate goal of Alzheimer’s disease (AD) treatment. Determining binding sites of these modulators on Aβ monomer is an important topic in the mechanistic understanding of AD pathology and drug development. However, Aβ monomer binding sites have been reported for only a very limited number of Aβ modulators. In this article, we present a convenient method for determining aggregation-modulating polycyclic aromatic small molecule ligand binding sites on Aβ monomer using immunostaining with a panel of Aβ sequence-specific antibodies. To validate our technique, we first examined one modulating aromatic ligand, Congo Red, with known binding sites, which yielded consistent results with previous findings. Then, using the same technique, binding sites on Aβ of four known Aβ monomer aggregation modulators, Erythrosin B, Eosin Y, Phloxine B, and Rose Bengal, were determined. The identified ligand binding sites were also confirmed by a separate fluorescence quenching-based assay using a panel of overlapping Aβ sub-fragments. The technique described here greatly increases researchers’ ability to determine the Aβ monomer binding site(s) of aggregation-modulating aromatic small molecule ligands and to screen for new ligands that bind specific regions on Aβ.     

PDBToSDF: Create ligand structure files from PDB file

Protein Data Bank (PDB) file contains atomic data for protein and ligand in protein-ligand complexes. Structure data file (SDF) contains data for atoms, bonds, connectivity and coordinates of molecule for ligands. We describe PDBToSDF as a tool to separate the ligand data from pdb file for the calculation of ligand properties like molecular weight, number of hydrogen bond acceptors, hydrogen bond receptors easily.     

A crystal clear solution for determining G-protein-coupled receptor structures

G-protein-coupled receptors (GPCRs) are medically important membrane proteins that are targeted by over 30% of small molecule drugs. At the time of writing, 15 unique GPCR structures have been determined, with 77 structures deposited in the PDB database, which offers new opportunities for drug development and for understanding the molecular mechanisms of GPCR activation. Many different factors have contributed to this success, but if there is one single factor that can be singled out as the foundation for producing well-diffracting GPCR crystals, it is the stabilisation of the detergent-solubilised receptor-ligand complex. This review will focus predominantly on one of the successful strategies for the stabilisation of GPCRs, namely the thermostabilisation of GPCRs using systematic mutagenesis coupled with thermostability assays. Structures of thermostabilised GPCRs bound to a wide variety of ligands have been determined, which has led to an understanding of ligand specificity; why some ligands act as agonists as opposed to partial or inverse agonists; and the structural basis for receptor activation.     

Ligand-protein inverse docking and its potential use in the computer search of protein targets of a small molecule

Ligand-protein docking has been developed and used in facilitating new drug discoveries. In this approach, docking single or multiple small molecules to a receptor site is attempted to find putative ligands. A number of studies have shown that docking algorithms are capable of finding ligands and binding conformations at a receptor site close to experimentally determined structures. These algorithms are expected to be equally applicable to the identification of multiple proteins to which a small molecule can bind or weakly bind. We introduce a ligand-protein inverse-docking approach for finding potential protein targets of a small molecule by the computer-automated docking search of a protein cavity database. This database is developed from protein structures in the Protein Data Bank (PDB). Docking is conducted with a procedure involving multiple-conformer shape-matching alignment of a molecule to a cavity followed by molecular-mechanics torsion optimization and energy minimization on both the molecule and the protein residues at the binding region. Scoring is conducted by the evaluation of molecular-mechanics energy and, when applicable, by the further analysis of binding competitiveness against other ligands that bind to the same receptor site in at least one PDB entry. Testing results on two therapeutic agents, 4H-tamoxifen and vitamin E, showed that 50% of the computer-identified potential protein targets were implicated or confirmed by experiments. The application of this approach may facilitate the prediction of unknown and secondary therapeutic target proteins and those related to the side effects and toxicity of a drug or drug candidate. Proteins 2001;43:217-226.     

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.     

Quantitative analysis of individual hepatocyte growth factor receptor clusters in influenza A virus infected human epithelial cells using localization microscopy

In this report, we applied a special localization microscopy technique (Spectral Precision Distance/Spatial Position Determination Microscopy/SPDM) to quantitatively analyze the effect of influenza A virus (IAV) infection on the spatial distribution of individual HGFR (Hepatocyte Growth Factor Receptor) proteins on the membrane of human epithelial cells at the single molecule resolution level. We applied this SPDM method to Alexa 488 labeled HGFR proteins with two different ligands. The ligands were either HGF (Hepatocyte Growth Factor), or IAV. In addition, the HGFR distribution in a control group of mock-incubated cells without any ligands was investigated. The spatial distribution of 1 × 10⁶ individual HGFR proteins localized in large regions of interest on membranes of 240 cells was quantitatively analyzed and found to be highly non-random. Between 21% and 24% of the HGFR molecules were located in 44,304 small clusters with an average diameter of 54 nm. The mean density of HGFR molecule signals per individual cluster was very similar in control cells, in cells with ligand only, and in IAV infected cells, independent of the incubation time. From the density of HGFR molecule signals in the clusters and the diameter of the clusters, the number of HGFR molecule signals per cluster was estimated to be in the range between 4 and 11 (means 5–6). This suggests that the membrane bound HGFR clusters form small molecular complexes with a maximum diameter of few tens of nm, composed of a relatively low number of HGFR molecules. This article is part of a Special Issue entitled: Viral Membrane Proteins — Channels for Cellular Networking.     

