Druggable Hot Spots Research Articles

Molecular Mechanism of pH-Induced Protrusion Configuration Switching in Piscine Betanodavirus Implies a Novel Antiviral Strategy.

Many viruses contain surface spikes or protrusions that are essential for virus entry. These surface structures can thereby be targeted by antiviral drugs to treat viral infections. Nervous necrosis virus (NNV), a simple nonenveloped virus in the genus of betanodavirus, infects fish and damages aquaculture worldwide. NNV has 60 conspicuous surface protrusions, each comprising three protrusion domains (P-domain) of its capsid protein. NNV uses protrusions to bind to common receptors of sialic acids on the host cell surface to initiate its entry via the endocytic pathway. However, structural alterations of NNV in response to acidic conditions encountered during this pathway remain unknown, while detailed interactions of protrusions with receptors are unclear. Here, we used cryo-EM to discover that Grouper NNV protrusions undergo low-pH-induced compaction and resting. NMR and molecular dynamics (MD) simulations were employed to probe the atomic details. A solution structure of the P-domain at pH 7.0 revealed a long flexible loop (amino acids 311-330) and a pocket outlined by this loop. Molecular docking analysis showed that the N-terminal moiety of sialic acid inserted into this pocket to interact with conserved residues inside. MD simulations demonstrated that part of this loop converted to a β-strand under acidic conditions, allowing for P-domain trimerization and compaction. Additionally, a low-pH-favored conformation is attained for the linker connecting the P-domain to the NNV shell, conferring resting protrusions. Our findings uncover novel pH-dependent conformational switching mechanisms underlying NNV protrusion dynamics potentially utilized for facilitating NNV entry, providing new structural insights into complex NNV-host interactions with the identification of putative druggable hotspots on the protrusion.

The Interplay between Protein Frustration and Hotspot Formation

Hotspots situated at protein-protein interfaces, allosteric and orthosteric binding sites, traditionally play a crucial role in the initial phases of drug discovery, as they are useful to identify druggable targets and guide the optimization of fragments into lead compounds. Despite the high level of protein frustration, which can be thought of as the percentage of residues that cannot independently achieve minimum energy due to tridimensional restraints, observed at those locations, limited efforts have been made to investigate any potential connection between the localized frustration and hotspots in proteins. This review paper aims to partially address this knowledge gap by proposing that the origin of hotspots is, at least in part, due to localized frustration in proteins. While there is no hard evidence to support this hypothesis, the results obtained by integrating in silico tools predicting hot spot locations, with those calculating localized protein frustration, suggest that druggable hotspots are surrounded by residues involved in frustrated contacts. Additionally, the inclusion of long-range electrostatic terms in the protein frustration calculation enables the identification of an equal to a higher number of frustrated residues. These observations indirectly suggest that localized protein frustration around druggable hotspots could guide the development of more potent leads.

Exploring the mechanism of action of spirooxindoles as a class of CDK2 inhibitors: a structure-based computational approach.

Cyclin-dependent kinase 2 (CDK2) regulates cell cycle checkpoints in the synthesis and mitosis phases and plays a pivotal role in cancerous cell proliferation. The activation of CDK2, influenced by various protein signaling pathways, initiates the phosphorylation process. Due to its crucial role in carcinogenesis, CDK2 is a druggable hotspot target to suppress cancer cell proliferation. In this context, several studies have identified spirooxindoles as an effective class of CDK2 inhibitors. In the present study, three spirooxindoles (SOI1, SOI2, and SOI3) were studied to understand their inhibitory mechanism against CDK2 through a structure-based approach. Molecular docking and molecular dynamics (MD) simulations were performed to explore their interactions with CDK2 at the molecular level. The calculated binding free energy for the spirooxindole-based CDK2 inhibitors aligned well with experimental results regarding CDK2 inhibition. Energy decomposition (ED) analysis identified key binding residues, including I10, G11, T14, R36, F82, K89, L134, P155, T158, Y159, and T160, in the CDK2 active site and T-loop phosphorylation. Molecular mechanics (MM) energy was identified as the primary contributor to stabilizing inhibitor binding in the CDK2 protein structure. Furthermore, the analysis of binding affinity revealed that the inhibitor SOI1 binds more strongly to CDK2 compared to the other inhibitors under investigation. It demonstrated a robust interaction with the crucial residue T160 in the T-loop phosphorylation site, responsible for kinase activation. These insights into the inhibitory mechanism are anticipated to contribute to the development of potential CDK2 inhibitors using the spirooxindole scaffold.

