Pulsed Electromagnetic Fields and Tumor-Targeted Radiofrequencies in Cancer Therapy: A Review of Experimental and Clinical Evidence

Abstract
Pulsed electromagnetic fields (PEMF) and amplitude-modulated radiofrequency electromagnetic fields (AM-RF EMF) are emerging as non-invasive adjuncts in cancer therapy. Initially approved for bone healing and pain management, PEMF has shown potential in inhibiting tumor cell proliferation, inducing apoptosis, and impairing angiogenesis in various preclinical models. This review synthesizes findings from studies involving low- and high-intensity PEMF, tumor-treating alternating electric fields, and tumor-specific AM-RF EMF across cancer cell lines, animal models, and early-phase human trials. While results are promising, therapeutic responses remain highly context-dependent and mechanistically heterogeneous. Advances in structural biology and cancer genomics offer new opportunities to rationally design field parameters targeting specific molecular pathways. We propose a framework for estimating the electromagnetic field strength needed to disrupt key protein-ligand interactions and highlight pathways for future investigation. Rigorous clinical trials and optimized protocols will be essential to fully integrate PEMF into precision oncology.

Introduction
Pulsed Electromagnetic Field (PEMF) therapy is an FDA-approved, non-invasive modality widely used in orthopedics to support bone healing in conditions such as nonunion fractures, spinal fusions, and osteotomies. Devices like Orthofix’s Physio-Stim and Biomet’s EBI Bone Healing System deliver time-varying electromagnetic fields to stimulate cellular activity and promote osteogenesis and angiogenesis. These effects are mediated through mechanisms including modulation of ion binding, upregulation of growth factors (e.g., BMP-2, TGF-β), and increased expression of osteogenic markers (Bassett et al., 1974; Aaron et al., 2004).
Beyond orthopedics, PEMF is gaining attention as a complementary therapy for chronic pain, particularly musculoskeletal conditions such as osteoarthritis, fibromyalgia, and chronic low back pain. Although not FDA-approved for general pain relief, over-the-counter devices (e.g., Oska Pulse, BEMER) are marketed under Class I or II wellness exemptions. Studies suggest PEMF may reduce pain and improve function by modulating inflammation, nociceptive signaling, and microcirculation (Foley-Nolan et al., 1990; Thomas et al., 2007).
Emerging evidence now points to a potential role for PEMF in cancer therapy. Preclinical studies demonstrate that specific PEMF exposures can inhibit tumor cell proliferation, induce apoptosis, and impair angiogenesis in models of breast, lung, and brain cancers. Animal studies support these findings (Vadala et al., 2016), and early clinical trials suggest PEMF may improve quality of life and even exert direct antitumor effects in certain contexts (Zimmermann et al., 2012).
While clinical data are still limited, recent reviews emphasize the need for rigorous trials to optimize treatment protocols and clarify mechanisms (Xu et al., 2022, Egg & Kietzmann, 2025). This review examines key findings from cellular and animal studies, as well as preliminary human trials, tracing the evolution of this field from early research on alternating fields and modulated radiofrequencies to current interest in PEMF for cancer treatment.

