Improved Docking of Protein Models by a Combination of Alphafold2 and ClusPro

Ghani, Usman; Desta, Israel; Jindal, Akhil; Khan, Omeir; Jones, George; Kotelnikov, Sergey; Padhorny, Dzmitry; Vajda, Sándor; Kozakov, Dima

doi:10.1101/2021.09.07.459290

Cited by 62 publications

(75 citation statements)

References 19 publications

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“…We were able to obtain these results after only minor optimization of the default AF2 monomer structure prediction protocol for peptide docking (see Methods). Most importantly, we also modeled the interaction with separate chains, as has already been suggested for protein docking 33,34,37 . This implementation provided complementary results (see Supplementary Fig.…”

Section: Resultsmentioning

confidence: 99%

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Harnessing protein folding neural networks for peptide–protein docking

et al. 2022

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Highly accurate protein structure predictions by deep neural networks such as AlphaFold2 and RoseTTAFold have tremendous impact on structural biology and beyond. Here, we show that, although these deep learning approaches have originally been developed for the in silico folding of protein monomers, AlphaFold2 also enables quick and accurate modeling of peptide–protein interactions. Our simple implementation of AlphaFold2 generates peptide–protein complex models without requiring multiple sequence alignment information for the peptide partner, and can handle binding-induced conformational changes of the receptor. We explore what AlphaFold2 has memorized and learned, and describe specific examples that highlight differences compared to state-of-the-art peptide docking protocol PIPER-FlexPepDock. These results show that AlphaFold2 holds great promise for providing structural insight into a wide range of peptide–protein complexes, serving as a starting point for the detailed characterization and manipulation of these interactions.

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Section: Resultsmentioning

confidence: 99%

“…Best performance is obtained by combining our linker-based strategy with modeling of peptide-protein complexes by presenting two separate chains to AF2. The latter has been implemented for the modeling of homoand hetero-multimers in several recent studies on AF2 36,37 .…”

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confidence: 99%

Harnessing protein folding neural networks for peptide–protein docking

et al. 2022

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“…Similarly, a study across 11 different proteomes found that AlphaFold added structure determination for on average 25 percentage points of additional residues over existing experimental structures or those that could be derived by homology modelling 56 . Interestingly, despite being trained on single proteins, AlphaFold proved capable of modelling the structures of protein complexes 56 – 58 . Most recently, AlphaFold-Multimer has been released, featuring a model trained on multimeric protein structures, which clearly outperforms the standard AlphaFold for modelling protein complex structures 59 .…”

Section: Coevolution and Deep Learning Approachesmentioning

confidence: 99%

From systems to structure — using genetic data to model protein structures

et al. 2022

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Understanding the effects of genetic variation is a fundamental problem in biology that requires methods to analyse both physical and functional consequences of sequence changes at systems-wide and mechanistic scales. To achieve a systems view, protein interaction networks map which proteins physically interact, while genetic interaction networks inform on the phenotypic consequences of perturbing these protein interactions. Until recently, understanding the molecular mechanisms that underlie these interactions often required biophysical methods to determine the structures of the proteins involved. The past decade has seen the emergence of new approaches based on coevolution, deep mutational scanning and genome-scale genetic or chemical–genetic interaction mapping that enable modelling of the structures of individual proteins or protein complexes. Here, we review the emerging use of large-scale genetic datasets and deep learning approaches to model protein structures and their interactions, and discuss the integration of structural data from different sources.

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“…ColabFold, offering both AlphaFold and Ro-seTTAFold to the non-expert user, used a fast multiple sequence alignment generation by MMseqs2 to predict homo-and heterocomplexes (Mirdita et al, 2021). One study showed an improved performance of PPI docking by combining AlphaFold and ClusPro (Ghani et al, 2021). Despite the unprecedented success of such approaches and the extent to which they have empowered research in various branches of the life sciences, they do occasionally produce erroneous models and therefore still need to be followed up by in-depth analysis and validation, until we establish reliable metrics for model quality.…”

Section: Machine Learning-based Predictionmentioning

confidence: 99%