Kyasanur Forest disease virus NS3 helicase: Insights into structure, activity, and inhibitors

Kyasanur Forest disease virus (KFDV), a tick-borne flavivirus prevalent in India, presents a serious threat to human health. KFDV NS3 helicase (NS3hel) is considered a potential drug target due to its involvement in the viral replication complex. Here, we resolved the crystal structures of KFDV NS3hel apo and its complex with three phosphate molecules, which indicates a conformational switch during ATP hydrolysis. Our data revealed that KFDV NS3hel has a higher binding affinity for dsRNA, and its intrinsic ATPase activity was enhanced by dsRNA while being inhibited by DNA. Through mutagenesis analysis, several residues within motifs I, Ia, III, V, and VI were identified to be crucial for NS3hel ATPase activity. Notably, the M419A mutation drastically reduced NS3hel ATPase activity. We propose that the methionine-aromatic interaction between residues M419 and W294, located on the surface of the RNA-binding channel, could be a target for the design of efficient inhibitor probes. Moreover, epigallocatechin gallate (EGCG), a tea-derived polyphenol, strongly inhibited NS3hel ATPase activity with an IC50 value of 0.8 μM. Our computational docking data show that EGCG binds at the predicted druggable hotspots of NS3hel. Overall, these findings contribute to the development and design of more effective and specific inhibitors.

BMaps: A Web Application for Fragment-Based Drug Design and Compound Binding Evaluation.

Fragment-based drug design uses data about where, and how strongly, small chemical fragments bind to proteins, to assemble new drug molecules. Over the past decade, we have been successfully using fragment data, derived from thermodynamically rigorous Monte Carlo fragment-protein binding simulations, in dozens of preclinical drug programs. However, this approach has not been available to the broader research community because of the cost and complexity of doing simulations and using design tools. We have developed a web application, called BMaps, to make fragment-based drug design widely available with greatly simplified user interfaces. BMaps provides access to a large repository (>550) of proteins with 100s of precomputed fragment maps, druggable hot spots, and high-quality water maps. Users can also employ their own structures or those from the Protein Data Bank and AlphaFold DB. Multigigabyte data sets are searched to find fragments in bondable orientations, ranked by a binding-free energy metric. The designers use this to select modifications that improve affinity and other properties. BMaps is unique in combining conventional tools such as docking and energy minimization with fragment-based design, in a very easy to use and automated web application. The service is available at https://www.boltzmannmaps.com.

Application of Mixed-Solvent Molecular Dynamics Simulations for Prediction of Allosteric Sites on G Protein-Coupled Receptors.

The development of small molecule allosteric modulators acting at G protein-coupled receptors (GPCRs) is becoming increasingly attractive. Such compounds have advantages over traditional drugs acting at orthosteric sites on these receptors, in particular target specificity. However, the number and locations of druggable allosteric sites within most clinically relevant GPCRs are unknown. In the present study, we describe the development and application of a mixed-solvent molecular dynamics (MixMD)-based method for the identification of allosteric sites on GPCRs. The method employs small organic probes with druglike qualities to identify druggable hotspots in multiple replicate short-timescale simulations. As proof of principle, we first applied the method retrospectively to a test set of five GPCRs (cannabinoid receptor type 1, C-C chemokine receptor type 2, M2 muscarinic receptor, P2Y purinoceptor 1, and protease-activated receptor 2) with known allosteric sites in diverse locations. This resulted in the identification of the known allosteric sites on these receptors. We then applied the method to the μ-opioid receptor. Several allosteric modulators for this receptor are known, although the binding sites for these modulators are not known. The MixMD-based method revealed several potential allosteric sites on the mu-opioid receptor. Implementation of the MixMD-based method should aid future efforts in the structure-based drug design of drugs targeting allosteric sites on GPCRs. SIGNIFICANCE STATEMENT: Allosteric modulation of G protein-coupled receptors (GPCRs) has the potential to provide more selective drugs. However, there are limited structures of GPCRs bound to allosteric modulators, and obtaining such structures is problematic. Current computational methods utilize static structures and therefore may not identify hidden or cryptic sites. Here we describe the use of small organic probes and molecular dynamics to identify druggable allosteric hotspots on GPCRs. The results reinforce the importance of protein dynamics in allosteric site identification.

