The use of chemical design tools to transform proteomics data into drug candidates

The Human Genome Project has fueled the massive information-driven growth of genomics and proteomics and promises to deliver new insights into biology and medicine. Since proteins represent the majority of drug targets, these molecules are the focus of activity in pharmaceutical and biotechnology organizations. In this article, we describe the processes by which computational drug design may be used to exploit protein structural information to create virtual small molecules that may become novel medicines. Experimental protein structure determination, site exploration, and virtual screening provide a foundation for small molecule generation in silico, thus creating the bridge between proteomics and drug discovery.     

Structural proteomics in drug discovery

High-throughput, automated or semiautomated methodologies implemented by companies and structural genomics initiatives have accelerated the process of acquiring structural information for proteins via x-ray crystallography. This has enabled the application of structure-based drug design technologies to a variety of new structures that have potential pharmacologic relevance. Although there remain major challenges to applying these approaches more broadly to all classes of drug discovery targets, clearly the continued development and implementation of these structure-based drug design methodologies by the scientific community at large will help to address and provide solutions to these hurdles. The result will be a growing number of protein structures of important pharmacologic targets that will help to streamline the process of identification and optimization of lead compounds for drug development. These lead agonist and antagonist pharmacophores should, in turn, help to alleviate one of the current critical bottlenecks in the drug discovery process; that is, defining the functional relevance of potential novel targets to disease modification. The prospect of generating an increasing number of potential drug candidates will serve to highlight perhaps the most significant future bottleneck for drug development, the cost and complexity of the drug approval process.     

Structural proteomics in drug discovery

High-throughput, automated or semiautomated methodologies implemented by companies and structural genomics initiatives have accelerated the process of acquiring structural information for proteins via x-ray crystallography. This has enabled the application of structure-based drug design technologies to a variety of new structures that have potential pharmacologic relevance. Although there remain major challenges to applying these approaches more broadly to all classes of drug discovery targets, clearly the continued development and implementation of these structure-based drug design methodologies by the scientific community at large will help to address and provide solutions to these hurdles. The result will be a growing number of protein structures of important pharmacologic targets that will help to streamline the process of identification and optimization of lead compounds for drug development. These lead agonist and antagonist pharmacophores should, in turn, help to alleviate one of the current critical bottlenecks in the drug discovery process; that is, defining the functional relevance of potential novel targets to disease modification. The prospect of generating an increasing number of potential drug candidates will serve to highlight perhaps the most significant future bottleneck for drug development, the cost and complexity of the drug approval process.     

AlphaFold2 protein structure prediction: Implications for drug discovery

The drug discovery process involves designing compounds to selectively interact with their targets. The majority of therapeutic targets for low molecular weight (small molecule) drugs are proteins. The outstanding accuracy with which recent artificial intelligence methods compile the three-dimensional structure of proteins has made protein targets more accessible to the drug design process. Here, we present our perspective of the significance of accurate protein structure prediction on various stages of the small molecule drug discovery life cycle focusing on current capabilities and assessing how further evolution of such predictive procedures can have a more decisive impact in the discovery of new medicines.     

A computational approach to finding novel targets for existing drugs

Repositioning existing drugs for new therapeutic uses is an efficient approach to drug discovery. We have developed a computational drug repositioning pipeline to perform large-scale molecular docking of small molecule drugs against protein drug targets, in order to map the drug-target interaction space and find novel interactions. Our method emphasizes removing false positive interaction predictions using criteria from known interaction docking, consensus scoring, and specificity. In all, our database contains 252 human protein drug targets that we classify as reliable-for-docking as well as 4621 approved and experimental small molecule drugs from DrugBank. These were cross-docked, then filtered through stringent scoring criteria to select top drug-target interactions. In particular, we used MAPK14 and the kinase inhibitor BIM-8 as examples where our stringent thresholds enriched the predicted drug-target interactions with known interactions up to 20 times compared to standard score thresholds. We validated nilotinib as a potent MAPK14 inhibitor in vitro (IC50 40 nM), suggesting a potential use for this drug in treating inflammatory diseases. The published literature indicated experimental evidence for 31 of the top predicted interactions, highlighting the promising nature of our approach. Novel interactions discovered may lead to the drug being repositioned as a therapeutic treatment for its off-target's associated disease, added insight into the drug's mechanism of action, and added insight into the drug's side effects.     

