The use of virtual screening and differential scanning fluorimetry for the rapid identification of fragments active against MEK1

An Assessment of Dispersion-Corrected DFT Methods for Modeling Nonbonded Interactions in Protein Kinase Inhibitor Complexes

Accurate modeling of nonbonded interactions between protein kinases and their small molecule inhibitors is essential for structure-based drug design. Quantum chemical methods such as density functional theory (DFT) hold significant promise for quantifying the strengths of these key protein-ligand interactions. However, the accuracy of DFT methods can vary substantially depending on the choice of exchange-correlation functionals and associated basis sets. In this study, a comprehensive benchmarking of nine widely used DFT methods was carried out to identify an optimal approach for quantitative modeling of nonbonded interactions, balancing both accuracy and computational efficiency. From a database of 2139 kinase-inhibitor crystal structures, a diverse library of 49 nonbonded interaction motifs was extracted, encompassing CH-π, π-π stacking, cation-π, hydrogen bonding, and salt bridge interactions. The strengths of nonbonded interaction energies for all 49 motifs were calculated at the advanced CCSD(T)/CBS level of theory, which serve as references for a systematic benchmarking of BLYP, TPSS, B97, ωB97X, B3LYP, M062X, PW6B95, B2PLYP, and PWPB95 functionals with D3BJ dispersion correction alongside def2-SVP, def2-TZVP, and def2-QZVP basis sets. The RI, RIJK, and RIJCOSX approximations were used for selected functionals. It was found that the B3LYP/def2-TZVP and RIJK RI-B2PLYP/def2-QZVP methods delivered the best combination of accuracy and computational efficiency, making them well-suited for efficient modeling of nonbonded interactions responsible for molecular recognition of protein kinase inhibitors in their targets.

Response and Resistance to Trametinib in MAP2K1-Mutant Triple-Negative Melanoma

The development of targeted therapies for non-BRAF p.Val600-mutant melanomas remains a challenge. Triple wildtype (TWT) melanomas that lack mutations in BRAF, NRAS, or NF1 form 10% of human melanomas and are heterogeneous in their genomic drivers. MAP2K1 mutations are enriched in BRAF-mutant melanoma and function as an innate or adaptive resistance mechanism to BRAF inhibition. Here we report the case of a patient with TWT melanoma with a bona fide MAP2K1 mutation without any BRAF mutations. We performed a structural analysis to validate that the MEK inhibitor trametinib could block this mutation. Although the patient initially responded to trametinib, he eventually progressed. The presence of a CDKN2A deletion prompted us to combine a CDK4/6 inhibitor, palbociclib, with trametinib but without clinical benefit. Genomic analysis at progression showed multiple novel copy number alterations. Our case illustrates the challenges of combining MEK1 and CDK4/6 inhibitors in case of resistance to MEK inhibitor monotherapy.

Application of temperature-responsive HIS-tag fluorophores to differential scanning fluorimetry screening of small molecule libraries

Differential scanning fluorimetry is a rapid and economical biophysical technique used to monitor perturbations to protein structure during a thermal gradient, most often by detecting protein unfolding events through an environment-sensitive fluorophore. By employing an NTA-complexed fluorophore that is sensitive to nearby structural changes in histidine-tagged protein, a robust and sensitive differential scanning fluorimetry (DSF) assay is established with the specificity of an affinity tag-based system. We developed, optimized, and miniaturized this HIS-tag DSF assay (HIS-DSF) into a 1536-well high-throughput biophysical platform using the Borrelial high temperature requirement A protease (BbHtrA) as a proof of concept for the workflow. A production run of the BbHtrA HIS-DSF assay showed a tight negative control group distribution of T_m values with an average coefficient of variation of 0.51% and median coefficient of variation of compound T_m of 0.26%. The HIS-DSF platform will provide an additional assay platform for future drug discovery campaigns with applications in buffer screening and optimization, target engagement screening, and other biophysical assay efforts.

eSPC: an online data-analysis platform for molecular biophysics

All biological processes rely on the formation of protein-ligand, protein-peptide and protein-protein complexes. Studying the affinity, kinetics and thermodynamics of binding between these pairs is critical for understanding basic cellular mechanisms. Many different technologies have been designed for probing interactions between biomolecules, each based on measuring different signals (fluorescence, heat, thermophoresis, scattering and interference, among others). Evaluation of the data from binding experiments and their fitting is an essential step towards the quantification of binding affinities. Here, user-friendly online tools to analyze biophysical data from steady-state fluorescence spectroscopy, microscale thermophoresis and differential scanning fluorimetry experiments are presented. The modules of the data-analysis platform (https://spc.embl-hamburg.de/) contain classical thermodynamic models and clear user guidelines for the determination of equilibrium dissociation constants (Kd) and thermal unfolding parameters such as melting temperatures (Tm).

