From folding proteins to finding cures: How Machine Learning is shaping ion channel research
In the foreseeable future cancer will be detected before symptoms, drugs developed in months, and treatments tailored to your genetics. Thanks to Machine Learning (ML), this future is already on the horizon - revolutionizing biotech, pharmaceuticals, and diagnostics to make healthcare faster, more precise, and personalized.
In this second of our ‘Current Affairs’ series, we address where Machine Learning is already impacting ion channel research, such as structural folding and modeling and making ion channel drug discovery and development faster and more efficient.
Ion channel folding prediction with ML
Transmembrane proteins, like ion channels, have been challenging to generate 3D atomic-scale structures. By refining protein structure predictions and accurately modeling folding, Machine Learning folding prediction tools enable more detailed insights into ion channels, essential for cellular signaling.
New algorithms also enhance binding site identification and ligand prediction, critical for developing targeted therapies. This technology accelerates our understanding of channel physiology and pharmacology, in turn advancing the drug discovery and development process, uncovering new therapeutic possibilities, and improving the precision of treatments for channel-related diseases.
ML accelerates ion channel drug discovery
Artificial Intelligence is revolutionizing research into rare disease channelopathies – genetic disorders caused by dysfunctional ion channels. These tools help researchers analyze vast datasets, identify pathogenic mutant variants of uncertain significance (VUS), modeling their impact on ion channel function, which is often difficult to study due to limited data on rare diseases. For example, Montanucci et al. used Machine Learning to predict the pathogenicity of GRIN NMDAR VUS.
Additionally, Machine Learning accelerates drug repurposing for ion channel therapies, sifting through existing drugs’ on- and off-target side effects to find candidates that can target specific ion channels, reducing the time and cost of bringing new treatments to patients. This approach opens new doors for rare disease therapies, offering hope for conditions that previously lacked effective treatments.
Enhancing ion channel safety pharmacology with ML
Also, ion channel safety pharmacology, a critical aspect of drug development focused on evaluating potential cardiac and neurological side effects, has been enhanced via Machine Learning. New models can predict how drugs will interact with ion channels, particularly those involved in heart rhythms and neural signaling, where unintended interactions can lead to severe adverse effects. By simulating these interactions early in the drug development process, Machine Learning helps researchers identify and mitigate risks more effectively, refining drug safety profiles before clinical testing. This accelerates development timelines while reducing the likelihood of late-stage failures due to safety concerns, making the drug pipeline more efficient and safer for patients.
Advancing automated patch clamp research with ML
Automated patch clamp is a key technology in how Artificial Intelligence is being used in ion channel research. For each of the applications introduced above the following are a selection of examples where automated patch clamp and Machine Learning are working in concert:
Ion channel 3D folding prediction
ML folding prediction tools like AlphaFold offer detailed insights into ion channels, playing a vital role in advancing cellular signaling. Read Rafaelli et al. to learn how AlphaFold was used for venom toxin folding targeting ion channels.
Flipping the folding prediction process on its head, recent Nobel prizewinner David Baker uses ML to predict protein sequences to fold and fill 3D spaces. See Vazquez Torres et al. for how this was used to create de novo proteins to bind snakebite venom neurotoxins, neutralizing their activity against ion channels.
Ion channel drug discovery
For a review of how ML is being applied to ion channel drug discovery, see Mateos et al.
Machine Learning and safety pharmacology
Explore Liu et al.’s work on ML and deep learning approaches for enhanced prediction of hERG blockade.
The future of ion channel research will be driven by Machine Learning algorithms: in the snapshot of existing applications given above and a multitude of future applications barely conceived at present. The Machine Learning revolution is upon us and offers much potential in understanding ion channels and targeting them for disease therapeutics. Discover more!