Zhenling Peng

RiboDFM designs RNA sequences that fold into target 3D structures using a backbone-conditioned discrete flow-matching framework, achieving high sequence recovery and tertiary-structure consistency.

trFlow generates all-atom protein conformations faster and more accurately than existing methods by using flow matching with three key innovations.

This paper introduces trRosettaRNA2, a deep learning model for RNA 3D structure prediction that outperforms existing methods with fewer parameters, and demonstrates its effectiveness on RNase P RNA.

The paper benchmarks MSA pairing strategies for protein-protein complex structure prediction and finds that increased MSA depth, not specific pairing, improves accuracy.

IDPFunNet predicts IDR functions using a hybrid deep learning model, outperforming existing methods in binding prediction and DFL identification across multiple benchmarks.

RNArank assesses RNA 3D structure model quality using deep learning and intermediate 2D maps, outperforming existing methods in benchmark tests.

This paper introduces trX2-D, a method that combines deep learning with physics-based sampling to predict protein dynamic structures and alternative conformations.

PathDiffusion models protein folding pathways using evolution-guided diffusion, accurately reconstructing folding events and demonstrating versatility across multiple applications.

The paper introduces mTM-align2, a method for rapid and accurate protein structure database search that uses inverse folding models and contrastive learning, achieving high precision in seconds.

CryFold improves de novo model building for cryo-EM maps using local attention and 3D rotary position embedding, producing more complete models and reducing resolution requirements.

Q-BioLiP improves upon BioLiP by representing protein-ligand interactions using quaternary structures and providing both experimental and predicted binding affinities.

This paper introduces trRosettaX-Single, a single-sequence protein structure prediction method that outperforms AlphaFold2 and RoseTTAFold on natural and designed proteins.

This paper introduces trRosettaRNA, a deep learning method for RNA 3D structure prediction, showing improved accuracy over existing methods on benchmark datasets.

The paper evaluates 43 methods for predicting intrinsically disordered regions and binding regions in proteins, finding that deep learning methods outperform older physicochemical approaches but binding prediction remains challenging.

This paper introduces a deep residual network and Rosetta protocol for predicting inter-residue orientations and distances, outperforming existing methods in benchmark tests.