Hugging Face Blog
Aug 24, 2022
Proteins: A Mosaic Pattern to Rule Them All?
For decades, the existence of the hydrophobic core, a region in the 3D structure of proteins where hydrophobic amino acids reside together, has been considered a general property in proteins. What we have found now may extend that model.
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arXiv AI
Aug 18
Protein Structure Prediction: From Evolutionary Constraints to Generative Modeling
arXiv:2608. 16094v1 Announce Type: new Abstract: Accurate protein structure prediction is fundamental to structural biology because protein structure underlies molecular function and provides a basis for mechanistic interpretation.
arXiv Machine Learning
Jul 15
PRIME: Protein Representation via Physics-Informed Multiscale Equivariant Hierarchies
arXiv:2605. 01625v3 Announce Type: replace Abstract: Proteins are inherently multiscale physical systems whose functional properties emerge from coordinated structural organization across multiple spatial resolutions, ranging from atomic interactions to global fold topology.
arXiv Machine Learning
Jun 16
Learning Topological Representations for Molecular Dynamics
arXiv:2606. 14737v1 Announce Type: cross Abstract: Molecular dynamics (MD) simulations generate trajectories in a high-dimensional configuration space whose analysis critically depends on molecular descriptors, typically handcrafted observables or learned kinetic embeddings.
arXiv Machine Learning
Jul 20
Neural spectroscopy of AlphaFold2 reveals encoded protein conformational landscapes
arXiv:2607. 16087v1 Announce Type: new Abstract: AlphaFold2's 93 million parameters, shaped by the evolutionary record of protein structure encoded in the Protein Data Bank and in sequence alignments, are conventionally treated only as machinery for converting sequence to structure.
arXiv Machine Learning
Jun 25
Two Stages of Folding: Convergent Mechanisms in AI Protein Folding Trunks
arXiv:2602. 06020v3 Announce Type: replace Abstract: How do protein structure prediction models fold proteins?
arXiv Machine Learning
Jun 11
DeepRHP: A Hybrid Variational Autoencoder for Designing Random Heteropolymers as Protein Mimics
arXiv:2606. 11651v1 Announce Type: new Abstract: Synthetic random heteropolymers (RHPs), consisting of a predefined set of monomers, offer an approach toward the design of protein-like materials.
arXiv Machine Learning
Aug 28
Interpreting Latent Protein Language Model Features with Geometric Annotations
The paper introduces a scalable method to interpret sparse autoencoder (SAE) features in the ESM-2 protein language model by leveraging geometrically inspired features of the protein α‑carbon backbone. Across 8M layers of ESM-2, a false discovery rate–controlled analysis shows that local geometry is significantly associated with many SAE features, revealing substructure within known biological labels and enabling annotation of unannotated metagenomic proteins. Ablation experiments demonstrate that removing these geometric features shifts ESM-2’s predicted contact maps toward the descriptor, linking mechanistic interpretability with structural biology.
arXiv AI
Aug 25
PepLLM: ESM-Guided Llama for Structured Protein-Peptide Binding Interface Analysis
arXiv:2608.21367v1 Announce Type: cross Abstract: Protein-peptide interactions are central to cellular regulation and peptide-based drug discovery, yet existing computational methods mainly focus on...
MIT News AI
Aug 27
Looking beyond natural sequences
A new machine‑learning framework is being developed to enhance the success rate of computational protein design. It deliberately moves away from reproducing sequences found in nature, aiming instead for novel designs that may perform better in practical applications.
arXiv AI
Jun 9
SurfDesign: Effective Protein Design on Molecular Surfaces
arXiv:2606. 07567v1 Announce Type: cross Abstract: Protein function is largely determined by molecular surface geometry and physicochemical complementarity, yet most protein design methods condition only on backbone structure.
arXiv Machine Learning
Jun 11
ProHiFlo: Hierarchical Flow Matching with Functional Guidance for De Novo Protein Generation
arXiv:2606. 11243v1 Announce Type: new Abstract: De novo protein generation has transformative potential in therapeutic design, enzyme engineering, and synthetic biology.