Occurrence of ordered and disordered structural elements in postsynaptic proteins supports optimization for interaction diversity

AbstractThe human postsynaptic density is an elaborate network comprising thousands of proteins, playing a vital role in the molecular events of learning and the formation of memory. Despite our growing knowledge of specific proteins and their interactions, atomic-level details of their full three-dimensional structure and their rearrangements are mostly elusive. Advancements in structural bioinformatics enabled us to depict the characteristic features of proteins involved in different processes aiding neurotransmission. We show that postsynaptic protein-protein interactions are mediated through the delicate balance of intrinsically disordered regions and folded domains, and this duality is also imprinted in the amino acid sequence. We introduce Diversity of Potential Interactions (DPI), a structure and regulation based descriptor to assess the diversity of interactions. Our approach reveals that the postsynaptic proteome has its own characteristic features and these properties reliably discriminate them from other proteins of the human proteome. Our results suggest that postsynaptic proteins are especially susceptible to forming diverse interactions with each other, which might be key in the reorganization of the PSD in molecular processes related to learning and memory.

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Occurrence of Ordered and Disordered Structural Elements in Postsynaptic Proteins Supports Optimization for Interaction Diversity

Entropy ◽

10.3390/e21080761 ◽

2019 ◽

Vol 21 (8) ◽

pp. 761

Author(s):

Annamária Kiss-Tóth ◽

Laszlo Dobson ◽

Bálint Péterfia ◽

Annamária F. Ángyán ◽

Balázs Ligeti ◽

...

Keyword(s):

Protein Interactions ◽

Three Dimensional ◽

Postsynaptic Density ◽

Vital Role ◽

Dimensional Structure ◽

Intrinsically Disordered ◽

Intrinsically Disordered Regions ◽

Postsynaptic Protein ◽

Molecular Events ◽

Characteristic Features

The human postsynaptic density is an elaborate network comprising thousands of proteins, playing a vital role in the molecular events of learning and the formation of memory. Despite our growing knowledge of specific proteins and their interactions, atomic-level details of their full three-dimensional structure and their rearrangements are mostly elusive. Advancements in structural bioinformatics enabled us to depict the characteristic features of proteins involved in different processes aiding neurotransmission. We show that postsynaptic protein-protein interactions are mediated through the delicate balance of intrinsically disordered regions and folded domains, and this duality is also imprinted in the amino acid sequence. We introduce Diversity of Potential Interactions (DPI), a structure and regulation based descriptor to assess the diversity of interactions. Our approach reveals that the postsynaptic proteome has its own characteristic features and these properties reliably discriminate them from other proteins of the human proteome. Our results suggest that postsynaptic proteins are especially susceptible to forming diverse interactions with each other, which might be key in the reorganization of the postsynaptic density (PSD) in molecular processes related to learning and memory.

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Peptide-based Interaction Proteomics

Molecular & Cellular Proteomics ◽

10.1074/mcp.r120.002034 ◽

2020 ◽

Vol 19 (7) ◽

pp. 1070-1075 ◽

Cited By ~ 3

Author(s):

Katrina Meyer ◽

Matthias Selbach

Keyword(s):

Protein Interactions ◽

Synthetic Peptides ◽

Protein Protein Interactions ◽

Intrinsically Disordered ◽

Intrinsically Disordered Regions ◽

Interaction Partners ◽

Linear Motifs ◽

Cellulose Membranes ◽

Interaction Proteomics ◽

Proteins Interactions

Protein-protein interactions are often mediated by short linear motifs (SLiMs) that are located in intrinsically disordered regions (IDRs) of proteins. Interactions mediated by SLiMs are notoriously difficult to study, and many functionally relevant interactions likely remain to be uncovered. Recently, pull-downs with synthetic peptides in combination with quantitative mass spectrometry emerged as a powerful screening approach to study protein-protein interactions mediated by SLiMs. Specifically, arrays of synthetic peptides immobilized on cellulose membranes provide a scalable means to identify the interaction partners of many peptides in parallel. In this minireview we briefly highlight the relevance of SLiMs for protein-protein interactions, outline existing screening technologies, discuss unique advantages of peptide-based interaction screens and provide practical suggestions for setting up such peptide-based screens.