Low and intermediate frequency alternating electromagnetic fields
Alternating electric fields in the intermediate frequency range (100–300 kHz) at field strengths of 1–2.5 V/cm (corresponding to magnetic fields of 0.44–1.1 μT) have been shown to suppress the proliferation of various rodent and human tumor cell lines—including Patricia C, U-118, U-87, H-1299, MDA231, PC3, B16F1, F-98, C-6, RG2, and CT-26—as well as malignant tumors in animal models. This inhibitory effect is both frequency- and intensity-dependent and is selective for actively dividing cells; non-proliferating (quiescent) cells remain unaffected. The mechanism of action of these tumor treating fields (TTFields) involves disruption of mitotic spindle formation, resulting in mitotic arrest and cell death (Kirson et al., 2004). These findings have been extended to additional cell lines (e.g., MDA-MB-231 and H1299) and tumor models (e.g., intradermal B16F1 melanoma and intracranial F-98 glioma). In a pilot clinical study of 10 glioblastoma patients, TTFields therapy more than doubled both median progression-free survival and overall survival compared to historical controls (Kirson et al., 2007).
In contrast, exposure of Caco-2 human colon adenocarcinoma cells to low-frequency (50 Hz), higher-intensity (1 mT) magnetic fields promoted cell growth rather than inhibiting it. This stimulation was time-dependent and accompanied by increased protein oxidation and elevated intracellular reactive oxygen species (ROS). These changes coincided with increased intracellular calcium levels and global activation of the 20S proteasome, along with a reduction in the pro-apoptotic protein p27 (Eleuteri et al., 2009).
Other studies have reported no significant effects of 1 mT, 60 Hz electromagnetic fields on non-cancerous immortalized cell lines such as Jurkat (human T lymphocytes) and NIH3T3 (mouse embryonic fibroblasts). However, under the same conditions, both MCF-7 (human breast cancer) and MCF-10A (non-tumorigenic breast epithelial) cells exhibited significant reductions in cell number, viability, and DNA synthesis. These effects were attributed to cell cycle delay and induction of the pro-apoptotic gene PMAIP in a context-dependent manner (Lee et al., 2015).
Overall, the biological effects of low- and intermediate-frequency EMFs on cancer cells are complex and highly context-dependent. Responses vary by cell type, proliferative status, field parameters (frequency and intensity), and exposure duration, underscoring the importance of precise characterization in therapeutic and experimental applications.

Amplitude modulated radiofrequency electromagnetic fields
In 2009, Barbault et al. reported that cancer patients exhibited biofeedback responses to tumor-specific frequencies of amplitude-modulated (AM) radiofrequency electromagnetic fields (RF-EMF). These modulation frequencies, ranging from 0.1 Hz to 114 kHz, were specific to the type of tumor, while the carrier frequency was a fixed 27.12 MHz. The RF signal was generated at 100 mW into a 50-ohm load. In a limited compassionate-use clinical trial involving 28 patients with various cancer types, RF-EMF treatment was delivered intrabuccally for 60 minutes, three times daily, and continued until disease progression or death.
No significant side effects were reported. Of the 13 patients eligible for response evaluation, one breast cancer patient achieved a complete response, and another showed a partial response. Four additional patients (with thyroid, lung, pancreatic cancers, and leiomyosarcoma) exhibited stable disease. The authors concluded that the observed clinical responses were more likely due to systemic physiological effects rather than direct cytotoxic action, given the low field strength and the anatomical distance between the intrabuccal application site and the tumor sites. Estimated field strengths within 1 mm of the emitter were approximately 30 V/cm (electric) and 13 μT (magnetic).
A subsequent open-label phase I/II clinical trial involving 41 patients with hepatocellular carcinoma (HCC) confirmed the earlier findings. Using the same protocol of tumor-specific AM RF-EMF delivery, 28 patients were evaluable for response: 4 demonstrated partial responses, 16 had stable disease, and 8 showed disease progression. These preliminary outcomes were considered promising and formed the rationale for pursuing larger, randomized clinical trials (Costa et al., 2011).
Follow-up mechanistic studies investigated the effects of tumor-specific modulation frequencies on HCC (HepG2, Huh7) and breast cancer (MCF-7) cell lines. Direct in vitro exposure resulted in significant growth inhibition of malignant cells, whereas non-malignant counterparts—THLE-2 hepatocytes and MCF-10A breast epithelial cells—were unaffected. Growth suppression in HCC cells was accompanied by downregulation of the chemokine-related genes XCL2 and PLP2, as well as disruption of mitotic spindle architecture. Notably, reduced expression of XCL2 and PLP2 has been associated with improved prognosis in cancer patients (Zimmerman et al., 2012).

