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

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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Less is more: Coarse-grained integrative modeling of large biomolecular assemblies with HADDOCK

10.1101/715268 ◽

2019 ◽

Cited By ~ 2

Author(s):

Jorge Roel-Touris ◽

Charleen G. Don ◽

Rodrigo V. Honorato ◽

João P.G.L.M Rodrigues ◽

Alexandre M.J.J. Bonvin

Keyword(s):

Protein Interactions ◽

Degrees Of Freedom ◽

Energy Landscape ◽

3D Structure ◽

Coarse Graining ◽

Protein Docking ◽

Coarse Grained ◽

Speed Increase ◽

Less Is More ◽

Similar Accuracy

ABSTRACTPredicting the 3D structure of protein interactions remains a challenge in the field of computational structural biology. This is in part due to difficulties in sampling the complex energy landscape of multiple interacting flexible polypeptide chains. Coarse-graining approaches, which reduce the number of degrees of freedom of the system, help address this limitation by smoothing the energy landscape, allowing an easier identification of the global energy minimum. They also accelerate the calculations, allowing to model larger assemblies. Here, we present the implementation of the MARTINI coarse-grained force field for proteins into HADDOCK, our integrative modelling platform. Docking and refinement are performed at the coarse-grained level and the resulting models are then converted back to atomistic resolution through a distance restraints-guided morphing procedure. Our protocol, tested on the largest complexes of the protein docking benchmark 5, shows an overall ~7-fold speed increase compared to standard all-atom calculations, while maintaining a similar accuracy and yielding substantially more near-native solutions. To showcase the potential of our method, we performed simultaneous 7 body docking to model the 1:6 KaiC-KaiB complex, integrating mutagenesis and hydrogen/deuterium exchange data from mass spectrometry with symmetry restraints, and validated the resulting models against a recently published cryo-EM structure.

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Udock, the interactive docking entertainment system

Faraday Discussions ◽

10.1039/c3fd00147d ◽

2014 ◽

Vol 169 ◽

pp. 425-441 ◽

Cited By ~ 10

Author(s):

Guillaume Levieux ◽

Guillaume Tiger ◽

Stéphanie Mader ◽

Jean-François Zagury ◽

Stéphane Natkin ◽

...

Keyword(s):

Protein Interactions ◽

Protein Complexes ◽

Protein Structures ◽

Protein Docking ◽

Conformational Space ◽

Protein Protein Interactions ◽

Binding Interface ◽

Protein Interfaces ◽

Almost All ◽

Experimental Partner

Protein–protein interactions play a crucial role in biological processes. Protein docking calculations' goal is to predict, given two proteins of known structures, the associate conformation of the corresponding complex. Here, we present a new interactive protein docking system, Udock, that makes use of users' cognitive capabilities added up. In Udock, the users tackle simplified representations of protein structures and explore protein–protein interfaces’ conformational space using a gamified interactive docking system with on the fly scoring. We assumed that if given appropriate tools, a naïve user's cognitive capabilities could provide relevant data for (1) the prediction of correct interfaces in binary protein complexes and (2) the identification of the experimental partner in interaction among a set of decoys. To explore this approach experimentally, we conducted a preliminary two week long playtest where the registered users could perform a cross-docking on a dataset comprising 4 binary protein complexes. The users explored almost all the surface of the proteins that were available in the dataset but favored certain regions that seemed more attractive as potential docking spots. These favored regions were located inside or nearby the experimental binding interface for 5 out of the 8 proteins in the dataset. For most of them, the best scores were obtained with the experimental partner. The alpha version of Udock is freely accessible at http://udock.fr.

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Predicting Protein Interactions by Brownian Dynamics Simulations

Journal of Biomedicine and Biotechnology ◽

10.1155/2012/121034 ◽

2012 ◽

Vol 2012 ◽

pp. 1-11 ◽

Cited By ~ 5

Author(s):

Xuan-Yu Meng ◽

Yu Xu ◽

Hong-Xing Zhang ◽

Mihaly Mezei ◽

Meng Cui

Keyword(s):

Protein Interactions ◽

Brownian Dynamics ◽

Protein Complexes ◽

Complex Structure ◽

Protein Docking ◽

Receptor Protein ◽

Conformational Sampling ◽

Test Set ◽

Docking Approach ◽

Dynamics Simulations

We present a newly adapted Brownian-Dynamics (BD)-based protein docking method for predicting native protein complexes. The approach includes global BD conformational sampling, compact complex selection, and local energy minimization. In order to reduce the computational costs for energy evaluations, a shell-based grid force field was developed to represent the receptor protein and solvation effects. The performance of this BD protein docking approach has been evaluated on a test set of 24 crystal protein complexes. Reproduction of experimental structures in the test set indicates the adequate conformational sampling and accurate scoring of this BD protein docking approach. Furthermore, we have developed an approach to account for the flexibility of proteins, which has been successfully applied to reproduce the experimental complex structure from the structure of two unbounded proteins. These results indicate that this adapted BD protein docking approach can be useful for the prediction of protein-protein interactions.

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Mass spectrometry-based cross-linking study shows that the Psb28 protein binds to cytochrome b559 in Photosystem II

Proceedings of the National Academy of Sciences ◽

10.1073/pnas.1620360114 ◽

2017 ◽

Vol 114 (9) ◽

pp. 2224-2229 ◽

Cited By ~ 18

Author(s):

Daniel A. Weisz ◽

Haijun Liu ◽

Hao Zhang ◽

Sundarapandian Thangapandian ◽

Emad Tajkhorshid ◽

...

