Missing fragments: detecting cooperative binding in fragment-based drug design

Automation of Drug Discovery through Cutting-edge In-silico Research in Pharmaceuticals: Challenges and Future Scope

The rapidity and high-throughput nature of in silico technologies make them advantageous for predicting the properties of a large array of substances. In silico approaches can be used for compounds intended for synthesis at the beginning of drug development when there is either no or very little compound available. In silico approaches can be used for impurities or degradation products. Quantifying drugs and related substances (RS) with pharmaceutical drug analysis (PDA) can also improve drug discovery (DD) by providing additional avenues to pursue. Potential future applications of PDA include combining it with other methods to make insilico predictions about drugs and RS. One possible outcome of this is a determination of the drug potential of nontoxic RS. ADME estimation, QSAR research, molecular docking, bioactivity prediction, and toxicity testing all involve impurity profiling. Before committing to DD, RS with minimal toxicity can be utilised in silico. The efficacy of molecular docking in getting a medication to market is still debated despite its refinement and improvement. Biomedical labs and pharmaceutical companies were hesitant to adopt molecular docking algorithms for drug screening despite their decades of development and improvement. Despite the widespread use of "force fields" to represent the energy exerted within and between molecules, it has been impossible to reliably predict or compute the binding affinities between proteins and potential binding medications.

In silico methods and tools for drug discovery

In the past, conventional drug discovery strategies have been successfully employed to develop new drugs, but the process from lead identification to clinical trials takes more than 12 years and costs approximately $1.8 billion USD on average. Recently, in silico approaches have been attracting considerable interest because of their potential to accelerate drug discovery in terms of time, labor, and costs. Many new drug compounds have been successfully developed using computational methods. In this review, we briefly introduce computational drug discovery strategies and outline up-to-date tools to perform the strategies as well as available knowledge bases for those who develop their own computational models. Finally, we introduce successful examples of anti-bacterial, anti-viral, and anti-cancer drug discoveries that were made using computational methods.

Elucidating the multiple roles of hydration for accurate protein-ligand binding prediction via deep learning

Accurate and efficient prediction of protein-ligand interactions has been a long-lasting dream of practitioners in drug discovery. The insufficient treatment of hydration is widely recognized to be a major limitation for accurate protein-ligand scoring. Using an integration of molecular dynamics simulations on thousands of protein structures with novel big-data analytics based on convolutional neural networks and deep Taylor decomposition, we consistently identify here three different patterns of hydration to be essential for protein-ligand interactions. In addition to desolvation and water-mediated interactions, the formation of enthalpically favorable networks of first-shell water molecules around solvent-exposed ligand moieties is identified to be essential for protein-ligand binding. Despite being currently neglected in drug discovery, this hydration phenomenon could lead to new avenues in optimizing the free energy of ligand binding. Application of deep neural networks incorporating hydration to docking provides 89% accuracy in binding pose ranking, an essential step for rational structure-based drug design.

Predicting the Prevalence of Alternative Warfarin Tautomers in Solution

Warfarin, a widely used oral anticoagulant, is prescribed as a racemic mixture. Each enantiomer of neutral Warfarin can exist in 20 possible tautomeric states leading to complex pharmacokinetics and uncertainty as to the relevant species under different conditions. Here, the ability of alternative computational approaches to predict the preferred tautomeric form(s) of neutral Warfarin in different solvents is examined. It is shown that varying the method used to estimate the heat of formation in vacuum (direct or via homodesmic reactions), whether entropic corrections were included, and the method used to estimate the free enthalpy of solvation (i.e., PCM, COSMO, or SMD implicit models or explicit solvent) lead to large differences in the predicted rank and relative populations of the tautomers. In this case, only a combination of the enthalpy of formation using homodesmic reactions and explicit solvent to estimate the free enthalpy of solvation yielded results compatible with the available experimental data. The work also suggests that a small but significant subset of the possible Warfarin tautomers are likely to be physiologically relevant.