Low intensity PEMF
The biological effects of pulsed electromagnetic fields (PEMF) on tumor cell growth have been recognized for over two decades. Early studies demonstrated that PEMF exposure at a magnetic field intensity of 1.5 mT and pulse frequencies of 1 or 25 Hz enhanced the cytotoxicity of chemotherapeutic agents—vincristine, mitomycin, and cisplatin—against multidrug-resistant HCA-2/1cch human colon adenocarcinoma cells in vitro (Ruiz-Gómez, M.J., 2002). At the same field intensity but a higher pulse frequency (75 Hz), PEMF upregulated A3 adenosine receptor (A3AR) expression in neural tumor cell lines, including PC12 (rat adrenal pheochromocytoma) and U87MG (human glioblastoma). This upregulation was associated with inhibition of NF-κB signaling and induction of p53, ultimately leading to suppressed proliferation, increased lactate dehydrogenase (LDH) release, and elevated caspase-3 activity—markers of cytotoxicity and apoptosis, respectively (Vincenzi, F., 2012). Similarly, growth inhibition and apoptotic induction were observed in the SKOV3 human ovarian cancer cell line following PEMF exposure at 1 mT with pulse frequencies ranging from 8 to 64 Hz (Wang et al., 2012).
More recent studies have extended these findings to other tumor types. In vitro and in vivo experiments involving human breast cancer MCF-7 cells and human lung adenocarcinoma A549 cells revealed that low-intensity PEMF (0.68 and 1.19 mT) applied at higher pulse frequencies (3.846 and 40.85 kHz, respectively) significantly inhibited tumor growth (Chen et al., 2022). This effect was attributed to increased apoptotic activity, evidenced by elevated caspase-3/7 expression and greater annexin V staining, as well as an accumulation of cells in the G0 phase of the cell cycle. Gene expression analysis further indicated activation of pathways associated with DNA damage, cell cycle arrest, and growth suppression.
Notably, the systemic impact of PEMF has also been demonstrated in humans. In a recent double-blind, randomized clinical trial involving healthy female volunteers, participants were exposed to PEMF at 1 mT intensity and 50 Hz pulse frequency (Iversen et al., 2025). Sera collected from treated individuals exhibited significant anti-cancer properties up to one month post-exposure, reducing breast cancer cell proliferation, migration, and invasiveness in vitro. These effects correlated with a reduction in epithelial-mesenchymal transition (EMT) markers, suggesting a systemic modulation of anti-tumor signaling pathways.

Higher intensity PEMF exposure
For the purposes of this review, high-intensity pulsed electromagnetic fields (PEMF) are defined as those with magnetic field strengths ranging from 2 to 400 mT, remaining within the public exposure limits recommended by the International Commission on Non-Ionizing Radiation Protection (ICNIRP) (Yamaguchi-Sakeno et al., 2011). The specific pulse frequencies and exposure durations varied across the studies discussed below.
Over the past two decades, a growing body of research involving both tumor cell lines and animal models has demonstrated that PEMF—either as a stand-alone treatment or in combination with chemotherapy or gamma irradiation—can exert antiproliferative and antiangiogenic effects. These studies typically used field intensities between 2 and 20 mT, pulse frequencies from 8 to 120 Hz, and diverse exposure regimens. Tumor types studied included breast, bladder, liver, hematopoietic cancers, osteosarcoma, and fibrosarcoma.
Breast cancer cell lines, particularly MCF-7, have shown notable sensitivity to PEMF. Exposure to PEMF at 3 mT and 20 Hz for 60 minutes daily over three days significantly inhibited proliferation (Crocetti et al., 2013). A separate protocol using full-square wave PEMF at 11 mT and 8 Hz, applied for 30 minutes twice daily over five days, yielded similar antiproliferative outcomes in both MCF-7 and MDA-MB-231 breast cancer cells (Pantelis et al., 2024). These effects were mechanistically linked to DNA damage, apoptosis, and the induction of cellular senescence markers. Importantly, these effects appeared to be selective for malignant cells; normal epithelial and fibroblast cells remained unaffected under the same treatment conditions.
The potential for PEMF to enhance chemotherapy has also been explored. In MCF-7 cells, PEMF exposure significantly potentiated the cytotoxic effects of doxorubicin (Woo & Kim, 2024) and etoposide (Woo et al., 2022). Both agents inhibit cell proliferation through topoisomerase II inhibition and reactive oxygen species (ROS) generation, and these pathways appeared to be further activated in the presence of PEMF.
Findings from in vitro studies have been validated in vivo. For example, PEMF exposure inhibited the growth and vascularization of 16/C murine mammary adenocarcinoma tumors implanted in syngeneic C3H/HeJ mice. Treatment consisted of 10-minute daily exposures for 12 days using a 120 Hz pulse frequency and field intensities up to 20 mT (Williams et al., 2001). Further studies confirmed that tumor inhibition was dependent on increasing magnetic field intensity rather than increased exposure time at a fixed intensity (Cameron et al., 2014). PEMF also enhanced the effects of gamma irradiation and the chemotherapeutic agent bleomycin in mouse models bearing human MDA-MB-231 breast cancer xenografts (Cameron et al., 2005) and triple-negative breast cancer (TNBC) MX-1 xenografts in SCID mice (Berg et al., 2010).
Beyond breast cancer, PEMF has demonstrated antitumor activity in models of bladder cancer (Sanberg et al., 2025), hematologic malignancies (Radeva & Berg, 2004; Berg et al., 2010), osteosarcoma (Muramatsu et al., 2017), and fibrosarcoma (Omote et al., 1990). Of particular interest is the reported synergy between PEMF and molecularly targeted therapies: in BCR/ABL(+) leukemia-derived TCC-S cells, PEMF enhanced the efficacy of the tyrosine kinase inhibitor imatinib (Yamaguchi-Sakeno et al., 2011).