17β-Estradiol-Induced Conformational Changes of Human Microsomal Triglyceride Transfer Protein: A Computational Molecular Modelling Study

Human microsomal triglyceride transfer protein (hMTP) plays an essential role in the assembly of apoB-containing lipoproteins, and has become an important drug target for the treatment of several disease states, such as abetalipoproteinemia, fat malabsorption and familial hypercholesterolemia. hMTP is a heterodimer composed of a larger hMTPα subunit and a smaller hMTPβ subunit (namely, protein disulfide isomerase, hPDI). hPDI can interact with 17β-estradiol (E₂), an endogenous female sex hormone. It has been reported that E₂ can significantly reduce the blood levels of low-density lipoprotein, cholesterol and triglyceride, and modulate liver lipid metabolism in vivo. However, some of the estrogen’s actions on lipid metabolism are not associated with estrogen receptors (ER), and the exact mechanism underlying estrogen’s ER-independent lipid-modulating action is still not clear at present. In this study, the potential influence of E₂ on the stability of the hMTP complex is investigated by jointly using multiple molecular dynamics analyses based on available experimental structures. The molecular dynamics analyses indicate that the hMTP complex in the presence of E₂ has reduced interface contacts and surface areas. A steered molecular dynamics analysis shows that the forces required to separate the two subunits (namely, hPDI and hMTPα subunit) of the hMTP complex in the absence of E₂ are significantly higher than the forces required to separate the complex in which its hPDI is already bound with E₂. E₂ makes the interface between hMTPα and hPDI subunits more flexible and less stable. The results of this study suggest that E₂-induced conformational changes of the hMTP complex might be a novel mechanism partly accounting for the ER-independent lipid-modulating effect of E₂.

Fragment-Based Discovery of Novel Allosteric MEK1 Binders

The MEK1 kinase plays a critical role in key cellular processes, and as such, its dysfunction is strongly linked to several human diseases, particularly cancer. MEK1 has consequently received considerable attention as a drug target, and a significant number of small-molecule inhibitors of this kinase have been reported. The majority of these inhibitors target an allosteric pocket proximal to the ATP binding site which has proven to be highly druggable, with four allosteric MEK1 inhibitors approved to date. Despite the significant attention that the MEK1 allosteric site has received, chemotypes which have been shown structurally to bind to this site are limited. With the aim of discovering novel allosteric MEK1 inhibitors using a fragment-based approach, we report here a screening method which resulted in the discovery of multiple allosteric MEK1 binders, one series of which was optimized to sub-μM affinity for MEK1 with promising physicochemical and ADMET properties.

NMR as a "Gold Standard" Method in Drug Design and Discovery

Studying disease models at the molecular level is vital for drug development in order to improve treatment and prevent a wide range of human pathologies. Microbial infections are still a major challenge because pathogens rapidly and continually evolve developing drug resistance. Cancer cells also change genetically, and current therapeutic techniques may be (or may become) ineffective in many cases. The pathology of many neurological diseases remains an enigma, and the exact etiology and underlying mechanisms are still largely unknown. Viral infections spread and develop much more quickly than does the corresponding research needed to prevent and combat these infections; the present and most relevant outbreak of SARS-CoV-2, which originated in Wuhan, China, illustrates the critical and immediate need to improve drug design and development techniques. Modern day drug discovery is a time-consuming, expensive process. Each new drug takes in excess of 10 years to develop and costs on average more than a billion US dollars. This demonstrates the need of a complete redesign or novel strategies. Nuclear Magnetic Resonance (NMR) has played a critical role in drug discovery ever since its introduction several decades ago. In just three decades, NMR has become a "gold standard" platform technology in medical and pharmacology studies. In this review, we present the major applications of NMR spectroscopy in medical drug discovery and development. The basic concepts, theories, and applications of the most commonly used NMR techniques are presented. We also summarize the advantages and limitations of the primary NMR methods in drug development.

Key Topics in Molecular Docking for Drug Design

Molecular docking has been widely employed as a fast and inexpensive technique in the past decades, both in academic and industrial settings. Although this discipline has now had enough time to consolidate, many aspects remain challenging and there is still not a straightforward and accurate route to readily pinpoint true ligands among a set of molecules, nor to identify with precision the correct ligand conformation within the binding pocket of a given target molecule. Nevertheless, new approaches continue to be developed and the volume of published works grows at a rapid pace. In this review, we present an overview of the method and attempt to summarise recent developments regarding four main aspects of molecular docking approaches: (i) the available benchmarking sets, highlighting their advantages and caveats, (ii) the advances in consensus methods, (iii) recent algorithms and applications using fragment-based approaches, and (iv) the use of machine learning algorithms in molecular docking. These recent developments incrementally contribute to an increase in accuracy and are expected, given time, and together with advances in computing power and hardware capability, to eventually accomplish the full potential of this area.

