scholarly journals AutoDock CrankPep: combining folding and docking to predict protein–peptide complexes

2019 ◽  
Vol 35 (24) ◽  
pp. 5121-5127 ◽  
Author(s):  
Yuqi Zhang ◽  
Michel F Sanner
Keyword(s):  
Amino Acids ◽  
Protein Interactions ◽  
Disordered Proteins ◽  
Supplementary Information ◽  
Protein Protein Interactions ◽  
Peptide Docking ◽  
Novel Approach ◽  
Peptide Interactions ◽  
Therapeutic Molecules ◽  
Peptide Complexes

Abstract Motivation Protein–peptide interactions mediate a wide variety of cellular and biological functions. Methods for predicting these interactions have garnered a lot of interest over the past few years, as witnessed by the rapidly growing number of peptide-based therapeutic molecules currently in clinical trials. The size and flexibility of peptides has shown to be challenging for existing automated docking software programs. Results Here we present AutoDock CrankPep or ADCP in short, a novel approach to dock flexible peptides into rigid receptors. ADCP folds a peptide in the potential field created by the protein to predict the protein–peptide complex. We show that it outperforms leading peptide docking methods on two protein–peptide datasets commonly used for benchmarking docking methods: LEADS-PEP and peptiDB, comprised of peptides with up to 15 amino acids in length. Beyond these datasets, ADCP reliably docked a set of protein–peptide complexes containing peptides ranging in lengths from 16 to 20 amino acids. The robust performance of ADCP on these longer peptides enables accurate modeling of peptide-mediated protein–protein interactions and interactions with disordered proteins. Availability and implementation ADCP is distributed under the LGPL 2.0 open source license and is available at http://adcp.scripps.edu. The source code is available at https://github.com/ccsb-scripps/ADCP. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals YAPP-CD: Yet another protein-peptide complex database

2021 ◽  
Author(s):  
Joon-Sang Park
Keyword(s):  
Structural Biology ◽  
Protein Interactions ◽  
Pharmaceutical Products ◽  
Protein Protein Interactions ◽  
Peptide Complex ◽  
Prediction Quality ◽  
Peptide Docking ◽  
Peptide Interactions ◽  
Peptide Complexes ◽  
Increasing Demand

Protein-peptide interactions are of great interest to the research community not only because they serve as mediators in many protein-protein interactions but also because of the increasing demand for peptide-based pharmaceutical products. Protein-peptide docking is a major tool for studying protein-peptide interactions, and several docking methods are currently available. Among various protein-peptide docking algorithms, template-based approaches, which utilize known protein-peptide complexes or templates to predict a new one, have been shown to yield more reliable results than template-free methods in recent comparative research. To obtain reliable results with a template-based docking method, the template database must be comprehensive enough; that is, there must be similar templates of protein-peptide complexes to the protein and peptide being investigated. Thus, the template database must be updated to leverage recent advances in structural biology. However, the template database distributed with GalaxyPepDock, one of the most widely used peptide docking programs, is outdated, limiting the prediction quality of the method. Here, we present an up-to-date protein-peptide complex database called YAPP-CD, which can be directly plugged into the GalaxyPepDock binary package to improve GalaxyPepDock's prediction quality by drawing on recent discoveries in structural biology. Experimental results show that YAPP-CD significantly improves GalaxyPepDock's prediction quality, e.g., the average Ligand/Interface RMSD of a benchmark set is reduced from 7.60 A/3.62 A to 3.47 A/1.71 A.

Structure-aware protein–protein interaction site prediction using deep graph convolutional network

2021 ◽  
Author(s):  
Qianmu Yuan ◽  
Jianwen Chen ◽  
Huiying Zhao ◽  
Yaoqi Zhou ◽  
Yuedong Yang
Keyword(s):  
Protein Interactions ◽  
Spatial Information ◽  
Screening Tools ◽  
Supplementary Information ◽  
Protein Protein Interactions ◽  
Convolutional Network ◽  
Source Codes ◽  
Site Prediction ◽  
Protein Protein Interaction ◽  
Mapping Techniques

Abstract Motivation Protein–protein interactions (PPI) play crucial roles in many biological processes, and identifying PPI sites is an important step for mechanistic understanding of diseases and design of novel drugs. Since experimental approaches for PPI site identification are expensive and time-consuming, many computational methods have been developed as screening tools. However, these methods are mostly based on neighbored features in sequence, and thus limited to capture spatial information. Results We propose a deep graph-based framework deep Graph convolutional network for Protein–Protein-Interacting Site prediction (GraphPPIS) for PPI site prediction, where the PPI site prediction problem was converted into a graph node classification task and solved by deep learning using the initial residual and identity mapping techniques. We showed that a deeper architecture (up to eight layers) allows significant performance improvement over other sequence-based and structure-based methods by more than 12.5% and 10.5% on AUPRC and MCC, respectively. Further analyses indicated that the predicted interacting sites by GraphPPIS are more spatially clustered and closer to the native ones even when false-positive predictions are made. The results highlight the importance of capturing spatially neighboring residues for interacting site prediction. Availability and implementation The datasets, the pre-computed features, and the source codes along with the pre-trained models of GraphPPIS are available at https://github.com/biomed-AI/GraphPPIS. The GraphPPIS web server is freely available at https://biomed.nscc-gz.cn/apps/GraphPPIS. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals Enhancing Binding Affinity of an Intrinsically Disordered Protein by α-Methylation of Key Amino Acid Residues

