Max Stammnitz @maxstammnitz.bsky.social

Incoming PI at CRG and AR+I
previously PhD at Cambridge University
Genomics | Chemical Biology | Mutational Scanning | Evolution 🧬🧪🧑‍💻⛰️🌊

[Max Stammnitz @maxstammnitz.bsky.social @maxi_prep]

Yay!

Soon I'm starting my lab at the Centre for Genomic Regulation in Barcelona (@CRGenomica), as a joint Independent Fellow with Andorra Research + Innovation (@Andorra_RI) . 🧬🤠

https://t.co/Efn0b5EaY8

[Max Stammnitz @maxstammnitz.bsky.social @maxi_prep]

We are looking for PhD and Master’s students to join our team from September/October 2027. 🧑‍🔬🧑‍💻

Curious? Please get in touch with me via email.

CRG PhD call link (deadline 22 November): https://t.co/4SyfNSGRBK

[Max Stammnitz @maxstammnitz.bsky.social @maxi_prep]

Yay!

Soon I'm starting my lab at the Centre for Genomic Regulation in Barcelona (@CRGenomica), as a joint Independent Fellow with Andorra Research + Innovation (@Andorra_RI) . 🧬🤠

https://t.co/Efn0b5EaY8

[Max Stammnitz @maxstammnitz.bsky.social @maxi_prep]

Our Molecular Sensing Group will use large-scale protein × chemical interaction screens, metagenomic protein mining and evolutionary tracing to get closer to an answer: how do proteins recognise small molecules, exactly? 🧬🤠

[Centre for Genomic Regulation (CRG) @CRGenomica]

Introducing Human Domainome v1, the largest and most comprehensive library of human protein variants to date, which maps the effects of +500K mutations across 522 domains. The study by @BenLehner and Toni Beltran @mutatoni is out in @Nature.

✍️ Illustration by @QueraltTolosa

[Pablo Iván Nikel @pabnik.bsky.social @PabloINik]

Leveraging engineered #Pseudomonas putida minicells for bioconversion of organic acids into short-chain methyl ketones by @KaterinaKozaeva, @ManuelNietoDom1, @maxi_prep et al. @ACSSynBio @LabNikel @DTUBiosustain #OpenAccess
https://t.co/rFm2ypWNYH

[Max Stammnitz @maxstammnitz.bsky.social @maxi_prep]

@phoebefordd @GfGenetik @tcgcambridge @CamVetSchool @sangerinstitute @emblebi @ResearchMenzies thanks a lot, Phoebe – still haven't had the chance to catch up after all those years, I hope you are well !!

[Max Stammnitz @maxstammnitz.bsky.social @maxi_prep]

Deeply honoured to receive this year's Gateff Prize by the German Genetics Society @GfGenetik 🎓🧬

Recognition for an incredible team I had the privilege to work with during my PhD. @tcgcambridge @CamVetSchool @sangerinstitute @emblebi @ResearchMenzies

[Max Stammnitz @maxstammnitz.bsky.social @maxi_prep]

@Hake_Chromatin @GfGenetik @tcgcambridge @CamVetSchool @sangerinstitute @emblebi @ResearchMenzies thank you Sandra!

[Max Stammnitz @maxstammnitz.bsky.social @maxi_prep]

@multicellgenome @GfGenetik @tcgcambridge @CamVetSchool @sangerinstitute @emblebi @ResearchMenzies thanks a lot Iñaki!

[Max Stammnitz @maxstammnitz.bsky.social @maxi_prep]

@EvaPillai @GfGenetik @tcgcambridge @CamVetSchool @sangerinstitute @emblebi @ResearchMenzies Thank you Eva!! (Hope Heidelberg has been treating you well!)

[Max Stammnitz @maxstammnitz.bsky.social @maxi_prep]

Can't help but also mention these fascinating works by award sponsor Elisabeth Gateff (photo). Already back in 1978 (!), she documented the genetics of various tumour types in Drosophila larvae – years before the first human oncogenes were cloned:
https://t.co/7JOmgYhOZb 🪰

[Taylor Mighell @taylor_mighell]

A major challenge in treating rare genetic diseases is the huge number of causal variants in different individuals. This led to assumptions that any given treatment would be suitable only for a small fraction of patients

[EvoMG Program @EvoMG_Bcn]

Opening the session on “Comparative genomic models of disease”, Elizabeth Murchison from @Cambridge_Uni talks about transmissible cancers and when cancer cells become infectious agents.

[Inigo Martincorena @imartincorena]

Excited to share our latest work. We introduce an improved version of NanoSeq, a duplex sequencing protocol with <5 errors per billion bp in single DNA molecules, and use it to study the somatic mutation landscape of oral epithelium in >1000 people. 1/n https://t.co/KBjrDfrxlj

[Max Stammnitz @maxstammnitz.bsky.social @maxi_prep]

@TimCoorens @emblebi Yay, congrats Tim!!

[Max Stammnitz @maxstammnitz.bsky.social @maxi_prep]

@themetzgerm @RobertEdgarPhD on multi-allelic phasing, how about Strainy (fresh software piece by @MishaKolmogorov, @KaterinaKazants, @donmezataberkk)?

https://t.co/vjNXdsapfr

https://t.co/R6QAaz3hSA

[Misha Kolmogorov @MishaKolmogorov]

Strainy is finally out! It enables assembly of individual strains from ONT and Pacbio metagenomes. The secret ingredient is multi-allelic phasing algorithm that does not make assumptions about number and abundance of haplotypes. Unpaywalled link - https://t.co/g1TShFOQ5T

[Max Stammnitz @maxstammnitz.bsky.social @maxi_prep]

@themetzgerm have you considered UCHIME by @RobertEdgarPhD?
https://t.co/adnwPUiwPA

classic tool for detecting chimeric PCR sequences

[Hannah Benisty @Hannah_Benisty]

⛰️🧬🥾Last weekend 30 @CRGenomica colleagues from both scientific and administration communities teamed up for a pilot #CitizenScience activity🧑‍🤝‍🧑, collecting water samples from Pyrenean lakes! #PyriSentinel @CTPPOCTEFA 🇪🇺

[Biology+AI Daily @BiologyAIDaily]

The Genetic Architecture of Protein Stability @Nature

1/ 🧬 Simplified protein stability landscape: This paper reveals that the genetic architecture governing protein stability can be surprisingly simple. It demonstrates that additive energy models can predict phenotypic outcomes from complex mutations, debunking the idea that highly complex models are necessary.

2/ 🔍 Large-scale mutation experiments: The study explored vast sequence spaces with over 10 billion genotypes and found that most phenotypic changes could be predicted using additive energy models, where interactions between mutations are minimal and sparse.

3/ 💡 Energetic couplings: While most genetic interactions are additive, the paper highlights that some pairwise energetic couplings (interactions between mutations) are structurally related and important for more accurate predictions in some proteins.

4/ 🧠 Simple and interpretable: Instead of relying on deep learning models with millions of parameters, the authors used simple thermodynamic models that are interpretable and mechanistically insightful, showing that complexity isn't always necessary for understanding protein function.

5/ 🔬 Practical applications: These models could have broad applications in fields such as clinical variant effect prediction, protein engineering, and drug design, providing a robust tool for predicting the stability of protein structures with numerous mutations.

6/ 🌐 General principle: The study suggests that this simplicity in the genotype–phenotype relationship might be a general principle across macromolecules, offering insights into the evolutionary and functional constraints of proteins.

@BenLehner @JoernSchmiedel @ainamartiaranda @aj4re
💻 Code: https://t.co/zO8gDbBzF9
📜 Paper: https://t.co/LdCdB83d0v