Landscape and function of multiple mutations within individual oncogenes.

Sporadic reports have described cancer cases in which multiple driver mutations (MMs) occur in the same oncogene1,2. However, the overall landscape and relevance of MMs remain elusive. Here we carried out a pan-cancer analysis of 60,954 cancer samples, and identified 14 pan-cancer and 6 cancer-type-specific oncogenes in which MMs occur more frequently than expected: 9% of samples with at least one mutation in these genes harboured MMs. In various oncogenes, MMs are preferentially present in cis and show markedly different mutational patterns compared with single mutations in terms of type (missense mutations versus in-frame indels), position and amino-acid substitution, suggesting a cis-acting effect on mutational selection. MMs show an overrepresentation of functionally weak, infrequent mutations, which confer enhanced oncogenicity in combination. Cells with MMs in the PIK3CA and NOTCH1 genes exhibit stronger dependencies on the mutated genes themselves, enhanced downstream signalling activation and/or greater sensitivity to inhibitory drugs than those with single mutations. Together oncogenic MMs are a relatively common driver event, providing the underlying mechanism for clonal selection of suboptimal mutations that are individually rare but collectively account for a substantial proportion of oncogenic mutations.

Binding Mechanism between Platelet Glycoprotein and Cyclic Peptide Elucidated by McMD-Based Dynamic Docking.

The cyclic peptide OS1 (amino acid sequence: CTERMALHNLC), which has a disulfide bond between both termini cysteine residues, inhibits complex formation between the platelet glycoprotein Ibα (GPIbα) and the von Willebrand factor (vWF) by forming a complex with GPIbα. To study the binding mechanism between GPIbα and OS1 and, therefore, the inhibition mechanism of the protein-protein GPIbα-vWF complex, we have applied our multicanonical molecular dynamics (McMD)-based dynamic docking protocol starting from the unbound state of the peptide. Our simulations have reproduced the experimental complex structure, although the top-ranking structure was an intermediary one, where the peptide was bound in the same location as in the experimental structure; however, the ß-switch of GPIbα attained a different conformation. Our analysis showed that subsequent refolding of the ß-switch results in a more stable binding configuration, although the transition to the native configuration appears to take some time, during which OS1 could dissociate. Our results show that conformational changes in the ß-switch are crucial for successful binding of OS1. Furthermore, we identified several allosteric binding sites of GPIbα that might also interfere with vWF binding, and optimization of the peptide to target these allosteric sites might lead to a more effective inhibitor, as these are not dependent on the ß-switch conformation.

Protein-ligand binding affinity prediction of cyclin-dependent kinase-2 inhibitors by dynamically averaged fragment molecular orbital-based interaction energy.

Fragment molecular orbital (FMO) method is a powerful computational tool for structure-based drug design, in which protein-ligand interactions can be described by the inter-fragment interaction energy (IFIE) and its pair interaction energy decomposition analysis (PIEDA). Here, we introduced a dynamically averaged (DA) FMO-based approach in which molecular dynamics simulations were used to generate multiple protein-ligand complex structures for FMO calculations. To assess this approach, we examined the correlation between the experimental binding free energies and DA-IFIEs of six CDK2 inhibitors whose net charges are zero. The correlation between the experimental binding free energies and snapshot IFIEs for X-ray crystal structures was R2  = 0.75. Using the DA-IFIEs, the correlation significantly improved to 0.99. When an additional CDK2 inhibitor with net charge of -1 was added, the DA FMO-based scheme with the dispersion energies still achieved R2  = 0.99, whereas R2 decreased to 0.32 employing all the energy terms of PIEDA.

Molecular dynamics simulation-guided drug sensitivity prediction for lung cancer with rare EGFR mutations.

