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Yusuf Hamied Department of Chemistry


Royal Society University Research Fellow

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Dr Pietro Sormanni is a group leader supported by a Royal Society University Research Fellowship. His research focuses on the development of innovative data-driven technologies of rational antibody design, to obtain antibodies against targets that have been challenging to access using conventional approaches, and to improve or predict biophysical properties crucial for the successful development of antibody therapeutics. In his work he has established numerous collaborations and industrial partnerships, whose outcomes are beginning to demonstrate that computational approaches can be applied alongside established procedures to streamline antibody development, and to offer time- and cost-effective novel alternatives.  

Antibodies are key tools to address questions in biomedical research, are widely employed in diagnostics, and are increasingly used as therapeutics to treat many diseases, including cancer and neurodegeneration. Existing methods of antibody discovery and optimisation rely on the laboratory screening of large numbers of variants produced by library construction or by the immune system, which can be time consuming and costly, and sometimes result in antibodies exhibiting sub-optimal properties. Conversely, computational design could drastically reduce time and costs of antibody discovery, and in principle allow for a highly controlled parallel screening of multiple biophysical properties. Moreover, rational design inherently allows targeting specific regions on the target protein (epitopes), which can be particularly daunting using available techniques but is very important for many therapeutic applications.


Prior to taking up this post, Pietro held a postdoctoral Borysiewicz Biomedical Sciences  Fellowship from the University of Cambridge, obtained a PhD in Chemistry from the University of Cambridge, and an MSc in Theoretical Physics from the University of Milan.

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Selected publications



Dr Sormanni discusses his research


Automated optimisation of solubility and conformational stability of antibodies and proteins
A Rosace, A Bennett, M Oeller, M Mortensen, L Sakhnini, N Lorenzen, C Poulsen, P Sormanni
Conformational Entropy as a Potential Liability of Computationally Designed Antibodies.
T Löhr, P Sormanni, M Vendruscolo
– Biomolecules
An aggregation inhibitor specific to oligomeric intermediates of Aβ42 derived from phage display libraries of stable, small proteins.
S Linse, P Sormanni, DJ O'Connell
– Proceedings of the National Academy of Sciences of the United States of America
Sequence-based pH-dependent prediction of protein solubility using CamSol
M Oeller, R Kang, P Sormanni, M Vendruscolo
Current advances in biopharmaceutical informatics: guidelines, impact and challenges in the computational developability assessment of antibody therapeutics.
R Khetan, R Curtis, CM Deane, JT Hadsund, U Kar, K Krawczyk, D Kuroda, SA Robinson, P Sormanni, K Tsumoto, J Warwicker, ACR Martin
– mAbs
Sequence-based prediction and measurement of pH-dependent protein solubility
M Oeller, P Sormanni, M Vendruscolo
– Biophysical Journal
Sequence-Based Prediction of Ph-Dependent Protein Solubility Using Camsol
M Oeller, R Kang, R Bell, H Ausserwoger, P Sormanni, M Vendruscolo
– SSRN Electronic Journal
Neuroserpin and transthyretin are extracellular chaperones that preferentially inhibit amyloid formation
J West, S Satapathy, DR Whiten, M Kelly, NJ Geraghty, E-J Proctor, P Sormanni, M Vendruscolo, JN Buxbaum, M Ranson, MR Wilson
– Science Advances
An open-source automated PEG precipitation assay to measure the relative solubility of proteins with low material requirement
M Oeller, P Sormanni, M Vendruscolo
– Scientific reports
Computational maturation of a single-domain antibody against Aβ42 aggregation
J Lin, C Figazzolo, MA Metrick, P Sormanni, M Vendruscolo
– Chemical Science
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