Structural interaction fingerprint (SIFt): a novel method for analyzing three-dimensional protein-ligand binding interactions

Representing and understanding the three-dimensional (3D) structural information of protein-ligand complexes is a critical step in the rational drug discovery process. Traditional analysis methods are proving inadequate and inefficient in dealing with the massive amount of structural information being generated from X-ray crystallography, NMR, and in silico approaches such as structure-based docking experiments. Here, we present SIFt (structural interaction fingerprint), a novel method for representing and analyzing 3D protein-ligand binding interactions. Key to this approach is the generation of an interaction fingerprint that translates 3D structural binding information from a protein-ligand complex into a one-dimensional binary string. Each fingerprint represents the "structural interaction profile" of the complex that can be used to organize, analyze, and visualize the rich amount of information encoded in ligand-receptor complexes and also to assist database mining. We have applied SIFt to tackle three common tasks in structure-based drug design. The first involved the analysis and organization of a typical set of results generated from a docking study. Using SIFt, docking poses with similar binding modes were identified, clustered, and subsequently compared with conventional scoring function information. A second application of SIFt was to analyze approximately 90 known X-ray crystal structures of protein kinase-inhibitor complexes obtained from the Protein Databank. Using SIFt, we were able to organize the structures and reveal striking similarities and diversity between their small molecule binding interactions. Finally, we have shown how SIFt can be used as an effective molecular filter during the virtual chemical library screening process to select molecules with desirable binding mode(s) and/or desirable interaction patterns with the protein target. In summary, SIFt shows promise to fully leverage the wealth of information being generated in rational drug design.     

The future of crystallography in drug discovery

Introduction: X-ray crystallography plays an important role in structure-based drug design (SBDD), and accurate analysis of crystal structures of target macromolecules and macromolecule–ligand complexes is critical at all stages. However, whereas there has been significant progress in improving methods of structural biology, particularly in X-ray crystallography, corresponding progress in the development of computational methods (such as in silico high-throughput screening) is still on the horizon. Crystal structures can be overinterpreted and thus bias hypotheses and follow-up experiments. As in any experimental science, the models of macromolecular structures derived from X-ray diffraction data have their limitations, which need to be critically evaluated and well understood for structure-based drug discovery.

Areas covered: This review describes how the validity, accuracy and precision of a protein or nucleic acid structure determined by X-ray crystallography can be evaluated from three different perspectives: i) the nature of the diffraction experiment; ii) the interpretation of an electron density map; and iii) the interpretation of the structural model in terms of function and mechanism. The strategies to optimally exploit a macromolecular structure are also discussed in the context of ‘Big Data' analysis, biochemical experimental design and structure-based drug discovery.

Expert opinion: Although X-ray crystallography is one of the most detailed ‘microscopes' available today for examining macromolecular structures, the authors would like to re-emphasize that such structures are only simplified models of the target macromolecules. The authors also wish to reinforce the idea that a structure should not be thought of as a set of precise coordinates but rather as a framework for generating hypotheses to be explored. Numerous biochemical and biophysical experiments, including new diffraction experiments, can and should be performed to verify or falsify these hypotheses. X-ray crystallography will find its future application in drug discovery by the development of specific tools that would allow realistic interpretation of the outcome coordinates and/or support testing of these hypotheses.     

Impact of recombinant DNA technology and protein engineering on structure-based drug design: case studies of HIV-1 and HCMV proteases

Structure-based drug design is an organized, multidisciplinary endeavor undertaken by scientists from many different scientific fields. The success of structure-based drug design was only made possible by advances in structure biology that provides the three-dimensional structure of the drug design target with which small molecular chemical ligands interact. Visualization of the conformation and interactions of a small molecule ligand bound to the protein target in the co-crystal structure of the protein:ligand complex enables the design of new chemical compounds with improved binding affinity and specificity. With the advances in molecular biology, lab automation, and computational science, genomic data have now become available for the human genome, as well as various other organisms. The pharmaceutical industry is currently putting forth tremendous effort in the area of functional genomics and structural genomics in attempts to decipher functions and structures of protein encoded by genes, with the ultimate goal of identifying novel targets for drug discovery and development. This chapter discusses the significant impact made by recombinant DNA technology and protein engineering on structural biology and, more specifically, on structure-based drug design.     