Next generation sequencing (NGS)-based tumor profiling identified an overwhelming number of uncharacterized somatic mutations, also known as variants of unknown significance (VUS). The therapeutic significance of EGFR mutations outside mutational hotspots, consisting of >50 types, in nonsmall cell lung carcinoma (NSCLC) is largely unknown. In fact, our pan-nation screening of NSCLC without hotspot EGFR mutations (n = 3,779) revealed that the majority (>90%) of cases with rare EGFR mutations, accounting for 5.5% of the cohort subjects, did not receive EGFR-tyrosine kinase inhibitors (TKIs) as a first-line treatment. To tackle this problem, we applied a molecular dynamics simulation-based model to predict the sensitivity of rare EGFR mutants to EGFR-TKIs. The model successfully predicted the diverse in vitro and in vivo sensitivities of exon 20 insertion mutants, including a singleton, to osimertinib, a third-generation EGFR-TKI (R2 = 0.72, P = 0.0037). Additionally, our model showed a higher consistency with experimentally obtained sensitivity data than other prediction approaches, indicating its robustness in analyzing complex cancer mutations. Thus, the in silico prediction model will be a powerful tool in precision medicine for NSCLC patients carrying rare EGFR mutations in the clinical setting. Here, we propose an insight to overcome mutation diversity in lung cancer.

Oncogenic mutations Q61L and Q61H confer active form-like structural features to the inactive state (state 1) conformation of H-Ras protein.

GTP-bound forms of Ras proteins (Ras•GTP) assume two interconverting conformations, "inactive" state 1 and "active" state 2. Our previous study on the crystal structure of the state 1 conformation of H-Ras in complex with guanosine 5'-(ß, γ-imido)triphosphate (GppNHp) indicated that state 1 is stabilized by intramolecular hydrogen-bonding interactions formed by Gln61. Since Ras are constitutively activated by substitution mutations of Gln61, here we determine crystal structures of the state 1 conformation of H-Ras•GppNHp carrying representative mutations Q61L and Q61H to observe the effect of the mutations. The results show that these mutations alter the mode of hydrogen-bonding interactions of the residue 61 with Switch II residues and induce conformational destabilization of the neighboring regions. In particular, Q61L mutation results in acquirement of state 2-like structural features. Moreover, the mutations are likely to impair an intramolecular structural communication between Switch I and Switch II. Molecular dynamics simulations starting from these structures support the above observations. These findings may give a new insight into the molecular mechanism underlying the aberrant activation of the Gln61 mutants.

Discovery of natural TRPA1 activators through pharmacophore-based virtual screening and a biological assay.

Transient receptor potential cation channel subfamily A member 1 (TRPA1), a member of the transient receptor potential family, detects a wide range of environmental stimuli, such as low temperature, abnormal pH, and reactive irritants. TRPA1 is of great interest as a target protein in fields related to pharmaceuticals and foods. In this study, a library of natural products was explored to identify TRPA1 activators by pharmacophore screening of known TRPA1 agonists and biological assays for agonist activity. The study identified six natural compounds as novel TRPA1 agonists. The discovery of these compounds may prove useful in elucidating the TRPA1 activation mechanism.

Accurate Binding Configuration Prediction of a G-Protein-Coupled Receptor to Its Antagonist Using Multicanonical Molecular Dynamics-Based Dynamic Docking.

We have performed dynamic docking between a prototypic G-protein-coupled receptor (GPCR) system, the ß2-adrenergic receptor, and its antagonist, alprenolol, using one of the enhanced conformation sampling methods, multicanonical molecular dynamics (McMD), which does not rely on any prior knowledge for the definition of the reaction coordinate. Although we have previously applied our McMD-based dynamic docking protocol to various globular protein systems, its application to GPCR systems would be difficult because of their complicated design, which include a lipid bilayer, and because of the difficulty in sampling the configurational space of a binding site that exists deep inside the GPCR. Our simulations sampled a wide array of ligand-bound and ligand-unbound structures, and we measured 427 binding events during our 48 µs production run. Analysis of the ensemble revealed several stable and meta-stable structures, where the most stable structure at the global free energy minimum matches the experimental one. Additional canonical MD simulations were used for refinement and validation of the structures, revealing that most of the intermediates are sufficiently stable to trap the ligand in these intermediary states and furthermore validated our prediction results. Given the difficulty in reaching the orthosteric binding site, chemical optimization of the compound for the second ranking configuration, which binds near the pocket's entrance, might lead to a high-affinity allosteric inhibitor. Accordingly, we show that the application of our methodology can be used to provide crucial insights for the rational design of drugs that target GPCRs.