Future directions
The growing body of evidence supports the continued development of pulsed electromagnetic fields (PEMF) as a complementary modality in cancer therapeutics. At present, PEMF demonstrates its greatest efficacy when combined with conventional treatments, such as cytotoxic chemotherapy or ionizing radiation. Several novel laboratory protocols have yielded promising results and merit translation into large-scale, double-blind clinical trials to evaluate therapeutic utility and safety in a controlled setting. However, current findings also highlight important areas for further refinement and optimization.
To date, no PEMF or EMF protocol has consistently achieved irreversible tumor regression as a stand-alone intervention. Moreover, the heterogeneity in field parameters—such as frequency, waveform, intensity, and exposure duration—across studies has impeded the establishment of a unified mechanism of action. Inhibition of cancer cell growth by PEMF appears to involve multiple, and potentially interacting, cellular processes, including alterations in membrane potential, disruption of mitochondrial function, interference with mitotic spindle formation, modulation of growth signaling pathways, increased generation of reactive oxygen species (ROS), and induction of apoptosis. While the preferential sensitivity of malignant cells compared to normal cells is encouraging, the underlying basis of this selectivity remains incompletely understood and warrants further mechanistic investigation.
Reliance on empirical trial-and-error testing may be inefficient and limiting, especially given current advances in cancer genomics and the characterization of key oncogenic driver mutations (Kinnersley et al., 2024). Moving forward, it would be advantageous to rationally design PEMF protocols that specifically disrupt molecular pathways activated by such driver genes. This approach could yield targeted, mechanism-informed applications of PEMF with improved therapeutic indices. Table 1 summarizes major classes of cancer-relevant signaling pathways and representative driver genes identified through high-throughput genome sequencing.
Many of the downstream effectors of cancer-associated gene products—such as enzymes, receptors, transcription factors, DNA-binding proteins, and scaffolding proteins—have had their structures experimentally determined and deposited in the Protein Data Bank (PDB), or accurately predicted by AlphaFold. This structural information provides a powerful foundation for estimating the electromagnetic field (EMF) intensities required to perturb or disrupt their functional interactions. In particular, these insights open the door to the rational design of PEMF protocols targeting specific molecular interactions central to tumor growth and survival.
Functionally disrupting a protein's activity via EMF can be conceptualized as interfering with its interactions with a substrate, ligand, DNA target, or binding partner. This disruption can occur if the energy imparted by the EMF is sufficient to overcome the standard free energy of binding (ΔG⁰), or alternatively, the free energy required to partially unfold one or both interacting molecules (ΔGᵤ).
For binding interference, the minimum energy supplied by the EMF must match or exceed the standard free energy of binding. On a per-molecule basis, this requirement can be written as:

