Relibase: design and development of a database for comprehensive analysis of protein-ligand interactions

Knowledge discovery from the exponentially growing body of structurally characterised protein-ligand complexes as a source of information in structure-based drug design is a major challenge in contemporary drug research. Given the need for powerful data retrieval, integration and analysis tools, Relibase was developed as a database system particularly designed to handle protein-ligand related problems and tasks. Here, we describe the design and functionality of the Relibase core database system. Features of Relibase include, e.g. the detailed analysis of superimposed ligand binding sites, ligand similarity and substructure searches, and 3D searches for protein-ligand and protein-protein interaction patterns. The broad range of functions provided in Relibase and its high level of data integration, along with its flexible and intuitive interface, makes Relibase an invaluable data mining tool which can significantly enhance the drug development process. An example, illustrating a 3D query for quarternary ligand nitrogen atoms interacting with aromatic ring systems in proteins, a pattern found in pharmaceutically relevant target proteins such as, e.g. acetylcholine-esterase, is discussed.     

Use of Relibase for retrieving complex three-dimensional interaction patterns including crystallographic packing effects

Relibase is a database system that has been specially designed to handle protein-ligand data. Included within Relibase is a tool that can be used to systematically analyse protein-ligand interaction patterns specified by three-dimensional (3D) constraints, revealing favorable combinations of interacting functional groups and their preferred interaction geometries. This paper describes the Relibase 3D query tools, including novel extensions (Relibase+) for handling crystallographic packing effects. Examples illustrating the broad range of functionality for defining 3D interaction patterns and the application of such queries in drug design comprise carbonyl-carbonyl interactions, zinc binding site environments, and ligand-ligand interactions in the crystal packing.     

Towards structure-based protein drug design

Structure-based drug design for chemical molecules has been widely used in drug discovery in the last 30 years. Many successful applications have been reported, especially in the field of virtual screening based on molecular docking. Recently, there has been much progress in fragment-based as well as de novo drug discovery. As many protein-protein interactions can be used as key targets for drug design, one of the solutions is to design protein drugs based directly on the protein complexes or the target structure. Compared with protein-ligand interactions, protein-protein interactions are more complicated and present more challenges for design. Over the last decade, both sampling efficiency and scoring accuracy of protein-protein docking have increased significantly. We have developed several strategies for structure-based protein drug design. A grafting strategy for key interaction residues has been developed and successfully applied in designing erythropoietin receptor-binding proteins. Similarly to small-molecule design, we also tested de novo protein-binder design and a virtual screen of protein binders using protein-protein docking calculations. In comparison with the development of structure-based small-molecule drug design, we believe that structure-based protein drug design has come of age.     

Optimizing a widely used protein structure alignment measure in expected polynomial time

Protein structure alignment is an important tool in many biological applications, such as protein evolution studies, protein structure modeling, and structure-based, computer-aided drug design. Protein structure alignment is also one of the most challenging problems in computational molecular biology, due to an infinite number of possible spatial orientations of any two protein structures. We study one of the most commonly used measures of pairwise protein structure similarity, defined as the number of pairs of atoms in two proteins that can be superimposed under a predefined distance cutoff. We prove that the expected running time of a recently published algorithm for optimizing this (and some other, derived measures of protein structure similarity) is polynomial.     

Think Twice: Understanding the High Potency of Bis(phenyl)methane Inhibitors of Thrombin

Successful design of potent and selective protein inhibitors, in terms of structure-based drug design, strongly relies on the correct understanding of the molecular features determining the ligand binding to the target protein. We present a case study of serine protease inhibitors with a bis(phenyl)methane moiety binding into the S3 pocket. These inhibitors bind with remarkable potency to the active site of thrombin, the blood coagulation factor IIa. A combination of X-ray crystallography and isothermal titration calorimetry provides conclusive insights into the driving forces responsible for the surprisingly high potency of these inhibitors. Analysis of six well-resolved crystal structures (resolution 1.58-2.25 Å) along with the thermodynamic data allows an explanation of the tight binding of the bis(phenyl)methane inhibitors. Interestingly, the two phenyl rings contribute to binding affinity for very different reasons — a fact that can only be elucidated by a structure-based approach. The first phenyl moiety occupies the hydrophobic S3 pocket, resulting in a mainly entropic advantage of binding. This observation is based on the displacement of structural water molecules from the S3 pocket that are observed in complexes with inhibitors that do not bind in the S3 pocket. The same classic hydrophobic effect cannot explain the enhanced binding affinity resulting from the attachment of the second, more solvent-exposed phenyl ring. For the bis(phenyl)methane inhibitors, an observed adaptive rotation of a glutamate residue adjacent to the S3 binding pocket attracted our attention. The rotation of this glutamate into salt-bridging distance with a lysine moiety correlates with an enhanced enthalpic contribution to binding for these highly potent thrombin binders. This explanation for the magnitude of the attractive force is confirmed by data retrieved by a Relibase search of several thrombin-inhibitor complexes deposited in the Protein Data Bank exhibiting similar molecular features.Special attention was attributed to putative changes in the protonation states of the interaction partners. For this purpose, two analogous inhibitors differing mainly in their potential to change the protonation state of a hydrogen-bond donor functionality were compared. Buffer dependencies of the binding enthalpy associated with complex formation could be traced by isothermal titration calorimetry, which revealed, along with analysis of the crystal structures (resolution 1.60 and 1.75 Å), that a virtually compensating proton interchange between enzyme, inhibitor and buffer is responsible for the observed buffer-independent thermodynamic signatures.     

