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

Ozden, Burcu; Kryshtafovych, Andriy; Karaca, Ezgi

doi:10.1101/2023.07.10.548341

Cited by 5 publications

(1 citation statement)

References 90 publications

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“…Another early study showed that a combination of AF2 predictions with the ClusPro docking protocol improved the success rate over using AF2 alone and confirmed that the quality of the resulting model was much higher than the usual docking model quality . A specific version of AF2, called AlphaFold-Multimer, was retrained on protein complexes and displayed improved performances for interface modeling over AF2, reaching a 67% success rate. ,, The potential of AlphaFold-Multimer has recently been confirmed in the CASP15 experiment . In parallel, RF was developed for the prediction not only of protein structures but also of protein complexes .…”

Section: Predicting Oligomeric Modelsmentioning

confidence: 92%

A Perspective on the Prospective Use of AI in Protein Structure Prediction

Versini,

Sritharan,

Aykac Fas

et al. 2023

J. Chem. Inf. Model.

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AlphaFold2 (AF2) and RoseTTaFold (RF) have revolutionized structural biology, serving as highly reliable and effective methods for predicting protein structures. This article explores their impact and limitations, focusing on their integration into experimental pipelines and their application in diverse protein classes, including membrane proteins, intrinsically disordered proteins (IDPs), and oligomers. In experimental pipelines, AF2 models help X-ray crystallography in resolving the phase problem, while complementarity with mass spectrometry and NMR data enhances structure determination and protein flexibility prediction. Predicting the structure of membrane proteins remains challenging for both AF2 and RF due to difficulties in capturing conformational ensembles and interactions with the membrane. Improvements in incorporating membrane-specific features and predicting the structural effect of mutations are crucial. For intrinsically disordered proteins, AF2's confidence score (pLDDT) serves as a competitive disorder predictor, but integrative approaches including molecular dynamics (MD) simulations or hydrophobic cluster analyses are advocated for accurate dynamics representation. AF2 and RF show promising results for oligomeric models, outperforming traditional docking methods, with AlphaFold-Multimer showing improved performance. However, some caveats remain in particular for membrane proteins. Real-life examples demonstrate AF2's predictive capabilities in unknown protein structures, but models should be evaluated for their agreement with experimental data. Furthermore, AF2 models can be used complementarily with MD simulations. In this Perspective, we propose a "wish list" for improving deep-learning-based protein folding prediction models, including using experimental data as constraints and modifying models with binding partners or post-translational modifications. Additionally, a meta-tool for ranking and suggesting composite models is suggested, driving future advancements in this rapidly evolving field.

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Section: Predicting Oligomeric Modelsmentioning

confidence: 92%

A Perspective on the Prospective Use of AI in Protein Structure Prediction

Versini,

Sritharan,

Aykac Fas

et al. 2023

J. Chem. Inf. Model.

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Assessment of the assessment—All about complexes

Studer,

Tauriello,

Schwede

2023

Proteins

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Predicting model quality is a fundamental component of any modeling procedure, and blind assessment of these methods constitutes a crucial aspect of the Critical Assessment of Protein Structure Prediction (CASP) experiment. Historically, the main focus was on assessing methods that predict global and per‐residue accuracies in tertiary structure models. This focus shifted with the community's increased efforts in modeling complexes and assemblies. We asked the community to process the models from the CASP15 assembly category and provide estimates of the accuracy of the predicted quaternary structure, both globally and at the local interface level. Besides identifying remarkable accuracy of modeling groups in assessing their own predictions, we set up a benchmarking pipeline to highlight different aspects of quaternary structure models and introduced a simple consensus EMA method as baseline. While participating methods showed commendable performance, the baseline was difficult to surpass. It is important to point out that prediction performance varies for the individual CASP targets, highlighting potential areas of improvement and challenges ahead.

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

Kryshtafovych,

Schwede,

Topf

et al. 2023

Proteins

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Computing protein structure from amino acid sequence information has been a long‐standing grand challenge. Critical assessment of structure prediction (CASP) conducts community experiments aimed at advancing solutions to this and related problems. Experiments are conducted every 2 years. The 2020 experiment (CASP14) saw major progress, with the second generation of deep learning methods delivering accuracy comparable with experiment for many single proteins. There is an expectation that these methods will have much wider application in computational structural biology. Here we summarize results from the most recent experiment, CASP15, in 2022, with an emphasis on new deep learning‐driven progress. Other papers in this special issue of proteins provide more detailed analysis. For single protein structures, the AlphaFold2 deep learning method is still superior to other approaches, but there are two points of note. First, although AlphaFold2 was the core of all the most successful methods, there was a wide variety of implementation and combination with other methods. Second, using the standard AlphaFold2 protocol and default parameters only produces the highest quality result for about two thirds of the targets, and more extensive sampling is required for the others. The major advance in this CASP is the enormous increase in the accuracy of computed protein complexes, achieved by the use of deep learning methods, although overall these do not fully match the performance for single proteins. Here too, AlphaFold2 based method perform best, and again more extensive sampling than the defaults is often required. Also of note are the encouraging early results on the use of deep learning to compute ensembles of macromolecular structures. Critically for the usability of computed structures, for both single proteins and protein complexes, deep learning derived estimates of both local and global accuracy are of high quality, however the estimates in interface regions are slightly less reliable. CASP15 also included computation of RNA structures for the first time. Here, the classical approaches produced better agreement with experiment than the new deep learning ones, and accuracy is limited. Also, for the first time, CASP included the computation of protein–ligand complexes, an area of special interest for drug design. Here too, classical methods were still superior to deep learning ones. Many new approaches were discussed at the CASP conference, and it is clear methods will continue to advance.

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

Cited by 5 publications

References 90 publications

A Perspective on the Prospective Use of AI in Protein Structure Prediction

A Perspective on the Prospective Use of AI in Protein Structure Prediction

Assessment of the assessment—All about complexes

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

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