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Glutamate promotes SSB protein–protein Interactions via intrinsically disordered regions

Journal of Molecular Biology ◽

10.1016/j.jmb.2017.07.021 ◽

2017 ◽

Vol 429 (18) ◽

pp. 2790-2801 ◽

Cited By ~ 23

Author(s):

Alexander G. Kozlov ◽

Min Kyung Shinn ◽

Elizabeth A. Weiland ◽

Timothy M. Lohman

Keyword(s):

Protein Interactions ◽

Protein Protein Interactions ◽

Intrinsically Disordered ◽

Intrinsically Disordered Regions ◽

Disordered Regions

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Affinity versus specificity in coupled binding and folding reactions

Protein Engineering Design and Selection ◽

10.1093/protein/gzz020 ◽

2019 ◽

Author(s):

Stefano Gianni ◽

Per Jemth

Keyword(s):

Protein Interactions ◽

Intrinsically Disordered Proteins ◽

Intrinsically Disordered Protein ◽

High Specificity ◽

Disordered Proteins ◽

Protein Protein Interactions ◽

Intrinsically Disordered ◽

Intrinsically Disordered Regions ◽

Interaction Interface ◽

Disordered Regions

Abstract Intrinsically disordered protein regions may fold upon binding to an interaction partner. It is often argued that such coupled binding and folding enables the combination of high specificity with low affinity. The basic tenet is that an unfavorable folding equilibrium will make the overall binding weaker while maintaining the interaction interface. While theoretically solid, we argue that this concept may be misleading for intrinsically disordered proteins. In fact, experimental evidence suggests that interactions of disordered regions usually involve extended conformations. In such cases, the disordered region is exceptionally unlikely to fold into a bound conformation in the absence of its binding partner. Instead, these disordered regions can bind to their partners in multiple different conformations and then fold into the native bound complex, thus, if anything, increasing the affinity through folding. We concede that (de)stabilization of native structural elements such as helices will modulate affinity, but this could work both ways, decreasing or increasing the stability of the complex. Moreover, experimental data show that intrinsically disordered binding regions display a range of affinities and specificities dictated by the particular side chains and length of the disordered region and not necessarily by the fact that they are disordered. We find it more likely that intrinsically disordered regions are common in protein–protein interactions because they increase the repertoire of binding partners, providing an accessible route to evolve interactions rather than providing a stability–affinity trade-off.

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Protein Interaction Z Score Assessment (PIZSA): an empirical scoring scheme for evaluation of protein–protein interactions

Nucleic Acids Research ◽

10.1093/nar/gkz368 ◽

2019 ◽

Vol 47 (W1) ◽

pp. W331-W337 ◽

Cited By ~ 3

Author(s):

Ankit A Roy ◽

Abhilesh S Dhawanjewar ◽

Parichit Sharma ◽

Gulzar Singh ◽

M S Madhusudhan

Keyword(s):

Protein Interactions ◽

Protein Complexes ◽

Three Dimensional ◽

Optimal Number ◽

Dimensional Structure ◽

Side Chain ◽

Interface Residue ◽

Z Score ◽

Protein Protein Interactions ◽

Residue Contacts

Abstract Our web server, PIZSA (http://cospi.iiserpune.ac.in/pizsa), assesses the likelihood of protein–protein interactions by assigning a Z Score computed from interface residue contacts. Our score takes into account the optimal number of atoms that mediate the interaction between pairs of residues and whether these contacts emanate from the main chain or side chain. We tested the score on 174 native interactions for which 100 decoys each were constructed using ZDOCK. The native structure scored better than any of the decoys in 146 cases and was able to rank within the 95th percentile in 162 cases. This easily outperforms a competing method, CIPS. We also benchmarked our scoring scheme on 15 targets from the CAPRI dataset and found that our method had results comparable to that of CIPS. Further, our method is able to analyse higher order protein complexes without the need to explicitly identify chains as receptors or ligands. The PIZSA server is easy to use and could be used to score any input three-dimensional structure and provide a residue pair-wise break up of the results. Attractively, our server offers a platform for users to upload their own potentials and could serve as an ideal testing ground for this class of scoring schemes.