2019 ◽  
Author(s):  
Valentin Bauer ◽  
Boris Schmidtgall ◽  
Gergő Gógl ◽  
Jozica Dolenc ◽  
Judit Osz ◽  
...  
Keyword(s):  
Amino Acids ◽  
Protein Interactions ◽  
Intrinsically Disordered Proteins ◽  
Intrinsically Disordered Protein ◽  
Selective Inhibition ◽  
Disordered Proteins ◽  
Creb Binding Protein ◽  
Alpha Helices ◽  
Intrinsically Disordered ◽  
Order Of Magnitude

Intrinsically disordered proteins (IDPs), which undergo folding upon binding to their targets, are critical players in protein interaction networks. Here we demonstrate that incorporation of non-canonical alpha-methylated amino acids into the unstructured activation domain of the transcriptional coactivator ACTR can stabilize helical conformations and strengthen binding interactions with the nuclear coactivator binding domain (NCBD) of CREB-binding protein (CBP). A combinatorial alpha-methylation scan of the ACTR sequence converged on two substitutions at positions 1055 and 1076 that increase affinity for both NCBD and the full length 270 kDa CBP by one order of magnitude. The first X-ray structure of the modified ACTR domain bound to NCBD revealed that the key alpha-methylated amino acids were localized within alpha-helices. Biophysical studies showed that the observed changes in binding energy are the result of long-range interactions and redistribution of enthalpy and entropy. This proof-of-concept study establishes a potential strategy for selective inhibition of protein-protein interactions involving IDPs in cells.<br>

Engineering an acetyllysine reader with photocrosslinking amino acid for interactome profiling

2021 ◽  
Author(s):  
Babu Sudhamalla ◽  
Anirban Roy ◽  
Soumen Barman ◽  
Jyotirmayee Padhan
Keyword(s):  
Amino Acids ◽  
Amino Acid ◽  
Protein Interactions ◽  
Unnatural Amino Acids ◽  
Protein Protein Interactions ◽  
Site Specific ◽  
Powerful Approach

The site-specific installation of light-activable crosslinker unnatural amino acids offers a powerful approach to trap transient protein-protein interactions both in vitro and in vivo. Herein, we engineer a bromodomain to...

Protein Interactions for Functional Genomics

2012 ◽  
Vol 3 (4) ◽  
pp. 15-30
Author(s):  
Pablo Minguez ◽  
Joaquin Dopazo
Keyword(s):  
Graph Theory ◽  
Protein Interactions ◽  
Biological Networks ◽  
State Of The Art ◽  
Protein Protein Interactions ◽  
Pairwise Interactions ◽  
Novel Approach ◽  
Functional Profiling ◽  
Available Resources

Here the authors review the state of the art in the use of protein-protein interactions (ppis) within the context of the interpretation of genomic experiments. They report the available resources and methodologies used to create a curated compilation of ppis introducing a novel approach to filter interactions. Special attention is paid in the complexity of the topology of the networks formed by proteins (nodes) and pairwise interactions (edges). These networks can be studied using graph theory and a brief introduction to the characterization of biological networks and definitions of the more used network parameters is also given. Also a report on the available resources to perform different modes of functional profiling using ppi data is provided along with a discussion on the approaches that have typically been applied into this context. They also introduce a novel methodology for the evaluation of networks and some examples of its application.

scholarly journals Coevolution-based prediction of protein–protein interactions in polyketide biosynthetic assembly lines

2020 ◽  
Vol 36 (19) ◽  
pp. 4846-4853 ◽  
Author(s):  
Yan Wang ◽  
Miguel Correa Marrero ◽  
Marnix H Medema ◽  
Aalt D J van Dijk
Keyword(s):  
Protein Interactions ◽  
Antitumor Agents ◽  
Acyl Carrier Protein ◽  
Specific Protein ◽  
Combinatorial Biosynthesis ◽  
Supplementary Information ◽  
Protein Protein Interactions ◽  
Assembly Lines ◽  
Specific Order ◽  
Encoding Genes

