Non-Canonical Crosslinks Confound Evolutionary Protein Structure Models
- URL: http://arxiv.org/abs/2503.17368v1
- Date: Sun, 09 Mar 2025 08:18:11 GMT
- Title: Non-Canonical Crosslinks Confound Evolutionary Protein Structure Models
- Abstract summary: We present an out-of-domain benchmark based on sacRitipeptides, a rare class of ribosomally synthesized and post-translationally modified peptides.<n>We evaluate recent models on predicting conformations compatible with cross-links bridges for the 10 known sactipeptides with elucidated post-translational modifications.<n>Our results point to the need for physics-informed models to sustain progress in biomolecular structure prediction.
- Score: 0.0
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Evolution-based protein structure prediction models have achieved breakthrough success in recent years. However, they struggle to generalize beyond evolutionary priors and on sequences lacking rich homologous data. Here we present a novel, out-of-domain benchmark based on sactipeptides, a rare class of ribosomally synthesized and post-translationally modified peptides (RiPPs) characterized by sulfur-to-$\alpha$-carbon thioether bridges creating cross-links between cysteine residues and backbone. We evaluate recent models on predicting conformations compatible with these cross-links bridges for the 10 known sactipeptides with elucidated post-translational modifications. Crucially, the structures of 5 of them have not yet been experimentally resolved. This makes the task a challenging problem for evolution-based models, which we find exhibit limited performance (0.0% to 19.2% GDT-TS on sulfur-to-$\alpha$-carbon distance). Our results point at the need for physics-informed models to sustain progress in biomolecular structure prediction.
Related papers
- Co-folding model guided by structural proteomics [0.0]
AIMS-Fold is an inference-time guided-diffusion framework for protein structure generative models.<n>It steers the generative sampling trajectory using differentiable physical potentials derived from XL-MS spatial restraints and HDX-MS solvent accessibility profiles.<n>This establishes our framework as a powerful, integrative computational approach for the structure based drug design.
arXiv Detail & Related papers (2026-05-25T14:54:08Z) - EvoStruct: Bridging Evolutionary and Structural Priors for Antibody CDR Design via Protein Language Model Adaptation [3.8079978718120473]
EvoStruct bridges a frozen protein language model with 3D structural context from an E(3)-equivariant GNN via a cross-attention adapter.<n>On the CHIMERA-Bench dataset, EvoStruct achieves the highest amino acid recovery and lowest perplexity among several antibody design methods.
arXiv Detail & Related papers (2026-05-20T17:59:16Z) - PepEDiff: Zero-Shot Peptide Binder Design via Protein Embedding Diffusion [3.9876702935151225]
We present PepEDiff, a novel peptide binder generator that designs binding sequences given a target receptor protein sequence and its pocket residues.<n>Our approach departs from existing methods by generating binder sequences directly in a continuous latent space derived from a pretrained protein embedding model.<n>Despite its simplicity, our method outperforms state-of-the-art approaches across benchmark tests and in the TIGIT case study.
arXiv Detail & Related papers (2026-01-19T19:07:32Z) - La-Proteina: Atomistic Protein Generation via Partially Latent Flow Matching [36.32031583420137]
We introduce La-Proteina for atomistic protein design based on a novel partially latent protein representation.<n>La-Proteina achieves state-of-the-art performance on multiple generation benchmarks, including all-atom co-designability, diversity, and structural validity.<n>La-Proteina is able to generate co-designable proteins of up to 800 residues, a regime where most baselines collapse and fail to produce valid samples.
arXiv Detail & Related papers (2025-07-13T03:01:50Z) - Generation of structure-guided pMHC-I libraries using Diffusion Models [0.0]
We introduce a structure-guided benchmark of pMHC-I peptides designed using diffusion models conditioned on crystal distances.<n>This benchmark is independent of previously characterized peptides yet reproduces canonical anchor residue preferences.<n>We demonstrate that state-of-the-art sequence-based predictors perform poorly at recognizing the binding potential of these structurally stable designs.
arXiv Detail & Related papers (2025-07-11T08:29:18Z) - Protein Inverse Folding From Structure Feedback [78.27854221882572]
We introduce a novel approach to fine-tune an inverse folding model using feedback from a protein folding model.<n>Our results on the CATH 4.2 test set demonstrate that DPO fine-tuning leads to a significant improvement in average TM-Score.
arXiv Detail & Related papers (2025-06-03T16:02:12Z) - Learning conformational ensembles of proteins based on backbone geometry [1.1874952582465603]
We propose a flow matching model for sampling protein conformations based solely on backbone geometry.