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Three-dimensional structure of a regular bacterial surface layer: The HPI-layer of Deinococcus radiodurans

Proceedings, annual meeting, Electron Microscopy Society of America ◽

10.1017/s0424820100075920 ◽

1983 ◽

Vol 41 ◽

pp. 438-439

Author(s):

W. Baumeister ◽

M. Hahn ◽

W.O. Saxton

Keyword(s):

Outer Membrane ◽

Protein Interactions ◽

Deinococcus Radiodurans ◽

Bacterial Species ◽

Hexagonal Lattice ◽

Three Dimensional ◽

Dimensional Structure ◽

Protein Protein Interactions ◽

Bacterial Surface ◽

Freeze Dried

Regularly organized surface (RS) layers are a feature common to many bacterial species; they are clearly more abundant than was anticipated even a few years ago. The RS-layers are believed to fulfil a variety of functions in the interaction between the cell and its environment (see e.g. [1]). The so-called HPI-layer of the radiotolerant bacterium Deinococcus radiodurans is a typical example of such a layer: It is composed of a single polypeptide species (Mr 105 kDa) arranged on a hexagonal lattice to form a network that covers the entire surface of the bacterium; it is associated with the outer membrane via hydrophobic protein-protein interactions.Isolated HPI-layer sheets, released from the outer membrane by detergent treatment, have been studied in the electron microscope making extensive use of the present arsenal of preparation techniques: negative staining, (auro- thio)glucose embedding, freeze-dried/unstained, freeze-dried/metal shadowed etc.Because of the notorious problem of lattice imperfections image processing usually followed the strategy of correlation averaging as outlined in some detail elsewhere.

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InterPep2: global peptide–protein docking using interaction surface templates

Bioinformatics ◽

10.1093/bioinformatics/btaa005 ◽

2020 ◽

Vol 36 (8) ◽

pp. 2458-2465 ◽

Cited By ~ 2

Author(s):

Isak Johansson-Åkhe ◽

Claudio Mirabello ◽

Björn Wallner

Keyword(s):

Protein Interactions ◽

Protein Complexes ◽

Structural Features ◽

Protein Docking ◽

Supplementary Information ◽

Peptide Ligand ◽

Protein Protein Interactions ◽

Intrinsically Disordered ◽

Intrinsically Disordered Regions ◽

Improved Performance

Abstract Motivation Interactions 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 difficult because of the inherent flexibility of the peptide ligand. Although several prediction-methods exist, most are limited in performance or availability. Results InterPep2 is a freely available method for predicting the structure of peptide–protein interactions. Improved performance is obtained by using templates from both peptide–protein and regular protein–protein interactions, and by a random forest trained to predict the DockQ-score for a given template using sequence and structural features. When tested on 252 bound peptide–protein complexes from structures deposited after the complexes used in the construction of the training and templates sets of InterPep2, InterPep2-Refined correctly positioned 67 peptides within 4.0 Å LRMSD among top10, similar to another state-of-the-art template-based method which positioned 54 peptides correctly. However, InterPep2 displays a superior ability to evaluate the quality of its own predictions. On a previously established set of 27 non-redundant unbound-to-bound peptide–protein complexes, InterPep2 performs on-par with leading methods. The extended InterPep2-Refined protocol managed to correctly model 15 of these complexes within 4.0 Å LRMSD among top10, without using templates from homologs. In addition, combining the template-based predictions from InterPep2 with ab initio predictions from PIPER-FlexPepDock resulted in 22% more near-native predictions compared to the best single method (22 versus 18). Availability and implementation The program is available from: http://wallnerlab.org/InterPep2. Supplementary information Supplementary data are available at Bioinformatics online.

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PPI_SVM: Prediction of protein-protein interactions using machine learning, domain-domain affinities and frequency tables

Cellular & Molecular Biology Letters ◽

10.2478/s11658-011-0008-x ◽

2011 ◽

Vol 16 (2) ◽

Cited By ~ 41

Author(s):

Piyali Chatterjee ◽

Subhadip Basu ◽

Mahantapas Kundu ◽

Mita Nasipuri ◽

Dariusz Plewczynski

Keyword(s):