Substituted anthraquinones represent a potential scaffold for DNA methyltransferase 1-specific inhibitors

In humans, the most common epigenetic DNA modification is methylation of the 5-carbon of cytosines, predominantly in CpG dinucleotides. DNA methylation is an important epigenetic mark associated with gene repression. Disruption of the normal DNA methylation pattern is known to play a role in the initiation and progression of many cancers. DNA methyltransferase 1 (DNMT1), the most abundant DNA methyltransferase in humans, is primarily responsible for maintenance of the DNA methylation pattern and is considered an important cancer drug target. Recently, laccaic acid A (LCA), a highly substituted anthraquinone natural product, was identified as a direct, DNA-competitive inhibitor of DNMT1. Here, we have successfully screened a small library of simplified anthraquinone compounds for DNMT1 inhibition. Using an endonuclease-coupled DNA methylation assay, we identified two anthraquinone compounds, each containing an aromatic substituent, that act as direct DNMT1 inhibitors. These simplified anthraquinone compounds retain the DNA-competitive mechanism of action of LCA and exhibit some selectivity for DNMT1 over DNMT3a. The newly identified compounds are at least 40-fold less potent than LCA, but have significantly less complex structures. Collectively, this data indicates that substituted anthraquinone compounds could serve as a novel scaffold for developing DNMT1-specific inhibitors.

Docking, Interaction Fingerprint, and Three-Dimensional Quantitative Structure-Activity Relationship (3D-QSAR) of Sigma1 Receptor Ligands, Analogs of the Neuroprotective Agent RC-33

The human Sigma1 receptor (S1R), which has been identified as a target with an important role in neuropsychological disorders, was first crystallized 3 years ago. Since S1R structure has no relation with another previous crystallized structures, the presence of the new crystal is an important hallmark for the design of agonists and antagonists against this important target. Some years ago, our group identified RC-33, a potent and selective S1R agonist, endowed with neuroprotective properties. In this work, drawing on new structural information, we studied the interactions of RC-33 and its analogs with the S1R binding site by using computational methods such as docking, interaction fingerprints, and receptor-guided alignment three dimensional quantitative structure–activity relationship (3D-QSAR). We found that RC-33 and its analogs adopted similar orientations within S1R binding site, with high similitude with orientations of the crystallized ligands; such information was used for identifying the residues involved in chemical interactions with ligands. Furthermore, the structure-activity relationship of the studied ligands was adequately described considering classical QSAR tests. All relevant aspects of the interactions between the studied compounds and S1R were covered here, through descriptions of orientations, binding interactions, and features that influence differential affinities. In this sense, the present results could be useful in the future design of novel S1R modulators.

In silico fragment-based drug design with SEED

We report on two fragment-based drug design protocols, SEED2XR and ALTA, which start by high-throughput docking. SEED2XR is a two-stage protocol for fragment-based drug design. The first stage is in silico and consists of the automatic docking of 10³-10⁴ fragments using SEED, which requires about 1 s per fragment. SEED is a docking software developed specifically for fragment docking and binding energy evaluation by a force field with implicit solvent. In the second stage of SEED2XR, the 10-10² fragments with the most favorable predicted binding energies are validated by protein X-ray crystallography. The recent applications of SEED2XR to bromodomains demonstrate that the whole SEED2XR protocol can be carried out in about a week of working time, with hit rates ranging from 10% to 40%. Information on fragment-target interactions generated by the SEED2XR protocol or directly from SEED docking has been used for the discovery of hundreds of hits. ALTA is a computational protocol for screening which identifies candidate ligands that preserve the interactions between the optimal SEED fragments and the protein target. Medicinal chemistry optimization of ligands predicted by ALTA has resulted in pre-clinical candidates for protein kinases and bromodomains. The high-throughput, very low cost, sustainability, and high hit rate of the SEED-based protocols, unreachable by purely experimental techniques, make them perfectly suitable for both academic and industrial drug discovery research.

Is It Reliable to Take the Molecular Docking Top Scoring Position as the Best Solution without Considering Available Structural Data?