The resulting model is orders of magnitudes faster than current state-of-the-art approaches at comparable accuracy and can be trained from scratch in a few GPU days.
arXiv Detail & Related papers (2025-02-19T17:16:27Z) - GENERator: A Long-Context Generative Genomic Foundation Model [66.46537421135996]
We present GENERator, a generative genomic foundation model featuring a context length of 98k base pairs (bp) and 1.2B parameters.<n>Trained on an expansive dataset comprising 386B bp of DNA, the GENERator demonstrates state-of-the-art performance across both established and newly proposed benchmarks.<n>It also shows significant promise in sequence optimization, particularly through the prompt-responsive generation of enhancer sequences with specific activity profiles.
arXiv Detail & Related papers (2025-02-11T05:39:49Z) - SFM-Protein: Integrative Co-evolutionary Pre-training for Advanced Protein Sequence Representation [97.99658944212675]
We introduce a novel pre-training strategy for protein foundation models.
It emphasizes the interactions among amino acid residues to enhance the extraction of both short-range and long-range co-evolutionary features.
Trained on a large-scale protein sequence dataset, our model demonstrates superior generalization ability.
arXiv Detail & Related papers (2024-10-31T15:22:03Z) - Retrieval-Enhanced Mutation Mastery: Augmenting Zero-Shot Prediction of Protein Language Model [3.4494754789770186]
Deep learning methods for protein modeling have demonstrated superior results at lower costs compared to traditional approaches.
In mutation effect prediction, the key to pre-training deep learning models lies in accurately interpreting the complex relationships among protein sequence, structure, and function.
This study introduces a retrieval-enhanced protein language model for comprehensive analysis of native properties from sequence and local structural interactions.
arXiv Detail & Related papers (2024-10-28T15:28:51Z) - NovoBench: Benchmarking Deep Learning-based De Novo Peptide Sequencing Methods in Proteomics [58.03989832372747]
We present the first unified benchmark NovoBench for emphde novo peptide sequencing.
It comprises diverse mass spectrum data, integrated models, and comprehensive evaluation metrics.
Recent methods, including DeepNovo, PointNovo, Casanovo, InstaNovo, AdaNovo and $pi$-HelixNovo are integrated into our framework.
arXiv Detail & Related papers (2024-06-16T08:23:21Z) - A Hierarchical Training Paradigm for Antibody Structure-sequence
Co-design [54.30457372514873]
We propose a hierarchical training paradigm (HTP) for the antibody sequence-structure co-design.
HTP consists of four levels of training stages, each corresponding to a specific protein modality.
Empirical experiments show that HTP sets the new state-of-the-art performance in the co-design problem.
arXiv Detail & Related papers (2023-10-30T02:39:15Z) - AbDiffuser: Full-Atom Generation of in vitro Functioning Antibodies [44.149969082612486]
AbDiffuser is an equivariant and physics-informed diffusion model for antibody 3D structures and sequences.
Our approach improves protein diffusion by taking advantage of domain knowledge and physics-based constraints.
Numerical experiments showcase the ability of AbDiffuser to generate antibodies that closely track the sequence and structural properties of a reference set.
arXiv Detail & Related papers (2023-07-28T11:57:44Z) - Efficient Prediction of Peptide Self-assembly through Sequential and
Graphical Encoding [57.89530563948755]
This work provides a benchmark analysis of peptide encoding with advanced deep learning models.
It serves as a guide for a wide range of peptide-related predictions such as isoelectric points, hydration free energy, etc.
arXiv Detail & Related papers (2023-07-17T00:43:33Z) - Predicting protein variants with equivariant graph neural networks [0.0]
We compare the abilities of equivariant graph neural networks (EGNNs) and sequence-based approaches to identify promising amino-acid mutations.
Our proposed structural approach achieves a competitive performance to sequence-based approaches while being trained on significantly fewer molecules.
arXiv Detail & Related papers (2023-06-21T12:44:52Z) - State-specific protein-ligand complex structure prediction with a
multi-scale deep generative model [68.28309982199902]
We present NeuralPLexer, a computational approach that can directly predict protein-ligand complex structures.
Our study suggests that a data-driven approach can capture the structural cooperativity between proteins and small molecules, showing promise in accelerating the design of enzymes, drug molecules, and beyond.
arXiv Detail & Related papers (2022-09-30T01:46:38Z) - Unsupervisedly Prompting AlphaFold2 for Few-Shot Learning of Accurate
Folding Landscape and Protein Structure Prediction [28.630603355510324]
We present EvoGen, a meta generative model, to remedy the underperformance of AlphaFold2 for poor MSA targets.
By prompting the model with calibrated or virtually generated homologue sequences, EvoGen helps AlphaFold2 fold accurately in low-data regime.
arXiv Detail & Related papers (2022-08-20T10:23:17Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.