UNRES-Dock—protein–protein and peptide–protein docking by coarse-grained replica-exchange MD simulations

Abstract Motivation The majority of the proteins in living organisms occur as homo- or hetero-multimeric structures. Although there are many tools to predict the structures of single-chain proteins or protein complexes with small ligands, peptide–protein and protein–protein docking is more challenging. In this work, we utilized multiplexed replica-exchange molecular dynamics (MREMD) simulations with the physics-based heavily coarse-grained UNRES model, which provides more than a 1000-fold simulation speed-up compared with all-atom approaches to predict structures of protein complexes. Results We present a new protein–protein and peptide–protein docking functionality of the UNRES package, which includes a variable degree of conformational flexibility. UNRES-Dock protocol was tested on a set of 55 complexes with size from 43 to 587 amino-acid residues, showing that structures of the complexes can be predicted with good quality, if the sampling of the conformational space is sufficient, especially for flexible peptide–protein systems. The developed automatized protocol has been implemented in the standalone UNRES package and in the UNRES server. Availability and implementation UNRES server: http://unres-server.chem.ug.edu.pl; UNRES package and data used in testing of UNRES-Dock: http://unres.pl. Supplementary information Supplementary data are available at Bioinformatics online.

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Dynamical Behavior and Conformational Selection Mechanism of the Intrinsically Disordered Sic1 Kinase-Inhibitor Domain

Life ◽

10.3390/life10070110 ◽

2020 ◽

Vol 10 (7) ◽

pp. 110 ◽

Cited By ~ 1

Author(s):

Davide Sala ◽

Ugo Cosentino ◽

Anna Ranaudo ◽

Claudio Greco ◽

Giorgio Moro

Keyword(s):

Kinase Inhibitor ◽

Dynamical Behavior ◽

Md Simulations ◽

Molecular Shape ◽

Conformational Space ◽

Coarse Grained ◽

Conformational Ensemble ◽

Selection Mechanism ◽

Conformational Selection ◽

Intrinsically Disordered

Intrinsically Disordered Peptides and Proteins (IDPs) in solution can span a broad range of conformations that often are hard to characterize by both experimental and computational methods. However, obtaining a significant representation of the conformational space is important to understand mechanisms underlying protein functions such as partner recognition. In this work, we investigated the behavior of the Sic1 Kinase-Inhibitor Domain (KID) in solution by Molecular Dynamics (MD) simulations. Our results point out that application of common descriptors of molecular shape such as Solvent Accessible Surface (SAS) area can lead to misleading outcomes. Instead, more appropriate molecular descriptors can be used to define 3D structures. In particular, we exploited Weighted Holistic Invariant Molecular (WHIM) descriptors to get a coarse-grained but accurate definition of the variegated Sic1 KID conformational ensemble. We found that Sic1 is able to form a variable amount of folded structures even in absence of partners. Among them, there were some conformations very close to the structure that Sic1 is supposed to assume in the binding with its physiological complexes. Therefore, our results support the hypothesis that this protein relies on the conformational selection mechanism to recognize the correct molecular partners.

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Conformational and functional characterization of artificially conjugated non-canonical ubiquitin dimers

Scientific Reports ◽

10.1038/s41598-019-56458-z ◽

2019 ◽

Vol 9 (1) ◽

Cited By ~ 3

Author(s):

Tobias Schneider ◽

Andrej Berg ◽

Zeynel Ulusoy ◽

Martin Gamerdinger ◽

Christine Peter ◽

...

Keyword(s):

Experimental Data ◽

Functional Characterization ◽

Isotopic Labeling ◽

Md Simulations ◽

Conformational Space ◽

Coarse Grained ◽

Covalent Attachment ◽

Binding Studies ◽

Polyubiquitin Chains ◽

Polypeptide Chains

AbstractUbiquitylation is an eminent posttranslational modification referring to the covalent attachment of single ubiquitin molecules or polyubiquitin chains to a target protein dictating the fate of such labeled polypeptide chains. Here, we have biochemically produced artificially Lys11-, and Lys27-, and Lys63-linked ubiquitin dimers based on click-chemistry generating milligram quantities in high purity. We show that the artificial linkage used for the conjugation of two ubiquitin moieties represents a fully reliable surrogate of the natural isopeptide bond by acquiring highly resolved nuclear magnetic resonance (NMR) spectroscopic data including ligand binding studies. Extensive coarse grained and atomistic molecular dynamics (MD) simulations allow to extract structures representing the ensemble of domain-domain conformations used to verify the experimental data. Advantageously, this methodology does not require individual isotopic labeling of both ubiquitin moieties as NMR data have been acquired on the isotopically labeled proximal moiety and complementary MD simulations have been used to fully interpret the experimental data in terms of domain-domain conformation. This combined approach intertwining NMR spectroscopy with MD simulations makes it possible to describe the conformational space non-canonically Lys11-, and Lys27-linked ubiquitin dimers occupy in a solution averaged ensemble by taking atomically resolved information representing all residues in ubiquitin dimers into account.

