On Monday, May 04, at 17:30 EEST we are delighted to host our guests  Dr. Michael Heinzinger and Jan-Philipp Leusch, who will present latest research in the context of #protein #folding, by modeling local energetic #frustation using #AI.

Talk:

Dr. Michael Heinzinger and Jan-Philipp Leusch, "FrustrAI-Seq: Scaling Local Energetic Frustration to the Protein Sequence Space", Helmholtz Zentrum Munich, Munich, Germany

Abstract: Proteins fold into their native three-dimensional (3D) structures
by navigating complex energy landscapes shaped by the biophysical and biochemical properties of their sequence. Once folded, some sequence positions (dubbed residues) remain locally frustrated, reflecting functional constraints incompatible with optimal packing. This local energetic frustration provides important insights into protein function and dynamics, but its analysis typically relies on structure-based energy calculations and remains energetically costly at scale. Here, we introduce an ultra-fast sequence-based prediction of local energetic frustration directly from protein sequences using embeddings from protein language models (pLMs). Our method, coined FrustrAI-Seq, enables proteome-wide frustration profiling in minutes (∼17 minutes for the entire human proteome on a single Nvidia H100 GPU) while retaining biologically relevant performance as shown for the α-globin and β-lactamase family. By eliminating the need for explicit structural or evolutionary information, this approach expands frustration analysis to protein regions and classes that were previously inaccessible, including intrinsically disordered regions and high-throughput de novo designed protein datasets. To support reproducibility and large-scale applications, we provide the largest freely available resource of precomputed local frustration scores to date (∼ 106 proteins), along with model weights and complete training and inference code at: github.com/leuschjanphilipp/FrustrAI-Seq.

Keywords: Local Energetic Frustration, Frustration, Protein Language Model, Frus- tratometer, FrustraEvo

Resources:

• Leusch et al, bioRxiv, https://www.biorxiv.org/content/10.64898/2026.02.03.703498v1.full.pdf
• FrustrAI-Seq Github -> https://github.com/leuschjanphilipp/FrustrAI-Seq
• Dataset -> https://huggingface.co/datasets/leuschj/Funstration
• FrustrAI-seq Model -> https://huggingface.co/leuschj/FrustrAI-Seq
• Heinzinger Lab -> https://www.helmholtz-munich.de/icb/research-groups/heinzinger-lab

Format: Online, free to attend, registration required: zoom

Organization: YMS is organized by RSBI and is linked to RoBioinfo Seminars

Chair: Madalina Giurgiu-Kraljic

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