Druggable sites/pockets of thep53-DNAJA1 protein-protein interaction: In silico modeling and in vitro/in vivo validation.

Mutation of p53 is the most common genetic alteration in human cancer. The vast majority of p53 mutations found in cancer are missense mutations, with some single nucleotide point mutations leading to the accumulation of mutant p53 protein with potential gain of oncogenic function. The mechanism for stabilization and accumulation of missense mutant p53 protein in malignant cells is not fully understood. It is thought that DNAJA1 plays a crucial role as a co-chaperone protein by stabilizing mutant p53 and amplifying oncogenic potential. As such, identifying small molecule inhibitors to disrupt the protein-protein interaction between mutant p53 and DNAJA1 may lead to an effective treatment for preventing carcinogenesis. Studying protein-protein interactions and identifying potential druggable hotspots has historically been limited-protein-protein binding sites require more complex characterization than those of single proteins and the crystal structures of many proteins have not been identified. Due to these issues, identifying salient druggable targets in protein-protein interactions through bench research may take years to complete. However, in silico modeling approaches allow for rapid characterization of protein-protein interfaces and the druggable binding sites they contain. In this chapter, we first review the oncogenic potential of mutant p53 and the crucial role of DNAJA1 in stabilizing missense mutant p53. We then detail our methodology for using in silico modeling and molecular biology to identify druggable protein-protein interaction sites/pockets between mutant p53 and DNAJA1. Finally, we discuss screening for and validating the utility of a small molecule inhibitor identified through our in silico framework. Specifically, we describe GY1-22, a unique compound with activity against mutant p53 that demonstrates therapeutic potential to inhibit cancer cell growth both in vivo and in vitro.

NS1 protein as a novel anti-influenza target: Map-and-mutate antiviral rationale reveals new putative druggable hot spots with an important role on viral replication

Influenza NS1 is a promising anti-influenza target, considering its conserved and druggable structure, and key function in influenza replication and pathogenesis. Notwithstanding, target identification and validation, strengthened by experimental data, are lacking.Here, we further explored our previously designed structure-based antiviral rationale directed to highly conserved druggable NS1 regions across a broad spectrum of influenza A viruses. We aimed to identify NS1-mutated viruses exhibiting a reduced growth phenotype and/or an altered cell apoptosis profile.We found that NS1 mutations Y171A, K175A (consensus druggable pocket 1), W102A (consensus druggable pocket 3), Q121A and G184P (multiple consensus druggable pockets) – located at hot spots amenable for pharmacological modulation - significantly impaired A(H1N1)pdm09 virus replication, in vitro. This is the first time that NS1–K175A, -W102A, and -Q121A mutations are characterized. Our map-and-mutate strategy provides the basis to establish the NS1 as a promising target using a rationale with a higher resilience to resistance development.

Mixed-solvent molecular dynamics simulation-based discovery of a putative allosteric site on regulator of G protein signaling 4.

Regulator of G protein signaling 4 (RGS4) is an intracellular protein that binds to the Gα subunit ofheterotrimeric G proteins and aids in terminating G protein coupled receptor signaling. RGS4 has been implicated in pain, schizophrenia, and the control of cardiac contractility. Inhibitors of RGS4 have been developed but bind covalently to cysteine residues on the protein. Therefore, we sought to identify alternative druggable sites on RGS4 using mixed-solvent molecular dynamics simulations, which employ low concentrations of organic probes to identify druggable hotspots on the protein. Pseudo-ligands were placed in consensus hotspots, and perturbation with normal mode analysis led to the identification and characterization of a putative allosteric site, which would be invaluable for structure-based drug design of non-covalent, small molecule inhibitors. Future studies on the mechanism of this allostery will aid in the development of novel therapeutics targeting RGS4.

Druggable hot spots in trypanothione reductase: novel insights and opportunities for drug discovery revealed by DRUGpy.

