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Yusuf Hamied Department of Chemistry

 

Royal Society University Research Fellow

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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

Tour of the Sormanni lab

Publications

Assessing antibody and nanobody nativeness for hit selection and humanization with AbNatiV
A Ramon, M Ali, M Atkinson, A Saturnino, K Didi, C Visentin, S Ricagno, X Xu, M Greenig, P Sormanni
– Nature Machine Intelligence
(2024)
6,
74
Sequence-based prediction of the intrinsic solubility of peptides containing non-natural amino acids
M Oeller, RJD Kang, HL Bolt, AL Gomes Dos Santos, AL Weinmann, A Nikitidis, P Zlatoidsky, W Su, W Czechtizky, L De Maria, P Sormanni, M Vendruscolo
– Nat Commun
(2023)
14,
7475
Automated optimisation of solubility and conformational stability of antibodies and proteins.
P Sormanni, A Rosace, A Bennett, M Oeller, M Mortensen, L Sakhnini, N Lorenzen, C Poulsen
– Nature Communications
(2023)
14,
1937
Automated optimisation of solubility and conformational stability of antibodies and proteins
A Rosace, A Bennett, M Oeller, MM Mortensen, L Sakhnini, N Lorenzen, C Poulsen, P Sormanni
– Nature Communications
(2023)
14,
1937
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)
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)
Sequence-based prediction of pH-dependent protein solubility using CamSol.
M Oeller, R Kang, R Bell, H Ausserwöger, P Sormanni, M Vendruscolo
– Brief Bioinform
(2023)
24,
bbad004
Approaches to expand the conventional toolbox for discovery and selection of antibodies with drug-like physicochemical properties.
HL Svilenov, P Arosio, T Menzen, P Tessier, P Sormanni
– mAbs
(2023)
15,
2164459
Fragment-based computational design of antibodies targeting structured epitopes.
M Aguilar Rangel, A Bedwell, E Costanzi, RJ Taylor, R Russo, GJL Bernardes, S Ricagno, J Frydman, M Vendruscolo, P Sormanni
– Science advances
(2022)
8,
eabp9540
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Research Group

Research Interest Group

Email address

ps589@cam.ac.uk

College

Clare Hall