Abstract Motivation Polyketide synthases (PKSs) are enzymes that generate diverse molecules of great pharmaceutical importance, including a range of clinically used antimicrobials and antitumor agents. Many polyketides are synthesized by cis-AT modular PKSs, which are organized in assembly lines, in which multiple enzymes line up in a specific order. This order is defined by specific protein–protein interactions (PPIs). The unique modular structure and catalyzing mechanism of these assembly lines makes their products predictable and also spurred combinatorial biosynthesis studies to produce novel polyketides using synthetic biology. However, predicting the interactions of PKSs, and thereby inferring the order of their assembly line, is still challenging, especially for cases in which this order is not reflected by the ordering of the PKS-encoding genes in the genome. Results Here, we introduce PKSpop, which uses a coevolution-based PPI algorithm to infer protein order in PKS assembly lines. Our method accurately predicts protein orders (93% accuracy). Additionally, we identify new residue pairs that are key in determining interaction specificity, and show that coevolution of N- and C-terminal docking domains of PKSs is significantly more predictive for PPIs than coevolution between ketosynthase and acyl carrier protein domains. Availability and implementation The code is available on http://www.bif.wur.nl/ (under ‘Software’). Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals Chasing coevolutionary signals in intrinsically disordered proteins complexes

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Javier A. Iserte ◽  
Tamas Lazar ◽  
Silvio C. E. Tosatto ◽  
Peter Tompa ◽  
Cristina Marino-Buslje
Keyword(s):  
Protein Interactions ◽  
Intrinsically Disordered Proteins ◽  
Disordered Proteins ◽  
Protein Protein Interactions ◽  
Contact Prediction ◽  
Intrinsically Disordered ◽  
Regulatory Processes ◽  
Wide Range ◽  
Predict Protein Structure ◽  
Biological Interpretation

Abstract Intrinsically disordered proteins/regions (IDPs/IDRs) are crucial components of the cell, they are highly abundant and participate ubiquitously in a wide range of biological functions, such as regulatory processes and cell signaling. Many of their important functions rely on protein interactions, by which they trigger or modulate different pathways. Sequence covariation, a powerful tool for protein contact prediction, has been applied successfully to predict protein structure and to identify protein–protein interactions mostly of globular proteins. IDPs/IDRs also mediate a plethora of protein–protein interactions, highlighting the importance of addressing sequence covariation-based inter-protein contact prediction of this class of proteins. Despite their importance, a systematic approach to analyze the covariation phenomena of intrinsically disordered proteins and their complexes is still missing. Here we carry out a comprehensive critical assessment of coevolution-based contact prediction in IDP/IDR complexes and detail the challenges and possible limitations that emerge from their analysis. We found that the coevolutionary signal is faint in most of the complexes of disordered proteins but positively correlates with the interface size and binding affinity between partners. In addition, we discuss the state-of-art methodology by biological interpretation of the results, formulate evaluation guidelines and suggest future directions of development to the field.

Inhibiting protein-protein interactions of Hsp90 as a novel approach for targeting cancer

2019 ◽  
Vol 178 ◽  
pp. 48-63 ◽  
Author(s):  
Sayan Dutta Gupta ◽  
Manish Kumar Bommaka ◽  
Anindita Banerjee
Keyword(s):  
Protein Interactions ◽  
Protein Protein Interactions ◽  
Novel Approach ◽  
Inhibiting Protein

scholarly journals The Disordered Cellular Multi-Tasker WIP and Its Protein–Protein Interactions: A Structural View

Biomolecules ◽  
2020 ◽  
Vol 10 (7) ◽  
pp. 1084 ◽  
Author(s):  
Chana G. Sokolik ◽  
Nasrin Qassem ◽  
Jordan H. Chill
Keyword(s):  
Protein Interactions ◽  
Cell Biology ◽  
Intrinsically Disordered Proteins ◽  
Disordered Proteins ◽  
Actin Binding ◽  
Structural Description ◽  
Protein Protein Interactions ◽  
Intrinsically Disordered ◽  
Terminal Domain ◽  
Binding Partners

WASp-interacting protein (WIP), a regulator of actin cytoskeleton assembly and remodeling, is a cellular multi-tasker and a key member of a network of protein–protein interactions, with significant impact on health and disease. Here, we attempt to complement the well-established understanding of WIP function from cell biology studies, summarized in several reviews, with a structural description of WIP interactions, highlighting works that present a molecular view of WIP’s protein–protein interactions. This provides a deeper understanding of the mechanisms by which WIP mediates its biological functions. The fully disordered WIP also serves as an intriguing example of how intrinsically disordered proteins (IDPs) exert their function. WIP consists of consecutive small functional domains and motifs that interact with a host of cellular partners, with a striking preponderance of proline-rich motif capable of interactions with several well-recognized binding partners; indeed, over 30% of the WIP primary structure are proline residues. We focus on the binding motifs and binding interfaces of three important WIP segments, the actin-binding N-terminal domain, the central domain that binds SH3 domains of various interaction partners, and the WASp-binding C-terminal domain. Beyond the obvious importance of a more fundamental understanding of the biology of this central cellular player, this approach carries an immediate and highly beneficial effect on drug-design efforts targeting WIP and its binding partners. These factors make the value of such structural studies, challenging as they are, readily apparent.

Sign in / Sign up

Export Citation Format

Share Document