Structure-based drug design and structural biology study of novel nonpeptide inhibitors of severe acute respiratory syndrome coronavirus main protease

Severe acute respiratory syndrome coronavirus (SARS-CoV) main protease (M(pro)), a protein required for the maturation of SARS-CoV, is vital for its life cycle, making it an attractive target for structure-based drug design of anti-SARS drugs. The structure-based virtual screening of a chemical database containing 58,855 compounds followed by the testing of potential compounds for SARS-CoV M(pro) inhibition leads to two hit compounds. The core structures of these two hits, defined by the docking study, are used for further analogue search. Twenty-one analogues derived from these two hits exhibited IC50 values below 50 microM, with the most potent one showing 0.3 microM. Furthermore, the complex structures of two potent inhibitors with SARS-CoV M(pro) were solved by X-ray crystallography. They bind to the protein in a distinct manner compared to all published SARS-CoV M(pro) complex structures. They inhibit SARS-CoV M(pro) activity via intensive H-bond network and hydrophobic interactions, without the formation of a covalent bond. Interestingly, the most potent inhibitor induces protein conformational changes, and the inhibition mechanisms, particularly the disruption of catalytic dyad (His41 and Cys145), are elaborated.     

Microbial genomics - new targets,new drugs

Genomics has changed our view of the biological world in the past decade, providing both new information and new tools to characterise biological systems. Over 100 microbial genomes - including many of substantial clinical importance - have been fully or partially sequenced, pushing the search for novel antimicrobial compounds into the post-genomic era. Genomic information and associated new technologies have the potential to revolutionise the drug discovery process. Genomic methods have created a wealth of potential new antimicrobial targets; strategies are evolving to provide validation for these targets before chemical inhibitors are identified. The ability to obtain large amounts of purified target proteins and advances in X-ray crystallography have caused significant increases in available protein structures, which may foreshadow an increased effort in structure-based drug design. The post-genomics strategies used in antimicrobial drug discovery may have application for small molecule drug discovery in numerous therapeutic areas.     

Microbial genomics – new targets,new drugs

Genomics has changed our view of the biological world in the past decade, providing both new information and new tools to characterise biological systems. Over 100 microbial genomes – including many of substantial clinical importance – have been fully or partially sequenced, pushing the search for novel antimicrobial compounds into the post-genomic era. Genomic information and associated new technologies have the potential to revolutionise the drug discovery process. Genomic methods have created a wealth of potential new antimicrobial targets; strategies are evolving to provide validation for these targets before chemical inhibitors are identified. The ability to obtain large amounts of purified target proteins and advances in X-ray crystallography have caused significant increases in available protein structures, which may foreshadow an increased effort in structure-based drug design. The post-genomics strategies used in antimicrobial drug discovery may have application for small molecule drug discovery in numerous therapeutic areas.     

Structural biology and drug discovery

In the past few years macromolecular crystallography has become a standard technique used by many pharmaceutical and biotechnology companies. This methodology offers details of protein-ligand interactions at levels of resolution virtually unmatched by any other technique, and this approach holds the promise of novel, more effective, safer and cheaper drugs. Although crystallography remains a laborious and rather expensive technique, remarkable advances in structure determination and structure based drug design (SBDD) have been made in recent years. This process has been aided by recent technological innovations such as high-throughput crystallization, high performance synchrotron beamlines, and new methods in structural bioinformatics and computational chemistry prompted by the structural genomics effort. As a consequence of the increased availability of structural data, the use of structure-based information has expanded from simple protein-ligand interaction analysis to include other aspects of the drug discovery process like target selection and initial lead discovery that used to be almost the exclusive property of biology and chemistry. This review will cover recent examples to illustrate how macromolecular crystallography has evolved and how structural information is now being used in the different stages of the drug discovery process. Advantages and shortcomings of the methodology will also be discussed.     

The hunt for antimitotic agents: an overview of structure-based design strategies

ABSTRACT

Introduction: Structure-based drug discovery offers a rational approach for the design and development of novel anti-mitotic agents which target specific proteins involved in mitosis. This strategy has paved the way for development of a new generation of chemotypes which selectively interfere with the target proteins. The interference of these anti-mitotic targets implicated in diverse stages of mitotic cell cycle progression culminates in cancer cell apoptosis.

Areas covered: This review covers the various mitotic inhibitors developed against validated mitotic checkpoint protein targets using structure-based design and optimization strategies. The protein-ligand interactions and the insights gained from these studies, culminating in the development of more potent and selective inhibitors, have been presented.