Assessment of target druggability guided by search and characterization of hot spots is a pivotal step in early stages of drug-discovery. The raw output of FTMap provides the data to perform this task, but it relies on manual intervention to properly combine different sets of consensus sites, therefore allowing identification of hot spots and evaluation of strength, shape and distance among them. Thus, the user's previous experience on the target and the software has a direct impact on how data generated by FTMap server can be explored. DRUGpy plugin was developed to overcome this limitation. By automatically assembling and scoring all possible combinations of consensus sites, DRUGpy plugin provides FTMap users a straight-forward method to identify and characterize hot spots in protein targets. DRUGpy is available in all operating systems that support PyMOL software.DRUGpy promptly identifies and characterizes pockets that are predicted by FTMap to bind druglike molecules with high-affinity (druggable sites) or low-affinity (borderline sites) and reveals how protein conformational flexibility impacts on the target's druggability. The use of DRUGpy on the analysis of trypanothione reductases (TR), a validated drug target against trypanosomatids, showcases the usefulness of the plugin, and led to the identification of a druggable pocket in the conserved dimer interface present in this class of proteins, opening new perspectives to the design of selective inhibitors.

Druggable Hot Spots in the Schistosomiasis Cathepsin B1 Target Identified by Functional and Binding Mode Analysis of Potent Vinyl Sulfone Inhibitors.

Schistosomiasis, a parasitic disease caused by blood flukes of the genus Schistosoma, is a global health problem with over 200 million people infected. Treatment relies on just one drug, and new chemotherapies are needed. Schistosoma mansoni cathepsin B1 (SmCB1) is a critical peptidase for the digestion of host blood proteins and a validated drug target. We screened a library of peptidomimetic vinyl sulfones against SmCB1 and identified the most potent SmCB1 inhibitors reported to date that are active in the subnanomolar range with second order rate constants (k2nd) of ∼2 × 105 M–1 s–1. High resolution crystal structures of the two best inhibitors in complex with SmCB1 were determined. Quantum chemical calculations of their respective binding modes identified critical hot spot interactions in the S1′ and S2 subsites. The most potent inhibitor targets the S1′ subsite with an N-hydroxysulfonic amide moiety and displays favorable functional properties, including bioactivity against the pathogen, selectivity for SmCB1 over human cathepsin B, and reasonable metabolic stability. Our results provide structural insights for the rational design of next-generation SmCB1 inhibitors as potential drugs to treat schistosomiasis.

Targeting SARS-CoV-2 RBD Interface: a Supervised Computational Data-Driven Approach to Identify Potential Modulators.

Coronavirus disease 2019 (COVID‐19) has spread out as a pandemic threat affecting over 2 million people. The infectious process initiates via binding of SARS‐CoV‐2 Spike (S) glycoprotein to host angiotensin‐converting enzyme 2 (ACE2). The interaction is mediated by the receptor‐binding domain (RBD) of S glycoprotein, promoting host receptor recognition and binding to ACE2 peptidase domain (PD), thus representing a promising target for therapeutic intervention. Herein, we present a computational study aimed at identifying small molecules potentially able to target RBD. Although targeting PPI remains a challenge in drug discovery, our investigation highlights that interaction between SARS‐CoV‐2 RBD and ACE2 PD might be prone to small molecule modulation, due to the hydrophilic nature of the bi‐molecular recognition process and the presence of druggable hot spots. The fundamental objective is to identify, and provide to the international scientific community, hit molecules potentially suitable to enter the drug discovery process, preclinical validation and development.

Plasticity of the 340-Loop in Influenza Neuraminidase Offers New Insight for Antiviral Drug Development.

The recently discovered 340-cavity in influenza neuraminidase (NA) N6 and N7 subtypes has introduced new possibilities for rational structure-based drug design. However, the plasticity of the 340-loop (residues 342–347) and the role of the 340-loop in NA activity and substrate binding have not been deeply exploited. Here, we investigate the mechanism of 340-cavity formation and demonstrate for the first time that seven of nine NA subtypes are able to adopt an open 340-cavity over 1.8 μs total molecular dynamics simulation time. The finding that the 340-loop plays a role in the sialic acid binding pathway suggests that the 340-cavity can function as a druggable pocket. Comparing the open and closed conformations of the 340-loop, the side chain orientation of residue 344 was found to govern the formation of the 340-cavity. Additionally, the conserved calcium ion was found to substantially influence the stability of the 340-loop. Our study provides dynamical evidence supporting the 340-cavity as a druggable hotspot at the atomic level and offers new structural insight in designing antiviral drugs.

In Silico Insights towards the Identification of NLRP3 Druggable Hot Spots.

