Nigel Beaton, PhD & Yuehan Feng, PhD (Biognosys)
A key step in the drug development pipeline is having a deep understanding of a compound’s target protein space including target identification, protein binding affinities and binding site affirmation. Traditionally, this pathway from small molecule target deconvolution to binding site characterization has been highly time and labour intensive. Proteomics-based strategies have gained popularity in recent years as robust and highly sensitive techniques that can provide vital compound target information. Limited proteolysis (LiP-MS) is a peptide-centric strategy that exploits 3D structural alterations and steric hindrance induced by drug binding to determine protein binding, estimate affinity (EC50) and predict binding sites from across the proteome.
Traditional LiP-MS is an unbiased, label-free mass spectrometry-based technique that uses machine learning derived parameters to not only characterize a compound’s target protein space but also to rank proteins based upon these known parameters. To date, LiP-MS has been used extensively to characterize small molecules across species including recently published data using a novel CDK9 inhibitor developed by AstraZeneca. A LiP-MS-specific attribute that can be exploited to add significant value during drug development is its peptide centric approach, as LiP peptides often contain critical information beyond simply their protein source. This peptide-level resolution can be used to characterize drug-protein interactions through a novel high-throughput technique known as high-resolution LiP (HR-LiP).
This approach can be used in place of traditional structure-activity relationship (SAR) studies such as x-ray crystallography and nuclear magnetic resonance imaging which are highly resource-intensive and challenging to perform. A key attribute of HR-LiP is the ability to use the technique to obtain structural data for proteins that would traditionally be considered challenging to characterize such as membrane proteins or very large proteins. Here we demonstrate the applicability of HR-LiP in two test cases (BRD4 and EGFR), as well as highlight a recent publication in which HR-LiP was used to map a compound’s binding site in a large, multimeric protein complex (VCP).
Collectively, we believe that both classic LiP-MS and HR-LiP are power technologies that can help researchers along the drug development pipeline from target validation and lead optimization in small molecule drug discovery pipelines in a label-free, high-resolution and high-throughput manner.
Short Bio Nigel Beaton, PhD:
Nigel obtained his BSc (Hons) in physiology from the University of Western Ontario in London, Canada. He then received his MSc in Bioengineering from University of Strathclyde in Glasgow, Scotland before moving on to a PhD in Translational Biomedicine at ETH Zurich in Switzerland where he focused on obesity and diabetes. He continued his training as a post-doc studying the role lncRNAs play in acute-on-chronic liver failure (ACLF) at University College London in England. He joined Biognosys in 2017, working as a senior scientist developing their limited proteolysis platform for target deconvolution and structural proteomics. He currently works as a scientific director at Biognosys.
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Short Bio Yuehan Feng, PhD:
Yuehan Feng, PhD is Director, Scientific Alliances at Biognosys. His scientific expertise is centered around proteomics technologies and applications with a strong emphasis on mass spectrometry and chemical biology. Yuehan obtained his BSc, MSc in interdisciplinary sciences from the Department of Chemistry at ETH Zurich. He stayed at ETH for his PhD training at the Institute for Biochemistry and contributed to the initial development of LiP-MS as well as structural biomarkers for protein misfolding in the lab of Paola Picotti. Before joining Biognosys in 2018, he was a postdoctoral researcher at the Stanford Genome Technology Center.
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