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MODE-TASK: Large-scale protein motion tools

10.1101/217505 ◽

2017 ◽

Author(s):

Caroline Ross ◽

Bilal Nizami ◽

Michael Glenister ◽

Olivier Sheik Amamuddy ◽

Ali Rana Atilgan ◽

...

Keyword(s):

Large Scale ◽

Protein Complexes ◽

Normal Mode Analysis ◽

Md Simulations ◽

Supplementary Information ◽

Mode Analysis ◽

Analysis Tool ◽

Link Type ◽

Supplementary Material ◽

Anisotropic Network

AbstractSummaryMODE-TASK, a novel software suite, comprises Principle Component Analysis, Multidimensional Scaling, and t-Distributed Stochastic Neighbor Embedding techniques using molecular dynamics trajectories. MODE-TASK also includes a Normal Mode Analysis tool based on Anisotropic Network Model so as to provide a variety of ways to analyse and compare large-scale motions of protein complexes for which long MD simulations are prohibitive.Availability and ImplementationMODE-TASK has been open-sourced, and is available for download from https://github.com/RUBi-ZA/MODE-TASK, implemented in Python and C++.Supplementary informationDocumentation available at http://mode-task.readthedocs.io.

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DDT - Drug Discovery Tool: a fast and intuitive graphics user interface for docking and molecular dynamics analysis

Bioinformatics ◽

10.1093/bioinformatics/btz543 ◽

2019 ◽

Author(s):

Simone Aureli ◽

Daniele Di Marino ◽

Stefano Raniolo ◽

Vittorio Limongelli

Keyword(s):

Molecular Dynamics ◽

User Interface ◽

Drug Discovery ◽

Protein Binding ◽

Protein Complexes ◽

Md Simulations ◽

Supplementary Information ◽

Chemical Information ◽

Binding Interaction ◽

Fast Analysis

Abstract Motivation The ligand/protein binding interaction is typically investigated by docking and molecular dynamics (MD) simulations. In particular, docking-based virtual screening (VS) is used to select the best ligands from database of thousands of compounds, while MD calculations assess the energy stability of the ligand/protein binding complexes. Considering the broad use of these techniques, it is of great demand to have one single software that allows a combined and fast analysis of VS and MD results. With this in mind, we have developed the Drug Discovery Tool (DDT) that is an intuitive graphics user interface able to provide structural data and physico-chemical information on the ligand/protein interaction. Results DDT is designed as a plugin for the Visual Molecular Dynamics (VMD) software and is able to manage a large number of ligand/protein complexes obtained from AutoDock4 (AD4) docking calculations and MD simulations. DDT delivers four main outcomes: i) ligands ranking based on an energy score; ii) ligand ranking based on a ligands’ conformation cluster analysis; iii) identification of the aminoacids forming the most occurrent interactions with the ligands; iv) plot of the ligands’ center-of-mass coordinates in the Cartesian space. The flexibility of the software allows saving the best ligand/protein complexes using a number of user-defined options. Availability and implementation DDT_site_1 (alternative DDT_site_2); the DDT tutorial movie is available here. Supplementary information Supplementary data are available at Bioinformatics online.

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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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Mapping the Energy Landscape of PROTAC-Mediated Protein-protein Interactions

10.1101/2021.08.31.458424 ◽

2021 ◽

Author(s):

Jose A Villegas ◽

Tasneem M Vaid ◽

Michael E Johnson ◽

Terry W Moore

Keyword(s):

