Matching protein surface structural patches for high-resolution blind peptide docking

Khramushin, Alisa; Ben-Aharon, Ziv; Tsaban, Tomer; Varga, Julia K; Avraham, Orly; Schueler‐Furman, Ora

doi:10.1073/pnas.2121153119

Cited by 14 publications

(15 citation statements)

References 45 publications

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“…Moreover, the method can be extensively used to find substrate targets for molecular glue-mediated degradation systems. The increment of the experimental complex structures of PPI and motif–receptor protein reveals more and more motif–protein recognition patterns, which are often very useful information for applications in antibody design, , peptide–protein docking, and drug design. Hence, our method can be used for those purposes and discovery of new PPI pairs, extending to mining PPI interaction network, motif grafting-based protein design, and potential substrate identification for E3 ligase.…”

Section: Discussionmentioning

confidence: 99%

PPI-Miner: A Structure and Sequence Motif Co-Driven Protein–Protein Interaction Mining and Modeling Computational Method

Wang¹,

Li²,

Ma³

et al. 2022

J. Chem. Inf. Model.

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Protein−protein interactions (PPIs) play important roles in biological processes of life, and predicting PPIs becomes a critical scientific issue of concern. Most PPIs occur through small domains or motifs (fragments), which are challenging and laborious to map by standard biochemical approaches because they generally require the cloning of several truncation mutants. Here, we present a computational method, named as PPI-Miner, to fish potential protein interacting partners utilizing protein motifs as queries. In brief, this work first developed a motif-matching algorithm designed to identify the proteins that contain sequential or structural similar motifs with the given query motif. Being aligned to the query motif, the binding mode of the discovered motif and its receptor protein will be initially determined to be used to build PPI complexes accordingly. Eventually, a PPI complex structure could be built and optimized with a designed automatic protocol. Besides discovering PPIs, PPI-Miner can also be applied to other areas, i.e., the rational design of molecular glues and protein vaccines. In this work, PPI-Miner was employed to mine the potential cereblon (CRBN) substrates from human proteome. As a result, 1,739 candidates were predicted, and 16 of them have been experimentally validated in previous studies. The source code of PPI-Miner can be obtained from the GitHub repository (https://github.com/Wang-Lin-boop/PPI-Miner), the webserver is freely available for users (https://bailab.siais.shanghaitech.edu. cn/services/ppi-miner), and the database of predicted CRBN substrates is accessible at https://bailab.siais.shanghaitech.edu.cn/ services/crbn-subslib.

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Section: Discussionmentioning

confidence: 99%

PPI-Miner: A Structure and Sequence Motif Co-Driven Protein–Protein Interaction Mining and Modeling Computational Method

Wang¹,

Li²,

Ma³

et al. 2022

J. Chem. Inf. Model.

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“…For example, when we only consider sampling efficiency, MDockPeP has success rate of 95% when starting from bound conformations and 93% when https://doi.org/10.1017/qrd.2022.14 Published online by Cambridge University Press starting with challenging unbound structures (Yan et al, 2016). The recent method patchMAN can sample within 5Å RMSD from the native complex in 100% cases (Khramushin et al, 2022). This implies that currently, the limitation and overall successes of the docking tools can be attributed to the scoring stage majorly.…”

Section: Scoringmentioning

confidence: 99%

Modelling peptide–protein complexes: docking, simulations and machine learning

2022

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Peptides mediate up to 40% of protein interactions, their high specificity and ability to bind in places where small molecules cannot make them potential drug candidates. However, predicting peptide-protein complexes remains more challenging than protein-protein or protein-small molecule interactions, in part due to the high flexibility peptides have. In this review we look at the advances in docking, molecular simulations, and machine learning to tackle problems related to peptides such as predicting structures, binding affinities or even kinetics. We specifically focus on explaining the number of docking programs and force fields used in molecular simulations, so a prospective user can have an educated guess as to why choose one modeling tool or another to address their scientific questions.

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“…Both are needed to develop computational software that predicts high affinity peptide binders (Cunningham et al, 2020;Motmaen et al, 2022). Two advances will play key roles to increase database knowledge to feed machine learning databases: 1) curating protein-protein structural databases to identify peptide epitopes and their interaction patterns, as recently shown by PatchMAN (Khramushin et al, 2022) and others (Peterson et al, 2017;Aderinwale et al, 2020), 2) establishment of high throughput/high sensitivity techniques for determining binding affinities (Nguyen et al, 2019).…”

Section: Key Areas Of Synergy For Modeling Peptide Behaviormentioning

confidence: 99%

“…Surprisingly, a recent implementation of AlphaFold (AF) for peptide docking showed an unprecedented success-despite being trained for a different task (protein structure prediction) (Jumper et al, 2021;Ko and Lee, 2021;Tsaban et al, 2022). Combining search strategies and template-based strategies, PatchMAN has recently surpassed even the successes from AF under certain scenarios, leading the way into the structural characterization of previously unknown peptide-protein interactions (Khramushin et al, 2022).…”

Section: Introductionmentioning

confidence: 99%

Towards rational computational peptide design

2022

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Peptides are prevalent in biology, mediating as many as 40% of protein-protein interactions, and involved in other cellular functions such as transport and signaling. Their ability to bind with high specificity make them promising therapeutical agents with intermediate properties between small molecules and large biologics. Beyond their biological role, peptides can be programmed to self-assembly, and they are already being used for functions as diverse as oligonuclotide delivery, tissue regeneration or as drugs. However, the transient nature of their interactions has limited the number of structures and knowledge of binding affinities available–and their flexible nature has limited the success of computational pipelines that predict the structures and affinities of these molecules. Fortunately, recent advances in experimental and computational pipelines are creating new opportunities for this field. We are starting to see promising predictions of complex structures, thermodynamic and kinetic properties. We believe in the following years this will lead to robust rational peptide design pipelines with success similar to those applied for small molecule drug discovery.

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Matching protein surface structural patches for high-resolution blind peptide docking

Cited by 14 publications

References 45 publications

PPI-Miner: A Structure and Sequence Motif Co-Driven Protein–Protein Interaction Mining and Modeling Computational Method

PPI-Miner: A Structure and Sequence Motif Co-Driven Protein–Protein Interaction Mining and Modeling Computational Method

Modelling peptide–protein complexes: docking, simulations and machine learning

Towards rational computational peptide design

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