Structure-Based de Novo Molecular Generator Combined with Artificial Intelligence and Docking Simulations.

Recently, molecular generation models based on deep learning have attracted significant attention in drug discovery. However, most existing molecular generation models have serious limitations in the context of drug design wherein they do not sufficiently consider the effect of the three-dimensional (3D) structure of the target protein in the generation process. In this study, we developed a new deep learning-based molecular generator, SBMolGen, that integrates a recurrent neural network, a Monte Carlo tree search, and docking simulations. The results of an evaluation using four target proteins (two kinases and two G protein-coupled receptors) showed that the generated molecules had a better binding affinity score (docking score) than the known active compounds, and the generated molecules possessed a broader chemical space distribution. SBMolGen not only generates novel binding active molecules but also presents 3D docking poses with target proteins, which will be useful in subsequent drug design. The code is available at https://github.com/clinfo/SBMolGen.

E487K-Induced Disorder in Functionally Relevant Dynamics of Mitochondrial Aldehyde Dehydrogenase 2.

Mitochondrial aldehyde dehydrogenase 2 (ALDH2), which is a homotetramer assembled by two equivalent dimers, is an important enzyme that metabolizes ethanol-derived acetaldehyde to acetate in a coenzyme-dependent manner. The highly reactive acetaldehyde exhibits a toxic effect, indicating that the proper functioning of ALDH2 is essential to counteract aldehyde-associated diseases. It is known that the catalytic activity of ALDH2 is drastically impaired by a frequently observed mutation, E487K, in a dominant fashion. However, the molecular basis of the inactivation mechanism is elusive because of the complex nature of the dynamic behavior. Here, we performed microsecond-timescale molecular dynamics simulations of the proteins complexed with coenzymes. The E487K mutation elevated the conformational heterogeneity of the dimer interfaces, which are relatively distal from the substituted residue. Dynamic network analyses showed that Glu487 and the dimer interface were dynamically communicated, and the dynamic community further spanned throughout all of the subunits in the wild-type; however, this network was completely rearranged by the E487K mutation. The perturbation of the dynamic properties led to alterations of the global conformational motions and destabilization of the coenzyme binding required for receiving a proton from the catalytic nucleophile. The insights into the dynamic behavior of the dominant negative mutant in this work will provide clues to restore its function.

Exhaustive search of the configurational space of heat-shock protein 90 with its inhibitor by multicanonical molecular dynamics based dynamic docking.

Multicanonical molecular dynamics based dynamic docking was used to exhaustively search the configurational space of an inhibitor binding to the N-terminal domain of heat-shock protein 90 (Hsp90). The obtained structures at 300 K cover a wide structural ensemble, with the top two clusters ranked by their free energy coinciding with the native binding site. The representative structure of the most stable cluster reproduced the experimental binding configuration, but an interesting conformational change in Hsp90 could be observed. The combined effects of solvation and ligand binding shift the equilibrium from a preferred loop-in conformation in the unbound state to an α-helical one in the bound state for the flexible lid region of Hsp90. Thus, our dynamic docking method is effective at predicting the native binding site while exhaustively sampling a wide configurational space, modulating the protein structure upon binding.

Identification of a new class of non-electrophilic TRPA1 agonists by a structure-based virtual screening approach.

