Maximize your next value creation opportunity for peptides, proteins, enzymes, antibodies & VHHs. Meet Peaccel’s delegates

6 December 2022

Maximize your next value creation opportunity for peptides, proteins, enzymes, antibodies & VHHs. Stand out of the reach of your competitors.

EPI_technology™ (Epistasis Processing Inside): Unique on the market, better & quicker for protein design & optimization

Meet Peaccel’s delegates Jean-Marie Vallet and Jacob Weintraub at the Synthetic Biology-Based Therapies Summit, December 14-15 2022, Boston, US: https://synthetic-biology-therapeutics-summit.com

For more information:

PEACCEL
Making the world disease free
Contact: AI-team@peaccel.com
http://www.peaccel.com/

How the success rate of protein engineering projects is influenced by epistatic effects? A comparison of 15 state-of-the-art approaches

29 November 2022

The recent development of structure prediction deep learning (DL) tools such as Alphafold2 (DeepMind), ESMfold (Meta) or ProteinMPNN (David Baker’s team) has revolutionized this area. Nevertheless, these DL tools are not suitable for predicting how individual amino acid changes alter protein function: they can’t predict epistaticeffects.

After protein folding powered by DeepMind and Meta the next challenge is to accurately predict epistasis ie. the impact of non-linear interactions of mutations within the protein sequence on the function.

Our recent review of Machine Learning (ML) and Deep Learning (DL) strategies examines how epistatic effects influence the success rate of protein engineering projects by comparing 15 state-of-the-art approaches (see Table 4: https://link.springer.com/protocol/10.1007/978-1-0716-2152-3_15/tables/4) and provides a general workflow for non-experts when using such learning strategies.

For more information:

PEACCEL
Making the world disease free
Contact: AI-team@peaccel.com
http://www.peaccel.com/

After protein folding powered by Meta and DeepMind, the next challenge is to accurately predict epistasis

9 November 2022

Epistasis dramatically influences the success of drug discovery projects: The real lead drug candidate is missed if epistasis is not taken into account.

After protein folding powered by Meta and DeepMind , the next challenge is to accurately predict epistasis ie. the impact of non-linear interactions of mutations within the protein sequence. PEACCEL’s founder, co-authored in ACS Catalysis a critical review on epistasis with its partners at Leibniz Institute of Plant Biochemistry.

For more information :

PEACCEL
Making the world disease free
Contact: AI-team@peaccel.com
http://www.peaccel.com/

PEACCEL (Paris, Fr) and JWIP & Patent Services, LLC (Boston, USA) sign a strategic partnership to increase the chances of success of your drugs

13 October 2022

PEACCEL (Paris, Fr) and JWIP & Patent Services, LLC (Boston, USA) have signed a strategic partnership agreement.

As of October 2022, PEACCEL – The AI company for life sciences in Paris, and JWIP & Patent Services, LLC a Leading IP Firm in Boston, USA, combine their know-how and expertise to address the increasing needs of emerging AI-based drug discovery challenges for their large portfolio of customers and partners.
Combining the innov’SAR industrial AI platform developed by PEACCEL and the legal counsel provided by JWIP to accelerate critical collaborations and licensing between PEACCEL and key players in the pharmaceutical and chemical industries.

(more…)

Accurate modeling of metabolic pathways for drug target identification and industrial production processes

25 July 2022

PEACCEL and partners underline in the June 2022 issue of the peer-
reviewed journal Frontiers in Artificial intelligence how non-linearity is crucial when
modeling metabolic pathways for the identification of biomarkers of diseases or
optimizing industrial production processes.
Exemplified for:

The Entamoeba histolytica glycolysis, one of the major metabolic pathways of the parasite
The peroxide detoxification pathway of Trypanosoma cruzi
The industrial-scale penicillin fermentation process of Penicillium chrysogenum

PEACCEL co-authored a critical review on VHH structural modelling approaches with its academic partners

18 July 2022

VHH, i.e., VH domains of camelid single-chain antibodies, are very promising therapeutic agents due to their significant physicochemical advantages compared to classical mammalian antibodies. Unfortunately, most VHHs do not have 3D structures. This review presents the most extensive overview of structure prediction methods applied for the 3D modelling of a given VHH sequence (a total of 21). AlphaFold 2 and NanoNet appear to be the best tools to predict a structural model of VHH from its sequence.