Structural Biology and the Design of New Therapeutics: From HIV and Cancer to Mycobacterial Infections: A Paper Dedicated to John Kendrew

Structure of a novel form of phosphopantetheine adenylyltransferase from Klebsiella pneumoniae at 2.59 Å resolution

Phosphopantetheine adenylyltransferase (EC. 2.7.7.3, PPAT) catalyzes the penultimate step of the multistep reaction in the coenzyme A (CoA) biosynthesis pathway. In this step, an adenylyl group from adenosine triphosphate (ATP) is transferred to 4'-phosphopantetheine (PNS) yielding 3'-dephospho-coenzyme A (dpCoA) and pyrophosphate (PPi). PPAT from strain C3 of Klebsiella pneumoniae (KpPPAT) was cloned, expressed and purified. It was crystallized using 0.1 M HEPES buffer and PEG10000 at pH 7.5. The crystals belonged to tetragonal space group P41212 with cell dimensions of a = b = 72.82 Å and c = 200.37 Å. The structure was determined using the molecular replacement method and refined to values of 0.208 and 0.255 for Rcryst and Rfree factors, respectively. The structure determination showed the presence of three crystallographically independent molecules A, B and C in the asymmetric unit. The molecules A and B are observed in the form of a dimer in the asymmetric unit while molecule C belongs to the second dimer whose partner is related by crystallographic twofold symmetry. The polypeptide chain of KpPPAT folds into a β/α structure. The conformations of the side chains of several residues in the substrate binding site in KpPPAT are significantly different from those reported in other PPATs. As a result, the modes of binding of substrates, phosphopantetheine (PNS) and adenosine triphosphate (ATP) differ considerably. The binding studies using fluorescence spectroscopy indicated a KD value of 3.45 × 10-4 M for ATP which is significantly lower than the corresponding values reported for PPAT from other species.

Mapping Single-Molecule Protein Complexes in 3D with DNA Nanoswitch Calipers

The ability to accurately map the 3D geometry of single-molecule complexes in trace samples is a challenging goal that would lead to new insights into molecular mechanics and provide an approach for single-molecule structural proteomics. To enable this, we have developed a high-resolution force spectroscopy method capable of measuring multiple distances between labeled sites in natively folded protein complexes. Our approach combines reconfigurable nanoscale devices, we call DNA nanoswitch calipers, with a force-based barcoding system to distinguish each measurement location. We demonstrate our approach by reconstructing the tetrahedral geometry of biotin-binding sites in natively folded streptavidin, with 1.5-2.5 Å agreement with previously reported structures.

Mapping Single-molecule Protein Complexes in 3D with DNA Nanoswitch Calipers

The ability to accurately map the 3D geometry of single-molecule complexes in trace samples would lead to new insights into molecular mechanics and provide an approach for single-molecule structural proteomics. To enable this, we have developed a high-resolution force-spectroscopy method capable of measuring multiple distances between labeled sites in natively folded protein complexes. Our approach combines reconfigurable nanoscale devices we call DNA Nanoswitch Calipers, which we have previously introduced, with a force-based barcoding system to distinguish each measurement location. We demonstrate our approach by reconstructing the tetrahedral geometry of biotin-binding sites in natively folded streptavidin, with 1.5-2.5 Å agreement to previously reported structures.

Fragment-Based Drug Discovery against Mycobacteria: The Success and Challenges

The emergence of drug-resistant mycobacteria, including Mycobacterium tuberculosis (Mtb) and non-tuberculous mycobacteria (NTM), poses an increasing global threat that urgently demands the development of new potent anti-mycobacterial drugs. One of the approaches toward the identification of new drugs is fragment-based drug discovery (FBDD), which is the most ingenious among other drug discovery models, such as structure-based drug design (SBDD) and high-throughput screening. Specialized techniques, such as X-ray crystallography, nuclear magnetic resonance spectroscopy, and many others, are part of the drug discovery approach to combat the Mtb and NTM global menaces. Moreover, the primary drawbacks of traditional methods, such as the limited measurement of biomolecular toxicity and uncertain bioavailability evaluation, are successfully overcome by the FBDD approach. The current review focuses on the recognition of fragment-based drug discovery as a popular approach using virtual, computational, and biophysical methods to identify potent fragment molecules. FBDD focuses on designing optimal inhibitors against potential therapeutic targets of NTM and Mtb (PurC, ArgB, MmpL3, and TrmD). Additionally, we have elaborated on the challenges associated with the FBDD approach in the identification and development of novel compounds. Insights into the applications and overcoming the challenges of FBDD approaches will aid in the identification of potential therapeutic compounds to treat drug-sensitive and drug-resistant NTMs and Mtb infections.

