NMR‐assisted protein structure prediction with MELDxMD

Robertson, J.C.; Nassar, Roy; Liu, Cong; Brini, Emiliano; Dill, Ken A.; Pérez, Alberto

doi:10.1002/prot.25788

Cited by 20 publications

(20 citation statements)

References 33 publications

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“…These results confirm the expectation that, generally speaking, inclusion of sparse NMR data improves the accuracy of predictions. Detailed descriptions of the methods used by Laufer and Meilerlab are presented in their own papers on NMR‐assisted prediction in CASP13.…”

Section: Discussionmentioning

confidence: 99%

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: Discussionmentioning

confidence: 99%

Protein structure prediction assisted with sparse NMR data in CASP13

et al. 2019

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“…Laufer used molecular dynamics with a filtering technique to remove nonconsistent restraints. 39 We hope the encouraging results will lead to larger scale participation in this category in CASP14.…”

Section: Nmrmentioning

confidence: 96%

“…Of the nine groups submitting NMR assisted structures, two (Laufer and Meiler) had markedly better results than the controls. Laufer used molecular dynamics with a filtering technique to remove nonconsistent restraints . We hope the encouraging results will lead to larger scale participation in this category in CASP14.…”

Section: Data Assisted Modelingmentioning

confidence: 99%

Critical assessment of methods of protein structure prediction (CASP)—Round XIII

et al. 2019

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CASP (critical assessment of structure prediction) assesses the state of the art in modeling protein structure from amino acid sequence. The most recent experiment (CASP13 held in 2018) saw dramatic progress in structure modeling without use of structural templates (historically “ab initio” modeling). Progress was driven by the successful application of deep learning techniques to predict inter‐residue distances. In turn, these results drove dramatic improvements in three‐dimensional structure accuracy: With the proviso that there are an adequate number of sequences known for the protein family, the new methods essentially solve the long‐standing problem of predicting the fold topology of monomeric proteins. Further, the number of sequences required in the alignment has fallen substantially. There is also substantial improvement in the accuracy of template‐based models. Other areas—model refinement, accuracy estimation, and the structure of protein assemblies—have again yielded interesting results. CASP13 placed increased emphasis on the use of sparse data together with modeling and chemical crosslinking, SAXS, and NMR all yielded more mature results. This paper summarizes the key outcomes of CASP13. The special issue of PROTEINS contains papers describing the CASP13 assessments in each modeling category and contributions from the participants.

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“…To allow MD to overcome the high energy barrier between different zones of the phase space that the smart springs create, a hamiltonian and temperature replica exchange protocol is used. Previously, MELD has been successfully applied to the problems of protein folding [27][28][29] , peptide docking 30,31 , and protein dimer structure predictions 32 . More details are provided in the Supplementary Materials.…”

Section: Modeling Round 38 Target T121 Using Refinement With Modeling Employing Limited Data (Meld) Molecular Dynamics Simulationsmentioning

confidence: 99%

Modeling beta‐sheet peptide‐protein interactions: Rosetta FlexPepDock in CAPRI rounds 38‐45

et al. 2020

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Peptide-protein docking is challenging due to the considerable conformational freedom of the peptide. CAPRI Rounds 38-45 included two peptide-protein interactions, both characterized by a peptide forming an additional beta strand of a beta sheet in the receptor. Using the Rosetta FlexPepDock peptide docking protocol we generated top-performing, high-accuracy models for Targets 134 and 135, involving an interaction between a peptide derived from L-MAG with DLC8. In addition, we were able to generate the only medium-accuracy model for a particularly challenging target, T121.

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NMR‐assisted protein structure prediction with MELDxMD

Cited by 20 publications

References 33 publications

Protein structure prediction assisted with sparse NMR data in CASP13

Protein structure prediction assisted with sparse NMR data in CASP13

Critical assessment of methods of protein structure prediction (CASP)—Round XIII

Modeling beta‐sheet peptide‐protein interactions: Rosetta FlexPepDock in CAPRI rounds 38‐45

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