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.     

A fast protein-ligand docking algorithm based on hydrogen bond matching and surface shape complementarity

With the rapid development of structural determination of target proteins for human diseases, high throughout virtual screening based drug discovery is gaining popularity gradually. In this paper, a fast docking algorithm (H-DOCK) based on hydrogen bond matching and surface shape complementarity was developed. In H-DOCK, firstly a divide-and-conquer strategy based enumeration approach is applied to rank the intermolecular modes between protein and ligand by maximizing their hydrogen bonds matching, then each docked conformation of the ligand is calculated according to the matched hydrogen bonding geometry, finally a simple but effective scoring function reflecting mainly the van der Waals interaction is used to evaluate the docked conformations of the ligand. H-DOCK is tested for rigid ligand docking and flexible one, the latter is implemented by repeating rigid docking for multiple conformations of a small molecule and ranking all together. For rigid ligands, H-DOCK was tested on a set of 271 complexes where there is at least one intermolecular hydrogen bond, and H-DOCK achieved success rate (RMSD<2.0?Å) of 91.1%. For flexible ligands, H-DOCK was tested on another set of 93 complexes, where each case was a conformation ensemble containing native ligand conformation as well as 100 decoy ones generated by AutoDock [1], and the success rate reached 81.7%. The high success rate of H-DOCK indicates that the hydrogen bonding and steric hindrance can grasp the key interaction between protein and ligand. H-DOCK is quite efficient compared with the conventional docking algorithms, and it takes only about 0.14 seconds for a rigid ligand docking and about 8.25 seconds for a flexible one on average. According to the preliminary docking results, it implies that H-DOCK can be potentially used for large scale virtual screening as a pre-filter for a more accurate but less efficient docking algorithm.     

Assisted assignment of ligands corresponding to unknown electron density

A semi-automated computational procedure to assist in the identification of bound ligands from unknown electron density has been developed. The atomic surface surrounding the density blob is compared to a library of three-dimensional ligand binding surfaces extracted from the Protein Data Bank (PDB). Ligands corresponding to surfaces which share physicochemical texture and geometric shape similarities are considered for assignment. The method is benchmarked against a set of well represented ligands from the PDB, in which we show that we can identify the correct ligand based on the corresponding binding surface. Finally, we apply the method during model building and refinement stages from structural genomics targets in which unknown density blobs were discovered. A semi-automated computational method is described which aims to assist crystallographers with assigning the identity of a ligand corresponding to unknown electron density. Using shape and physicochemical similarity assessments between the protein surface surrounding the density and a database of known ligand binding surfaces, a plausible list of candidate ligands are identified for consideration. The method is validated against highly observed ligands from the Protein Data Bank and results are shown from its use in a high-throughput structural genomics pipeline.     

Ligand binding site superposition and comparison based on Atomic Property Fields: identification of distant homologues, convergent evolution and PDB-wide clustering of binding sites

A new binding site comparison algorithm using optimal superposition of the continuous pharmacophoric property distributions is reported. The method demonstrates high sensitivity in discovering both, distantly homologous and convergent binding sites. Good quality of superposition is also observed on multiple examples. Using the new approach, a measure of site similarity is derived and applied to clustering of ligand binding pockets in PDB.     

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.     

Landscape of protein–small ligand binding modes

Elucidating the mechanisms of specific small‐molecule (ligand) recognition by proteins is a long‐standing conundrum. While the structures of these molecules, proteins and ligands, have been extensively studied, protein–ligand interactions, or binding modes, have not been comprehensively analyzed. Although methods for assessing similarities of binding site structures have been extensively developed, the methods for the computational treatment of binding modes have not been well established. Here, we developed a computational method for encoding the information about binding modes as graphs, and assessing their similarities. An all‐against‐all comparison of 20,040 protein–ligand complexes provided the landscape of the protein–ligand binding modes and its relationships with protein‐ and chemical spaces. While similar proteins in the same SCOP Family tend to bind relatively similar ligands with similar binding modes, the correlation between ligand and binding similarities was not very high (R² = 0.443). We found many pairs with novel relationships, in which two evolutionally distant proteins recognize dissimilar ligands by similar binding modes (757,474 pairs out of 200,790,780 pairs were categorized into this relationship, in our dataset). In addition, there were an abundance of pairs of homologous proteins binding to similar ligands with different binding modes (68,217 pairs). Our results showed that many interesting relationships between protein–ligand complexes are still hidden in the structure database, and our new method for assessing binding mode similarities is effective to find them.