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Yusuf Hamied Department of Chemistry

 

Research

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.

Background

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.

Join our group

We are always looking for talented and enthusiastic individuals to join the team. If you are interested, please get in touch to discuss potential opportunities.

Selected publications

Dr Sormanni discusses his research

Publications

Supersaturated proteins are enriched at synapses and underlie cell and tissue vulnerability in Alzheimer's disease
R Freer, P Sormanni, P Ciryam, B Rammner, SO Rizzoli, CM Dobson, M Vendruscolo
– Heliyon
(2019)
5,
e02589
Biochemical and biophysical comparison of human and mouse beta‐2 microglobulin reveals the molecular determinants of low amyloid propensity
A Achour, L Broggini, X Han, R Sun, C Santambrogio, J Buratto, C Visentin, A Barbiroli, CMG De Luca, P Sormanni, F Moda, A De Simone, T Sandalova, R Grandori, C Camilloni, S Ricagno
– FEBS Journal
(2019)
287,
546
A chemical kinetic basis for measuring translation initiation and elongation rates from ribosome profiling data.
AK Sharma, P Sormanni, N Ahmed, P Ciryam, UA Friedrich, G Kramer, EP O'Brien
– PLOS Computational Biology
(2019)
15,
e1007070
Identifying A- and P-site locations on ribosome-protected mRNA fragments using Integer Programming
N Ahmed, P Sormanni, P Ciryam, M Vendruscolo, CM Dobson, EP O'Brien
– Scientific reports
(2019)
9,
6256
Different soluble aggregates of A beta 42 can give rise to cellular toxicity through different mechanisms
S De, DC Wirthensohn, P Flagmeier, C Hughes, FA Aprile, FS Ruggeri, DR Whiten, D Emin, Z Xia, JA Varela, P Sormanni, F Kundel, TPJ Knowles, CM Dobson, C Bryant, M Vendruscolo, D Klenerman
– Nature communications
(2019)
10,
1541
A method of predicting the in vitro fibril formation propensity of A beta 40 mutants based on their inclusion body levels in E-coli
K Sanagavarapu, E Nüske, I Nasir, G Meisl, JN Immink, P Sormanni, M Vendruscolo, TPJ Knowles, A Malmendal, C Cabaleiro-Lago, S Linse
– Scientific reports
(2019)
9,
3680
Protein Solubility Predictions Using the CamSol Method in the Study of Protein Homeostasis
P Sormanni, M Vendruscolo
– Cold Spring Harb Perspect Biol
(2019)
11,
a033845
In vitro and in silico assessment of the developability of a designed monoclonal antibody library.
A-M Wolf Pérez, P Sormanni, JS Andersen, LI Sakhnini, I Rodriguez-Leon, JR Bjelke, AJ Gajhede, L De Maria, DE Otzen, M Vendruscolo, N Lorenzen
– mAbs
(2019)
11,
388
Attentive Cross-Modal Paratope Prediction
A Deac, P VeliČković, P Sormanni
– Journal of computational biology : a journal of computational molecular cell biology
(2018)
26,
536
Developability Assessment of Engineered Monoclonal Antibody Variants with a Complex Self-Association Behavior Using Complementary Analytical and in Silico Tools
L Shan, N Mody, P Sormani, KL Rosenthal, MM Damschroder, R Esfandiary
– Mol Pharm
(2018)
15,
5697
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Telephone number

01223 761480

Email address

ps589@cam.ac.uk

College

Clare Hall