Structural and Biochemical Basis for Development of Influenza Virus Inhibitors Targeting the PA Endonuclease

Emerging influenza viruses are a serious threat to human health because of their pandemic potential. A promising target for the development of novel anti-influenza therapeutics is the PA protein, whose endonuclease activity is essential for viral replication. Translation of viral mRNAs by the host ribosome requires mRNA capping for recognition and binding, and the necessary mRNA caps are cleaved or “snatched” from host pre-mRNAs by the PA endonuclease. The structure-based development of inhibitors that target PA endonuclease is now possible with the recent crystal structure of the PA catalytic domain. In this study, we sought to understand the molecular mechanism of inhibition by several compounds that are known or predicted to block endonuclease-dependent polymerase activity. Using an in vitro endonuclease activity assay, we show that these compounds block the enzymatic activity of the isolated PA endonuclease domain. Using X-ray crystallography, we show how these inhibitors coordinate the two-metal endonuclease active site and engage the active site residues. Two structures also reveal an induced-fit mode of inhibitor binding. The structures allow a molecular understanding of the structure-activity relationship of several known influenza inhibitors and the mechanism of drug resistance by a PA mutation. Taken together, our data reveal new strategies for structure-based design and optimization of PA endonuclease inhibitors.     

Molecular docking towards drug discovery

Fueled by advances in molecular structure determination, tools for structure-based drug design are proliferating rapidly. Lead discovery through searching of ligand databases with molecular docking techniques represents an attractive alternative to high-throughput random screening. The size of commercial databases imposes severe computational constraints on molecular docking, compromising the level of calculational detail permitted for each putative ligand. We describe alternative philosophies for docking which effectively address this challenge. With respect to the dynamic aspects of molecular recognition, these strategies lie along a spectrum of models bounded by the Lock-and-Key and Induced-Fit theories for ligand binding. We explore the potential of a rigid model in exploiting species specificity and of a tolerant model in predicting absolute ligand binding affinity. Current molecular docking methods are limited primarily by their ability to rank docked complexes; we therefore place particular emphasis on this aspect of the problem throughout our validation of docking strategies.     

Design of RNA-binding proteins and ligands

The rapidly expanding database of RNA structures and protein complexes is beginning to lead to the successful design of specific RNA-binding molecules. Recent combinatorial and structure-based approaches have utilized known nucleic-acid-binding scaffolds from both proteins and small molecules to display a relatively small set of functional groups often used in protein--RNA recognition. Several studies have shown that the tethering of multiple binding modules can enhance RNA-binding affinity and specificity, a strategy also commonly used in DNA recognition.     

Specificity in structure-based drug design: Identification of a novel,selective inhibitor of Pneumocystis carinii dihydrofolate reductase

Specificity is an important aspect of structure-based drug design. Distinguishing between related targets in different organisms is often the key to therapeutic success. Pneumocystis carinii is a fungal opportunist which causes a crippling pneumonia in immunocompromised individuals. We report the identification of novel inhibitors of P. cariniidihydrofolate reductase (DHFR) that are selective versus inhibition of human DHFR using computational molecular docking techniques. The Fine Chemicals Directory, a database of commercially available compounds, was screened with the DOCK program suite to produce a list of potential P. carinii DHFR inhibitors. We then used a postdocking refinement directed at discerning subtle structural and chemical features that might reflect species specificity. Of 40 compounds predicted to exhibit anti-PneumocystisDHFR activity, each of novel chemical framework, 13 (33%) show IC₅₀ values better than 150 μM in an enzyme assay. These inhibitors were further assayed against human DHFR: 10 of the 13 (77%) bind preferentially to the fungal enzyme. The most potent compound identified is a 7 μM inhibitor of P. carinii DHFR with 25-fold selectivity. The ability of molecular docking methods to locate selective inhibitors reinforces our view of structure-based drug discovery as a valuable strategy, not only for identifying lead compounds, but also for addressing receptor specificity. Proteins 29:59–67, 1997. © 1997 Wiley-Liss, Inc.     

