Recent Advances in NMR Protein Structure Prediction with ROSETTA

Leman, Julia Koehler; Künze, Georg

doi:10.3390/ijms24097835

Cited by 14 publications

(12 citation statements)

References 190 publications

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Section: Protein Structure Determination Using In-cell Nmrmentioning

confidence: 99%

Progress, Challenges and Opportunities of NMR and XL-MS for Cellular Structural Biology

Zhang,

Zhao,

Gong

et al. 2024

JACS Au

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The validity of protein structures and interactions, whether determined under ideal laboratory conditions or predicted by AI tools such as Alphafold2, to precisely reflect those found in living cells remains to be examined. Moreover, understanding the changes in protein structures and interactions in response to stimuli within living cells, under both normal and disease conditions, is key to grasping proteins' functionality and cellular processes. Nevertheless, achieving high-resolution identification of these protein structures and interactions within living cells presents a technical challenge. In this Perspective, we summarize the recent advancements in in-cell nuclear magnetic resonance (NMR) and in vivo cross-linking mass spectrometry (XL-MS) for studying protein structures and interactions within a cellular context. Additionally, we discuss the challenges, opportunities, and potential benefits of integrating in-cell NMR and in vivo XL-MS in future research to offer an exhaustive approach to studying proteins in their natural habitat.

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Section: Protein Structure Determination Using In-cell Nmrmentioning

confidence: 99%

Progress, Challenges and Opportunities of NMR and XL-MS for Cellular Structural Biology

Zhang,

Zhao,

Gong

et al. 2024

JACS Au

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show abstract

“…Various strategies, including coevolution integration and the use of available experimental data, have been implemented to enhance docking accuracy. [6][7][8][9][10][11][12] For intricate cases involving intertwined complexes, fold-and-dock strategies are employed. 13,14 The CAPRI blind docking competition has been evaluating the state-of-the-art in assembly modeling since 2002.…”

Section: Introductionmentioning

confidence: 99%

The impact of AI‐based modeling on the accuracy of protein assembly prediction: Insights from CASP15

Ozden,

Kryshtafovych,

Karaca

2023

Proteins

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In CASP15, 87 predictors submitted around 11 000 models on 41 assembly targets. The community demonstrated exceptional performance in overall fold and interface contact predictions, achieving an impressive success rate of 90% (compared to 31% in CASP14). This remarkable accomplishment is largely due to the incorporation of DeepMind's AF2‐Multimer approach into custom‐built prediction pipelines. To evaluate the added value of participating methods, we compared the community models to the baseline AF2‐Multimer predictor. In over 1/3 of cases, the community models were superior to the baseline predictor. The main reasons for this improved performance were the use of custom‐built multiple sequence alignments, optimized AF2‐Multimer sampling, and the manual assembly of AF2‐Multimer‐built subcomplexes. The best three groups, in order, are Zheng, Venclovas, and Wallner. Zheng and Venclovas reached a 73.2% success rate over all (41) cases, while Wallner attained 69.4% success rate over 36 cases. Nonetheless, challenges remain in predicting structures with weak evolutionary signals, such as nanobody–antigen, antibody–antigen, and viral complexes. Expectedly, modeling large complexes also remains challenging due to their high memory compute demands. In addition to the assembly category, we assessed the accuracy of modeling interdomain interfaces in the tertiary structure prediction targets. Models on seven targets featuring 17 unique interfaces were analyzed. Best predictors achieved a 76.5% success rate, with the UM‐TBM group being the leader. In the interdomain category, we observed that the predictors faced challenges, as in the case of the assembly category, when the evolutionary signal for a given domain pair was weak or the structure was large. Overall, CASP15 witnessed unprecedented improvement in interface modeling, reflecting the AI revolution seen in CASP14.

show abstract

“…Various strategies, including coevolution integration and the use of available experimental data, have been implemented to enhance docking accuracy [6][7][8][9][10][11][12] . For intricate cases involving intertwined complexes, fold-and-dock strategies are employed 13,14 .…”

Section: Introductionmentioning

confidence: 99%

The Impact of AI-Based Modeling on the Accuracy of Protein Assembly Prediction: Insights from CASP15

Ozden

Kryshtafovych

Karaca

2023

Preprint

View full text Add to dashboard Cite

In CASP15, 87 predictors submitted around 11,000 models on 41 assembly targets. The community demonstrated exceptional performance in overall fold and interface contact prediction, achieving an impressive success rate of 90% (compared to 31% in CASP14). This remarkable accomplishment is largely due to the incorporation of DeepMind's AF2-Multimer approach into custom-built prediction pipelines. To evaluate the added value of participating methods, we compared the community models to the baseline AF2-Multimer predictor. In over 1/3 of cases the community models were superior to the baseline predictor. The main reasons for this improved performance were the use of custom-built multiple sequence alignments, optimized AF2-Multimer sampling, and the manual assembly of AF2-Multimer-built subcomplexes. The best three groups, in order, are Zheng, Venclovas and Wallner. Zheng and Venclovas reached a 73.2% success rate over all (41) cases, while Wallner attained 69.4% success rate over 36 cases. Nonetheless, challenges remain in predicting structures with weak evolutionary signals, such as nanobody-target, antibody-antigen, and viral complexes. Expectedly, modeling large complexes remains also challenging due to their high memory compute demands. In addition to the assembly category, we assessed the accuracy of modeling interdomain interfaces in the tertiary structure prediction targets. Models on seven targets featuring 17 unique interfaces were analyzed. Best predictors achieved the 76.5% success rate, with the UM-TBM group being the leader. In the interdomain category, we observed that the predictors faced challenges, as in the case of the assembly category, when the evolutionary signal for a given domain pair was weak or the structure was large. Overall, CASP15 witnessed unprecedented improvement in interface modeling, reflecting the AI revolution seen in CASP14.

show abstract

Recent Advances in NMR Protein Structure Prediction with ROSETTA

Cited by 14 publications

References 190 publications

Progress, Challenges and Opportunities of NMR and XL-MS for Cellular Structural Biology

Progress, Challenges and Opportunities of NMR and XL-MS for Cellular Structural Biology

The impact of AI‐based modeling on the accuracy of protein assembly prediction: Insights from CASP15

The Impact of AI-Based Modeling on the Accuracy of Protein Assembly Prediction: Insights from CASP15

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