Binding Modes of Ligands Using Enhanced Sampling (BLUES): Rapid Decorrelation of Ligand Binding Modes via Nonequilibrium Candidate Monte Carlo

Accurately predicting protein-ligand binding affinities and binding modes is a major goal in computational chemistry, but even the prediction of ligand binding modes in proteins poses major challenges. Here, we focus on solving the binding mode prediction problem for rigid fragments. That is, we focus on computing the dominant placement, conformation, and orientations of a relatively rigid, fragment-like ligand in a receptor, and the populations of the multiple binding modes which may be relevant. This problem is important in its own right, but is even more timely given the recent success of alchemical free energy calculations. Alchemical calculations are increasingly used to predict binding free energies of ligands to receptors. However, the accuracy of these calculations is dependent on proper sampling of the relevant ligand binding modes. Unfortunately, ligand binding modes may often be uncertain, hard to predict, and/or slow to interconvert on simulation time scales, so proper sampling with current techniques can require prohibitively long simulations. We need new methods which dramatically improve sampling of ligand binding modes. Here, we develop and apply a nonequilibrium candidate Monte Carlo (NCMC) method to improve sampling of ligand binding modes. In this technique, the ligand is rotated and subsequently allowed to relax in its new position through alchemical perturbation before accepting or rejecting the rotation and relaxation as a nonequilibrium Monte Carlo move. When applied to a T4 lysozyme model binding system, this NCMC method shows over 2 orders of magnitude improvement in binding mode sampling efficiency compared to a brute force molecular dynamics simulation. This is a first step toward applying this methodology to pharmaceutically relevant binding of fragments and, eventually, drug-like molecules. We are making this approach available via our new Binding modes of ligands using enhanced sampling (BLUES) package which is freely available on GitHub.

In silico structure-based approaches to discover protein-protein interaction-targeting drugs

A core concept behind modern drug discovery is finding a small molecule that modulates a function of a target protein. This concept has been successfully applied since the mid-1970s. However, the efficiency of drug discovery is decreasing because the druggable target space in the human proteome is limited. Recently, protein-protein interaction (PPI) has been identified asan emerging target space for drug discovery. PPI plays a pivotal role in biological pathways including diseases. Current human interactome research suggests that the number of PPIs is between 130,000 and 650,000, and only a small number of them have been targeted as drug targets. For traditional drug targets, in silico structure-based methods have been successful in many cases. However, their performance suffers on PPI interfaces because PPI interfaces are different in five major aspects: From a geometric standpoint, they have relatively large interface regions, flat geometry, and the interface surface shape tends to fluctuate upon binding. Also, their interactions are dominated by hydrophobic atoms, which is different from traditional binding-pocket-targeted drugs. Finally, PPI targets usually lack natural molecules that bind to the target PPI interface. Here, we first summarize characteristics of PPI interfaces and their known binders. Then, we will review existing in silico structure-based approaches for discovering small molecules that bind to PPI interfaces.

Six Biophysical Screening Methods Miss a Large Proportion of Crystallographically Discovered Fragment Hits: A Case Study

Fragment-based lead discovery (FBLD) has become a pillar in drug development. Typical applications of this method comprise at least two biophysical screens as prefilter and a follow-up crystallographic experiment on a subset of fragments. Clearly, structural information is pivotal in FBLD, but a key question is whether such a screening cascade strategy will retrieve the majority of fragment-bound structures. We therefore set out to screen 361 fragments for binding to endothiapepsin, a representative of the challenging group of aspartic proteases, employing six screening techniques and crystallography in parallel. Crystallography resulted in the very high number of 71 structures. Yet alarmingly, 44% of these hits were not detected by any biophysical screening approach. Moreover, any screening cascade, building on the results from two or more screening methods, would have failed to predict at least 73% of these hits. We thus conclude that, at least in the present case, the frequently applied biophysical prescreening filters deteriorate the number of possible X-ray hits while only the immediate use of crystallography enables exhaustive retrieval of a maximum of fragment structures, which represent a rich source guiding hit-to-lead-to-drug evolution.

New Biological Insights from Better Structure Models

Structure validation is a key component of all steps in the structure determination process, from structure building, refinement, deposition, and evaluation all the way to post-deposition optimisation of structures in the Protein Data Bank (PDB) by re-refinement and re-building. Today, many aspects of protein structures are understood better than 10years ago, and combined with improved software and more computing power, the automated PDB_REDO procedure can significantly improve about 85% of all X-ray structures ever deposited in the PDB. We review structure validation, structure improvement, and a series of validation resources and facilities that give access to improved PDB files and to reports on the quality of the original and the improved structures. Post-deposition optimisation generally leads to improved protein structures and a series of examples will illustrate how that, in turn, leads to improved or even novel biological insights.

Considerations of Protein Subpockets in Fragment-Based Drug Design

While the fragment-based drug design approach continues to gain importance, gaps in the tools and methods available in the identification and accurate utilization of protein subpockets have limited the scope. The importance of these features of small molecule-protein recognition is highlighted with several examples. A generalized solution for the identification of subpockets and corresponding chemical fragments remains elusive, but there are numerous advancements in methods that can be used in combination to address subpockets. Finally, additional examples of approaches that consider the relative importance of small-molecule co-dependence of protein conformations are highlighted to emphasize an increased significance of subpockets, especially at protein interfaces.