Recent work has gradually been clarifying the binding site of non-electrophilic agonists on the transient receptor potential A1 (TRPA1). This study searched for non-electrophilic TRPA1 agonists by means of in silico drug discovery techniques based on three-dimensional (3-D) protein structure. First, agonist-bound pocket structures were explored using an advanced molecular dynamics simulation starting from the cryo-electron microscopic structure of TRPA1, and several pocket structures suitable for virtual screening were extracted by structure evaluation using known non-electrophilic TRPA1 agonists. Next, 49 compounds were selected as new non-electrophilic agonist candidates from a library of natural products comprising 10,555 compounds by molecular docking toward these pocket structures. Measurement of the TRPA1 agonist activity of these compounds showed notable TRPA1 activation with three compounds (decanol, 2-ethyl-1-hexanol, phenethyl butanoate). Decanol and 2-ethyl-1-hexanol, which are categorized as fatty alcohols, in particular have a novel chemical scaffold for TRPA1 activation. The results of this study are expected to be of considerable use in understanding the molecular mechanism of TRPA1 recognition by non-electrophilic agonists.

Machine learning accelerates MD-based binding pose prediction between ligands and proteins.

Motivation: Fast and accurate prediction of protein-ligand binding structures is indispensable for structure-based drug design and accurate estimation of binding free energy of drug candidate molecules in drug discovery. Recently, accurate pose prediction methods based on short Molecular Dynamics (MD) simulations, such as MM-PBSA and MM-GBSA, among generated docking poses have been used. Since molecular structures obtained from MD simulation depend on the initial condition, taking the average over different initial conditions leads to better accuracy. Prediction accuracy of protein-ligand binding poses can be improved with multiple runs at different initial velocity. Results: This paper shows that a machine learning method, called Best Arm Identification, can optimally control the number of MD runs for each binding pose. It allows us to identify a correct binding pose with a minimum number of total runs. Our experiment using three proteins and eight inhibitors showed that the computational cost can be reduced substantially without sacrificing accuracy. This method can be applied for controlling all kinds of molecular simulations to obtain best results under restricted computational resources. Availability and implementation: Code and data are available on GitHub at https://github.com/tsudalab/bpbi. Contact: terayama@cbms.k.u-tokyo.ac.jp or tsuda@k.u-tokyo.ac.jp. Supplementary information: Supplementary data are available at Bioinformatics online.

Structural modification of indomethacin toward selective inhibition of COX-2 with a significant increase in van der Waals contributions.

We have synthesized a fluorinated analogue of indomethacin bearing a 3,3,3-trifluoroprop-1-enyl group at its 2-position and evaluated its inhibitory activity towards the COX-1 and COX-2 enzymes in vitro. The results revealed that this fluorinated analogue exhibited much greater inhibitory activity and selectivity towards COX-2 than indomethacin. The increased affinity between the fluorinated analogue and COX-2 was attributed to a significant increase in van der Waals contacts (i.e. van der Waals contributions in ΔG were -13.80 kcal/mol for COX-1 and -18.46 kcal/mol for COX-2), explaining an effect of the fluorine substituent in enzyme selectivity. This newly synthesized fluorinated analogue therefore represents a potent and selective COX-2 inhibitor.

Molecular Basis for Allosteric Inhibition of GTP-Bound H-Ras Protein by a Small-Molecule Compound Carrying a Naphthalene Ring.

The ras oncogene products (H-Ras, K-Ras, and N-Ras) have been regarded as some of the most promising targets for anticancer drug discovery because their activating mutations are frequently found in human cancers. Nonetheless, molecular targeted therapy for them is currently unavailable. Here, we report the discovery of a small-molecule compound carrying a naphthalene ring, named KBFM123, which binds to the GTP-bound form of H-Ras. The solution structure of its complex with the guanosine 5'-(ß,γ-imide) triphosphate-bound form of H-RasT35S (H-RasT35S·GppNHp) indicates that the naphthalene ring of KBFM123 interacts directly with a hydrophobic pocket located between switch I and switch II and allosterically inhibits the effector interaction by inducing conformational changes in switch I and its flanking region in strand ß2, which are directly involved in recognition of the effector molecules, including c-Raf-1. In particular, Asp38 of H-Ras, a crucial residue for the interaction with c-Raf-1 via the formation of a salt bridge with Arg89 of the Ras-binding domain (RBD) of c-Raf-1, shows a drastic conformational change: its side chain orients toward the opposite direction. Consistent with these results, KBFM123 exhibits an activity to inhibit, albeit weakly, the association of H-RasG12V·GppNHp with the c-Raf-1 RBD. The binding of the naphthalene ring to the hydrophobic pocket of H-RasT35S·GppNHp is further supported by nuclear magnetic resonance analyses showing that two other naphthalene-containing compounds with distinct structures also exhibit similar binding properties with KBFM123. These results indicate that the naphthalene ring could become a promising scaffold for the development of Ras inhibitors.