FGDB: a comprehensive graph database of ligand fragments from the Protein Data Bank

This work presents Fragment Graph DataBase (FGDB), a graph database of ligand fragments extracted and generated from the protein entries available in the Protein Data Bank (PDB). FGDB is meant to support and elicit campaigns of fragment-based drug design, by enabling users to query it in order to construct ad hoc, target-specific libraries. In this regard, the database features more than 17 000 fragments, typically small, highly soluble and chemically stable molecules expressed via their canonical Simplified Molecular Input Line Entry System (SMILES) representation. For these fragments, the database provides information related to their contact frequencies with the amino acids, the ligands they are contained in and the proteins the latter bind to. The graph database can be queried via standard web forms and textual searches by a number of identifiers (SMILES, ligand and protein PDB ids) as well as via graphical queries that can be performed against the graph itself, providing users with an intuitive and effective view upon the underlying biological entities. Further search mechanisms via advanced conjunctive/disjunctive/negated textual queries are also possible, in order to allow scientists to look for specific relationships and export their results for further studies. This work also presents two sample use cases where maternal embryonic leucine zipper kinase and mesotrypsin are used as a target, being proteins of high biomedical relevance for the development of cancer therapies.

Database URL: http://biochimica3.bio.uniroma3.it/fragments-web/

Structural Characterization of Mycobacterium abscessus Phosphopantetheine Adenylyl Transferase Ligand Interactions: Implications for Fragment-Based Drug Design

Anti-microbial resistance is a rising global healthcare concern that needs urgent attention as growing number of infections become difficult to treat with the currently available antibiotics. This is particularly true for mycobacterial infections like tuberculosis and leprosy and those with emerging opportunistic pathogens such as Mycobacterium abscessus, where multi-drug resistance leads to increased healthcare cost and mortality. M. abscessus is a highly drug-resistant non-tuberculous mycobacterium which causes life-threatening infections in people with chronic lung conditions such as cystic fibrosis. In this study, we explore M. abscessus phosphopantetheine adenylyl transferase (PPAT), an enzyme involved in the biosynthesis of Coenzyme A, as a target for the development of new antibiotics. We provide structural insights into substrate and feedback inhibitor binding modes of M. abscessus PPAT, thereby setting the basis for further chemical exploration of the enzyme. We then utilize a multi-dimensional fragment screening approach involving biophysical and structural analysis, followed by evaluation of compounds from a previous fragment-based drug discovery campaign against M. tuberculosis PPAT ortholog. This allowed the identification of an early-stage lead molecule exhibiting low micro molar affinity against M. abscessus PPAT (Kd 3.2 ± 0.8 µM) and potential new ways to design inhibitors against this enzyme. The resulting crystal structures reveal striking conformational changes and closure of solvent channel of M. abscessus PPAT hexamer providing novel strategies of inhibition. The study thus validates the ligandability of M. abscessus PPAT as an antibiotic target and identifies crucial starting points for structure-guided drug discovery against this bacterium.

The impact of structural bioinformatics tools and resources on SARS-CoV-2 research and therapeutic strategies

SARS-CoV-2 is the causative agent of COVID-19, the ongoing global pandemic. It has posed a worldwide challenge to human health as no effective treatment is currently available to combat the disease. Its severity has led to unprecedented collaborative initiatives for therapeutic solutions against COVID-19. Studies resorting to structure-based drug design for COVID-19 are plethoric and show good promise. Structural biology provides key insights into 3D structures, critical residues/mutations in SARS-CoV-2 proteins, implicated in infectivity, molecular recognition and susceptibility to a broad range of host species. The detailed understanding of viral proteins and their complexes with host receptors and candidate epitope/lead compounds is the key to developing a structure-guided therapeutic design. Since the discovery of SARS-CoV-2, several structures of its proteins have been determined experimentally at an unprecedented speed and deposited in the Protein Data Bank. Further, specialized structural bioinformatics tools and resources have been developed for theoretical models, data on protein dynamics from computer simulations, impact of variants/mutations and molecular therapeutics. Here, we provide an overview of ongoing efforts on developing structural bioinformatics tools and resources for COVID-19 research. We also discuss the impact of these resources and structure-based studies, to understand various aspects of SARS-CoV-2 infection and therapeutic development. These include (i) understanding differences between SARS-CoV-2 and SARS-CoV, leading to increased infectivity of SARS-CoV-2, (ii) deciphering key residues in the SARS-CoV-2 involved in receptor-antibody recognition, (iii) analysis of variants in host proteins that affect host susceptibility to infection and (iv) analyses facilitating structure-based drug and vaccine design against SARS-CoV-2.

