Proteins are complex and heterogeneous biomolecules. They owe their complicated nature to a combination of structure and dynamics. We are working on the prediction of structural features and intrinsic dynamics from their sequences and known structural models.
Intrinsic protein disorder is difficult to model, yet necessary for advancing protein structure, function and dynamics prediction. Our algorithms can quantify protein disorder at the residue level with unprecedented level of accuracy thanks to inclusion of dSPP™ data.
Thermal stability influences protein production on the laboratory and industrial scale. Our research into intrinsic protein dynamics and protein sequence-structure relationships yields predictive models, which will help to identify production bottlenecks and gain high protein yields.
Protein electrostatics has profound consequences for prediction of structure, dynamics and stability. Our propriety, hybrid machine learning algorithms can predict electrostatic effects of single mutations on protein stability, isoelectric point (pI) and optimal pH. Therefore, production and purification protocols can be fine tuned for individual protein mutants.
Laboratory evolution of proteins made a seminal impact for protein engineers in the last two decades. Now, we must harness machine learning to advance protein engineering to the next level. Through streamlined integration of our prediction tools, we are able to perform in silico evolution for smarter engineering.
Kamil Tamiola is the architect of Peptone, leveraging a profound understanding of intricate workings of machine learning and high performance computing techniques.
Matthew Heberling brings an invaluable blend of problem solving skills and professional experience to Peptone with his business and biotechnology background.
Emanuele Paci brings his unique scientific insight and research experience born out of prominent academic career in computational biology, and statistical mechanics of proteins.