Improving the Accuracy of Protein-Ligand Binding Mode Prediction Using a Molecular Dynamics-Based Pocket Generation Approach.

Protein-drug binding mode prediction from the apo-protein structure is challenging because drug binding often induces significant protein conformational changes. Here, the authors report a computational workflow that incorporates a novel pocket generation method. First, the closed protein pocket is expanded by repeatedly filling virtual atoms during molecular dynamics (MD) simulations. Second, after ligand docking toward the prepared pocket structures, binding mode candidates are ranked by MD/Molecular Mechanics Poisson-Boltzmann Surface Area. The authors validated our workflow using CDK2 kinase, which has an especially-closed ATP-binding pocket in the apo-form, and several inhibitors. The crystallographic pose coincided with the top-ranked docking pose for 59% (34/58) of the compounds and was within the top five-ranked ones for 88% (51/58), while those estimated by a conventional prediction protocol were 9% (5/58) and 50% (29/58), respectively. Our study demonstrates that the prediction accuracy is significantly improved by preceding pocket expansion, leading to generation of conformationally-diverse binding mode candidates. © 2018 Wiley Periodicals, Inc.

The Effect of Conformational Flexibility on Binding Free Energy Estimation between Kinases and Their Inhibitors.

Accurate prediction of binding affinities of drug candidates to their targets remains challenging because of protein flexibility in solution. Conformational flexibility of the ATP-binding site in the CDK2 and ERK2 kinases was identified using molecular dynamics simulations. The binding free energy (ΔG) of twenty-four ATP-competitive inhibitors toward these kinases was assessed using an alchemical free energy perturbation method, MP-CAFEE. However, large calculation errors of 2-3 kcal/mol were observed using this method, where the free energy simulation starts from a single equilibrated conformation. Here, we developed a new ΔG computation method, where the starting structure was set to multiconformations to cover flexibility. The calculation accuracy was successfully improved, especially for larger molecular size compounds, leading to reliable prediction of a broader range of drug candidates. The present study demonstrates that conformational flexibility of interactions between a compound and the glycine-rich loop in the kinases is a key factor in ΔG estimation.

In silico discovery of small-molecule Ras inhibitors that display antitumor activity by blocking the Ras-effector interaction.

Mutational activation of the Ras oncogene products (H-Ras, K-Ras, and N-Ras) is frequently observed in human cancers, making them promising anticancer drug targets. Nonetheless, no effective strategy has been available for the development of Ras inhibitors, partly owing to the absence of well-defined surface pockets suitable for drug binding. Only recently, such pockets have been found in the crystal structures of a unique conformation of Ras⋅GTP. Here we report the successful development of small-molecule Ras inhibitors by an in silico screen targeting a pocket found in the crystal structure of M-Ras⋅GTP carrying an H-Ras-type substitution P40D. The selected compound Kobe0065 and its analog Kobe2602 exhibit inhibitory activity toward H-Ras⋅GTP-c-Raf-1 binding both in vivo and in vitro. They effectively inhibit both anchorage-dependent and -independent growth and induce apoptosis of H-ras(G12V)-transformed NIH 3T3 cells, which is accompanied by down-regulation of downstream molecules such as MEK/ERK, Akt, and RalA as well as an upstream molecule, Son of sevenless. Moreover, they exhibit antitumor activity on a xenograft of human colon carcinoma SW480 cells carrying the K-ras(G12V) gene by oral administration. The NMR structure of a complex of the compound with H-Ras⋅GTP(T35S), exclusively adopting the unique conformation, confirms its insertion into one of the surface pockets and provides a molecular basis for binding inhibition toward multiple Ras⋅GTP-interacting molecules. This study proves the effectiveness of our strategy for structure-based drug design to target Ras⋅GTP, and the resulting Kobe0065-family compounds may serve as a scaffold for the development of Ras inhibitors with higher potency and specificity.

