D-QUARK ('Distance-assisted QUARK') is a method for ab initio (or template-free modeling, FM) protein structure prediction. D-QUARK is an extension of QUARK with the fragment assembly simulations guided by deep-learning based distance- and orientation-map predictions. Starting from a query sequence, D-QUARK first constructs a muliple sequence alignment (MSA) by searching through whole-genome and metagenome sequence databases using DeepMSA2. Next, inter-residue distance-map and dihedral-angle orientation are predicted by DeepPotential through deep residual convolutional nueral network training, where local fragment structures are created from the distance and orientation models by Limited-memory Broyden–Fletcher–Goldfarb–Shanno (L-BFGS) optimizations. Finally, full-length structural models are assembled by the replica-exchange fragment-assembly Monte Carlo simulations which are guided by the deep-learning distance and orientation restraints. D-QUARK participated in community-wide CASP14 challenge as the "QUARK" group, which was ranked as the top automated server for FM targets. (Read more about D-QUARK)

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Online server (view example output)

Cut and paste your sequence (in FASTA format) below: Example input

Or upload the sequence from your local computer:

Email: (mandatory, where results will be sent to)

ID: (optional, your given name of the protein)

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Dataset:

• Download training and testing datasets of D-QUARK

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Reference:

• Chengxin Zhang, Yang Li, Yang Zhang (2021) D-QUARK: ab initio protein structure prediction guided by multiple deep learning predicted distance and orientation restraints