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Yusuf Hamied Department of Chemistry

Research Fellow

I am a post-doctoral researcher supported by the Schmidt Science Foundation in partnership with the Rhodes Trust and a fellow at St. John's College. My research focusses on developing new experimental and predictive computational methods for examining the behaviour of biological molecules and systems, in particular proteins.

Proteins are the executive molecules of life that through diverse set of highly integrated and tightly regulated interactions set the molecular basis for our well-being. Yet the diverse and dynamic nature of proteins has made it challenging to devise methods that could be effectively used for understanding the roles of proteins in human health and disease. As part of my research, I bring the state-of-the-art micron and nano scale fabrication and flow engineering approaches that I developed during my PhD research together with single molecule optical tools and machine-learning based computational approaches to develop methods for understanding the diversity of the biological roles of proteins. One of my key interests is to develop an explorative probe-free platform for single cell proteomic analysis.

My personal website:


Deformable and Robust Core–Shell Protein Microcapsules Templated by Liquid–Liquid Phase-Separated Microdroplets
Y Xu, Y Shen, TCT Michaels, KN Baumann, D Vigolo, Q Peter, Y Lu, KL Saar, D Vella, H Zhu, B Li, H Yang, APM Guttenplan, M Rodriguez-Garcia, D Klenerman, TPJ Knowles
– Advanced Materials Interfaces
Learning the molecular grammar of protein condensates from sequence determinants and embeddings
K Saar
– Proceedings of the National Academy of Sciences of USA
Learning the molecular grammar of protein condensates from sequence determinants and embeddings
KL Saar, AS Morgunov, R Qi, WE Arter, G Krainer, AA Lee, TPJ Knowles
– Proceedings of the National Academy of Sciences
Rapid highly sensitive general protein quantification through on-chip chemiluminescence
HK Chiu, T Kartanas, KL Saar, CM Luxhøj, S Devenish, TPJ Knowles
– Biomicrofluidics
Direct Digital Sensing of Proteins in Solution through Single-Molecule Optofluidics
G Krainer, KL Saar, WE Arter, TPJ Knowles
Machine learning-aided protein identification from multidimensional signatures.
Y Zhang, MA Wright, KL Saar, P Challa, AS Morgunov, QAE Peter, S Devenish, CM Dobson, TPJ Knowles
– Lab on a Chip
Machine learning aided top-down proteomics on a microfluidic platform
Y Zhang, M Wright, K Saar, P Challa, A Morgunov, Q Peter, S Devenish, C Dobson, TPJ Knowles
Machine learning models for predicting protein condensate formation from sequence determinants and embeddings
K Saar, A Morgunov, R Qi, W Arter, G Krainer, A Lee, T Knowles
Rapid structural, kinetic, and immunochemical analysis of alpha-synuclein oligomers in solution
WE Arter, CK Xu, M Castellana-Cruz, TW Herling, G Krainer, KL Saar, JR Kumita, CM Dobson, TPJ Knowles
– Nano Letters
Multidimensional protein characterisation using microfluidic post-column analysis
T Scheidt, T Kartanas, Q Peter, MM Schneider, KL Saar, T Müller, PK Challa, A Levin, S Devenish, TPJ Knowles
– Lab Chip
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Research Group

Telephone number

01223 336359

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