Contact‐assisted protein structure modeling by global optimization in CASP11

Joo, Keehyoung; Cheng, Qianyi; Lee, Sung Jong; Lee, Jooyoung

doi:10.1002/prot.24975

Cited by 12 publications

(14 citation statements)

References 40 publications

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“…Data set N1008 was designed to test the ability to combine backbone‐only assignments with advanced structure prediction methods. Several studies have previously explored the combination of sparse NMR data obtained on perdeuterated protein samples with advanced molecular modeling methods . For such data sets, the NOESY cross peak assignments are not complicated by the presence of unassigned sidechain resonances.…”

Section: Resultsmentioning

confidence: 99%

“…During the CASP10 International Meeting, it was suggested that rather than using predicted contacts, which at the time were not very reliable, it might be more productive to explore the impact of a few real experimental contacts, as can be obtained from NMR, cross‐linking, fluorescence energy transfer, or other experimental methods. This concept developed in CASP11 into the first NMR‐assisted contact prediction experiment . In the meantime, the accuracy of contact prediction based on evolutionary sequence covariance analysis and machine learning has increased dramatically, making the original proposal of replacing predicted contacts with real, sparse experimental contacts moot.…”

Section: Introductionmentioning

confidence: 99%

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Protein structure prediction assisted with sparse NMR data in CASP13

et al. 2019

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CASP13 has investigated the impact of sparse NMR data on the accuracy of protein structure prediction. NOESY and 15N‐1H residual dipolar coupling data, typical of that obtained for 15N,13C‐enriched, perdeuterated proteins up to about 40 kDa, were simulated for 11 CASP13 targets ranging in size from 80 to 326 residues. For several targets, two prediction groups generated models that are more accurate than those produced using baseline methods. Real NMR data collected for a de novo designed protein were also provided to predictors, including one data set in which only backbone resonance assignments were available. Some NMR‐assisted prediction groups also did very well with these data. CASP13 also assessed whether incorporation of sparse NMR data improves the accuracy of protein structure prediction relative to nonassisted regular methods. In most cases, incorporation of sparse, noisy NMR data results in models with higher accuracy. The best NMR‐assisted models were also compared with the best regular predictions of any CASP13 group for the same target. For six of 13 targets, the most accurate model provided by any NMR‐assisted prediction group was more accurate than the most accurate model provided by any regular prediction group; however, for the remaining seven targets, one or more regular prediction method provided a more accurate model than even the best NMR‐assisted model. These results suggest a novel approach for protein structure determination, in which advanced prediction methods are first used to generate structural models, and sparse NMR data is then used to validate and/or refine these models.

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Protein structure prediction assisted with sparse NMR data in CASP13

et al. 2019

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“…For the modeling of Tx targets, we introduced a flat‐bottom Lorentzian‐function‐based contact energy term,

E_{Tx}

as follows:

E_{Tx} = \sum_{i j}^{N} true{\begin{array}{l} left & w_{i j} \frac{{(r_{i j} - r_{upper})}^{2}}{{(r_{i j} - r_{upper})}^{2} + {normalσ}^{2}}, & r_{i j} > r_{upper} \\ left & 0, & otherwise \end{array}

where the summation is over all N given cross‐linking pairs between residue indices of i and j , r ij is the distance between two

C_{normalα}

atoms of residues i and j . The energy weight parameter w ij is set to 24.0 × confidence score which was given in the cross‐linking data where the 24.0 value was determined by analysis for CASP11 Tx targets.…”

Section: Methodsmentioning

confidence: 95%

Data‐assisted protein structure modeling by global optimization in CASP12

et al. 2018

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In CASP12, 2 types of data-assisted protein structure modeling were experimented. Either SAXS experimental data or cross-linking experimental data was provided for a selected number of CASP12 targets that the CASP12 predictor could utilize for better protein structure modeling. We devised 2 separate energy terms for SAXS data and cross-linking data to drive the model structures into more native-like structures that satisfied the given experimental data as much as possible. In CASP11, we successfully performed protein structure modeling using simulated sparse and ambiguously assigned NOE data and/or correct residue-residue contact information, where the only energy term that folded the protein into its native structure was the term which was originated from the given experimental data. However, the 2 types of experimental data provided in CASP12 were far from being sufficient enough to fold the target protein into its native structure because SAXS data provides only the overall shape of the molecule and the cross-linking contact information provides only very low-resolution distance information. For this reason, we combined the SAXS or cross-linking energy term with our regular modeling energy function that includes both the template energy term and the de novo energy terms. By optimizing the newly formulated energy function, we obtained protein models that fit better with provided SAXS data than the X-ray structure of the target. However, the improvement of the model relative to the 1 modeled without the SAXS data, was not significant. Consistent structural improvement was achieved by incorporating cross-linking data into the protein structure modeling.

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“…Since CASP7, we formulated protein 3D modeling in terms of 3 layers of combinatorial optimization problems; multiple sequence alignment, chain building using the alignment containing multiple templates, and side‐chain remodeling . When solving each combinatorial problem, due to the intrinsic nature of solving frustrated systems, obtaining a solution that satisfies all the restraints applied is seemingly impossible.…”

Section: Discussionmentioning

confidence: 99%

“…A flat‐bottom Lorentzian‐function‐based contact energy term was implemented as follows:

E_{c o n t a c t} = \sum_{i j}^{N} true{\begin{array}{l} left & \frac{{(r_{i j} - r_{cut})}^{2}}{{(r_{i j} - r_{cut})}^{2} + {normalσ}_{contact}^{2}} c true(p true), & r_{i j} \geq r_{cut} \\ left & 0, & otherwise \end{array},

c true(p true) = a p + 1,

where the summation is over all N MetaPSICOV predictions with p > 0.2, and i and j correspond to the residue indices of the predicted contact. Here, r ij is the distance between 2 C β atoms of residues i and j .…”

Section: Methodsmentioning

confidence: 99%

Protein structure modeling and refinement by global optimization in CASP12

et al. 2017

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For protein structure modeling in the CASP12 experiment, we have developed a new protocol based on our previous CASP11 approach. The global optimization method of conformational space annealing (CSA) was applied to 3 stages of modeling: multiple sequence-structure alignment, three-dimensional (3D) chain building, and side-chain re-modeling. For better template selection and model selection, we updated our model quality assessment (QA) method with the newly developed SVMQA (support vector machine for quality assessment). For 3D chain building, we updated our energy function by including restraints generated from predicted residue-residue contacts. New energy terms for the predicted secondary structure and predicted solvent accessible surface area were also introduced. For difficult targets, we proposed a new method, LEEab, where the template term played a less significant role than it did in LEE, complemented by increased contributions from other terms such as the predicted contact term. For TBM (template-based modeling) targets, LEE performed better than LEEab, but for FM targets, LEEab was better. For model refinement, we modified our CASP11 molecular dynamics (MD) based protocol by using explicit solvents and tuning down restraint weights. Refinement results from MD simulations that used a new augmented statistical energy term in the force field were quite promising. Finally, when using inaccurate information (such as the predicted contacts), it was important to use the Lorentzian function for which the maximal penalty arising from wrong information is always bounded.

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Contact‐assisted protein structure modeling by global optimization in CASP11

Cited by 12 publications

References 40 publications

Protein structure prediction assisted with sparse NMR data in CASP13

Protein structure prediction assisted with sparse NMR data in CASP13

Data‐assisted protein structure modeling by global optimization in CASP12

Protein structure modeling and refinement by global optimization in CASP12

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