E(emf) = (ΔG⁰/N)                   [1]

where N is the Avogadro number (6.022 x 1023 mol-1) since ΔG⁰ values are often given per mole.
The energy provided by an electric field (E) to a dipole or charge depends on the interaction between the field and the molecule dipole moment (μ​) or charge (q). For a dipole in an electric field, the potential energy (U) is the vectorial product of the electric field (E) and the dipole moment (μ), and maximum energy occurs when the dipole is aligned with the field and

U(max) = μE                                [2]

where μ is the magnitude of the dipole moment in Debyes or C.m, and E the electric field strength in V/m.
Since the energy required by the field to disrupt binding must at least be equal to the free binding energy,

μE = ΔG⁰/N                        [3]

Solving for E,

E = ΔG⁰/(Nμ)                      [4]

In practice, ΔG⁰ could be derived from the equilibrium binding constant Keq using,

ΔG⁰ = -RTlnKeq                  [5]

where R is the universal gas constant ( ≈ 8.314 J⋅mol⁻¹⋅K⁻¹) and T the temperature in Kelvin (K).
The corresponding magnetic field strength could be derived from,

B = E/c                                  [6]
​
 a consequence of Maxwell’s equations and where c is the speed of light (≈ 3 x 108 m/s).
Table 2 shows the calculated magnetic field intensities required for disrupting the functions of selected therapeutic targets in cancer pathways. These calculated values should be regarded as preliminary approximations, given several limitations in the underlying dipole moment estimations. Notably, solvent effects and electrostatic contributions from bound ligands were not included, even though they may significantly alter the net dipole moment of the complex. Despite these limitations, the estimates can serve as a useful starting point for the design of more targeted and effective pulsed electromagnetic field (PEMF) protocols—particularly with respect to field strength, pulse frequency, and exposure duration.
As discussed earlier, a complementary approach to estimating the required magnetic field intensity involves the use of the free energy of unfolding (ΔGᵤ) of the protein or protein-ligand complex. However, accurate ΔGᵤ values typically require differential scanning calorimetry (DSC) data, which remain sparse in the literature for many of the cancer-associated targets included in this analysis.
Table 2 indicates that, in most cases, the magnetic field strengths needed to interfere with key oncogenic pathways fall within the operational range of FDA-approved PEMF devices used for specific clinical indications. Furthermore, some PEMF systems marketed for general wellness are capable of producing high peak magnetic field intensities—up to 100 mT—though often at lower frequencies. Clinical experience in areas such as bone regeneration and pain control suggests that efficacy is not solely determined by maximum field strength. Rather, an optimized combination of intensity, frequency, pulse width, and duty cycle is likely required for therapeutic benefit.
Future advances in PEMF technology—guided by personalized cancer genomic data and supported by rigorous clinical trials—may help establish electromagnetic field modulation as a viable adjunct or alternative in cancer therapy. Such progress will be essential to gaining broader acceptance within the medical community and among the general public.

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Table 1. Selected Cancer Driver Genes

Classical Tumor Suppressors & Oncogenes

Gene	Mutation Frequency	Primary Pathway	Major Cancer Types	Therapeutic Relevance
TP53	>50% all cancers	Cell cycle checkpoint/Apoptosis	Pan-cancer (highest in ovarian, lung)	MDM2 inhibitors, p53 reactivators
KRAS	~30% all cancers	RAS/MAPK signaling	Pancreatic (90%), Colorectal (40%), Lung (25%)	KRAS G12C inhibitors (sotorasib, adagrasib)
PIK3CA	~20% solid tumors	PI3K/AKT/mTOR	Breast (45%), Colorectal (15%), Endometrial	PI3K inhibitors (alpelisib)
APC	80% colorectal	Wnt signaling	Colorectal, Gastric	Wnt pathway modulators (experimental)

