Dockground resource for protein recognition studies

Collins, Keeley W; Copeland, Matthew M.; Kotthoff, Ian; Singh, Awatar; Kundrotas, Petras J.; Vakser, Ilya A.

doi:10.1002/pro.4481

Cited by 11 publications

(8 citation statements)

References 36 publications

(65 reference statements)

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“…Heterodimeric protein complexes were downloaded from the DockGround database [28]. Complexes that were downloaded were published in the PDB [46] between up to 01/26/23 with a maximum of 3 Å resolution.…”

Section: Methodsmentioning

confidence: 99%

“…To build relevant and diverse structures of active protein pairings ( Figure 1a ), we used the DockGround database [28] to assemble the complete set of heterodimeric protein complexes in the PDB excluding homodimers, antibody/antigen complexes, and complexes that were either very large or very small (see Methods ). Upon filtering for a maximum of 30% sequence identity, there remained 1,481 non-redundant protein-protein complexes with experimentally defined structures.…”

Section: Introductionmentioning

confidence: 99%

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PPIscreenML: Structure-based screening for protein-protein interactions using AlphaFold

Mischley,

Maier,

Chen

et al. 2024

Preprint

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Protein-protein interactions underlie nearly all cellular processes. With the advent of protein structure prediction methods such as AlphaFold2 (AF2), models of specific protein pairs can be built extremely accurately in most cases. However, determining the relevance of a given protein pair remains an open question. It is presently unclear how to use best structure-based tools to infer whether a pair of candidate proteins indeed interact with one another: ideally, one might even use such information to screen amongst candidate pairings to build up protein interaction networks. Whereas methods for evaluating quality of modeled protein complexes have been co-opted for determining which pairings interact (e.g., pDockQ and iPTM), there have been no rigorously benchmarked methods for this task. Here we introduce PPIscreenML, a classification model trained to distinguish AF2 models of interacting protein pairs from AF2 models of compelling decoy pairings. We find that PPIscreenML out-performs methods such as pDockQ and iPTM for this task, and further that PPIscreenML exhibits impressive performance when identifying which ligand/receptor pairings engage one another across the structurally conserved tumor necrosis factor superfamily (TNFSF). Analysis of benchmark results using complexes not seen in PPIscreenML development strongly suggest that the model generalizes beyond training data, making it broadly applicable for identifying new protein complexes based on structural models built with AF2.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

PPIscreenML: Structure-based screening for protein-protein interactions using AlphaFold

Mischley,

Maier,

Chen

et al. 2024

Preprint

View full text Add to dashboard Cite

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“…Dataset selection: A dataset of hetero-dimers X-ray structures with a resolution better than 3 Å and a maximum sequence redundancy of 30% was retrieved from the Dockground resource (https://dockground.compbio.ku.edu/bound/index.php) 26 . Separate lists were built for proteins acquired before or after the AlphaFold training date (May 2018).…”

Section: Methodsmentioning

confidence: 99%

AlphaFold-Multimer struggles in predicting PROTAC-mediated protein-protein interfaces

Pereira,

Gouzien,

Souza

et al. 2024

Preprint

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AlphaFold2 (AF2) made its debut in the CASP14 competition, generating structures which could rival experimentally determined ones and causing a paradigm shift in the structural biology community. From then onwards, further developments enabled the prediction of multimeric protein structures while improving calculation efficiency, leading to the widespread usage of AF2. However, previous work noted that AF2 does not consider ligands and thus suggesting that ligand-mediated protein-protein interfaces (PPIs) are challenging to predict. In this letter, we explore this hypothesis by evaluating AF-Multimers' accuracy on four datasets, composed of: (i) 31 large PPIs, (ii) 31 small PPIs, (iii) 31 PPIs mediated by ligands and (iv) 28 PROTAC-mediated PPIs. Our results show that AF-Multimer is able to accurately predict the structure of the majority of the protein-protein complexes within the first three datasets (DockQ: 0.7-0.8) but fails to do so for the PROTAC-mediated set (DockQ < 0.2). One explanation is that AF-Multimers' underlying energy function was trained on naturally occurring complexes and PROTACs mediate interactions between proteins which do not naturally interact with each other. As these artificial interfaces fall outside AFs' applicability domain, their prediction is challenging for AF-Multimer.

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“…For the prediction of protein-protein binding sites (PPBS), we obtained 41,466 distinct PDB files, involved in 240,506 protein-protein interfaces from the Dockground database [42]. Following Tubiana et al [16], we investigated the impact of homology between train and test set examples on generalization of our framework and baseline models and therefore grouped validation and test examples into four subgroups based on their degrees of homology:…”

Section: Datasets With Multi-scale Learningmentioning

confidence: 99%

A Variational Expectation-Maximization Framework for Balanced Multi-scale Learning of Protein and Drug Interactions

Yang,

Rao,

Xie

et al. 2024

Preprint

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Protein functions are characterized by interactions with proteins, drugs, and other biomolecules. Understanding these interactions is essential for deciphering the molecular mechanisms underlying biological processes and developing new therapeutic strategies. Current computational methods mostly predict interactions based on either molecular network or structural information, without integrating them within a unified multi-scale framework. While a few multi-view learning methods are devoted to fusing the multi-scale information, these methods tend to rely intensively on a single scale and under-fitting the others, likely attributed to the imbalanced nature and inherent greediness of multi-scale learning. To alleviate the optimization imbalance, we present MUSE, a multi-scale representation learning framework based on a variant expectation maximization to optimize different scales in an alternating procedure over multiple iterations. This strategy efficiently fuses multi-scale information between atomic structure and molecular network scale through mutual supervision and iterative optimization. MUSE outperforms the current state-of-the-art models not only in molecular interaction (protein-protein, drug-protein, and drug-drug) tasks but also in protein interface prediction at the atomic structure scale. More importantly, the multi-scale learning framework shows potential for extension to other scales of computational drug discovery.

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Dockground resource for protein recognition studies

Cited by 11 publications

References 36 publications

PPIscreenML: Structure-based screening for protein-protein interactions using AlphaFold

PPIscreenML: Structure-based screening for protein-protein interactions using AlphaFold

AlphaFold-Multimer struggles in predicting PROTAC-mediated protein-protein interfaces

A Variational Expectation-Maximization Framework for Balanced Multi-scale Learning of Protein and Drug Interactions

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