Zhiye Guo

The paper introduces MULTICOM, a system that enhances AlphaFold-Multimer for protein complex structure prediction, achieving higher TM-scores in CASP15.

The paper improves AlphaFold2-based protein structure prediction using MULTICOM_refine, achieving higher accuracy in CASP15 compared to standard AlphaFold2.

The paper introduces a hybrid method combining pairwise similarity and deep learning for estimating protein complex model accuracy, achieving high correlation and low ranking loss in CASP15.

CDPred predicts inter-chain residue-residue distances in protein complexes using 2D attention-based deep learning, outperforming existing methods on homodimer and heterodimer datasets.

This paper introduces DistDom, a deep learning method that predicts protein domain boundaries using 1D sequence features and 2D distance maps, achieving higher accuracy than existing methods on CASP14 data.

This paper introduces a gradient descent algorithm to reconstruct protein quaternary structures from inter-chain contacts, showing it outperforms other methods and can produce high-quality models even with predicted contacts.

The paper improves protein tertiary structure prediction by enhancing deep learning and distance prediction methods in the MULTICOM system, achieving better performance in CASP14.

The paper develops deep learning-based EMA predictors for protein model accuracy estimation in CASP14, achieving top rankings in GDT-TS score loss.

The paper develops deep learning-based protein distance predictors for CASP14, achieving top rankings in contact prediction and showing the importance of MSAs and features in accuracy.

This study introduces an attention-based convolutional neural network for protein contact prediction, showing improved interpretability and predictive performance on CASP13 targets.

DeepDist predicts real-value inter-residue distances using a deep residual network, improving contact prediction and tertiary structure reconstruction compared to multi-class methods.

DNSS2 improves protein secondary structure prediction using advanced deep learning architectures, achieving high accuracy on benchmark datasets.