Miguel Arbesú Andrés

Miguel Arbesú Andrés

Researcher in Bio ∩ AI

Instadeep, Berlin, Germany

Hello

My name is Miguel. I am a researcher working at the intersection between Biology and Artificial Intelligence.

Nowadays I am interested in AI-driven protein science and optimization problems.

Interests
  • Protein Science
  • Drug Discovery
  • Machine Learning
  • Large Language Models
Education
  • PhD in Organic Chemistry, 2018

    University of Barcelona, Spain

  • MSc in Organic Chemistry, 2013

    University of Barcelona, Spain

  • BSc+MSc in Chemistry, 2012

    University of Oviedo, Spain

Experience

 
 
 
 
 
InstaDeep.
Senior applied research scientist
Sep 2024 – Present Berlin, Germany
Develop and apply state-of-the-art methods in protein engineering and other optimization problems.
 
 
 
 
 
InstaDeep.
Research engineer
Feb 2023 – Aug 2024 Berlin, Germany
 
 
 
 
 
Max-Delbrück-Centrum für Molekulare Medizin (MDC)
Visiting researcher
Sep 2022 – Nov 2022 Berlin, Germany

Helmholtz Information & Data Science Academy (HIDA) grantee at the MDC Bioinformatics unit.

Projects:

  • Protein:drug interaction prediction with geometric deep learning and protein language models.
 
 
 
 
 
Leibniz-Forschungsinstitut für Molekulare Pharmakologie (FMP-Berlin)
Postdoctoral researcher
Mar 2018 – Jun 2022 Berlin, Germany

Projects:

  • Liquid-liquid phase separation of human FUS by solid state Nuclear Magnetic Resonance .
  • Regulation of plant salt-stress response by phosphorylation of protein CC1.
  • Structural disorder in enteropathogenic bacterial proteomes.

Tasks:

  • Method development
  • Solid state and solution Nuclear Magnetic Resonance data acquisition
  • Data analysis
 
 
 
 
 
BioNMR group - University of Barcelona
PhD student
Sep 2013 – Feb 2018 Barcelona, Spain

Thesis: A novel regulatory unit in the N-terminal region of c-src.

Tasks:

  • Protein expression and purification.
  • Solution Nuclear Magnetic Resonance data acquisition.
  • Data analysis.

Publications

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(2024). Generative Model for Small Molecules with Latent Space RL Fine-Tuning to Protein Targets. In Arxiv.

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(2023). Offline RL for generative design of protein binders. Accepted in New Frontiers of AI for Drug Discovery and Development workshop, NeurIPS 2023..

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(2023). FrameDiPT: SE(3) Diffusion Model for Protein Structure Inpainting. Accepted in Machine Learning in Structural Biology workshop, NeurIPS 2023..

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(2021). The pathogen-encoded signaling receptor Tir exploits host-like intrinsic disorder to assist infection. Accepted in Nature Communications biology (February 2024). Find the published version here.

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(2019). Integrating disorder in globular multidomain proteins: Fuzzy sensors and the role of SH3 domains. In Archives of Biochemistry and Biophysics.

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(2019). A Myristoyl-Binding Site in the SH3 Domain Modulates c-Src Membrane Anchoring. In Iscience.

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(2018). Farseer-NMR: Automatic Treatment, Analysis and Plotting of Large, Multi-Variable NMR Data. In Journal of Biomolecular NMR.

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(2018). Intramolecular Fuzzy Interactions Involving Intrinsically Disordered Domains. In Frontiers in Molecular Biosciences.

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(2018). A novel regulatory unit in the N-terminal region of c-Src. Universitat de Barcelona.

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(2017). The Unique Domain Forms a Fuzzy Intramolecular Complex in Src Family Kinases. In Structure.

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(2015). The SH3 Domain Acts as a Scaffold for the N-Terminal Intrinsically Disordered Regions of c-Src. In Structure.

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(2013). Lipid binding by disordered proteins. In Protocol Exchange.

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