High-throughput ligand screening via preclustering and evolved neural networks

The pathway for novel lead drug discovery has many major deficiencies, the most significant of which is the immense size of small molecule diversity space. Methods that increase the search efficiency and/or reduce the size of the search space, increase the rate at which useful lead compounds are identified. Artificial neural networks optimized via evolutionary computation provide a cost and time-effective solution to this problem. Here, we present results that suggest preclustering of small molecules prior to neural network optimization is useful for generating models of quantitative structure-activity relationships for a set of HIV inhibitors. Using these methods, it is possible to prescreen compounds to separate active from inactive compounds or even actives and mildly active compounds from inactive compounds with high predictive accuracy while simultaneously reducing the feature space. It is also possible to identify "human interpretable" features from the best models that can be used for proposal and synthesis of new compounds in order to optimize potency and specificity.     

Mapping the binding sites of challenging drug targets

An increasing number of medically important proteins are challenging drug targets because their binding sites are too shallow or too polar, are cryptic and thus not detectable without a bound ligand or located in a protein–protein interface. While such proteins may not bind druglike small molecules with sufficiently high affinity, they are frequently druggable using novel therapeutic modalities. The need for such modalities can be determined by experimental or computational fragment based methods. Computational mapping by mixed solvent molecular dynamics simulations or the FTMap server can be used to determine binding hot spots. The strength and location of the hot spots provide very useful information for selecting potentially successful approaches to drug discovery.     

Assay Optimization and Screening of RNA-Protein Interactions by AlphaScreen

The lack of lead compounds that specifically recognize and manipulate the function of RNA molecules limits our ability to consider RNA targets valid for drug discovery. Herein is reported a high-throughput biochemical screen for inhibitors of RNA-protein interactions based on AlphaScreen technology that incorporates several layers of specificity measurements into the primary screen. This screen was used to analyze approximately 5500 compounds from a collection of bioactive small molecules to detect inhibitors of the HIV-1 Rev-RRE and BIV Tat-TAR interactions. This proof-of-concept screen validates the assay as one that accurately identifies hit molecules and determines the selectivity of those hits.     

Functional genomics: identifying drug targets for parasitic diseases

The genomic sequences of parasitic diseases are rapidly becoming available and, recently, the full sequence of Plasmodium falciparum has been published. Much has been promised from this genomic revolution including the identification of new drug targets and novel chemotherapeutic treatments for the control of parasitic diseases. The challenge to use this information efficiently will require functional genomics tools such as bioinformatics, microarrays, proteomics and chemical genomics to identify potential drug targets, and to allow the development of optimized lead compounds. The information generated from these tools will provide a crucial link from genomic analysis to drug discovery.     

Kinomics-structural biology and chemogenomics of kinase inhibitors and targets

Classifying kinases based entirely on small molecule selectivity data is a new approach to drug discovery that allows scientists to understand relationships between targets. This approach combines the understanding of small molecules and targets, and thereby assists the researcher in finding new targets for existing molecules or understanding selectivity and polypharmacology of molecules in related targets. Currently, structural information is available for relatively few of the protein kinases encoded in the human genome (7% of the estimated 518); however, even the current knowledge base, when paired with structure-based design techniques, can assist in the identification and optimization of novel kinase inhibitors across the entire protein class. Chemogenomics attempts to combine genomic data, structural biological data, classical dendrograms, and selectivity data to explore, define, and classify the medicinally relevant kinase space. Exploitation of this information in the discovery of kinase inhibitors defines practical kinase chemogenomics (kinomics). In this paper, we review the available information on kinase targets and their inhibitors, and present the relationships between the various classification schema for kinase space. In particular, we present the first dendrogram of kinases based entirely on small molecule selectivity data. We find that the selectivity dendrogram differs from sequence-based clustering mostly in the higher-level groupings of the smaller clusters, and remains very comparable for closely homologous targets. Highly homologous kinases are, on average, inhibited comparably by small molecules. This observation, although intuitive, is very important to the process of target selection, as one would expect difficulty in achieving inhibitor selectivity for kinases that share high sequence identity.     