Expert opinion: The advent of structure-based drug design coupled with advances in X-ray crystallography has revolutionized the discovery of candidate lead molecules. The structural insights gleaned from the co-complex protein-drug interactions have provided a new dimension in the design of anti-mitotic molecules to develop drugs with a higher selectivity and specificity profile. Targeting non-catalytic domains has provided an alternate approach to address cross-reactivity and broad selectivity among kinase inhibitors. The elucidation of structures of emerging mitotic drug targets has opened avenues for the design of inhibitors that target cancer.     

Using X-ray crystallography in drug discovery

One of the most dramatic benefits of using crystallography in drug discovery has been the development of the structure-based drug design (SBDD) cycle: structure, analysis, synthesis, testing and back to structure. This review covers recent examples of SBDD to illustrate how this design cycle has evolved from its early implementation. The dimension of the cycle has increased to include the consideration of structures related to the target, optimization of global drug parameters and binding affinity. Other technologies, such as combinatorial chemistry and virtual screening, have been integrated into the design cycle. In addition, novel new ways of directly using the crystal have been developed.     

Receptor-ligand docking simulation for membrane proteins

Protein structure-based molecular design using the computational techniques of protein structure prediction, ligand docking, and virtual screening is an integral part of drug discovery for limiting the application of the structure-based approach to target proteins such as G-protein-coupled receptors (GPCRs). GPCRs play an important role in living organisms and are of major interest to the pharmaceutical industry. However, structural data on ligand-binding forms for GPCRs from experiments to elucidate structural templates for docking simulations are lacking due to the difficulties associated with crystallization and crystallography. Therefore structural prediction of GPCRs in the ligand-bound state using computational methods has been introduced, but the prediction of ligand conformation onto target GPCRs is still constructed manually by human experts. We developed a molecular modeling technique for the prediction of ligand-receptor binding using comparative ligand-binding analysis (CoLBA) that not only considers interaction energy but also the similarity of interaction profiles among ligands. The advantage of CoLBA is that it can facilitate intuitive and flexible screening based on docking results when protein structures with low resolution (or theoretical models) are targeted. We applied CoLBA to ligand-binding prediction in several GPCRs. The predicted ligand-binding models were evaluated in site-directed mutagenesis experiments in collaborative research, and the enrichment rate of activated ligands was compared with random compounds in virtual screening simulations. We propose that CoLBA can be applied in large-scale modeling of ligand-receptor complexes and virtual screening for GPCRs.     

Structure-based drug design (SBDD): Every structure tells a story...

Summary In this article we report how protein-ligand X-ray structures can be used to identify and develop new drug candidates. The structure-based drug design approach (SBDD) is described, illustrating the way crystallographic information is utilized in an iterative manner to identify and optimize novel lead compounds, thereby accelerating the overall drug discovery process. The requirements for the effective use of SBDD and its implementation, drawing upon case histories from our Thymidylate Synthase (TS) research program, are described.     

Small molecule crystallography in drug design

Crystal structures of small molecules (i.e. isolated ligands) are a source of valuable structural information helpful in the process of drug design (pharmacophore model elaborations, 3D QSAR, docking, and de novo design). Indeed, structural data obtained from small molecules crystallography can approach ligand-receptor binding by providing unique structural features both about the conformation (internal geometry) of the ligand (s) and about the intermolecular interaction potentially occurring within the active site of a target (enzyme/receptor). Small molecule crystal structure databases can also be used in three dimensional search to identify new drug candidates. Future development in small molecule crystallography (e.g. powder diffraction) should also provide original solutions to complex problems related to polymorphism.     

From structure--based to knowledge--based drug design through x-ray protein crystallography: sketching glycogen phosphorylase binding sites

The knowledge derived from the three-dimensional structure of a macromolecular receptor either in the native form or in complex with different ligands has given new insights to the development of improved drug candidates contributing to the drug development pipeline. The structure-based drug design approach has been tested on a number of macromolecular targets implicated in various diseases such as hypertension, glaucoma, HIV and influenza. This approach has also been employed for the development of new antidiabetic agents targeting glycogen phosphorylase (GP), an enzyme that modulates glucose levels in blood circulation. The key role of x-ray protein crystallography in the structure-based inhibitor design process is presented by the case of rabbit muscle GP (RMGPb) that shares increased homology with the liver isoenzyme. The properties of the allosteric binding sites of RMGPb are revealed by filing the interactions formed upon binding of characteristic functional groups and documenting the changes induced in the residues lining the site of interest.     