Multiple binding modes of a small molecule to human Keap1 revealed by X-ray crystallography and molecular dynamics simulation

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Keap1 is useful target for the design of drugs that regulate the response to oxidative stresses.

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We determined two complex crystal structures of Keap1 with a small molecule ligand.

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The ligand binds to Keap1 so as to mimic the physiological substrate Nrf2.

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From molecular dynamics simulation results, the binding modes observed may be atypical in solution.

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Key residues for ligand binding are common between crystal and MD structures.

Keap1 protein acts as a cellular sensor for oxidative stresses and regulates the transcription level of antioxidant genes through the ubiquitination of a corresponding transcription factor, Nrf2. A small molecule capable of binding to the Nrf2 interaction site of Keap1 could be a useful medicine. Here, we report two crystal structures, referred to as the soaking and the cocrystallization forms, of the Kelch domain of Keap1 with a small molecule, Ligand1. In these two forms, the Ligand1 molecule occupied the binding site of Keap1 so as to mimic the ETGE motif of Nrf2, although the mode of binding differed in the two forms. Because the Ligand1 molecule mediated the crystal packing in both the forms, the influence of crystal packing on the ligand binding was examined using a molecular dynamics (MD) simulation in aqueous conditions. In the MD structures from the soaking form, the ligand remained bound to Keap1 for over 20 ns, whereas the ligand tended to dissociate in the cocrystallization form. The MD structures could be classified into a few clusters that were related to but distinct from the crystal structures, indicating that the binding modes observed in crystals might be atypical of those in solution. However, the dominant ligand recognition residues in the crystal structures were commonly used in the MD structures to anchor the ligand. Therefore, the present structural information together with the MD simulation will be a useful basis for pharmaceutical drug development.

High throughput screening using acoustic droplet ejection to combine protein crystals and chemical libraries on crystallization plates at high density

We describe a high throughput method for screening up to 1728 distinct chemicals with protein crystals on a single microplate. Acoustic droplet ejection (ADE) was used to co-position 2.5nL of protein, precipitant, and chemicals on a MiTeGen in situ-1 crystallization plate™ for screening by co-crystallization or soaking. ADE-transferred droplets follow a precise trajectory which allows all components to be transferred through small apertures in the microplate lid. The apertures were large enough for 2.5nL droplets to pass through them, but small enough so that they did not disrupt the internal environment created by the mother liquor. Using this system, thermolysin and trypsin crystals were efficiently screened for binding to a heavy-metal mini-library. Fluorescence and X-ray diffraction were used to confirm that each chemical in the heavy-metal library was correctly paired with the intended protein crystal. A fragment mini-library was screened to observe two known lysozyme ligands using both co-crystallization and soaking. A similar approach was used to identify multiple, novel thaumatin binding sites for ascorbic acid. This technology pushes towards a faster, automated, and more flexible strategy for high throughput screening of chemical libraries (such as fragment libraries) using as little as 2.5nL of each component.

Molecular dynamics simulations: from structure function relationships to drug discovery

Molecular dynamics (MD) simulation is an emerging in silico technique with potential applications in diverse areas of pharmacology. Over the past three decades MD has evolved as an area of importance for understanding the atomic basis of complex phenomena such as molecular recognition, protein folding, and the transport of ions and small molecules across membranes. The application of MD simulations in isolation and in conjunction with experimental approaches have provided an increased understanding of protein structure-function relationships and demonstrated promise in drug discovery.

Hitting the target: fragment screening with acoustic in situ co-crystallization of proteins plus fragment libraries on pin-mounted data-collection micromeshes

Acoustic droplet ejection (ADE) is a powerful technology that supports crystallographic applications such as growing, improving and manipulating protein crystals. A fragment-screening strategy is described that uses ADE to co-crystallize proteins with fragment libraries directly on MiTeGen MicroMeshes. Co-crystallization trials can be prepared rapidly and economically. The high speed of specimen preparation and the low consumption of fragment and protein allow the use of individual rather than pooled fragments. The Echo 550 liquid-handling instrument (Labcyte Inc., Sunnyvale, California, USA) generates droplets with accurate trajectories, which allows multiple co-crystallization experiments to be discretely positioned on a single data-collection micromesh. This accuracy also allows all components to be transferred through small apertures. Consequently, the crystallization tray is in equilibrium with the reservoir before, during and after the transfer of protein, precipitant and fragment to the micromesh on which crystallization will occur. This strict control of the specimen environment means that the crystallography experiments remain identical as the working volumes are decreased from the few microlitres level to the few nanolitres level. Using this system, lysozyme, thermolysin, trypsin and stachydrine demethylase crystals were co-crystallized with a small 33-compound mini-library to search for fragment hits. This technology pushes towards a much faster, more automated and more flexible strategy for structure-based drug discovery using as little as 2.5 nl of each major component.

