Induced fit with replica exchange improves protein complex structure prediction

Despite the progress in prediction of protein complexes over the last decade, recent blind protein complex structure prediction challenges revealed limited success rates (less than 20% models with DockQ score > 0.4) on targets that exhibit significant conformational change upon binding. To overcome limitations in capturing backbone motions, we developed a new, aggressive sampling method that incorporates temperature replica exchange Monte Carlo (T-REMC) and conformational sampling techniques within docking protocols in Rosetta. Our method, ReplicaDock 2.0, mimics induced-fit mechanism of protein binding to sample backbone motions across putative interface residues on-the-fly, thereby recapitulating binding-partner induced conformational changes. Furthermore, ReplicaDock 2.0 clocks in at 150-500 CPU hours per target (protein-size dependent); a runtime that is significantly faster than Molecular Dynamics based approaches. For a benchmark set of 88 proteins with moderate to high flexibility (unbound-to-bound iRMSD over 1.2 Angstroms), ReplicaDock 2.0 successfully docks 61% of moderately flexible complexes and 35% of highly flexible complexes. Additionally, we demonstrate that by biasing backbone sampling particularly towards residues comprising flexible loops or hinge domains, highly flexible targets can be predicted to under 2 angstrom accuracy. This indicates that additional gains are possible when mobile protein segments are known.

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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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Protein Complex Structure Prediction Powered by Multiple Sequence Alignment of Interologs from Multiple Taxonomic Ranks and AlphaFold2

10.1101/2021.12.21.473437 ◽

2021 ◽

Author(s):

Yunda Si ◽

Chengfei Yan

Keyword(s):

Sequence Alignment ◽

Multiple Sequence Alignment ◽

Protein Complex ◽

Structure Prediction ◽

Complex Structure ◽

Complex Structures ◽

Success Rates ◽

Multiple Sequence ◽

Taxonomic Rank ◽

Protein Protein Interaction

AlphaFold2 is expected to be able to predict protein complex structures as long as a multiple sequence alignment (MSA) of the interologs of the target protein-protein interaction (PPI) can be provided. However, preparing the MSA of protein-protein interologs is a non-trivial task. In this study, a simplified phylogeny-based approach was applied to generate the MSA of interologs, which was then used as the input of AlphaFold2 for protein complex structure prediction. Extensively benchmarked this protocol on non-redundant PPI dataset, we show complex structures of 79.5% of the bacterial PPIs and 49.8% of the eukaryotic PPIs can be successfully predicted. Considering PPIs may not be conserved in species with long evolutionary distances, we further restricted interologs in the MSA to different taxonomic ranks of the species of the target PPI in protein complex structure prediction. We found the success rates can be increased to 87.9% for the bacterial PPIs and 56.3% of the eukaryotic PPIs if interologs in the MSA are restricted to a specific taxonomic rank of the species of each target PPI. Finally, we show the optimal taxonomic ranks for protein complex structure prediction can be selected with the application of the predicted TM-scores of the output models.

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Faculty Opinions recommendation of Evaluating template-based and template-free protein-protein complex structure prediction.

Faculty Opinions – Post-Publication Peer Review of the Biomedical Literature ◽

10.3410/f.718025638.793480806 ◽

2013 ◽

Author(s):

Yaoqi Zhou

Keyword(s):

Protein Complex ◽

Structure Prediction ◽

Complex Structure ◽

Free Protein ◽

Template Free

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Comprehensive analysis of heterotrimeric G-protein complex diversity and their interactions with GPCRs in solution

Proceedings of the National Academy of Sciences ◽

10.1073/pnas.1417573112 ◽

2015 ◽

Vol 112 (11) ◽

pp. E1181-E1190 ◽

Cited By ~ 23

Author(s):

Matthias Hillenbrand ◽

Christian Schori ◽

Jendrik Schöppe ◽

Andreas Plückthun

Keyword(s):

G Protein ◽

G Proteins ◽

Conformational Changes ◽

Protein Complex ◽

Protein Complexes ◽

Heterotrimeric G Proteins ◽

Agonist Binding ◽

Transient Nature ◽

The Stability ◽

Neurotensin Receptor 1

Agonist binding to G-protein–coupled receptors (GPCRs) triggers signal transduction cascades involving heterotrimeric G proteins as key players. A major obstacle for drug design is the limited knowledge of conformational changes upon agonist binding, the details of interaction with the different G proteins, and the transmission to movements within the G protein. Although a variety of different GPCR/G protein complex structures would be needed, the transient nature of this complex and the intrinsic instability against dissociation make this endeavor very challenging. We have previously evolved GPCR mutants that display higher stability and retain their interaction with G proteins. We aimed at finding all G-protein combinations that preferentially interact with neurotensin receptor 1 (NTR1) and our stabilized mutants. We first systematically analyzed by coimmunoprecipitation the capability of 120 different G-protein combinations consisting of αi1or αsLand all possible βγ-dimers to form a heterotrimeric complex. This analysis revealed a surprisingly unrestricted ability of the G-protein subunits to form heterotrimeric complexes, including βγ-dimers previously thought to be nonexistent, except for combinations containing β5. A second screen on coupling preference of all G-protein heterotrimers to NTR1 wild type and a stabilized mutant indicated a preference for those Gαi1βγ combinations containing γ1and γ11. Heterotrimeric G proteins, including combinations believed to be nonexistent, were purified, and complexes with the GPCR were prepared. Our results shed new light on the combinatorial diversity of G proteins and their coupling to GPCRs and open new approaches to improve the stability of GPCR/G-protein complexes.