Protein Interactions ◽

Degrees Of Freedom ◽

Protein Complexes ◽

Energy Landscape ◽

Search Space ◽

Protein Docking ◽

Conformational Space ◽

Conformational Sampling ◽

Test Case ◽

Coordinate Descent Algorithm

One of the principal difficulties in computational modeling of macromolecules is the vast conformational space that arises out of large numbers of atomic degrees of freedom. This problem is a familiar issue in the area of protein-protein docking, where models of protein complexes are generated from the monomeric subunits. Although restriction of molecular flexibility is a commonly used approximation that decreases the dimensionality of the problem, the seemingly endless number of possible ways two binding partners can interact generally necessitates the use of further approximations to explore the search space. Recently, growing interest in using computational tools to build predictive models of PROTAC-mediated complexes has led to the application of state-of-the-art protein-protein docking techniques to tackle this problem. Additionally, the atomic degrees of freedom introduced by flexibility of linkers used in the construction of PROTACs further expands the configurational search space, a problem that can be tackled with conformational sampling tools. However, repurposing existing tools to carry out protein-protein docking and linker conformer generation independently results in extensive sampling of structures incompatible with PROTAC-mediated complex formation. Here we show that it is possible to restrict the search to the space of protein-protein conformations that can be bridged by a PROTAC molecule with a given linker composition by using a cyclic coordinate descent algorithm to position PROTACs into complex-bound configurations. We use this methodology to construct a picture of the energy landscape of PROTAC-mediated interactions in a model test case, and show that the global minimum lies in the space of native-like conformations.

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iScore: a novel graph kernel-based function for scoring protein–protein docking models

Bioinformatics ◽

10.1093/bioinformatics/btz496 ◽

2019 ◽

Vol 36 (1) ◽

pp. 112-121 ◽

Cited By ~ 9

Author(s):

Cunliang Geng ◽

Yong Jung ◽

Nicolas Renaud ◽

Vasant Honavar ◽

Alexandre M J J Bonvin ◽

...

Keyword(s):

Protein Complexes ◽

Three Dimensional ◽

Protein Docking ◽

Graph Representation ◽

Supplementary Information ◽

Scoring Functions ◽

Computational Docking ◽

3D Structures ◽

Novel Approach ◽

Protein Interfaces

Abstract Motivation Protein complexes play critical roles in many aspects of biological functions. Three-dimensional (3D) structures of protein complexes are critical for gaining insights into structural bases of interactions and their roles in the biomolecular pathways that orchestrate key cellular processes. Because of the expense and effort associated with experimental determinations of 3D protein complex structures, computational docking has evolved as a valuable tool to predict 3D structures of biomolecular complexes. Despite recent progress, reliably distinguishing near-native docking conformations from a large number of candidate conformations, the so-called scoring problem, remains a major challenge. Results Here we present iScore, a novel approach to scoring docked conformations that combines HADDOCK energy terms with a score obtained using a graph representation of the protein–protein interfaces and a measure of evolutionary conservation. It achieves a scoring performance competitive with, or superior to, that of state-of-the-art scoring functions on two independent datasets: (i) Docking software-specific models and (ii) the CAPRI score set generated by a wide variety of docking approaches (i.e. docking software-non-specific). iScore ranks among the top scoring approaches on the CAPRI score set (13 targets) when compared with the 37 scoring groups in CAPRI. The results demonstrate the utility of combining evolutionary, topological and energetic information for scoring docked conformations. This work represents the first successful demonstration of graph kernels to protein interfaces for effective discrimination of near-native and non-native conformations of protein complexes. Availability and implementation The iScore code is freely available from Github: https://github.com/DeepRank/iScore (DOI: 10.5281/zenodo.2630567). And the docking models used are available from SBGrid: https://data.sbgrid.org/dataset/684). Supplementary information Supplementary data are available at Bioinformatics online.

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Integrating ab initio and template-based algorithms for protein–protein complex structure prediction

Bioinformatics ◽

10.1093/bioinformatics/btz623 ◽

2019 ◽

Vol 36 (3) ◽

pp. 751-757 ◽

Cited By ~ 1

Author(s):

Sweta Vangaveti ◽

Thom Vreven ◽

Yang Zhang ◽

Zhiping Weng

Keyword(s):

Protein Complex ◽

Structure Prediction ◽

Protein Complexes ◽

Complex Structure ◽

Protein Docking ◽

Supplementary Information ◽

Test Case ◽

Binding Modes ◽

Success Rates ◽

Template Free

Abstract Motivation Template-based and template-free methods have both been widely used in predicting the structures of protein–protein complexes. Template-based modeling is effective when a reliable template is available, while template-free methods are required for predicting the binding modes or interfaces that have not been previously observed. Our goal is to combine the two methods to improve computational protein–protein complex structure prediction. Results Here, we present a method to identify and combine high-confidence predictions of a template-based method (SPRING) with a template-free method (ZDOCK). Cross-validated using the protein–protein docking benchmark version 5.0, our method (ZING) achieved a success rate of 68.2%, outperforming SPRING and ZDOCK, with success rates of 52.1% and 35.9% respectively, when the top 10 predictions were considered per test case. In conclusion, a statistics-based method that evaluates and integrates predictions from template-based and template-free methods is more successful than either method independently. Availability and implementation ZING is available for download as a Github repository (https://github.com/weng-lab/ZING.git). Supplementary information Supplementary data are available at Bioinformatics online.

