Biology subjects

Johansson-Akhe, I.

Publications and source records attributed to Johansson-Akhe, I..

6 recordsLinked to original sources

bioRxiv2021

InterPepScore: A Deep Learning Score for Improving the FlexPepDock Refinement Protocol

MotivationInteractions between peptide fragments and protein receptors are vital to cell function yet difficult to experimentally determine the structural details of. As such, many computational methods have been developed to aid in peptide-protein docking or structure prediction. One such method is Rosetta FlexPepDock which consistently refines coarse peptide-protein models into sub-[A]ngstrom precision using Monte-Carlo simulations and statistical potentials. Deep learning has recently seen increased use in protein structure prediction, with graph neural network seeing use in protein model quality assessment. ResultsHere, we introduce a graph neural network, InterPepScore, as an additional scoring term to complement and improve the Rosetta FlexPepDock refinement protocol. InterPepScore is trained on simulation trajectories from FlexPepDock refinement starting from thousands of peptide-protein complexes generated by a wide variety of docking schemes. The addition of InterPepScore into the refinement protocol consistently improves the quality of models created, and on an independent benchmark on 109 peptide-protein complexes its inclusion results in an increase in the number of complexes for which the top-scoring model had a DockQ-score of 0.49 (Medium quality) or better from 14.8% to 26.1%. AvailabilityInterPepScore is available online at http://wallnerlab.org/InterPepScore.

bioinformatics↗

bioRxiv2021

The PNUTS-PAD domain recruits MYC to the PNUTS:PP1 phosphatase complex via the oncogenic MYC-MB0 region

Despite MYC dysregulation in most human cancers, strategies to target this potent oncogenic driver remains an urgent unmet need. Recent evidence shows the PP1 phosphatase and its regulatory subunit PNUTS control MYC phosphorylation and stability, however the molecular basis remains unclear. Here we demonstrate that MYC interacts directly with PNUTS through the MYC homology Box 0 (MB0), a highly conserved region recently shown to be important for MYC oncogenic activity. MB0 interacts with PNUTS residues 1-148, a functional unit here termed, PNUTS amino-terminal domain (PAD). Using NMR spectroscopy we determined the solution structure of PAD, and characterised its interaction with MYC. Point mutations of residues at the MYC-PNUTS interface significantly weaken their interaction both in vitro and in vivo. These data demonstrate the MB0 binding pocket of the PAD represents an attractive site for pharmacological disruption of the MYC-PNUTS interaction. In BriefSolving the structure of MYC-PNUTS direct interaction reveals how the intrinsically disordered MYC-Box0 (MB0) region anchors into a binding pocket in the N-terminal PAD domain of PNUTS. These data provide insight into the molecular mechanism of how the PNUTS:PP1 phosphatase complex regulates MYC phosphorylation. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=197 SRC="FIGDIR/small/470928v1_ufig1.gif" ALT="Figure 1"> View larger version (86K): org.highwire.dtl.DTLVardef@179b89corg.highwire.dtl.DTLVardef@47338eorg.highwire.dtl.DTLVardef@89b6f4org.highwire.dtl.DTLVardef@14a80bd_HPS_FORMAT_FIGEXP M_FIG C_FIG HighlightsO_LIA region critical for MYC oncogenesis, MYC-Box0 (MB0), directly interacts with PNUTS C_LIO_LIPNUTS amino-terminal domain (PAD) is a structural domain that interacts with MYC MB0 C_LIO_LIMutation of single residues at the interaction interface disrupts MYC-PNUTS binding in cells C_LIO_LIMYC-PNUTS binding releases MYC intramolecular interactions to enable PP1substrate access C_LI

cancer biology↗

bioRxiv2021

Benchmarking Peptide-Protein Docking and Interaction Prediction with AlphaFold-Multimer

Protein interactions are key in vital biological process. In many cases, particularly often in regulation, this interaction is between a protein and a shorter peptide fragment. Such peptides are often part of larger disordered regions of other proteins. The flexible nature of peptides enable rapid, yet specific, regulation of important functions in the cell, such as the cell life-cycle. Because of this, understanding the molecular details of these interactions are crucial to understand and alter their function, and many specialized computational methods have been developed to study them. The recent release of AlphaFold and AlphaFold-Multimer has caused a leap in accuracy for computational modeling of proteins. In this study, the ability of AlphaFold to predict which peptides and proteins interact as well as its accuracy in modeling the resulting interaction complexes are benchmarked against established methods in the fields of peptide-protein interaction prediction and modeling. We find that AlphaFold-Multimer consistently produces predicted interaction complexes with a median DockQ of 0.47 for all 112 complexes investigated. Additionally, it can be used to separate interacting from non-interacting pairs of peptides and proteins with ROC-AUC and PR-AUC of 0.78 and 0.61, respectively, best among the method benchmarked. However, the most interestingly result is the possibility to improve AlphaFold by enabling dropout at inference to sample a wider part of the conformational space. This improves the median DockQ from 0.47 to 0.56 for rank 1 and the median best DockQ improves from 0.58 to 0.72. This scheme of generating more structures with AlphaFold should be generally useful for many application involving multiple states, flexible regions and disorder.