Strategy of computer-aided drug design

Modern strategies of computer-aided drug design (CADD) are reviewed. The task of CADD in the pipeline of drug discovery is accelerating of finding the new lead compounds and their structure optimization for the following pharmacological tests. The main directions in CADD are based on the availability of the experimentally determined three-dimensional structure of the target macromolecule. If spatial structure is known the methods of structure-based drug design are used. In the opposite case the indirect methods of CADD based on the structures of known ligands (ligand-based drug design) are used. The interrelationship between the main directions of CADD is reviewed. The main CADD approaches of molecule de novo design and database mining are described. They include methods of molecular docking, de novo design, design of pharmacophore and quantity structure-activity relationship models. New ways and perspectives of CADD are discussed.     

High-throughput crystallography for lead discovery in drug design

Knowledge of the three-dimensional structures of protein targets now emerging from genomic data has the potential to accelerate drug discovery greatly. X-ray crystallography is the most widely used technique for protein structure determination, but technical challenges and time constraints have traditionally limited its use primarily to lead optimization. Here, we describe how significant advances in process automation and informatics have aided the development of high-throughput X-ray crystallography, and discuss the use of this technique for structure-based lead discovery.     

Identification of Interaction Hot Spots in Structures of Drug Targets on the Basis of Three‐Dimensional Activity Cliff Information

Activity cliffs are defined as pairs or groups of structurally similar or analogous compounds that share the same specific activity but have large differences in potency. Although activity cliffs are mostly studied in medicinal chemistry at the level of molecular graphs, they can also be assessed by comparing compound binding modes. If such three‐dimensional activity cliffs (3D‐cliffs) are studied on the basis of X‐ray complex structures, experimental ligand–target interaction details can be taken into account. Rapid growth in the number of 3D‐cliffs that can be derived from X‐ray complex structures has made it possible to identify targets for which a substantial body of 3D‐cliff information is available. Activity cliffs are typically studied to identify structure–activity relationship determinants and aid in compound optimization. However, 3D‐cliff information can also be used to search for interaction hot spots and key residues, as reported herein. For six of seven drug targets for which more than 20 3D‐cliffs were available, series of 3D‐cliffs were identified that were consistently involved in interactions with different hot spots. These 3D‐cliffs often encoded chemical modifications resulting in interactions that were characteristic of highly potent compounds but absent in weakly potent ones, thus providing information for structure‐based design.     

Exploring the conformational energy landscape of acetylcholinesterase by kinetic crystallography

Acetylcholinesterase is a very rapid enzyme, essential in the process of nerve impulse transmission at cholinergic synapses. It is the target of all currently approved anti-Alzheimer drugs and further progress in the modulation of its activity requires structural as well as dynamical information. Exploration of the conformational energy landscape of a protein by means of X-ray crystallography requires the use of experimental tricks, to overcome the inherently static nature of crystallographic structures. Here we report three experimental approaches that allowed to gain structural insight into the dynamics of acetylcholinesterase, which is relevant for structure-based drug design.     

Chemical genomics strategy for the discovery of new anticancer agents

Chemical genomics represents a cooperation of biology and chemistry to identify and intervene the biological targets. Small molecules with diverse structural characteristics should be used to validate the target through interfering with the biological processes. Because of the limitation of existing chemical libraries, the diversity can be exploited using both the molecular design techniques; structure-based design and ligand-based design. These methods can guide the selection of small molecules with optimal binding properties to desired biological targets. Studies of potential molecular targets for novel anticancer drug discovery including in silico screening, QSAR, and de novo design demonstrated the importance of chemical genomics strategy to find the chemical probes and drug lead compounds.     

Protein models in drug discovery

Over the last few years, the utilization of protein structural information in drug discovery research has matured and is today applied throughout the process, ranging from genomics-derived target identification and selection to the final design of suitable drug candidates. An especially powerful methodology has arisen from the clear synergies of the combination of target structural information with combinatorial chemistry. Several structural genomics initiatives have recently been started and are now generating 3-D structures of target molecules at an unprecedented rate that will provide a wealth of novel information that can be utilized for rational drug design.