Physics‐based protein structure refinement in the era of artificial intelligence

Heo, Lim; Janson, Giacomo; Feig, Michael

doi:10.1002/prot.26161

Cited by 20 publications

(25 citation statements)

References 75 publications

(173 reference statements)

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“…Some consideration of potential future changes can be found elsewhere in this issue. 30,31 In conclusion, we have shown that the reLLG is a useful addition…”

Section: Relevance Of Refinement Category In Caspmentioning

confidence: 63%

“…Although the best refinement groups were consistently able to improve the server‐generated refinement targets, most refinement methods degrade the AlphaFold2 models, as seen here for MR as well as for other CASP assessment measures. 30 This is in spite of the lack, in the AlphaFold2 algorithm, 10 of the explicit physics‐based knowledge employed by the most successful refinement groups (e.g., Heo et al 31 ). Figure 12 shows that, with one marginal exception (a slight improvement on an AlphaFold2 starting model), the AlphaFold2 model would have scored equal or higher on the reLLG score compared to the best refined model, even including the double‐barrelled targets starting from AlphaFold2 models.…”

Section: Discussionmentioning

confidence: 99%

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Assessing the utility of CASP14 models for molecular replacement

et al. 2021

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The assessment of CASP models for utility in molecular replacement is a measure of their use in a valuable real-world application. In CASP7, the metric for molecular replacement assessment involved full likelihood-based molecular replacement searches; however, this restricted the assessable targets to crystal structures with only one copy of the target in the asymmetric unit, and to those where the search found the correct pose. In CASP10, full molecular replacement searches were replaced by likelihood-based rigid-body refinement of models superimposed on the target using the LGA algorithm, with the metric being the refined log-likelihood-gain (LLG) score. This enabled multi-copy targets and very poor models to be evaluated, but a significant further issue remained: the requirement of diffraction data for assessment. We introduce here the relative-expected-LLG (reLLG), which is independent of diffraction data. This reLLG is also independent of any crystal form, and can be calculated regardless of the source of the target, be it X-ray, NMR or cryo-EM.We calibrate the reLLG against the LLG for targets in CASP14, showing that it is a robust measure of both model and group ranking. Like the LLG, the reLLG shows that accurate coordinate error estimates add substantial value to predicted models. We find that refinement by CASP groups can often convert an inadequate initial model into a successful MR search model. Consistent with findings from others, we show that the AlphaFold2 models are sufficiently good, and reliably so, to surpass other current model generation strategies for attempting molecular replacement phasing.

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“…Some consideration of potential future changes can be found elsewhere in this issue. 30,31 In conclusion, we have shown that the reLLG is a useful addition…”

Section: Relevance Of Refinement Category In Caspmentioning

confidence: 63%

Section: Discussionmentioning

confidence: 99%

Assessing the utility of CASP14 models for molecular replacement

et al. 2021

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“…S6). However, one should be careful with simulations using AI-based structural models, since their conformation may be kinetically trapped into a specific state, inhibiting the study of conformational changes [ 39 ].…”

Section: Resultsmentioning

confidence: 99%

Ins and outs of AlphaFold2 transmembrane protein structure predictions

Hegedüs

Geisler

Lukacs

et al. 2022

Cell. Mol. Life Sci.

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Transmembrane (TM) proteins are major drug targets, but their structure determination, a prerequisite for rational drug design, remains challenging. Recently, the DeepMind’s AlphaFold2 machine learning method greatly expanded the structural coverage of sequences with high accuracy. Since the employed algorithm did not take specific properties of TM proteins into account, the reliability of the generated TM structures should be assessed. Therefore, we quantitatively investigated the quality of structures at genome scales, at the level of ABC protein superfamily folds and for specific membrane proteins (e.g. dimer modeling and stability in molecular dynamics simulations). We tested template-free structure prediction with a challenging TM CASP14 target and several TM protein structures published after AlphaFold2 training. Our results suggest that AlphaFold2 performs well in the case of TM proteins and its neural network is not overfitted. We conclude that cautious applications of AlphaFold2 structural models will advance TM protein-associated studies at an unexpected level.

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“…First, we modeled the structures of 25 FM/TBM and TBM-hard targets of CASP14. The average TM-score and lDDT score of the models were compared with those of the following server groups: FEIG-S [ 36 ], BAKER-ROSETTASERVER [ 37 ], Zhang-Server, and QUARK [ 38 ]. The model structures of the other groups were downloaded from the archive of the CASP14 website, and TM-score and lDDT scores were recalculated with the crystal structures and domain information for a fair comparison.…”

Section: Resultsmentioning

confidence: 99%

A-Prot: protein structure modeling using MSA transformer

Hong¹,

Lee

Ko³

2022

BMC Bioinformatics

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Background The accuracy of protein 3D structure prediction has been dramatically improved with the help of advances in deep learning. In the recent CASP14, Deepmind demonstrated that their new version of AlphaFold (AF) produces highly accurate 3D models almost close to experimental structures. The success of AF shows that the multiple sequence alignment of a sequence contains rich evolutionary information, leading to accurate 3D models. Despite the success of AF, only the prediction code is open, and training a similar model requires a vast amount of computational resources. Thus, developing a lighter prediction model is still necessary. Results In this study, we propose a new protein 3D structure modeling method, A-Prot, using MSA Transformer, one of the state-of-the-art protein language models. An MSA feature tensor and row attention maps are extracted and converted into 2D residue-residue distance and dihedral angle predictions for a given MSA. We demonstrated that A-Prot predicts long-range contacts better than the existing methods. Additionally, we modeled the 3D structures of the free modeling and hard template-based modeling targets of CASP14. The assessment shows that the A-Prot models are more accurate than most top server groups of CASP14. Conclusion These results imply that A-Prot accurately captures the evolutionary and structural information of proteins with relatively low computational cost. Thus, A-Prot can provide a clue for the development of other protein property prediction methods.

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Physics‐based protein structure refinement in the era of artificial intelligence

Cited by 20 publications

References 75 publications

Assessing the utility of CASP14 models for molecular replacement

Assessing the utility of CASP14 models for molecular replacement

Ins and outs of AlphaFold2 transmembrane protein structure predictions

A-Prot: protein structure modeling using MSA transformer

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