bioinformatics↗

bioRxiv2020

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

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 modelling such interactions is to exhaustively sample the conformational space by fast-fourier-transform docking, and then refine a top percentage of decoys. Commonly, methods capable of ranking the decoys for selection in short enough time for larger scale studies rely on first-principle energy terms such as electrostatics, Van der Waals forces, or on pre-calculated statistical pairwise potentials. ResultsWe 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 and sequence features as nodes. The graph-network is trained to predict the LRMSD of decoys by using edge-conditioned graph convolutions on a large set of peptide-protein complex decoys. InterPepRank is tested on a massive independent test set with no targets sharing CATH annotation nor 30% sequence identity with any target in training or validation data. On this set, InterPepRank has a median AUC of 0.86 for finding coarse peptide-protein complexes with LRMSD<4[A]. This is an improvement compared to other state-of-the-art ranking methods that have a median AUC of circa 0.69. When included as 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. AvailabilityThe program is available from: http://wallnerlab.org/InterPepRank ContactBjorn Wallner bjorn.wallner@liu.se Supplementary informationSupplementary data are available at BioRxiv online.

bioinformatics↗

bioRxiv2019

InterPep2: Global Peptide-Protein Docking with Structural Templates

MotivationInteractions between proteins and peptides or peptide-like intrinsically disordered regions are involved in many important biological processes, such as gene expression and cell life-cycle regulation. Experimentally determining the structure of such interactions is time-consuming, and because of the disordered nature of the ligand, the interactions are especially difficult to predict through software, requiring specialized solutions. Although several prediction-methods exist, most are limited in performance or availability.\n\nResultsInterPep2 is a freely available method for predicting the structure of peptide-protein interactions. We have previously shown that structural templates can be used to accurately predict peptide-protein binding sites, and that using templates from regular protein-protein interactions will increase the number of sites found. Here, we show that the same principle can be extended to dock the peptide to the binding surface using InterPep2. A key component of InterPep2 is the ability to score plausible interaction templates using a RandomForest trained to predict the DockQ-score using sequence and structural features. InterPep2 is tested on a difficult dataset of 251 peptide-protein complexes, where it correctly positions 136 (54%) at the correct site compared to 114 (45%) for the second best method. Analyzing the confidence score InterPep2 recalls more true positives across all specificity levels compared to the second best method, for example at 10% False Positive Rate it correctly identifies 59% of the complexes compared to 44% for the second best method.\n\nAvailabilityThe program is available from: http://wallnerlab.org/InterPep\n\nContactBjorn Wallner bjorn.wallner@liu.se

bioinformatics↗

bioRxiv2018

Predicting protein-peptide interaction sites using distant protein complexes as structural templates

Protein-peptide interactions play an important role in major cellular processes, and are associated with several human diseases. To understand and potentially regulate these cellular function and diseases it is important to know the molecular details of the interactions. However, because of peptide flexibility and the transient nature of protein-peptide interactions, peptides are difficult to study experimentally. Thus, computational methods for predicting structural information about protein-peptide interactions are needed. Here we present InterPep, a pipeline for predicting protein-peptide interaction sites. It is a novel pipeline that, given a protein structure and a peptide sequence, utilizes structural template matches, sequence information, random forest machine learning, and hierarchical clustering to predict what region of the protein structure the peptide is most likely to bind. When tested on its ability to predict binding sites, InterPep successfully pinpointed 255 of 502 (50.7%) binding sites in experimentally determined structures at rank 1 and 348 of 502 (69.3%) among the top five predictions using only structures with no significant sequence similarity as templates. InterPep is a powerful tool for identifying peptide-binding sites; with a precision of 80% at a recall of 20% it should be an excellent starting point for docking protocols or experiments investigating peptide interactions. The source code for InterPred is available at http://wallnerlab.org/InterPep/

bioinformatics↗