Machine Learning ◽

Protein Interactions ◽

Three Dimensional ◽

Prediction Method ◽

Protein Sequences ◽

Dimensional Structure ◽

Support Vector ◽

Interacting Proteins ◽

Protein Protein Interactions ◽

Protein Functions

AbstractProtein-protein interactions (PPI) control most of the biological processes in a living cell. In order to fully understand protein functions, a knowledge of protein-protein interactions is necessary. Prediction of PPI is challenging, especially when the three-dimensional structure of interacting partners is not known. Recently, a novel prediction method was proposed by exploiting physical interactions of constituent domains. We propose here a novel knowledge-based prediction method, namely PPI_SVM, which predicts interactions between two protein sequences by exploiting their domain information. We trained a two-class support vector machine on the benchmarking set of pairs of interacting proteins extracted from the Database of Interacting Proteins (DIP). The method considers all possible combinations of constituent domains between two protein sequences, unlike most of the existing approaches. Moreover, it deals with both single-domain proteins and multi domain proteins; therefore it can be applied to the whole proteome in high-throughput studies. Our machine learning classifier, following a brainstorming approach, achieves accuracy of 86%, with specificity of 95%, and sensitivity of 75%, which are better results than most previous methods that sacrifice recall values in order to boost the overall precision. Our method has on average better sensitivity combined with good selectivity on the benchmarking dataset. The PPI_SVM source code, train/test datasets and supplementary files are available freely in the public domain at: http://code.google.com/p/cmater-bioinfo/.

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Proteome-scale amino-acid resolution footprinting of protein-binding sites in the intrinsically disordered regions of the human proteome

10.1101/2021.04.13.439572 ◽

2021 ◽

Author(s):

Caroline Benz ◽

Muhammad Ali ◽

Izabella Krystkowiak ◽

Leandro Simonetti ◽

Ahmed Sayadi ◽

...

Keyword(s):

Amino Acid ◽

Protein Interactions ◽

Human Proteome ◽

Cell Physiology ◽

Protein Protein Interactions ◽

Linear Motif ◽

Intrinsically Disordered ◽

Intrinsically Disordered Regions ◽

Wide Range ◽

Disordered Regions

Specific protein-protein interactions are central to all processes that underlie cell physiology. Numerous studies using a wide range of experimental approaches have identified tens of thousands of human protein-protein interactions. However, many interactions remain to be discovered, and low affinity, conditional and cell type-specific interactions are likely to be disproportionately under-represented. Moreover, for most known protein-protein interactions the binding regions remain uncharacterized. We previously developed proteomic peptide phage display (ProP-PD), a method for simultaneous proteome-scale identification of short linear motif (SLiM)-mediated interactions and footprinting of the binding region with amino acid resolution. Here, we describe the second-generation human disorderome (HD2), an optimized ProP-PD library that tiles all disordered regions of the human proteome and allows the screening of ~1,000,000 overlapping peptides in a single binding assay. We define guidelines for how to process, filter and rank the results and provide PepTools, a toolkit for annotation and analysis of identified hits. We uncovered 2,161 interaction pairs for 35 known SLiM-binding domains and confirmed a subset of 38 interactions by biophysical or cell-based assays. Finally, we show how the amino acid resolution binding site information can be used to pinpoint functionally important disease mutations and phosphorylation events in intrinsically disordered regions of the human proteome. The HD2 ProP-PD library paired with PepTools represents a powerful pipeline for unbiased proteome-wide discovery of SLiM-based interactions.

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Computational prediction of MoRFs based on protein sequences and minimax probability machine

BMC Bioinformatics ◽

10.1186/s12859-019-3111-z ◽

2019 ◽

Vol 20 (1) ◽

Cited By ~ 1

Author(s):

Hao He ◽

Jiaxiang Zhao ◽

Guiling Sun

Keyword(s):

Protein Interactions ◽

Computational Prediction ◽

Specific Protein ◽

Protein Protein Interactions ◽

Intrinsically Disordered ◽

Intrinsically Disordered Regions ◽

Interaction Partners ◽

Test Sets ◽

Minimax Probability Machine ◽

Molecular Recognition Features

Abstract Background Molecular recognition features (MoRFs) are one important type of disordered segments that can promote specific protein-protein interactions. They are located within longer intrinsically disordered regions (IDRs), and undergo disorder-to-order transitions upon binding to their interaction partners. The functional importance of MoRFs and the limitation of experimental identification make it necessary to predict MoRFs accurately with computational methods. Results In this study, a new sequence-based method, named as MoRFMPM, is proposed for predicting MoRFs. MoRFMPM uses minimax probability machine (MPM) to predict MoRFs based on 16 features and 3 different windows, which neither relying on other predictors nor calculating the properties of the surrounding regions of MoRFs separately. Comparing with ANCHOR, MoRFpred and MoRFCHiBi on the same test sets, MoRFMPM not only obtains higher AUC, but also obtains higher TPR at low FPR. Conclusions The features used in MoRFMPM can effectively predict MoRFs, especially after preprocessing. Besides, MoRFMPM uses a linear classification algorithm and does not rely on results of other predictors which makes it accessible and repeatable.

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