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Text mining for modeling of protein complexes enhanced by machine learning

Bioinformatics ◽

10.1093/bioinformatics/btaa823 ◽

2020 ◽

Author(s):

Varsha D Badal ◽

Petras J Kundrotas ◽

Ilya A Vakser

Keyword(s):

Machine Learning ◽

Text Mining ◽

Protein Interactions ◽

Full Text ◽

Protein Complexes ◽

Protein Docking ◽

Supplementary Information ◽

Support Vector ◽

Learning Approaches ◽

Protein Protein Interactions

Abstract Motivation Procedures for structural modeling of protein-protein complexes (protein docking) produce a number of models which need to be further analyzed and scored. Scoring can be based on independently determined constraints on the structure of the complex, such as knowledge of amino acids essential for the protein interaction. Previously, we showed that text mining of residues in freely available PubMed abstracts of papers on studies of protein-protein interactions may generate such constraints. However, absence of post-processing of the spotted residues reduced usability of the constraints, as a significant number of the residues were not relevant for the binding of the specific proteins. Results We explored filtering of the irrelevant residues by two machine learning approaches, Deep Recursive Neural Network (DRNN) and Support Vector Machine (SVM) models with different training/testing schemes. The results showed that the DRNN model is superior to the SVM model when training is performed on the PMC-OA full-text articles and applied to classification (interface or non-interface) of the residues spotted in the PubMed abstracts. When both training and testing is performed on full-text articles or on abstracts, the performance of these models is similar. Thus, in such cases, there is no need to utilize computationally demanding DRNN approach, which is computationally expensive especially at the training stage. The reason is that SVM success is often determined by the similarity in data/text patterns in the training and the testing sets, whereas the sentence structures in the abstracts are, in general, different from those in the full text articles. Availability The code and the datasets generated in this study are available at https://gitlab.ku.edu/vakser-lab-public/text-mining/-/tree/2020-09-04. Supplementary information Supplementary data are available at Bioinformatics online.

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pyDockEneRes: per-residue decomposition of protein–protein docking energy

Bioinformatics ◽

10.1093/bioinformatics/btz884 ◽

2019 ◽

Vol 36 (7) ◽

pp. 2284-2285 ◽

Cited By ~ 1

Author(s):

Miguel Romero-Durana ◽

Brian Jiménez-García ◽

Juan Fernández-Recio

Keyword(s):

Binding Affinity ◽

Protein Interactions ◽

Structural Model ◽

Protein Complexes ◽

Complex Structure ◽

Protein Docking ◽

Supplementary Information ◽

Scoring Functions ◽

Residue Decomposition ◽

Docking Energy

Abstract Motivation Protein–protein interactions are key to understand biological processes at the molecular level. As a complement to experimental characterization of protein interactions, computational docking methods have become useful tools for the structural and energetics modeling of protein–protein complexes. A key aspect of such algorithms is the use of scoring functions to evaluate the generated docking poses and try to identify the best models. When the scoring functions are based on energetic considerations, they can help not only to provide a reliable structural model for the complex, but also to describe energetic aspects of the interaction. This is the case of the scoring function used in pyDock, a combination of electrostatics, desolvation and van der Waals energy terms. Its correlation with experimental binding affinity values of protein–protein complexes was explored in the past, but the per-residue decomposition of the docking energy was never systematically analyzed. Results Here, we present pyDockEneRes (pyDock Energy per-Residue), a web server that provides pyDock docking energy partitioned at the residue level, giving a much more detailed description of the docking energy landscape. Additionally, pyDockEneRes computes the contribution to the docking energy of the side-chain atoms. This fast approach can be applied to characterize a complex structure in order to identify energetically relevant residues (hot-spots) and estimate binding affinity changes upon mutation to alanine. Availability and implementation The server does not require registration by the user and is freely accessible for academics at https://life.bsc.es/pid/pydockeneres. Supplementary information Supplementary data are available at Bioinformatics online.

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