Epistasis is a crucial issue: Non-Linear responses can hinder drug target identification or optimization of industrial production processes
15 December 2022
Protein engineering in the context of metabolic engineering has a growing impact in industrial biotechnology and synthetic biology. Epistasis is a crucial issue: Non-Linear responses can hinder drug target identification or optimization of industrial production processes.
Discover our Editorial for a special issue of Frontiers in Molecular Biosciences: Machine learning, epistasis, and protein engineering: From sequence-structure-function relationships to regulation of metabolic pathways VOL I.
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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
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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.
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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.
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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.
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.
- 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