Chemical kinomics: a powerful strategy for target deconvolution

Kinomics is an emerging and promising approach for deciphering kinomes. Chemical kinomics is a discipline of chemical genomics that is also referred to as "chemogenomics", which is derived from chemistry and biology. Chemical kinomics has become a powerful approach to decipher complicated phosphorylation-based cellular signaling networks with the aid of small molecules that modulate kinase functions. Moreover, chemical kinomics has played a pivotal role in the field of kinase drug discovery as it enables identification of new molecular targets of small molecule kinase modulators and/or exploitation of novel functions of known kinases and has also provided novel chemical entities as hit/lead compounds. In this short review, contemporary chemical kinomics technologies such as activity-based protein profiling, T7 kinasetagged phages, kinobeads, three-hybrid systems, fluorescenttagged kinase binding assays, and chemical genomic profiling are discussed along with a novel allosteric Bcr-Abl kinase inhibitor (GNF-2/GNF-5) as a successful application of chemical kinomics approaches.     

The discovery of steroids and other novel FKBP inhibitors using a molecular docking program

The molecular docking computer program SANDOCK was used to screen small molecule three-dimensional databases in the hunt for novel FKBP inhibitors. Spectroscopic measurements confirmed binding of over 20 compounds to the target protein, some with dissociation constants in the low micromolar range. The discovery that FK506 binding protein is a steroid binding protein may be of wider biological significance. Two-dimensional NMR was used to determine the steroid binding mode and confirmed the interactions predicted by the docking program.     

Protein-protein interactions as targets for small molecule drug discovery

Protein-protein interactions represent a highly populated class of targets for drug discovery. However, such systems present a number of unique challenges. This review presents an analysis of individual protein-protein interaction systems which have recently yielded success in discovering drug-like inhibitors. The structural characteristics of the protein binding sites and the attributes of the small molecule ligands are focused upon, in an attempt to derive commonly shared principles that may be of general usefulness in future drug discovery efforts within this target class.     

NMR screening in drug discovery

NMR methods in drug discovery have traditionally been used to obtain structural information for drug targets or target-ligand complexes. Recently, it has been shown that NMR may be used as an alternative approach for identification of ligands that bind to protein drug targets, shifting the emphasis of many NMR laboratories towards screening and design of potential drug molecules, rather than structural characterization.     

Proteomic methods for drug target discovery

The field of drug target discovery is currently very popular with a great potential for advancing biomedical research and chemical genomics. Innovative strategies have been developed to aid the process of target identification, either by elucidating the primary mechanism-of-action of a drug, by understanding side effects involving unanticipated 'off-target' interactions, or by finding new potential therapeutic value for an established drug. Several promising proteomic methods have been introduced for directly isolating and identifying the protein targets of interest that are bound by active small molecules or for visualizing enzyme activities affected by drug treatment. Significant progress has been made in this rapidly advancing field, speeding the clinical validation of drug candidates and the discovery of the novel targets for lead compounds developed using cell-based phenotypic screens. Using these proteomic methods, further insight into drug activity and toxicity can be ascertained.     

Drug screening for influenza neuraminidase inhibitors

Neuraminidase (NA) is one of the most important targets to screen the drugs of anti-influenza virus A and B. After virtual screening approaches were applied to a compound database which possesses more than 10000 compound structures, 160 compounds were selected for bioactivity assay, then a High Throughput Screening (HTS) model established for influenza virus NA inhibitors was applied to detect these compounds. Finally, three compounds among them displayed higher inhibitory activities, the range of their IC50 was from 0.1 μmol/L to 3μmol/L. Their structural scaffolds are novel and different from those of NA inhibitors approved for influenza treatment, and will be useful for the design and research of new NA inhibitors. The resuit indicated that the combination of virtual screening with HTS was very significant to drug screening and drug discovery.     

Deletion of mammalian sterile 20-like kinase 1 attenuates neuronal loss and improves locomotor function in a mouse model of spinal cord trauma

Natural product-inspired libraries of molecules with diverse architectures have evolved as one of the most useful tools for discovering lead molecules for drug discovery. In comparison to conventional combinatorial libraries, these molecules have been inferred to perform better in phenotypic screening against complicated targets. Diversity-oriented synthesis (DOS) is a forward directional strategy to access such multifaceted library of molecules. From a successful DOS campaign of a natural product-inspired library, recently a small molecule with spiroindoline motif was identified as a potent anti-breast cancer compound. Herein we report the subcellular studies performed for this molecule on breast cancer cells. Our investigation revealed that it repositions microtubule cytoskeleton and displaces AKAP9 located at the microtubule organization centre. DNA ladder assay and cell cycle experiments further established the molecule as an apoptotic agent. This work further substantiated the amalgamation of DOS-phenotypic screening-sub-cellular studies as a consolidated blueprint for the discovery of potential pharmaceutical drug candidates.     