NLRP3 (NOD-like receptor family, pyrin domain-containing protein 3) activation has been linked to several chronic pathologies, including atherosclerosis, type-II diabetes, fibrosis, rheumatoid arthritis, and Alzheimer’s disease. Therefore, NLRP3 represents an appealing target for the development of innovative therapeutic approaches. A few companies are currently working on the discovery of selective modulators of NLRP3 inflammasome. Unfortunately, limited structural data are available for this target. To date, MCC950 represents one of the most promising noncovalent NLRP3 inhibitors. Recently, a possible region for the binding of MCC950 to the NLRP3 protein was described but no details were disclosed regarding the key interactions. In this communication, we present an in silico multiple approach as an insight useful for the design of novel NLRP3 inhibitors. In detail, combining different computational techniques, we propose consensus-retrieved protein residues that seem to be essential for the binding process and for the stabilization of the protein–ligand complex.

Chemical logic of MraY inhibition by antibacterial nucleoside natural products

Novel antibacterial agents are needed to address the emergence of global antibiotic resistance. MraY is a promising candidate for antibiotic development because it is the target of five classes of naturally occurring nucleoside inhibitors with potent antibacterial activity. Although these natural products share a common uridine moiety, their core structures vary substantially and they exhibit different activity profiles. An incomplete understanding of the structural and mechanistic basis of MraY inhibition has hindered the translation of these compounds to the clinic. Here we present crystal structures of MraY in complex with representative members of the liposidomycin/caprazamycin, capuramycin, and mureidomycin classes of nucleoside inhibitors. Our structures reveal cryptic druggable hot spots in the shallow inhibitor binding site of MraY that were not previously appreciated. Structural analyses of nucleoside inhibitor binding provide insights into the chemical logic of MraY inhibition, which can guide novel approaches to MraY-targeted antibiotic design.

Computational modeling of novel inhibitory peptides targeting proteoglycan like region of carbonic anhydrase IX and in vitro validation in HeLa cells

Carbonic anhydrase IX (CAIX) is a tumour-associated, hypoxia-induced, membrane-bound metallo-enzyme which catalyzes the reversible hydration of carbon dioxide (CO2) to bicarbonate (HCO3−) and proton (H+) ions. Over expression of CAIX is observed in cancers of colon, lung, kidney, breast, etc. CAIX plays a vital role in maintaining favourable intracellular pH for tumour cell growth and extracellular acidification which in-turn leads to drug resistance and spread of factors influencing tumour invasion. The N-terminal proteoglycan (PG) – like fragment of CAIX is unique to this isoform and is considered as potential druggable hotspot. Recently, M75 monoclonal antibody targeting the LPGEEDLPG epitope of PG like region has been proposed to reduce cellular adhesion in cancer cells. LPGEEDLPG fragment in complex with M75 has been crystallized and it serves as a strong base for development of peptide inhibitors based on interacting interfaces. Thus, in this study, an in-depth analysis of intermolecular interactions in LPGEEDLPG-M75 complex was carried out by implementing extensive molecular dynamics simulations, binding free energy calculations so as to infer the major determinant fragments of M75 that can be used as peptide inhibitors targeting PG region. Based on these analyses, 3 peptides (Pep1, Pep2 and Pep3) were synthesized and validated by in vitro assays involving cytotoxicity assessment, CAIX inhibition analysis through Direct and Indirect functional assays, and inhibition of Cell adhesion in HeLa cells. The results reveal Pep1 to be a promising inhibitor as it could efficiently modulate CAIX mediated pH homeostasis and cell adhesion in cancer cells.Communicated by Ramaswamy H. Sarma

Redefining Druggability using Chemoproteomic Platforms

The Nomura Research Group is focused on developing and applying chemoproteomic platforms to drug the undruggable proteome to develop next‐generation disease therapies. One of the greatest challenges that we face in discovering new disease therapies is that most proteins are considered “undruggable” or difficult to target with small‐molecules. Our research group addresses this challenge by developing chemoproteomic platforms to discover and pharmacologically target unique and novel therapeutic druggable hotspots for disease therapy. We currently have four major research directions. Our first research area focuses on coupling phenotypic screening of fragment‐based covalent ligand libraries with chemoproteomic platforms to rapidly discover novel druggable hotspots, targets, and small‐molecule leads for disease therapy. Our second research area focuses on covalent ligand discovery against druggable hotspots targeted by covalently‐acting natural products that show therapeutic activity using chemoproteomic platforms to discover new therapeutic targets and synthetically tractable therapies for cancer. Our third research area focuses on advancing chemoproteomic technologies to map and pharmacologically target druggable hotspots for targeted protein degradation. Our fourth research area focuses on using chemoproteomic platforms to map on and off‐targets of environmental and pharmaceutical chemicals towards discovering new toxicological mechanisms. Our fifth research area focuses on using chemoproteomic and metabolomic platforms to discover unique and novel metabolic drivers of cancer. The talk will highlight key examples of these themes within our lab.Support or Funding InformationNational Institutes of Health (R01CA172667, P42ES004705) and Novartis Institutes for BioMedical ResearchThis abstract is from the Experimental Biology 2018 Meeting. There is no full text article associated with this abstract published in The FASEB Journal.

