Protein lead design and optimisation through
artificial intelligence

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Complex Fundamentals

The fundamentals of protein structure and dynamics complicate drug design and industrial applications of proteins. Thus, advancing basic knowledge of proteins is pivotal for disease research and green chemistry solutions.

Big Data Issues

Fundamentals are obscured by the immense growth of public protein data. As a result, there is only stagnant growth of our knowledge, prediction models, and methods to advance protein biotech. This is why making sense of the data is paramount.

Complex Models

Developing models that predict or explain features of proteins are extremely complicated, yet required for smarter engineering and production. Big data is available to train models, but an effective digest of the data is lacking.

Artificial Intelligence

A key solution for bringing complex prediction models to protein biotech is through machine learning and AI. This fuels our mission to accelerate protein research, which is why we built the world's first protein database dSPP tailored for Deep Learning and AI applications with full Keras™ and Tensorflow™ integration.

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Complex Fundamentals

The fundamentals of protein structure and dynamics complicate drug design and industrial applications of proteins. Thus, advancing basic knowledge of proteins is pivotal for disease research and green chemistry solutions.

Big Data Issues

Fundamentals are obscured by the immense growth of public protein data. As a result, there is only stagnant growth of our knowledge, prediction models, and methods to advance protein biotech. This is why making sense of the data is paramount.

Complex Models

Developing models that predict or explain features of proteins are extremely complicated, yet required for smarter engineering and production. Big data is available to train models, but an effective digest of the data is lacking.

Artificial Intelligence

A key solution for bringing complex prediction models to protein biotech is through machine learning and AI. This fuels our mission to accelerate protein research, which is why we built the world's first protein database dSPP tailored for Deep Learning and AI applications with full Keras™ and Tensorflow™ integration.

Structural propensity

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.

Residual disorder

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

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.

Optimal pH and pI

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.

Directed evolution

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.

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Protein Data for Deep Learning

We have built the world's first protein database specificaly for Deep Learning and AI applications with full Keras™ and Tensorflow™ integration.

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Keras™ and Tensorflow™

Our dSPP database can be effortlessly setup and integrated with Keras™ and Tensorflow™ using Python pip manager.

pip install dspp-keras

To explore the full potential of dSPP, train example models from our Github repository.

Learn more
Keras™ and Tensorflow™

Our dSPP database can be effortlessly setup and integrated with Keras™ and Tensorflow™ using Python pip manager.

pip install dspp-keras

To explore the full potential of dSPP, train example models from our Github repository.

Learn more
Private research

We provide private research and consultancy for academic and industrial clients. Our research process includes: stringent statistical analyses of input data with thorough assessment of errors, state of the art numerical modelling supported by Machine Learning and Artificial Intelligence techniques, and training and development of powerful and intuitive tools that are complementary to existing software stack.

Please read dSPP™ research paper to see our workflow.

Read paper
Database of Structural Propensities of Proteins

We provide private research and consultancy for academic and industrial clients. Our research process includes: stringent statistical analyses of input data with thorough assessment of errors, state of the art numerical modelling supported by Machine Learning and Artificial Intelligence techniques, and training and development of powerful and intuitive tools that are complementary to existing software stack.

Please read dSPP™ research paper to see our workflow.

Read paper
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Kamil Tamiola
Chief Executive Officer
Bio

Kamil Tamiola is the architect of Peptone, leveraging a profound understanding of intricate workings of machine learning and high performance computing techniques.

Matt Heberling
Chief Operations Officer
Bio

Matthew Heberling brings an invaluable blend of problem solving skills and professional experience to Peptone with his business and biotechnology background.

Emanuele Paci
Chief Science Officer
Bio

Emanuele Paci brings his unique scientific insight and research experience born out of prominent academic career in computational biology, and statistical mechanics of proteins.

Accelerating protein research with GPUs

In silico protein engineering requires vast computational resources. Our technological partnership with NVIDIA, the leading manufacturer of high performance computing solutions for machine learning and AI, enables us to scale our protein calculations beyond the limits of standard supercomputers.

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Accelerating protein research with GPUs

In silico protein engineering requires vast computational resources. Our technological partnership with NVIDIA, the leading manufacturer of high performance computing solutions for machine learning and AI, enables us to scale our protein calculations beyond the limits of standard supercomputers.

Learn more
Peptone partners with NVIDIA