Photoaffinity labeling combined with mass spectrometric approaches as a tool for structural proteomics

Protein chemistry, such as crosslinking and photoaffinity labeling, in combination with modern mass spectrometric techniques, can provide information regarding protein–protein interactions beyond that normally obtained from protein identification and characterization studies. While protein crosslinking can make tertiary and quaternary protein structure information available, photoaffinity labeling can be used to obtain structural data about ligand–protein interaction sites, such as oligonucleotide–protein, drug–protein and protein–protein interaction. In this article, we describe mass spectrometry-based photoaffinity labeling methodologies currently used and discuss their current limitations. We also discuss their potential as a common approach to structural proteomics for providing 3D information regarding the binding region, which ultimately will be used for molecular modeling and structure-based drug design.     

Computational study of EGFR inhibition: molecular dynamics studies on the active and inactive protein conformations

The structural diversity observed across protein kinases, resulting in subtly different active site cavities, is highly desirable in the pursuit of selective inhibitors, yet it can also be a hindrance from a structure-based design perspective. An important challenge in structure-based design is to better understand the dynamic nature of protein kinases and the underlying reasons for specific conformational preferences in the presence of different inhibitors. To investigate this issue, we performed molecular dynamics simulation on both the active and inactive wild type epidermal growth factor receptor (EGFR) protein with both type-I and type-II inhibitors. Our goal is to better understand the origin of the two distinct EGFR protein conformations, their dynamic differences, and their relative preference for Type-I inhibitors such as gefitinib and Type-II inhibitors such as lapatinib. We discuss the implications of protein dynamics from a structure-based design perspective.     

Insights from free-energy calculations: protein conformational equilibrium, driving forces, and ligand-binding modes

Accurate free-energy calculations provide mechanistic insights into molecular recognition and conformational equilibrium. In this work, we performed free-energy calculations to study the thermodynamic properties of different states of molecular systems in their equilibrium basin, and obtained accurate absolute binding free-energy calculations for protein-ligand binding using a newly developed M2 algorithm. We used a range of Asp-Phe-Gly (DFG)-in/out p38α mitogen-activated protein kinase inhibitors as our test cases. We also focused on the flexible DFG motif, which is closely connected to kinase activation and inhibitor binding. Our calculations explain the coexistence of DFG-in and DFG-out states of the loop and reveal different components (e.g., configurational entropy and enthalpy) that stabilize the apo p38α conformations. To study novel ligand-binding modes and the key driving forces behind them, we computed the absolute binding free energies of 30 p38α inhibitors, including analogs with unavailable experimental structures. The calculations revealed multiple stable, complex conformations and changes in p38α and inhibitor conformations, as well as balance in several energetic terms and configurational entropy loss. The results provide relevant physics that can aid in designing inhibitors and understanding protein conformational equilibrium. Our approach is fast for use with proteins that contain flexible regions for structure-based drug design.     

Fold independent structural comparisons of protein-ligand binding sites for exploring functional relationships

The rapid growth in protein structural data and the emergence of structural genomics projects have increased the need for automatic structure analysis and tools for function prediction. Small molecule recognition is critical to the function of many proteins; therefore, determination of ligand binding site similarity is important for understanding ligand interactions and may allow their functional classification. Here, we present a binding sites database (SitesBase) that given a known protein-ligand binding site allows rapid retrieval of other binding sites with similar structure independent of overall sequence or fold similarity. However, each match is also annotated with sequence similarity and fold information to aid interpretation of structure and functional similarity. Similarity in ligand binding sites can indicate common binding modes and recognition of similar molecules, allowing potential inference of function for an uncharacterised protein or providing additional evidence of common function where sequence or fold similarity is already known. Alternatively, the resource can provide valuable information for detailed studies of molecular recognition including structure-based ligand design and in understanding ligand cross-reactivity. Here, we show examples of atomic similarity between superfamily or more distant fold relatives as well as between seemingly unrelated proteins. Assignment of unclassified proteins to structural superfamiles is also undertaken and in most cases substantiates assignments made using sequence similarity. Correct assignment is also possible where sequence similarity fails to find significant matches, illustrating the potential use of binding site comparisons for newly determined proteins.     

