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Yusuf Hamied Department of Chemistry

 

Professor of Biophysics

Our research

In the last 15 years our research has been focused on the development of methods of characterising the structure, dynamics and interactions of proteins in previously inaccessible states. These methods are based on the use of experimental data, in particular from nuclear magnetic resonance spectroscopy, as structural restraints in molecular dynamics simulations. Through this approach it is possible to obtain information about a variety of protein conformations, as for example those populated during the folding process, and about protein interactions in complex environments, including those generating aggregate species that are associated with neurodegenerative disorders such as Alzheimer's and Parkinson's diseases.

Application to neurodegenerative diseases

More recently, these studies have led us to investigate the physico-chemical principles of proteins homeostasis and their application to the development of therapeutic strategies against neurodegenerative diseases. Starting from the observation that proteins are expressed in the cell at levels close to their solubility limits, we are developing approaches to prevent or delay misfolding disorders based on the enhancement of our quality control mechanisms against protein aggregation.

Watch Professor Vendruscolo discuss his research

Take a tour of the Una Finlay Laboratory in the Centre for Misfolding Diseases

Publications

Characterization of Pairs of Toxic and Nontoxic Misfolded Protein Oligomers Elucidates the Structural Determinants of Oligomer Toxicity in Protein Misfolding Diseases.
R Limbocker, N Cremades, R Cascella, PM Tessier, M Vendruscolo, F Chiti
– Accounts of Chemical Research
(2023)
56,
1395
The amyloid-β pathway in Alzheimer's disease: a plain language summary
H Hampel, Y Hu, J Hardy, K Blennow, C Chen, G Perry, SH Kim, VL Villemagne, P Aisen, M Vendruscolo, T Iwatsubo, CL Masters, M Cho, L Lannfelt, JL Cummings, A Vergallo
– Neurodegenerative disease management
(2023)
13,
141
FuzPred: a web server for the sequence-based prediction of the context-dependent binding modes of proteins
A Hatos, JMC Teixeira, S Barrera-Vilarmau, A Horvath, SCE Tosatto, M Vendruscolo, M Fuxreiter
– Nucleic acids research
(2023)
51,
w198
Sequence-based prediction of the solubility of peptides containing non-natural amino acids
M Oeller, R Kang, H Bolt, AGD Santos, A Weinmann, A Nikitidis, P Zlatoidsky, W Su, W Czechtizky, L De Maria, P Sormanni, M Vendruscolo
(2023)
ANXA11 biomolecular condensates facilitate protein-lipid phase coupling on lysosomal membranes
J Nixon-Abell, FS Ruggeri, S Qamar, TW Herling, MA Czekalska, Y Shen, G Wang, C King, MS Fernandopulle, T Sneideris, JL Watson, VVS Pillai, W Meadows, JW Henderson, JE Chambers, JL Wagstaff, SH Williams, H Coyle, Y Lu, S Zhang, SJ Marciniak, SMV Freund, E Derivery, ME Ward, M Vendruscolo, TPJ Knowles, P St George-Hyslop
– bioRxiv
(2023)
AlphaFold Prediction of Structural Ensembles of Disordered Proteins
F Brotzakis, S Zhang, M Vendruscolo
(2023)
Exploration and Exploitation Approaches Based on Generative Machine Learning to Identify Potent Small Molecule Inhibitors of α‑Synuclein Secondary Nucleation
RI Horne, MH Murtada, D Huo, ZF Brotzakis, RC Gregory, A Possenti, S Chia, M Vendruscolo
– J Chem Theory Comput
(2023)
19,
4701
Multidimensional Protein Solubility Optimization with an Ultrahigh-Throughput Microfluidic Platform.
NA Erkamp, M Oeller, T Sneideris, H Ausserwoger, A Levin, TJ Welsh, R Qi, D Qian, N Lorenzen, H Zhu, P Sormanni, M Vendruscolo, TPJ Knowles
– Analytical Chemistry
(2023)
95,
5362
Sequence-based prediction of the solubility of peptides containing non-natural amino acids
M Oeller, R Kang, H Bolt, A Gomes dos Santos, A Langborg Weinmann, A Nikitidis, P Zlatoidsky, W Su, W Czechtizky, L De Maria, P Sormanni, M Vendruscolo
(2023)
Extracellular protein homeostasis in neurodegenerative diseases.
MR Wilson, S Satapathy, M Vendruscolo
– Nature reviews. Neurology
(2023)
19,
235
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Research Interest Groups

Telephone number

01223 763873

Email address

mv245@cam.ac.uk