DNA Repair Pathway Genes

Gene	Mutation Frequency	Primary Pathway	Major Cancer Types	Therapeutic Relevance
BRCA1/BRCA2	5-10% breast/ovarian	Homologous recombination	Breast, Ovarian, Prostate, Pancreatic	PARP inhibitors (olaparib, niraparib)
ATM	5-15% various	DNA damage response	CLL, Breast, Prostate	ATR inhibitors, PARP inhibitors
MLH1/MSH2/MSH6/PMS2	15% colorectal	Mismatch repair	Colorectal, Endometrial, Lynch syndrome	Immune checkpoint inhibitors

Receptor Tyrosine Kinases & Growth Factors

Gene	Mutation Frequency	Primary Pathway	Major Cancer Types	Therapeutic Relevance
EGFR	15% lung adenocarcinoma	RTK/MAPK signaling	Lung, Glioblastoma, Head/Neck	TKIs (erlotinib, osimertinib)
HER2 (ERBB2)	20% breast cancer	RTK signaling	Breast, Gastric	Trastuzumab, T-DM1, TKIs
ALK	5% lung adenocarcinoma	RTK fusion proteins	Lung, Lymphomas	ALK inhibitors (crizotinib, alectinib)

Chromatin Remodeling & Epigenetic Regulators

Gene	Mutation Frequency	Primary Pathway	Major Cancer Types	Therapeutic Relevance
ARID1A	10-50% various	SWI/SNF chromatin remodeling	Ovarian, Endometrial, Gastric	Synthetic lethal approaches
KMT2D	20% lymphomas	Histone methylation	Diffuse large B-cell lymphoma	Histone methyltransferase inhibitors
IDH1/IDH2	70% gliomas, 20% AML	Metabolic/Epigenetic	Glioma, AML, Cholangiocarcinoma	IDH inhibitors (ivosidenib, enasidenib)

Cell Cycle Regulation

Gene	Mutation Frequency	Primary Pathway	Major Cancer Types	Therapeutic Relevance
CDKN2A (p16)	30-50% various	G1/S checkpoint	Melanoma, Pancreatic, Lung	CDK4/6 inhibitors
RB1	90% retinoblastoma	G1/S checkpoint	Retinoblastoma, Small cell lung	CDK4/6 inhibitors, aurora kinase inhibitors
CCND1	15-20% breast	G1/S progression	Breast, Mantle cell lymphoma	CDK4/6 inhibitors (palbociclib)

Emerging Drivers (Recent WGS Studies 2024)

Discovery	Context	Primary Pathway	Cancer Types	Therapeutic Potential
74 New Candidate Genes	Nature Genetics 2024 (10,478 genomes)	RNA processing, protein degradation	Pan-cancer analysis	Under investigation
Non-coding drivers	Regulatory elements, lncRNAs	Gene expression regulation	Multiple cancer types	Epigenetic modulators

 

Table 2.

