Deep template-based protein structure prediction

Wu, Fandi; Xu, Jinbo

doi:10.1371/journal.pcbi.1008954

Cited by 26 publications

(25 citation statements)

References 33 publications

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“…These two approaches are not self-excluding and can be combined: for example, prediction of protein structure from a template and subsequent refinement of the conformation using energy functions. Machine learning methods and high performance of modern computing resources encourage the successfully combination of these methods [97]. Both approaches can be used to predict the SSS.…”

Section: Methods For Predicting Protein Structurementioning

confidence: 99%

Current Approaches in Supersecondary Structures Investigation

Rudnev

Kulikova

Nikolsky

et al. 2021

IJMS

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Proteins expressed during the cell cycle determine cell function, topology, and responses to environmental influences. The development and improvement of experimental methods in the field of structural biology provide valuable information about the structure and functions of individual proteins. This work is devoted to the study of supersecondary structures of proteins and determination of their structural motifs, description of experimental methods for their detection, databases, and repositories for storage, as well as methods of molecular dynamics research. The interest in the study of supersecondary structures in proteins is due to their autonomous stability outside the protein globule, which makes it possible to study folding processes, conformational changes in protein isoforms, and aberrant proteins with high productivity.

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Section: Methods For Predicting Protein Structurementioning

confidence: 99%

Current Approaches in Supersecondary Structures Investigation

Rudnev

Kulikova

Nikolsky

et al. 2021

IJMS

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“…Lately, several deep learning models (e.g. ResNet) have been developed to substantially improve sequence-template alignment for remotely similar templates [ 74 , 103 , 117 ]. ResNet-predicted contact and distance have also been used to improve sequence-template alignment [ 74 , 103 , 117–119 ].…”

Section: Neoantigen Identificationmentioning

confidence: 99%

“…ResNet) have been developed to substantially improve sequence-template alignment for remotely similar templates [ 74 , 103 , 117 ]. ResNet-predicted contact and distance have also been used to improve sequence-template alignment [ 74 , 103 , 117–119 ]. With very similar templates, traditional methods such as HHblits [ 73 ] and CNFpred [ 120 ] may already perform well on sequence-template alignment and thus deep learning is not essential for this step.…”

Section: Neoantigen Identificationmentioning

confidence: 99%

“…With very similar templates, traditional methods such as HHblits [ 73 ] and CNFpred [ 120 ] may already perform well on sequence-template alignment and thus deep learning is not essential for this step. A few years ago TBM was usually the first choice for protein structure prediction, but now FM may outperform TBM for many proteins unless very similar templates are available [ 74 ]. As such, improving TBM of a protein may not lead to substantial improvement in its final structure prediction since FM may produce a better prediction.…”

Section: Neoantigen Identificationmentioning

confidence: 99%

“…However, when similar templates are available, integrating sequence-template alignment and template (backbone angle and distance matrix) information into deep networks may help improve 3D structure prediction, especially when the protein under prediction is large and does not have many sequence homologs. Such an idea was first implemented in the RaptorX-DeepModeller server in CASP13 [ 45 , 74 ] and then adopted by a few other groups in CASP14 such as AlphaFold2 and Baker’s group [ 121 ]. It was shown that with template information AlphaFold2 may improve by a good margin the structure prediction of some proteins with fewer than 100 sequence homologs [ 112 ].…”

Section: Neoantigen Identificationmentioning

confidence: 99%

See 2 more Smart Citations

A tale of solving two computational challenges in protein science: neoantigen prediction and protein structure prediction

Tran

2021

Briefings in Bioinformatics

Self Cite

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In this article, we review two challenging computational questions in protein science: neoantigen prediction and protein structure prediction. Both topics have seen significant leaps forward by deep learning within the past five years, which immediately unlocked new developments of drugs and immunotherapies. We show that deep learning models offer unique advantages, such as representation learning and multi-layer architecture, which make them an ideal choice to leverage a huge amount of protein sequence and structure data to address those two problems. We also discuss the impact and future possibilities enabled by those two applications, especially how the data-driven approach by deep learning shall accelerate the progress towards personalized biomedicine.

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Improved Protein Structure Prediction Using a New Multi‐Scale Network and Homologous Templates

Wang

et al. 2021

Advanced Science

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The accuracy of de novo protein structure prediction has been improved considerably in recent years, mostly due to the introduction of deep learning techniques. In this work, trRosettaX, an improved version of trRosetta for protein structure prediction is presented. The major improvement over trRosetta consists of two folds. The first is the application of a new multi‐scale network, i.e., Res2Net, for improved prediction of inter‐residue geometries, including distance and orientations. The second is an attention‐based module to exploit multiple homologous templates to increase the accuracy further. Compared with trRosetta, trRosettaX improves the contact precision by 6% and 8% on the free modeling targets of CASP13 and CASP14, respectively. A preliminary version of trRosettaX is ranked as one of the top server groups in CASP14's blind test. Additional benchmark test on 161 targets from CAMEO (between Jun and Sep 2020) shows that trRosettaX achieves an average TM‐score ≈0.8, outperforming the top groups in CAMEO. These data suggest the effectiveness of using the multi‐scale network and the benefit of incorporating homologous templates into the network. The trRosettaX algorithm is incorporated into the trRosetta server since Nov 2020. The web server, the training and inference codes are available at: https://yanglab.nankai.edu.cn/trRosetta/.

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Deep template-based protein structure prediction

Cited by 26 publications

References 33 publications

Current Approaches in Supersecondary Structures Investigation

Current Approaches in Supersecondary Structures Investigation

A tale of solving two computational challenges in protein science: neoantigen prediction and protein structure prediction

Improved Protein Structure Prediction Using a New Multi‐Scale Network and Homologous Templates

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