Keyword(s):

Mass Spectrometry ◽

Photosystem Ii ◽

Protein Interactions ◽

Protein Complexes ◽

Protein Docking ◽

Cross Linking ◽

Protein Protein Interactions ◽

Cytochrome B559 ◽

Reaction Center Complex ◽

Assembly Intermediate

Photosystem II (PSII), a large pigment protein complex, undergoes rapid turnover under natural conditions. During assembly of PSII, oxidative damage to vulnerable assembly intermediate complexes must be prevented. Psb28, the only cytoplasmic extrinsic protein in PSII, protects the RC47 assembly intermediate of PSII and assists its efficient conversion into functional PSII. Its role is particularly important under stress conditions when PSII damage occurs frequently. Psb28 is not found, however, in any PSII crystal structure, and its structural location has remained unknown. In this study, we used chemical cross-linking combined with mass spectrometry to capture the transient interaction of Psb28 with PSII. We detected three cross-links between Psb28 and the α- and β-subunits of cytochrome b559, an essential component of the PSII reaction-center complex. These distance restraints enable us to position Psb28 on the cytosolic surface of PSII directly above cytochrome b559, in close proximity to the QB site. Protein–protein docking results also support Psb28 binding in this region. Determination of the Psb28 binding site and other biochemical evidence allow us to propose a mechanism by which Psb28 exerts its protective effect on the RC47 intermediate. This study also shows that isotope-encoded cross-linking with the “mass tags” selection criteria allows confident identification of more cross-linked peptides in PSII than has been previously reported. This approach thus holds promise to identify other transient protein–protein interactions in membrane protein complexes.

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Protein-protein docking using learned three-dimensional representations

10.1101/738690 ◽

2019 ◽

Cited By ~ 1

Author(s):

Georgy Derevyanko ◽

Guillaume Lamoureux

Keyword(s):

Protein Interactions ◽

Network Architecture ◽

Protein Complexes ◽

Three Dimensional ◽

Spatial Arrangement ◽

Protein Docking ◽

Protein Protein Interactions ◽

Translational Invariance ◽

Shape Complementarity ◽

Spatial Features

AbstractProtein-protein interactions are determined by a number of hard-to-capture features related to shape complementarity, electrostatics, and hydrophobicity. These features may be intrinsic to the protein or induced by the presence of a partner. A conventional approach to protein-protein docking consists in engineering a small number of spatial features for each protein, and in minimizing the sum of their correlations with respect to the spatial arrangement of the two proteins. To generalize this approach, we introduce a deep neural network architecture that transforms the raw atomic densities of each protein into complex three-dimensional representations. Each point in the volume containing the protein is described by 48 learned features, which are correlated and combined with the features of a second protein to produce a score dependent on the relative position and orientation of the two proteins. The architecture is based on multiple layers of SE(3)-equivariant convolutional neural networks, which provide built-in rotational and translational invariance of the score with respect to the structure of the complex. The model is trained end-to-end on a set of decoy conformations generated from 851 nonredundant protein-protein complexes and is tested on data from the Protein-Protein Docking Benchmark Version 4.0.

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

Bioinformatics ◽

10.1093/bioinformatics/btaa897 ◽

2020 ◽

Cited By ~ 1

Author(s):

Paweł Krupa ◽

Agnieszka S Karczyńska ◽

Magdalena A Mozolewska ◽

Adam Liwo ◽

Cezary Czaplewski

Keyword(s):

Protein Complexes ◽

Md Simulations ◽

Protein Docking ◽

Conformational Space ◽

Coarse Grained ◽

Supplementary Information ◽

Replica Exchange ◽

Variable Degree ◽

Single Chain ◽

Simulation Speed

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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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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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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In Silico Peptide Ligation: Iterative Residue Docking and Linking as a New Approach to Predict Protein-Peptide Interactions

Molecules ◽

10.3390/molecules24071351 ◽

2019 ◽

Vol 24 (7) ◽

pp. 1351 ◽

Cited By ~ 2

Author(s):

Julien Diharce ◽

Mickaël Cueto ◽

Massimiliano Beltramo ◽

Vincent Aucagne ◽

Pascal Bonnet

Keyword(s):

Protein Interactions ◽

In Silico ◽

Protein Complexes ◽

Binding Mode ◽

New Drugs ◽

Conformational Space ◽

Rational Development ◽

Theoretical Approaches ◽

Data Prediction ◽

High Flexibility

Peptide–protein interactions are corner-stones of living functions involved in essential mechanisms, such as cell signaling. Given the difficulty of obtaining direct experimental structural biology data, prediction of those interactions is of crucial interest for the rational development of new drugs, notably to fight diseases, such as cancer or Alzheimer’s disease. Because of the high flexibility of natural unconstrained linear peptides, prediction of their binding mode in a protein cavity remains challenging. Several theoretical approaches have been developed in the last decade to address this issue. Nevertheless, improvements are needed, such as the conformation prediction of peptide side-chains, which are dependent on peptide length and flexibility. Here, we present a novel in silico method, Iterative Residue Docking and Linking (IRDL), to efficiently predict peptide–protein interactions. In order to reduce the conformational space, this innovative method splits peptides into several short segments. Then, it uses the performance of intramolecular covalent docking to rebuild, sequentially, the complete peptide in the active site of its protein target. Once the peptide is constructed, a rescoring step is applied in order to correctly rank all IRDL solutions. Applied on a set of 11 crystallized peptide–protein complexes, the IRDL method shows promising results, since it is able to retrieve experimental binding conformations with a Root Mean Square Deviation (RMSD) below 2 Å in the top five ranked solutions. For some complexes, IRDL method outperforms two other docking protocols evaluated in this study. Hence, IRDL is a new tool that could be used in drug design projects to predict peptide–protein interactions.

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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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