Displacement of protein-bound aptamers with small molecules screened by fluorescence polarization

Small molecule inhibitors of proteins are invaluable tools in research and as starting points for drug development. However, their screening can be tedious, as most screening methods have to be tailored to the corresponding drug target. Here, we describe a detailed protocol for a modular and generally applicable assay for the identification of small organic compounds that displace an aptamer complexed to its target protein. The method relies on fluorescence-labeled aptamers and the increase of fluorescence polarization upon their binding to the target protein. The assay has high Z'-factors, making it compatible with high-throughput screening. It allows easy automation, making fluorescence readout the time-limiting step. As aptamers can be generated for virtually any protein target, the assay allows identification of small molecule inhibitors for targets or individual protein domains for which no functional screen is available. We provide the step-by-step protocol to screen for antagonists of the cytohesin class of small guanosine exchange factors.     

The use of peptides in Diogenesis: a novel approach to drug discovery and phenomics

The Diogenesis Process is an integrated drug discovery platform that allows target validation, partner identification, and the identification of small molecule drug candidates for protein:protein interactions. Diogenesis utilizes the well-established methods of peptide display, synthetic and recombinant peptide production, in vitro biochemical and cell-based testing to form a universal drug discovery engine with distinct advantages over competing protocols. The process creates a library of diverse peptides, and selects rare and unique binders that identify and simplify surface "hot spots" on protein targets through which target activity can be regulated. In many cases, these peptide "Surrogates" have the minimal sequence and structural information needed to induce a change in the biological activity of the target; in pharmacological terms, only after inducing agonism or antagonism. The use of Surrogates in hot spot identification also allows subdivision of rather large surface domains into smaller domains that alone, or in combination with another subdomain, offers sufficient territory for modification of target activity. These Surrogates, in turn, provide the necessary ligands to develop appropriate Site Directed Assays (SDAs) for each essential subdomain. The SDAs provide the screening mode for finding competitive small molecules by high throughput screening. The other arm of the Diogenesis system is an application in the new area of "Phenomics." This part of the discovery process is a form of phenotypic analysis of genomic information that has also been referred to as "functional" genomics. Phenomics, done via the Diogenesis system, uses peptide Surrogates as modifiers of the activity of, and identifiers of the partners of, gene products of known and unknown function. Actually, in many instances, the same Surrogate isolated for use in Phenomics will be used to create SDAs for discovery of small molecule drug candidates. In this simple fashion, the two applications of Diogenesis are integrated to provide savings in research time and money.     

Knowledge-based Fragment Binding Prediction

Target-based drug discovery must assess many drug-like compounds for potential activity. Focusing on low-molecular-weight compounds (fragments) can dramatically reduce the chemical search space. However, approaches for determining protein-fragment interactions have limitations. Experimental assays are time-consuming, expensive, and not always applicable. At the same time, computational approaches using physics-based methods have limited accuracy. With increasing high-resolution structural data for protein-ligand complexes, there is now an opportunity for data-driven approaches to fragment binding prediction. We present FragFEATURE, a machine learning approach to predict small molecule fragments preferred by a target protein structure. We first create a knowledge base of protein structural environments annotated with the small molecule substructures they bind. These substructures have low-molecular weight and serve as a proxy for fragments. FragFEATURE then compares the structural environments within a target protein to those in the knowledge base to retrieve statistically preferred fragments. It merges information across diverse ligands with shared substructures to generate predictions. Our results demonstrate FragFEATURE''s ability to rediscover fragments corresponding to the ligand bound with 74% precision and 82% recall on average. For many protein targets, it identifies high scoring fragments that are substructures of known inhibitors. FragFEATURE thus predicts fragments that can serve as inputs to fragment-based drug design or serve as refinement criteria for creating target-specific compound libraries for experimental or computational screening.