Target (substrate)	PDB/AlphaFold ID2	Keq (M-1)3	B (mT)1
GTPase switch protein
Hras (GTP)	PDB 8ELK	9.3x1010	625
Kras (Raf1 Ras BD)	PDB 6XHA	2.8 x 106	130
Protein tyrosine kinase
c-Abl (ATP)	AF-P00520-F1	8.3 x 104	18
ALK (ATP)	AF-Q9UM73-F1-v4	2.4 x 105	28
BTK (ATP)	AF-Q06187-F1	3.4 x 104	43
EGFR (ATP)	AF-P00533-F1	5.9 x 104	28
HER2 (ATP)	AF-P04626-F1	3.7 x 104	23
c-kit (ATP)	AF-P10721-F1-v4	1.9 x 104	18
SRC (ATP)	AF-P00523-F1-v4	1.2 x 104	102
VEGFR1 (ATP)	AF-P17948-F1	7.7 x 103	6
VEGFR2 (ATP)	AF-P35968-F1	7.7 x 103	10
Protein serine/threonine kinase
AKT1 (ATP)	AF-P31749-F1	7.6 x 103	36
AKT2 (ATP)	AF-P31751-F1	3.9 x 103	53
ATR (ATP)	AF-Q13535-F1-v4	2.0 x 107	28
Aurora 2 (ATP)	AF-O14965-F1	2.9 x 104	30
Cdk2-PO4/Cyclin A (ATP)	PDB 1JST	4.3 x 104	57
Cdk2/Cyclin E (ATP)	PDB 1W98	2.8 x 105	44
Cdk4/Cyclin D1 (ATP)	PDB 2W96	2.4 x 103	33
Cdk6/vCyclin (ATP)	PDB 1JOW	1.2 x 105	61
Chk1 (ATP)	AF-O14757-F1	7.1 x 105	72
Chk2 (ATP)	AF-O96017-F1	3.0 x 105	30
pERK1 (ATP)	PDB 2ZOQ	3.2 x 105	71
pERK2 (ATP)	PDB 6OPG	5.5 x 105	63
GSK3β (ATP)	AF-49841-F1	2.0 x 104	44
MEK1 (ATP)	AF-Q02750-F1	1.8 x 105	58
RAF1 (ATP)	AF-P04049-F1	8.6 x 104	36
Lipid phosphoinositol kinase
ATM (ATP)	PDB 7NI6	3.4 x 104	19
PIK3CA (ATP)	AF-P42336-F1	5.0 x 105	49
MTOR (ATP)	PDB 3JBZ	1.0 x 103	15
Other enzymes
PARP1 (NAD)	AF-P09874-F1-v4	1.3 x 106	29
PARP2 (NAD)	AF-Q9UGN5-F1-v4	5.3 x 105	25
IDH1 (Isocitrate)	PDB 3INM	1.5 x 104	306
IDH2 (Isocitrate)	PDB 5I95	1.7 x 105	230

 

¹ Calculation of B (mT) was based on equations [1]-[6].

² The dipole moment (μ​) of the proteins was calculated from coordinates provided in the corresponding PDB or AlphaFold files shown below, and using the Protein Dipole Moments Server . The server is described in Clifford E. Felder, Jaime Prilusky, Israel Silman, and Joel L. Sussman 2007, " A server and database for dipole moments of proteins", Nucleic Acids Research, 35, special Web Servers Issue.  https://academic.oup.com/nar/article/35/suppl_2/W512/2922221.

³ The K_eq in equation [5] is 1/K_d for simple protein-ligand interaction, or 1/K_m for enzyme substrate interaction. The catalytic K_m is used as a first approximation of substrate affinity. Kd or Km values were obtained from the following sources: Hras (GTP): John, J., et al., 1993, Kras (Raf1 Ras BD): Tran, T.H., et al., 2021, c-Abl (ATP), BTK (ATP), EGFR (ATP), HER2 (ATP), c-kit (ATP), SRC (ATP), VEGFR1 (ATP), VEGFR2 (ATP), AKT1 (ATP), AKT2 (ATP), Aurora 2 (ATP), Cdk2-PO4/Cyclin A (ATP), Cdk2/Cyclin E (ATP), Cdk4/Cyclin D1 (ATP), Chk1 (ATP), Chk2 (ATP), GSK3β (ATP), MEK1 (ATP), RAF1(ATP), ATM (ATP) & MTOR (ATP): Knight, Z.A. & Shokat, K.M., 2005, ALK (ATP): Bossi, R.T., et al., 2010, ATR: data from ReactionBiology and Eurofins, Cdk6/vCyclin (ATP): data from ReactionBiology, pERK1 (ATP) & pERK2 (ATP): Petrosino, M., et al., 2023, PIK3CA (ATP): Maheshwari, S., et al. 2017, PARP1 (NAD) & PARP2 (NAD): Thorsell A-G., et al., 2016, IDH1 (Isocitrate): Uniprot O75874, IDH2 (Isocitrate): Uniprot P48735

 

​Acknowledgement: The author thanks Bill Windsor for providing some of the literature cited in this review.

Dedication: To my brother Kiet, who in heaven will know why.