ConEVA: a toolbox for comprehensive assessment of protein contacts

Adhikari, Badri; Nowotny, Jackson; Bhattacharya, Debswapna; Hou, Jie; Cheng, Jianlin

doi:10.1186/s12859-016-1404-z

Cited by 25 publications

(30 citation statements)

References 42 publications

(54 reference statements)

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“…After the domain models were assembled, the full-length structural model was evaluated by the predicted contacts using ConEva. 68 The contacts in the model matched well with the contacts predicted by DNCON2 domain by domain, confirming that both domain parsing and structure modeling was largely correct ( Figure 10). This contact-based validation approach was applied to all CASP13 targets during CASP13, providing a complementary validation for structure modeling.…”

Section: What Went Right?supporting

confidence: 62%

“…Each domain region was modeled through MULTICOM domain‐based modeling pipeline. After the domain models were assembled, the full‐length structural model was evaluated by the predicted contacts using ConEva . The contacts in the model matched well with the contacts predicted by DNCON2 domain by domain, confirming that both domain parsing and structure modeling was largely correct (Figure ).…”

Section: Resultsmentioning

confidence: 60%

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Protein tertiary structure modeling driven by deep learning and contact distance prediction in CASP13

et al. 2019

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Predicting residue-residue distance relationships (e.g. contacts) has become the key direction to advance protein structure prediction since 2014 CASP11 experiment, while deep learning has revolutionized the technology for contact and distance distribution prediction since its debut in 2012 CASP10 experiment. During 2018 CASP13 experiment, we enhanced our MULTICOM protein structure prediction system with three major components: contact distance prediction based on deep convolutional neural networks, distance-driven template-free (ab initio) modeling, and protein model ranking empowered by deep learning and contact prediction. Our experiment demonstrates that contact distance prediction and deep learning methods are the key reasons that MULTICOM was ranked 3rd out of all 98 predictors in both template-free and template-based structure modeling in CASP13. Deep convolutional neural network can utilize global information in pairwise residue-residue features such as co-evolution scores to substantially improve contact distance prediction, which played a decisive role in correctly folding some free modeling and hard template-based modeling targets. Deep learning also successfully integrated 1D structural features, 2D contact information, and 3D structural quality scores to improve protein model quality assessment, where the contact prediction was demonstrated to consistently enhance ranking of protein models for the first time. The success of MULTICOM system clearly shows that protein contact distance prediction and model selection driven by deep learning holds the key of solving protein structure prediction problem. However, there are still challenges in accurately predicting protein contact distance when there are few homologous sequences, folding proteins from noisy contact distances, and ranking models of hard targets.

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Section: What Went Right?supporting

confidence: 62%

Section: Resultsmentioning

confidence: 60%

Protein tertiary structure modeling driven by deep learning and contact distance prediction in CASP13

et al. 2019

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“…On the same dataset, when we selected top five 72 models using contact satisfaction score of top L/5 or L/2 long-range contacts, we 73 achieved best-of-top-five TM-score of 0.50. The rationale for using top L/5 or L/2 74 contacts (instead of L or more) is that these subsets are found to best reflect the 75 accuracy of the predicted contacts [12]. In contrast, when we filter out the bottom 150 76 models, cluster the remaining 50 into five clusters, and select the cluster centroids, we 77 obtain best-of-top-five TM-score of 0.52, suggesting that the clustering approach is 78 effective in selecting models built from contacts.…”

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confidence: 99%

CONFOLD2: Improved contact-driven ab initio protein structure modeling

Adhikari

Cheng

2017

Preprint

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Background: Contact-guided protein structure prediction methods are becoming more and more successful because of the latest advances in residue-residue contact prediction. To support contact-driven structure prediction, effective tools that can quickly build tertiary structural models of good quality from predicted contacts need to be developed. Results:We develop an improved contact-driven protein modelling method, CONFOLD2, and study how it may be effectively used for ab initio protein structure prediction with predicted contacts as input. It builds models using various subsets of input contacts to explore the fold space under the guidance of a soft square energy function, and then clusters the models to obtain the top five models. CONFOLD2 obtains an average reconstruction accuracy of 0.57 TM-score for the 150 proteins in the PSICOV contact prediction dataset. When benchmarked on the CASP11 contacts predicted using CONSIP2 and CASP12 contacts predicted using Raptor-X, CONFOLD2 achieves a mean TM-score of 0.41 on both datasets. Conclusion: CONFOLD2 allows to quickly generate top five structural models for a protein sequence when its secondary structures and contacts predictions at hand. The source code of CONFOLD2 is publicly available at https:// github.com/multicom-toolbox/CONFOLD2/.

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“…Each domain region was modeled through MULTICOM domain-based modeling pipeline. After the domain models were assembled, the full-length structural model was evaluated by the predicted contacts using ConEva 61 . The contacts in the model matched well with the contacts predicted by DNCON2 domain by domain, confirming that both domain parsing and structure modeling was largely correct (Figure 10).…”

Section: What Went Right?mentioning

confidence: 99%

Protein tertiary structure modeling driven by deep learning and contact distance prediction in CASP13

Hou

Cao

et al. 2019

Preprint

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Prediction of residue-residue distance relationships (e.g. contacts) has become the key direction to advance protein tertiary structure prediction since 2014 CASP11 experiment, while deep learning has revolutionized the technology for contact and distance distribution prediction since its debut in 2012 CASP10 experiment. During 2018 CASP13 experiment, we enhanced our MULTICOM protein structure prediction system with three major components: contact distance prediction based on deep convolutional neural networks, contact distance-driven template-free (ab initio) modeling, and protein model ranking empowered by deep learning and contact prediction, in addition to an update of other components such as template library, sequence database, and alignment tools. Our experiment demonstrates that contact distance prediction and deep learning methods are the key reasons that MULTICOM was ranked 3rd out of all 98 predictors in both template-free and template-based protein structure modeling in CASP13. Deep convolutional neural network can utilize global information in pairwise residue-residue features such as co-evolution scores to substantially improve inter-residue contact distance prediction, which played a decisive role in correctly folding some free modeling and hard template-based modeling targets from scratch. Deep learning also successfully integrated 1D structural features, 2D contact information, and 3D structural quality scores to improve protein model quality assessment, where the contact prediction was demonstrated to consistently enhance ranking of protein models for the first time. The success of MULTICOM system in the CASP13 experiment clearly shows that protein contact distance prediction and model selection driven by powerful deep learning holds the key of solving protein structure prediction problem. However, there are still major challenges in accurately predicting protein contact distance when there are few homologous sequences to generate co-evolutionary signals, folding proteins from noisy contact distances, and ranking models of hard targets.

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ConEVA: a toolbox for comprehensive assessment of protein contacts

Cited by 25 publications

References 42 publications

Protein tertiary structure modeling driven by deep learning and contact distance prediction in CASP13

Protein tertiary structure modeling driven by deep learning and contact distance prediction in CASP13

CONFOLD2: Improved contact-driven ab initio protein structure modeling

Protein tertiary structure modeling driven by deep learning and contact distance prediction in CASP13

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