Phosphopantetheine Adenylyltransferase: A promising drug target to combat antibiotic resistance

Phosphopantetheine Adenylyltransferase (PPAT) is an enzyme that catalyzes the penultimate step in the biosynthesis of Coenzyme A (CoA), which is the active and physiologically functional form of dietary Vitamin B5. CoA serves as a cofactor for numerous metabolic reactions which makes it essential for cellular survival. This enzyme is also subject to feedback inhibition by CoA to maintain its cellular concentration. The steps of the CoA biosynthesis pathway remain conserved from prokaryotes to eukaryotes, with humans and pathogenic micro-organisms showing significant diversity on a sequence, structure and mechanistic level. This suggests that the development of selective inhibitors of microbial CoA biosynthesis should be possible using these enzymes as targets for drug development. Bacterial PPAT shows significant mechanistic difference from its human counterpart CoA synthase, which is a dual protein carrying the activity of both PPAT and next step in the pathway catalyzed by the enzyme Dephospho CoA kinase (DPCK). This review covers the detailed description of the mechanistic, structural and functional aspects of this enzyme. Also, all the attempts to design high efficiency inhibitors of this enzyme using the approach of structure based drug design have been discussed in detail. This comprehensive structural and functional discussion of PPAT will help in further exploiting it as a drug target.

Fragment-based discovery of a new class of inhibitors targeting mycobacterial tRNA modification

Translational frameshift errors are often deleterious to the synthesis of functional proteins and could therefore be promoted therapeutically to kill bacteria. TrmD (tRNA-(N(1)G37) methyltransferase) is an essential tRNA modification enzyme in bacteria that prevents +1 errors in the reading frame during protein translation and represents an attractive potential target for the development of new antibiotics. Here, we describe the application of a structure-guided fragment-based drug discovery approach to the design of a new class of inhibitors against TrmD in Mycobacterium abscessus. Fragment library screening, followed by structure-guided chemical elaboration of hits, led to the rapid development of drug-like molecules with potent in vitro TrmD inhibitory activity. Several of these compounds exhibit activity against planktonic M. abscessus and M. tuberculosis as well as against intracellular M. abscessus and M. leprae, indicating their potential as the basis for a novel class of broad-spectrum mycobacterial drugs.

A Personal History of Using Crystals and Crystallography to Understand Biology and Advanced Drug Discovery

Over the past 60 years, the use of crystals to define structures of complexes using X-ray analysis has contributed to the discovery of new medicines in a very significant way. This has been in understanding not only small-molecule inhibitors of proteins, such as enzymes, but also protein or peptide hormones or growth factors that bind to cell surface receptors. Experimental structures from crystallography have also been exploited in software to allow prediction of structures of important targets based on knowledge of homologues. Crystals and crystallography continue to contribute to drug design and provide a successful example of academia–industry collaboration.

Predicting the Effect of Chemical Factors on the pH of Crystallization Trials

In macromolecular crystallization, success is often dependent on the pH of the experiment. However, little is known about the pH of reagents used, and it is generally assumed that the pH of the experiment will closely match that of any buffering chemical in the solution. We use a large dataset of experimentally measured solution pH values to show that this assumption can be very wrong and generate a model that can be used to successfully predict the overall solution pH of a crystallization experiment. Furthermore, we investigate the time dependence of the pH of some polyethylene glycol polymers widely used in protein crystallization under different storage conditions.