Molecular docking is the most frequently used computational method for studying the interactions between organic molecules and biological macromolecules. In this context, docking allows predicting the preferred pose of a ligand inside a receptor binding site. However, the selection of the “best” solution is not a trivial task, despite the widely accepted selection criterion that the best pose corresponds to the best energy score. Here, several rigid-target docking methods were evaluated on the same dataset with respect to their ability to reproduce crystallographic binding orientations, to test if the best energy score is a reliable criterion for selecting the best solution. For this, two experiments were performed: (A) to reconstruct the ligand-receptor complex by performing docking of the ligand in its own crystal structure receptor (defined as self-docking), and (B) to reconstruct the ligand-receptor complex by performing docking of the ligand in a crystal structure receptor that contains other ligand (defined as cross-docking). Root-mean square deviation (RMSD) was used to evaluate how different the obtained docking orientation is from the corresponding co-crystallized pose of the same ligand molecule. We found that docking score function is capable of predicting crystallographic binding orientations, but the best ranked solution according to the docking energy is not always the pose that reproduces the experimental binding orientation. This happened when self-docking was achieved, but it was critical in cross-docking. Taking into account that docking is typically used with predictive purposes, during cross-docking experiments, our results indicate that the best energy score is not a reliable criterion to select the best solution in common docking applications. It is strongly recommended to choose the best docking solution according to the scoring function along with additional structural criteria described for analogue ligands to assure the selection of a correct docking solution.

Validating and enabling phosphoglycerate dehydrogenase (PHGDH) as a target for fragment-based drug discovery in PHGDH-amplified breast cancer

3-Phosphoglycerate dehydrogenase (PHGDH) has recently been identified as an attractive target in cancer therapy as it links upregulated glycolytic flux to increased biomass production in cancer cells. PHGDH catalyses the first step in the serine synthesis pathway and thus diverts glycolytic flux into serine synthesis. We have used siRNA-mediated suppression of PHGDH expression to show that PHGDH is a potential therapeutic target in PHGDH-amplified breast cancer. Knockdown caused reduced proliferation in the PHGDH-amplified cell line MDA-MB-468, whereas breast cancer cells with low PHGDH expression or with elevated PHGDH expression in the absence of genomic amplification were not affected. As a first step towards design of a chemical probe for PHGDH, we report a fragment-based drug discovery approach for the identification of PHGDH inhibitors. We designed a truncated PHGDH construct that gave crystals which diffracted to high resolution, and could be used for fragment soaking. 15 fragments stabilising PHGDH were identified using a thermal shift assay and validated by X-ray crystallography and ITC competition experiments to exhibit 1.5-26.2 mM affinity for PHGDH. A structure-guided fragment growing approach was applied to the PHGDH binders from the initial screen, yielding greater understanding of the binding site and suggesting routes to achieve higher affinity NAD-competitive inhibitors.

Flavonoids as CDK1 Inhibitors: Insights in Their Binding Orientations and Structure-Activity Relationship

In the last years, the interactions of flavonoids with protein kinases (PKs) have been described by using crystallographic experiments. Interestingly, different orientations have been found for one flavonoid inside different PKs and different chemical substitutions lead to different orientations of the flavonoid scaffold inside one PK. Accordingly, orientation predictions of novel analogues could help to the design of flavonoids with high PK inhibitory activities. With this in mind, we studied the binding modes of 37 flavonoids (flavones and chalcones) inside the cyclin-dependent PK CDK1 using docking experiments. We found that the compounds under study adopted two different orientations into the active site of CDK1 (orientations I and II in the manuscript). In addition, quantitative structure-activity relationship (QSAR) models using CoMFA and CoMSIA methodologies were constructed to explain the trend of the CDK1 inhibitory activities for the studied flavonoids. Template-based and docking-based alignments were used. Models developed starting from docking-based alignment were applied for describing the whole dataset and compounds with orientation I. Adequate R2 and Q2 values were obtained by each method; interestingly, only hydrophobic and hydrogen bond donor fields describe the differential potency of the flavonoids as CDK1 inhibitors for both defined alignments and subsets. Our current application of docking and QSAR together reveals important elements to be drawn for the design of novel flavonoids with increased PK inhibitory activities.

Is It Reliable to Use Common Molecular Docking Methods for Comparing the Binding Affinities of Enantiomer Pairs for Their Protein Target?