Protein crystallography and fragment-based drug design

Crystallography is a major tool for structure-driven drug design, as it allows knowledge of the 3D structure of protein targets and protein-ligand complexes. However, the route for crystal structure determination involves many steps, some of which may hamper its high-throughput use. Recent efforts have produced significant advances in experimental and computational tools and protocols. They include automatic crystallization tools, faster data collection devices, more efficient phasing methods and improved ligand-fitting procedures. The timescales of drug-discovery processes have been also reduced by using a fragment-based screening approach. Herein, the achievements in protein crystallography over the last 5 years are reviewed, and advantages and disadvantages of the fragment-based approaches to drug discovery that make use of x-ray crystallography as a primary screening method are examined. In particular, in some detail, five recent case studies pertaining to the development of new hits or leads in relevant therapeutic areas, such as cancer, immune response, inflammation, metabolic syndrome and neurology are described.

Advances in the design of a multipurpose fragment screening library

INTRODUCTION

Fragment-based lead discovery (FBLD) has evolved from an emerging technology to a state-of-the-art approach in drug research. The efficacious use of fragment libraries for the discovery of hits and generation of lead structures has to an increasing extent become implemented both within academia and the pharmaceutical industry but the careful or optimal selection of appropriate fragments remains a demanding task, especially when fragments are intended for the lead generation in more than one or even diverse and difficult targets.

AREAS COVERED

Progress in non-commercial screening collections of fragment-like compounds for multiple screening purposes deposited at academic institutions is reviewed as well as approaches for the generation of slim and shapely novel platforms for diversity. Recent literature on multipurpose fragment screening libraries and the papers presented at the EFMC-ISMC meeting in Berlin in August 2012 have been taken into account.

EXPERT OPINION

Existing fragment libraries tend to focus on sp (2)-rich compounds covering well-explored areas of chemical space. In order to improve the quality of the hits and to be able to tackle seemingly undruggable targets, flat scaffolds should be replaced by shapely molecular cores dominated by sp (3) hybridization. Structurally novel fragments are needed and in this respect, the role of halogen bonds has been underestimated. Pooling strategies for fragment cocktails must be designed to detect simultaneous binding of weak ligands in close proximity: cooperative binding is too important to rely on chance discoveries.

Mechanism of the ABC transporter ATPase domains: catalytic models and the biochemical and biophysical record

ABC transporters comprise a large, diverse, and ubiquitous superfamily of membrane active transporters. Their core architecture is a dimer of dimers, comprising two transmembrane domains that bind substrate and form the channel, and two ATP-binding cassettes, which bind and hydrolyze ATP to energize the translocase function. The prevailing paradigm for the ABC transport mechanism is the Switch Model, in which the nucleotide binding domains are proposed to dimerise upon binding of two ATP molecules, and thence dissociate upon sequential hydrolysis of the ATP. This idea appears consistent with crystal structures of both isolated subunits and whole transporters, as well as with a significant body of biochemical data. Nonetheless, an alternative Constant Contact Model has been proposed, in which the nucleotide binding domains do not fully dissociate, and ATP hydrolysis occurs alternately at each of the two active sites. Here, we review the biochemical and biophysical data relating to the ABC catalytic mechanism, to show how they may be construed as consistent with a Constant Contact Model, and to assess to what extent they support the Switch Model.

Fragment-based screening by protein crystallography: successes and pitfalls

Fragment-based drug discovery (FBDD) concerns the screening of low-molecular weight compounds against macromolecular targets of clinical relevance. These compounds act as starting points for the development of drugs. FBDD has evolved and grown in popularity over the past 15 years. In this paper, the rationale and technology behind the use of X-ray crystallography in fragment based screening (FBS) will be described, including fragment library design and use of synchrotron radiation and robotics for high-throughput X-ray data collection. Some recent uses of crystallography in FBS will be described in detail, including interrogation of the drug targets β-secretase, phenylethanolamine N-methyltransferase, phosphodiesterase 4A and Hsp90. These examples provide illustrations of projects where crystallography is straightforward or difficult, and where other screening methods can help overcome the limitations of crystallography necessitated by diffraction quality.