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The overall pH of crystallization solutions can be modeled

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The model was trained and tested on a set of more than 40,000 measured pH values

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A pH value can be assigned to a non-buffered crystallization cocktail

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A 12-month stability study of polyethylene glycol suggests ways to store PEGs

In silico Strategies to Support Fragment-to-Lead Optimization in Drug Discovery

Fragment-based drug (or lead) discovery (FBDD or FBLD) has developed in the last two decades to become a successful key technology in the pharmaceutical industry for early stage drug discovery and development. The FBDD strategy consists of screening low molecular weight compounds against macromolecular targets (usually proteins) of clinical relevance. These small molecular fragments can bind at one or more sites on the target and act as starting points for the development of lead compounds. In developing the fragments attractive features that can translate into compounds with favorable physical, pharmacokinetics and toxicity (ADMET-absorption, distribution, metabolism, excretion, and toxicity) properties can be integrated. Structure-enabled fragment screening campaigns use a combination of screening by a range of biophysical techniques, such as differential scanning fluorimetry, surface plasmon resonance, and thermophoresis, followed by structural characterization of fragment binding using NMR or X-ray crystallography. Structural characterization is also used in subsequent analysis for growing fragments of selected screening hits. The latest iteration of the FBDD workflow employs a high-throughput methodology of massively parallel screening by X-ray crystallography of individually soaked fragments. In this review we will outline the FBDD strategies and explore a variety of in silico approaches to support the follow-up fragment-to-lead optimization of either: growing, linking, and merging. These fragment expansion strategies include hot spot analysis, druggability prediction, SAR (structure-activity relationships) by catalog methods, application of machine learning/deep learning models for virtual screening and several de novo design methods for proposing synthesizable new compounds. Finally, we will highlight recent case studies in fragment-based drug discovery where in silico methods have successfully contributed to the development of lead compounds.

Structural and biochemical studies of phosphopantetheine adenylyltransferase from Acinetobacter baumannii with dephospho-coenzyme A and coenzyme A

Phosphopantetheine adenylyl transferase catalyzes a rate limiting penultimate step of the multistep reaction which produces coenzyme A (CoA) as a final product. CoA is required as an essential cofactor in a number of metabolic reactions. Therefore inhibiting the function of this enzyme will lead to cell death in bacteria. Acinetobacter baumannii is multi drug resistant pathogen and causes infections in immunocompromised patients. AbPPAT has been cloned, expressed, purified and crystallized and structures of two complexes of AbPPAT with dephospho coenzyme A (dPCoA) and coenzyme A (CoA) have been determined. Both dPCoA and CoA molecules are observed in the substrate binding site of AbPPAT. A comparison with the structures of the complexes of PPAT from other species shows that the orientations of dPCoA are identical in all the structures. On the other hand, as observed from the structures of the complexes of CoA with PPAT, the orientations of CoA are found to differ considerably. This shows that the substrates occupy identical positions in the substrate binding sites of enzymes whereas the positions of inhibitors may differ. The binding studies carried out using fluorescence method and surface plasmon resonance techniques showed that binding affinity of CoA towards AbPPAT is nearly three times higher than that of dPCoA.

Structure-guided fragment-based drug discovery at the synchrotron: screening binding sites and correlations with hotspot mapping

Structure-guided drug discovery emerged in the 1970s and 1980s, stimulated by the three-dimensional structures of protein targets that became available, mainly through X-ray crystal structure analysis, assisted by the development of synchrotron radiation sources. Structures of known drugs or inhibitors were used to guide the development of leads. The growth of high-throughput screening during the late 1980s and the early 1990s in the pharmaceutical industry of chemical libraries of hundreds of thousands of compounds of molecular weight of approximately 500 Da was impressive but still explored only a tiny fraction of the chemical space of the predicted 10⁴⁰ drug-like compounds. The use of fragments with molecular weights less than 300 Da in drug discovery not only decreased the chemical space needing exploration but also increased promiscuity in binding targets. Here we discuss advances in X-ray fragment screening and the challenge of identifying sites where fragments not only bind but can be chemically elaborated while retaining their positions and binding modes. We first describe the analysis of fragment binding using conventional X-ray difference Fourier techniques, with Mycobacterium abscessus SAICAR synthetase (PurC) as an example. We observe that all fragments occupy positions predicted by computational hotspot mapping. We compare this with fragment screening at Diamond Synchrotron Light Source XChem facility using PanDDA software, which identifies many more fragment hits, only some of which bind to the predicted hotspots. Many low occupancy sites identified may not support elaboration to give adequate ligand affinity, although they will likely be useful in drug discovery as 'warm spots' for guiding elaboration of fragments bound at hotspots. We discuss implications of these observations for fragment screening at the synchrotron sources. This article is part of the theme issue 'Fifty years of synchrotron science: achievements and opportunities'.