Molecular docking is a computational chemistry method which has become essential for the rational drug design process. In this context, it has had great impact as a successful tool for the study of ligand–receptor interaction modes, and for the exploration of large chemical datasets through virtual screening experiments. Despite their unquestionable merits, docking methods are not reliable for predicting binding energies due to the simple scoring functions they use. However, comparisons between two or three complexes using the predicted binding energies as a criterion are commonly found in the literature. In the present work we tested how wise is it to trust the docking energies when two complexes between a target protein and enantiomer pairs are compared. For this purpose, a ligand library composed by 141 enantiomeric pairs was used, including compounds with biological activities reported against seven protein targets. Docking results using the software Glide (considering extra precision (XP), standard precision (SP), and high-throughput virtual screening (HTVS) modes) and AutoDock Vina were compared with the reported biological activities using a classification scheme. Our test failed for all modes and targets, demonstrating that an accurate prediction when binding energies of enantiomers are compared using docking may be due to chance. We also compared pairs of compounds with different molecular weights and found the same results.

An Automated Microscale Thermophoresis Screening Approach for Fragment-Based Lead Discovery

Fragment-based lead discovery has proved to be an effective alternative to high-throughput screenings in identifying chemical matter that can be developed into robust lead compounds. The search for optimal combinations of biophysical techniques that can correctly and efficiently identify and quantify binding can be challenging due to the physicochemical properties of fragments. In order to minimize the time and costs of screening, optimal combinations of biophysical techniques with maximal information content, sensitivity, and robustness are needed. Here we describe an approach utilizing automated microscale thermophoresis (MST) affinity screening to identify fragments active against MEK1 kinase. MST identified multiple hits that were confirmed by X-ray crystallography but not detected by orthogonal methods. Furthermore, MST also provided information about ligand-induced aggregation and protein denaturation. The technique delivered a large number of binders while reducing experimentation time and sample consumption, demonstrating the potential of MST to execute and maximize the efficacy of fragment screening campaigns.

Enrichment of chemical libraries docked to protein conformational ensembles and application to aldehyde dehydrogenase 2

Molecular recognition is a complex process that involves a large ensemble of structures of the receptor and ligand. Yet, most structure-based virtual screening is carried out on a single structure typically from X-ray crystallography. Explicit-solvent molecular dynamics (MD) simulations offer an opportunity to sample multiple conformational states of a protein. Here we evaluate our recently developed scoring method SVMSP in its ability to enrich chemical libraries docked to MD structures of seven proteins from the Directory of Useful Decoys (DUD). SVMSP is a target-specific rescoring method that combines machine learning with statistical potentials. We find that enrichment power as measured by the area under the ROC curve (ROC-AUC) is not affected by increasing the number of MD structures. Among individual MD snapshots, many exhibited enrichment that was significantly better than the crystal structure, but no correlation between enrichment and structural deviation from crystal structure was found. We followed an innovative approach by training SVMSP scoring models using MD structures (SVMSPMD). The resulting models were applied to two difficult cases (p38 and CDK2) for which enrichment was not better than random. We found remarkable increase in enrichment power, particularly for p38, where the ROC-AUC increased by 0.30 to 0.85. Finally, we explored approaches for a priori identification of MD snapshots with high enrichment power from an MD simulation in the absence of active compounds. We found that the use of randomly selected compounds docked to the target of interest using SVMSP led to notable enrichment for EGFR and Src MD snapshots. SVMSP rescoring of protein-compound MD structures was applied for the search of small-molecule inhibitors of the mitochondrial enzyme aldehyde dehydrogenase 2 (ALDH2). Rank-ordering of a commercial library of 50 000 compounds docked to MD structures of ALDH2 led to five small-molecule inhibitors. Four compounds had IC50s below 5 μM. These compounds serve as leads for the design and synthesis of more potent and selective ALDH2 inhibitors.

An ultrasensitive high throughput screen for DNA methyltransferase 1-targeted molecular probes

DNA methyltransferase 1 (DNMT1) is the enzyme most responsible for epigenetic modification of human DNA and the intended target of approved cancer drugs such as 5-aza-cytidine and 5-aza-2'-deoxycytidine. 5-aza nucleosides have complex mechanisms of action that require incorporation into DNA, and covalent trapping and proteolysis of DNMT isozymes. Direct DNMT inhibitors are needed to refine understanding of the role of specific DNMT isozymes in cancer etiology and, potentially, to improve cancer prevention and treatment. Here, we developed a high throughput pipeline for identification of direct DNMT1 inhibitors. The components of this screen include an activated form of DNMT1, a restriction enzyme-coupled fluorigenic assay performed in 384 well plates with a z-factor of 0.66, a counter screen against the restriction enzyme, a screen to eliminate DNA intercalators, and a differential scanning fluorimetry assay to validate direct binders. Using the Microsource Spectrum collection of 2320 compounds, this screen identified nine compounds with dose responses ranging from 300 nM to 11 µM, representing at least two different pharmacophores with DNMT1 inhibitory activity. Seven of nine inhibitors identified exhibited two to four-fold selectivity for DNMT1 versus DNMT3A.