Structure Prediction of Protein Complexes

2007 ◽  
pp. 109-134 ◽  
Author(s):  
Brian Pierce ◽  
Zhiping Weng
Keyword(s):  
Structure Prediction ◽  
Protein Complexes

scholarly journals Bioinformatics Tools and Benchmarks for Computational Docking and 3D Structure Prediction of RNA-Protein Complexes

Genes ◽  
2018 ◽  
Vol 9 (9) ◽  
pp. 432 ◽  
Author(s):  
Chandran Nithin ◽  
Pritha Ghosh ◽  
Janusz Bujnicki
Keyword(s):  
Rna Splicing ◽  
Structure Prediction ◽  
Protein Complexes ◽  
3D Structure ◽  
Structural Models ◽  
Computational Prediction ◽  
Computational Docking ◽  
3D Structure Prediction ◽  
Rna Protein Complexes

RNA-protein (RNP) interactions play essential roles in many biological processes, such as regulation of co-transcriptional and post-transcriptional gene expression, RNA splicing, transport, storage and stabilization, as well as protein synthesis. An increasing number of RNP structures would aid in a better understanding of these processes. However, due to the technical difficulties associated with experimental determination of macromolecular structures by high-resolution methods, studies on RNP recognition and complex formation present significant challenges. As an alternative, computational prediction of RNP interactions can be carried out. Structural models obtained by theoretical predictive methods are, in general, less reliable compared to models based on experimental measurements but they can be sufficiently accurate to be used as a basis for to formulating functional hypotheses. In this article, we present an overview of computational methods for 3D structure prediction of RNP complexes. We discuss currently available methods for macromolecular docking and for scoring 3D structural models of RNP complexes in particular. Additionally, we also review benchmarks that have been developed to assess the accuracy of these methods.

On the Use of low-resolution Data to Improve Structure Prediction of Proteins and Protein Complexes

2009 ◽  
Vol 5 (11) ◽  
pp. 3129-3137 ◽  
Author(s):  
Marco D’Abramo ◽  
Tim Meyer ◽  
Pau Bernadó ◽  
Carles Pons ◽  
Juan Fernández Recio ◽  
...  
Keyword(s):  
Structure Prediction ◽  
Protein Complexes ◽  
Low Resolution ◽  
Resolution Data

scholarly journals LZerD Protein-Protein Docking Webserver Enhanced With de novo Structure Prediction

2021 ◽  
Vol 8 ◽  
Author(s):  
Charles Christoffer ◽  
Vijay Bharadwaj ◽  
Ryan Luu ◽  
Daisuke Kihara
Keyword(s):  
Protein Structure ◽  
Protein Structure Prediction ◽  
Structure Prediction ◽  
De Novo ◽  
Protein Complexes ◽  
Protein Sequences ◽  
Data Bank ◽  
Protein Docking ◽  
Functional Mechanisms ◽  
Established Technique

Protein-protein docking is a useful tool for modeling the structures of protein complexes that have yet to be experimentally determined. Understanding the structures of protein complexes is a key component for formulating hypotheses in biophysics regarding the functional mechanisms of complexes. Protein-protein docking is an established technique for cases where the structures of the subunits have been determined. While the number of known structures deposited in the Protein Data Bank is increasing, there are still many cases where the structures of individual proteins that users want to dock are not determined yet. Here, we have integrated the AttentiveDist method for protein structure prediction into our LZerD webserver for protein-protein docking, which enables users to simply submit protein sequences and obtain full-complex atomic models, without having to supply any structure themselves. We have further extended the LZerD docking interface with a symmetrical homodimer mode. The LZerD server is available at https://lzerd.kiharalab.org/.

scholarly journals Contact Potential for Structure Prediction of Proteins and Protein Complexes from Potts Model

2018 ◽  
Vol 115 (5) ◽  
pp. 809-821 ◽  
Author(s):  
Ivan Anishchenko ◽  
Petras J. Kundrotas ◽  
Ilya A. Vakser
Keyword(s):  
Potts Model ◽  
Structure Prediction ◽  
Protein Complexes ◽  
Contact Potential

scholarly journals Protein homodimers structure prediction based on deep neural network

Informatics ◽  
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.

DERIVING TOPOLOGY AND SEQUENCE ALIGNMENT FOR THE HELIX SKELETON IN LOW-RESOLUTION PROTEIN DENSITY MAPS

2008 ◽  
Vol 06 (01) ◽  
pp. 183-201 ◽  
Author(s):  
YONGGANG LU ◽  
JING HE ◽  
CHARLIE E. M. STRAUSS
Keyword(s):  
Sequence Alignment ◽  
Structure Prediction ◽  
Protein Complexes ◽  
3D Structure ◽  
Three Dimensional ◽  
Prediction Method ◽  
Density Maps ◽  
Ab Initio Structure ◽  
Geometrical Information ◽  
Protein Density

Cryoelectron microscopy (cryoEM) is an experimental technique to determine the three-dimensional (3D) structure of large protein complexes. Currently, this technique is able to generate protein density maps at 6–9 Å resolution, at which the skeleton of the structure (which is composed of α-helices and β-sheets) can be visualized. As a step towards predicting the entire backbone of the protein from the protein density map, we developed a method to predict the topology and sequence alignment for the skeleton helices. Our method combines the geometrical information of the skeleton helices with the Rosetta ab initio structure prediction method to derive a consensus topology and sequence alignment for the skeleton helices. We tested the method with 60 proteins. For 45 proteins, the majority of the skeleton helices were assigned a correct topology from one of our top ten predictions. The offsets of the alignment for most of the assigned helices were within ±2 amino acids in the sequence. We also analyzed the use of the skeleton helices as a clustering tool for the decoy structures generated by Rosetta. Our comparison suggests that the topology clustering is a better method than a general overlap clustering method to enrich the ranking of decoys, particularly when the decoy pool is small.

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

2019 ◽  
Vol 36 (3) ◽  
pp. 751-757 ◽  
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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