Classifying BRAF alterations in cancer: new rational therapeutic strategies for actionable mutations.

The RAS-RAF-MEK-ERK signaling cascade is among the most frequently mutated pathways in human cancer. Approximately 50% of melanoma patients possess a druggable hotspot V600E/K mutation in the BRAF protein kinase. FDA-approved combination therapies of BRAF and MEK inhibitors are available that provide survival benefits to patients with a BRAF V600 mutation. Non-V600 BRAF mutants are found in many cancers, and are more prevalent than V600 mutations in certain tumor types. For example, between 50-80% of BRAF mutations in non-small cell lung cancer and 22-30% in colorectal cancer encode for non-V600 mutants. As next generation sequencing becomes increasingly used in clinical practice, oncologists are frequently identifying non-V600 BRAF mutations in their patient's tumors, but are uncertain of viable therapeutic options that could be employed for optimal treatment. From recent studies, a new classification system is emerging for BRAF mutations based on biochemical and signaling mechanisms associated with these mutants. Class I BRAF mutations affect amino acid V600 and signal as RAS-independent active monomers, class II mutations function as RAS-independent activated dimers, and class III mutations are kinase impaired but increase signaling through the MAPK pathway due to enhanced RAS binding and subsequent CRAF activation. These distinct classes of BRAF mutations predict response to targeted therapies and have important implications for future drug development. Herein, we discuss pre-clinical and clinical findings that may lead to improved treatments for all classes of BRAF mutant cancers.

Target Identification of Bioactive Covalently Acting Natural Products.

There are countless natural products that have been isolated from microbes, plants, and other living organisms that have been shown to possess therapeutic activities such as antimicrobial, anticancer, or anti-inflammatory effects. However, developing these bioactive natural products into drugs has remained challenging in part because of their difficulty in isolation, synthesis, mechanistic understanding, and off-target effects. Among the large pool of bioactive natural products lies classes of compounds that contain potential reactive electrophilic centers that can covalently react with nucleophilic amino acid hotspots on proteins and other biological molecules to modulate their biological action. Covalently acting natural products are more amenable to rapid target identification and mapping of specific druggable hotspots within proteins using activity-based protein profiling (ABPP)-based chemoproteomic strategies. In addition, the granular biochemical insights afforded by knowing specific sites of protein modifications of covalently acting natural products enable the pharmacological interrogation of these sites with more synthetically tractable covalently acting small molecules whose structures are more easily tuned. Both discovering binding pockets and targets hit by natural products and exploiting druggable modalities targeted by natural products with simpler molecules may overcome some of the challenges faced with translating natural products into drugs.

Validation of Customized Cancer Panel for Detecting Somatic Mutations and Copy Number Alterations.

Accurate detection of genomic alterations, especially druggable hotspot mutations in tumors, has become an essential part of precision medicine. With targeted sequencing, we can obtain deeper coverage of reads and handle data more easily with a relatively lower cost and less time than whole-exome or whole-genome sequencing. Recently, we designed a customized gene panel for targeted sequencing of major solid cancers. In this study, we aimed to validate its performance. The cancer panel targets 95 cancer-related genes. In terms of the limit of detection, more than 86% of target mutations with a mutant allele frequency (MAF) <1% can be identified, and any mutation with >3% MAF can be detected. When we applied this system for the analysis of Acrometrix Oncology Hotspot Control DNA, which contains more than 500 COSMIC mutations across 53 genes, 99% of the expected mutations were robustly detected. We also confirmed the high reproducibility of the detection of mutations in multiple independent analyses. When we explored copy number alterations (CNAs), the expected CNAs were successfully detected, and this result was confirmed by target-specific genomic quantitative polymerase chain reaction. Taken together, these results support the reliability and accuracy of our cancer panel in detecting mutations. This panel could be useful for key mutation profiling research in solid tumors and clinical translation.