Dynamics Govern Specificity of a Protein-Protein Interface: Substrate Recognition by Thrombin

Biomolecular recognition is crucial in cellular signal transduction. Signaling is mediated through molecular interactions at protein-protein interfaces. Still, specificity and promiscuity of protein-protein interfaces cannot be explained using simplistic static binding models. Our study rationalizes specificity of the prototypic protein-protein interface between thrombin and its peptide substrates relying solely on binding site dynamics derived from molecular dynamics simulations. We find conformational selection and thus dynamic contributions to be a key player in biomolecular recognition. Arising entropic contributions complement chemical intuition primarily reflecting enthalpic interaction patterns. The paradigm “dynamics govern specificity” might provide direct guidance for the identification of specific anchor points in biomolecular recognition processes and structure-based drug design.     

Solvation influences flap collapse in HIV-1 protease

HIV-1 protease (HIVp) is an important target for the development of therapies to treat AIDS and is one of the classic examples of structure-based drug design. The flap region of HIVp is known to be highly flexible and undergoes a large conformational change upon binding a ligand. Accurately modeling the inherent flexibility of the HIVp system is critical for developing new methods for structure-based drug design. We report several 3-ns molecular dynamics simulations investigating the role of solvation in HIVp flap rearrangement. Using an unliganded crystal structure of HIVp, other groups have observed flap reorganization on the nanosecond timescale. We have also observed rapid, initial flap movement, but we propose that it may be caused by system setup. The initial solvation of the system creates vacuum regions around the protein that may encourage large conformational deformities. By reducing the vacuum space created by the solvation routine, the observed flap collapse is attenuated. Also, a more thorough equilibration procedure preserves a more stable protein conformation over the course of the simulation.     

New insights into the structure of PINK1 and the mechanism of ubiquitin phosphorylation

Mutations in PINK1 cause early-onset recessive Parkinson’s disease. This gene encodes a protein kinase implicated in mitochondrial quality control via ubiquitin phosphorylation and activation of the E3 ubiquitin ligase Parkin. Here, we review and analyze functional features emerging from recent crystallographic, nuclear magnetic resonance (NMR) and mass spectrometry studies of PINK1. We compare the apo and ubiquitin-bound PINK1 structures and reveal an allosteric switch, regulated by autophosphorylation, which modulates substrate recognition. We critically assess the conformational changes taking place in ubiquitin and the Parkin ubiquitin-like domain in relation to its binding to PINK1. Finally, we discuss the implications of these biophysical findings in our understanding of the role of PINK1 in mitochondrial function, and analyze the potential for structure-based drug design.     

Exploring the Composition of Protein-Ligand Binding Sites on a Large Scale

The residue composition of a ligand binding site determines the interactions available for diffusion-mediated ligand binding, and understanding general composition of these sites is of great importance if we are to gain insight into the functional diversity of the proteome. Many structure-based drug design methods utilize such heuristic information for improving prediction or characterization of ligand-binding sites in proteins of unknown function. The Binding MOAD database if one of the largest curated sets of protein-ligand complexes, and provides a source of diverse, high-quality data for establishing general trends of residue composition from currently available protein structures. We present an analysis of 3,295 non-redundant proteins with 9,114 non-redundant binding sites to identify residues over-represented in binding regions versus the rest of the protein surface. The Binding MOAD database delineates biologically-relevant “valid” ligands from “invalid” small-molecule ligands bound to the protein. Invalids are present in the crystallization medium and serve no known biological function. Contacts are found to differ between these classes of ligands, indicating that residue composition of biologically relevant binding sites is distinct not only from the rest of the protein surface, but also from surface regions capable of opportunistic binding of non-functional small molecules. To confirm these trends, we perform a rigorous analysis of the variation of residue propensity with respect to the size of the dataset and the content bias inherent in structure sets obtained from a large protein structure database. The optimal size of the dataset for establishing general trends of residue propensities, as well as strategies for assessing the significance of such trends, are suggested for future studies of binding-site composition.     

Photoaffinity labeling combined with mass spectrometric approaches as a tool for structural proteomics

Protein chemistry, such as crosslinking and photoaffinity labeling, in combination with modern mass spectrometric techniques, can provide information regarding protein-protein interactions beyond that normally obtained from protein identification and characterization studies. While protein crosslinking can make tertiary and quaternary protein structure information available, photoaffinity labeling can be used to obtain structural data about ligand-protein interaction sites, such as oligonucleotide-protein, drug-protein and protein-protein interaction. In this article, we describe mass spectrometry-based photoaffinity labeling methodologies currently used and discuss their current limitations. We also discuss their potential as a common approach to structural proteomics for providing 3D information regarding the binding region, which ultimately will be used for molecular modeling and structure-based drug design.