A macrocyclic kinase inhibitor overcomes triple resistant mutations in EGFR-positive lung cancer.

Brigatinib-based therapy was effective against osimertinib-resistant EGFR C797S mutants and is undergoing clinical studies. However, tumor relapse suggests additional resistance mutations might emerge. Here, we first demonstrated the binding mode of brigatinib to the EGFR-T790M/C797S mutant by crystal structure analysis and predicted brigatinib-resistant mutations through a cell-based assay including N-ethyl-N-nitrosourea (ENU) mutagenesis. We found that clinically reported L718 and G796 compound mutations appeared, consistent with their proximity to the binding site of brigatinib, and brigatinib-resistant quadruple mutants such as EGFR-activating mutation/T790M/C797S/L718M were resistant to all the clinically available EGFR-TKIs. BI-4020, a fourth-generation EGFR inhibitor with a macrocyclic structure, overcomes the quadruple and major EGFR-activating mutants but not the minor mutants, such as L747P or S768I. Molecular dynamics simulation revealed the binding mode and affinity between BI-4020 and EGFR mutants. This study identified potential therapeutic strategies using the new-generation macrocyclic EGFR inhibitor to overcome the emerging ultimate resistance mutants.

LTK mutations responsible for resistance to lorlatinib in non-small cell lung cancer harboring CLIP1-LTK fusion.

The CLIP1-LTK fusion was recently discovered as a novel oncogenic driver in non-small cell lung cancer (NSCLC). Lorlatinib, a third-generation ALK inhibitor, exhibited a dramatic clinical response in a NSCLC patient harboring CLIP1-LTK fusion. However, it is expected that acquired resistance will inevitably develop, particularly by LTK mutations, as observed in NSCLC induced by oncogenic tyrosine kinases treated with corresponding tyrosine kinase inhibitors (TKIs). In this study, we evaluate eight LTK mutations corresponding to ALK mutations that lead to on-target resistance to lorlatinib. All LTK mutations show resistance to lorlatinib with the L650F mutation being the highest. In vitro and in vivo analyses demonstrate that gilteritinib can overcome the L650F-mediated resistance to lorlatinib. In silico analysis suggests that introduction of the L650F mutation may attenuate lorlatinib-LTK binding. Our study provides preclinical evaluations of potential on-target resistance mutations to lorlatinib, and a novel strategy to overcome the resistance.

Mutual induced-fit mechanism drives binding between intrinsically disordered Bim and cryptic binding site of Bcl-xL.

The intrinsically disordered region (IDR) of Bim binds to the flexible cryptic site of Bcl-xL, a pro-survival protein involved in cancer progression that plays an important role in initiating apoptosis. However, their binding mechanism has not yet been elucidated. We have applied our dynamic docking protocol, which correctly reproduced both the IDR properties of Bim and the native bound configuration, as well as suggesting other stable/meta-stable binding configurations and revealed the binding pathway. Although the cryptic site of Bcl-xL is predominantly in a closed conformation, initial binding of Bim in an encounter configuration leads to mutual induced-fit binding, where both molecules adapt to each other; Bcl-xL transitions to an open state as Bim folds from a disordered to an α-helical conformation while the two molecules bind each other. Finally, our data provides new avenues to develop novel drugs by targeting newly discovered stable conformations of Bcl-xL.