Lattice engineering enables definition of molecular features allowing for potent small-molecule inhibition of HIV-1 entry

Diverse entry inhibitors targeting the gp120 subunit of the HIV-1 envelope (Env) trimer have been developed including BMS-626529, also called temsavir, a prodrug version of which is currently in phase III clinical trials. Here we report the characterization of a panel of small-molecule inhibitors including BMS-818251, which we show to be >10-fold more potent than temsavir on a cross-clade panel of 208-HIV-1 strains, as well as the engineering of a crystal lattice to enable structure determination of the interaction between these inhibitors and the HIV-1 Env trimer at higher resolution. By altering crystallization lattice chaperones, we identify a lattice with both improved diffraction and robust co-crystallization of HIV-1 Env trimers from different clades complexed to entry inhibitors with a range of binding affinities. The improved diffraction reveals BMS-818251 to utilize functional groups that interact with gp120 residues from the conserved β20-β21 hairpin to improve potency.

Temsavir, a compound that inhibits HIV entry by binding envelope (Env), is currently in clinical development. Here, Lai et al. identify a more than 10-fold improved compound and, using lattice engineering, obtain crystal structures that give insights into improved inhibition between small molecules and Env.

Methods for Discovering and Targeting Druggable Protein-Protein Interfaces and Their Application to Repurposing

Drug repurposing is a creative and resourceful approach to increase the number of therapies by exploiting available and approved drugs. However, identifying new protein targets for previously approved drugs is challenging. Although new strategies have been developed for drug repurposing, there is broad agreement that there is room for further improvements. In this chapter, we review protein-protein interaction (PPI) interface-targeting strategies for drug repurposing applications. We discuss certain features, such as hot spot residue and hot region prediction and their importance in drug repurposing, and illustrate common methods used in PPI networks to identify drug off-targets. We also collect available online resources for hot spot prediction, binding pocket identification, and interface clustering which are effective resources in polypharmacology. Finally, we provide case studies showing the significance of protein interfaces and hot spots in drug repurposing.

Mycobacterial genomics and structural bioinformatics: opportunities and challenges in drug discovery

Of the more than 190 distinct species of Mycobacterium genus, many are economically and clinically important pathogens of humans or animals. Among those mycobacteria that infect humans, three species namely Mycobacterium tuberculosis (causative agent of tuberculosis), Mycobacterium leprae (causative agent of leprosy) and Mycobacterium abscessus (causative agent of chronic pulmonary infections) pose concern to global public health. Although antibiotics have been successfully developed to combat each of these, the emergence of drug-resistant strains is an increasing challenge for treatment and drug discovery. Here we describe the impact of the rapid expansion of genome sequencing and genome/pathway annotations that have greatly improved the progress of structure-guided drug discovery. We focus on the applications of comparative genomics, metabolomics, evolutionary bioinformatics and structural proteomics to identify potential drug targets. The opportunities and challenges for the design of drugs for M. tuberculosis, M. leprae and M. abscessus to combat resistance are discussed.

Docking with SwissDock

Protein-ligand docking simulation is central in drug design and development. Therefore, the development of web servers intended to docking simulations is of pivotal importance. SwissDock is a web server dedicated to carrying out protein-ligand docking simulation intuitively and elegantly. SwissDock is based on the protein-ligand docking program EADock DSS and has a simple and integrated interface. The SwissDock allows the user to upload structure files for a protein and a ligand, and returns the results by e-mail. To facilitate the upload of the protein and ligand files, we can prepare these input files using the program UCSF Chimera. In this chapter, we describe how to use UCSF Chimera and SwissDock to perform protein-ligand docking simulations. To illustrate the process, we describe the molecular docking of the competitive inhibitor roscovitine against the structure of human cyclin-dependent kinase 2.

Current Strategies and Applications for Precision Drug Design

Since Human Genome Project (HGP) revealed the heterogeneity of individuals, precision medicine that proposes the customized healthcare has become an intractable and hot research. Meanwhile, as the Precision Medicine Initiative launched, precision drug design which aims at maximizing therapeutic effects while minimizing undesired side effects for an individual patient has entered a new stage. One of the key strategies of precision drug design is target based drug design. Once a key pathogenic target is identified, rational drug design which constitutes the major part of precision drug design can be performed. Examples of rational drug design on novel druggable targets and protein-protein interaction surfaces are summarized in this review. Besides, various kinds of computational modeling and simulation approaches increasingly benefit for the drug discovery progress. Molecular dynamic simulation, drug target prediction and in silico clinical trials are discussed. Moreover, due to the powerful ability in handling high-dimensional data and complex system, deep learning has efficiently promoted the applications of artificial intelligence in drug discovery and design. In this review, deep learning methods that tailor to precision drug design are carefully discussed. When a drug molecule is discovered, the development of specific targeted drug delivery system becomes another key aspect of precision drug design. Therefore, state-of-the-art techniques of drug delivery system including antibody-drug conjugates (ADCs), and ligand-targeted conjugates are also included in this review.