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1P007 Protein-protein complex structure prediction using the solution theory in the energy representation(Protein: Structure,Poster,The 52th Annual Meeting of the Biophysical Society of Japan(BSJ2014))

Seibutsu Butsuri ◽

10.2142/biophys.54.s142_1 ◽

2014 ◽

Vol 54 (supplement1-2) ◽

pp. S142

Author(s):

Takemura Kazuhiro ◽

Matubayasi Nobuyuki ◽

Kitao Akio

Keyword(s):

Protein Structure ◽

Annual Meeting ◽

Protein Complex ◽

Structure Prediction ◽

Complex Structure ◽

Solution Theory ◽

Biophysical Society

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Protein homodimers structure prediction based on deep neural network

Informatics ◽

10.37661/1816-0301-2020-17-2-44-53 ◽

2020 ◽

Vol 17 (2) ◽

pp. 44-53

Author(s):

A. Y. Hadarovich ◽

A. A. Kalinouski ◽

A. V. Tuzikov

Keyword(s):

Neural Network ◽

Protein Complex ◽

Structure Prediction ◽

Protein Complexes ◽

3D Structure ◽

Optimization Procedure ◽

Descent Method ◽

Gradient Descent Method ◽

Contact Map ◽

Suggested Approach

Structural prediction of protein-protein complexes has important application in such domains as modeling of biological processes and drug design. Homodimers (complexes which consist of two identical proteins) are the most common type of protein complexes in nature but there is still no universal algorithm to predict their 3D structures. Experimental techniques to identify the structure of protein complex require enormous amount of time and resources, and each method has its own limitations. Recently Deep Neural Networks allowed to predict structures of individual proteins greatly prevailing in accuracy over other algorithmic approaches. Building on the idea of this approach, we developed an algorithm to model the 3D structure of homodimer based on deep learning. It consists of two major steps: at the first step a protein complex contact map is predicted with the deep convolutional neural network, and the second stage is used to predict 3D structure of homodimer based on obtained contact map and optimization procedure. The use of the neural network in combination with optimization procedure based on gradient descent method allowed to predict structures for protein homodimers. The suggested approach was tested and validated on a dataset of protein homodimers from Protein Data Bank (PDB). The developed procedure could be also used for evaluating protein homodimer models as one of the stages in drug compounds developing.

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Using 3dRPC for RNA–protein complex structure prediction

Biophysics Reports ◽

10.1007/s41048-017-0034-y ◽

2016 ◽

Vol 2 (5-6) ◽

pp. 95-99 ◽

Cited By ~ 10

Author(s):

Yangyu Huang ◽

Haotian Li ◽

Yi Xiao

Keyword(s):

Protein Complex ◽

Structure Prediction ◽

Complex Structure

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Protein-complex structure completion usingIPCAS(Iterative Protein Crystal structure Automatic Solution)

Acta Crystallographica Section D Biological Crystallography ◽

10.1107/s1399004715008597 ◽

2015 ◽

Vol 71 (7) ◽

pp. 1487-1492 ◽

Cited By ~ 6

Author(s):

Weizhe Zhang ◽

Hongmin Zhang ◽

Tao Zhang ◽

Haifu Fan ◽

Quan Hao

Keyword(s):

Crystal Structure ◽

Protein Complex ◽

Model Building ◽

Protein Complexes ◽

Direct Method ◽

Complex Structure ◽

Protein Crystal ◽

Molecular Replacement ◽

Protein Crystal Structure ◽

Building Program

Protein complexes are essential components in many cellular processes. In this study, a procedure to determine the protein-complex structure from a partial molecular-replacement (MR) solution is demonstrated using a direct-method-aided dual-space iterative phasing and model-building program suite,IPCAS(Iterative Protein Crystal structure Automatic Solution). TheIPCASiteration procedure involves (i) real-space model building and refinement, (ii) direct-method-aided reciprocal-space phase refinement and (iii) phase improvement through density modification. The procedure has been tested with four protein complexes, including two previously unknown structures. It was possible to useIPCASto build the whole complex structure from one or less than one subunit once the molecular-replacement method was able to give a partial solution. In the most challenging case,IPCASwas able to extend to the full length starting from less than 30% of the complex structure, while conventional model-building procedures were unsuccessful.

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