InterPep2: Global Peptide-Protein Docking with Structural Templates

Johansson-Åkhe, Isak; Mirabello, Claudio; Wallner, Björn

doi:10.1101/813238

Cited by 5 publications

(1 citation statement)

References 39 publications

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“…In recent years, computational methods have been extensively used in medicinal chemistry, enabling virtual screening and drug design. [18][19][20][21][22] A key issue for computational chemistry is how to validate the accuracy and quality of different computational methods. To this end, the benchmarking of computational methods against experimental results is of high importance.…”

Section: Introductionmentioning

confidence: 99%

Experimental and Computational Models for Side Chain Discrimination in Peptide–Protein Interactions

Lidskog

Dawaigher

Arribas

et al. 2021

Chemistry A European J

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A bis(18-crown-6) Tröger's base receptor and 4substituted hepta-1,7-diyl bisammonium salt ligands have been used as a model system to study the interactions between non-polar side chains of peptides and an aromatic cavity of a protein. NMR titrations and NOESY/ROESY NMR spectroscopy were used to analyze the discrimination of the ligands by the receptor based on the substituent of the ligand, both quantitatively (free binding energies) and qualitatively (conformations). The analysis showed that an allanti conformation of the heptane chain was preferred for most of the ligands, both free and when bound to the receptor, and that for all of the receptor-ligand complexes, the substituent was located inside or partly inside of the aromatic cavity of the receptor. We estimated the free binding energy of a methyl-and a phenyl group to an aromatic cavity, via CH-π, and combined aromatic CH-π and π-π interactions to be À 1.7 and À 3.3 kJ mol À 1 , respectively. The experimental results were used to assess the accuracy of different computational methods, including molecular mechanics (MM) and density functional theory (DFT) methods, showing that MM was superior. Results and Discussion Description of the model systemReceptor 1 consists of a Tröger's base motif with 18-crown-6 moieties fused to each end of the aromatic cavity, and is synthesized in one step by the condensation of commercially [a] A

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Section: Introductionmentioning

confidence: 99%

Experimental and Computational Models for Side Chain Discrimination in Peptide–Protein Interactions

Lidskog

Dawaigher

Arribas

et al. 2021

Chemistry A European J

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Multiple direct interactions of TBP with the MYC oncoprotein

Wei

Resetca

et al. 2019

Nat Struct Mol Biol

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InterPepRank: Assessment of Docked Peptide Conformations by a Deep Graph Network

Johansson-Åkhe

Mirabello

Wallner

2020

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MotivationPeptide-protein interactions between a smaller or disordered peptide stretch and a folded receptor make up a large part of all protein-protein interactions. A common approach for modeling such interactions is to exhaustively sample the conformational space by fast-fourier transform based docking, and then refining a select top percentage of decoys. Commonly, the methods capable of ranking the decoys for selection in short enough time for larger scale studies rely only on first-principle energy terms derived directly from structure such as electrostatics, van der Waals forces, or on precalculated statistical pairwise potentials.ResultsIn this work, we present InterPepRank for peptide-protein complex scoring and ranking. InterPepRank is a machine-learning based method which encodes the structure of the complex as a graph; with physical pairwise interactions as edges and evolutionary features such as PSSM and sequence conservation as nodes. The graph representation is trained to predict the LRMSD of decoys by using edge-conditioned graph convolutions on a large set of peptide-protein complexes, and performance is maximised by an ensembled prediction of variying network architectures. InterPepRank is tested on an independent test set with no targets sharing CATH family nor 30% sequence identity with any target in training or validation data. On this set, InterPepRank has a mean AUC of 0.70 for finding peptide-protein complexes with LRMSD<4Å. This is an improvement compared to other state-of-the-art ranking methods that have a mean AUC in the 0.59-0.66 range. When included as the selection-method for selecting decoys for refinement in a previously established peptide docking pipeline, InterPepRank improves the number of Medium and High quality models produced by 80 and 40% respectively, proving its usefulness in decoy selection.AvailabilityThe program is available from: http://wallnerlab.org/InterPepRankContactBjörn Wallner bjorn.wallner@liu.seSupplementary informationSupplementary data are available at BioRxiv online.

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InterPep2: Global Peptide-Protein Docking with Structural Templates

Cited by 5 publications

References 39 publications

Experimental and Computational Models for Side Chain Discrimination in Peptide–Protein Interactions

Experimental and Computational Models for Side Chain Discrimination in Peptide–Protein Interactions

Multiple direct interactions of TBP with the MYC oncoprotein

InterPepRank: Assessment of Docked Peptide Conformations by a Deep Graph Network

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