Targeting Allostery with Avatars to Design Inhibitors Assessed by Cell Activity: Dissecting MRE11 Endo- and Exonuclease Activities

For inhibitor design, as in most research, the best system is question dependent. We suggest structurally defined allostery to design specific inhibitors that target regions beyond active sites. We choose systems allowing efficient quality structures with conformational changes as optimal for structure-based design to optimize inhibitors. We maintain that evolutionarily related targets logically provide molecular avatars, where this Sanskrit term for descent includes ideas of functional relationships and of being a physical embodiment of the target's essential features without requiring high sequence identity. Appropriate biochemical and cell assays provide quantitative measurements, and for biomedical impacts, any inhibitor's activity should be validated in human cells. Specificity is effectively shown empirically by testing if mutations blocking target activity remove cellular inhibitor impact. We propose this approach to be superior to experiments testing for lack of cross-reactivity among possible related enzymes, which is a challenging negative experiment. As an exemplary avatar system for protein and DNA allosteric conformational controls, we focus here on developing separation-of-function inhibitors for meiotic recombination 11 nuclease activities. This was achieved not by targeting the active site but rather by geometrically impacting loop motifs analogously to ribosome antibiotics. These loops are neighboring the dimer interface and active site act in sculpting dsDNA and ssDNA into catalytically competent complexes. One of our design constraints is to preserve DNA substrate binding to geometrically block competing enzymes and pathways from the damaged site. We validate our allosteric approach to controlling outcomes in human cells by reversing the radiation sensitivity and genomic instability in BRCA mutant cells.

Structure-based drug design: aiming for a perfect fit

Knowledge of the three-dimensional structure of therapeutically relevant targets has informed drug discovery since the first protein structures were determined using X-ray crystallography in the 1950s and 1960s. In this editorial we provide a brief overview of the powerful impact of structure-based drug design (SBDD), which has its roots in computational and structural biology, with major contributions from both academia and industry. We describe advances in the application of SBDD for integral membrane protein targets that have traditionally proved very challenging. We emphasize the major progress made in fragment-based approaches for which success has been exemplified by over 30 clinical drug candidates and importantly three FDA-approved drugs in oncology. We summarize the articles in this issue that provide an excellent snapshot of the current state of the field of SBDD and fragment-based drug design and which offer key insights into exciting new developments, such as the X-ray free-electron laser technology, cryo-electron microscopy, open science approaches and targeted protein degradation. We stress the value of SBDD in the design of high-quality chemical tools that are used to interrogate biology and disease pathology, and to inform target validation. We emphasize the need to maintain the scientific rigour that has been traditionally associated with structural biology and extend this to other methods used in drug discovery. This is particularly important because the quality and robustness of any form of contributory data determines its usefulness in accelerating drug design, and therefore ultimately in providing patient benefit.

Protein crystallography and drug discovery: recollections of knowledge exchange between academia and industry

The development of structure-guided drug discovery is a story of knowledge exchange where new ideas originate from all parts of the research ecosystem. Dorothy Crowfoot Hodgkin obtained insulin from Boots Pure Drug Company in the 1930s and insulin crystallization was optimized in the company Novo in the 1950s, allowing the structure to be determined at Oxford University. The structure of renin was developed in academia, on this occasion in London, in response to a need to develop antihypertensives in pharma. The idea of a dimeric aspartic protease came from an international academic team and was discovered in HIV; it eventually led to new HIV antivirals being developed in industry. Structure-guided fragment-based discovery was developed in large pharma and biotechs, but has been exploited in academia for the development of new inhibitors targeting protein-protein interactions and also antimicrobials to combat mycobacterial infections such as tuberculosis. These observations provide a strong argument against the so-called 'linear model', where ideas flow only in one direction from academic institutions to industry. Structure-guided drug discovery is a story of applications of protein crystallography and knowledge exhange between academia and industry that has led to new drug approvals for cancer and other common medical conditions by the Food and Drug Administration in the USA, as well as hope for the treatment of rare genetic diseases and infectious diseases that are a particular challenge in the developing world.