A Geometric Complementarity-Based Tool for Protein–Protein Docking

2021 ◽  
Author(s):  
Sharon Sunny ◽  
P. B. Jayaraj
Keyword(s):  
Structure Prediction ◽  
Complex Structure ◽  
Protein Docking ◽  
Geometric Algorithms ◽  
Coarse Grained ◽  
Biological Impact ◽  
Searching Strategy ◽  
Determining Factor ◽  
Docking Benchmark ◽  
Conformation Space

The computationally hard protein–protein complex structure prediction problem is continuously fascinating to the scientific community due to its biological impact. The field has witnessed the application of geometric algorithms, randomized algorithms, and evolutionary algorithms to name a few. These techniques improve either the searching or scoring phase. An effective searching strategy does not generate a large conformation space that perhaps demands computational power. Another determining factor is the parameter chosen for score calculation. The proposed method is an attempt to curtail the conformations by limiting the search procedure to probable regions. In this method, partial derivatives are calculated on the coarse-grained representation of the surface residues to identify the optimal points on the protein surface. Contrary to the existing geometric-based algorithms that align the convex and concave regions of both proteins, this method aligns the concave regions of the receptor with convex regions of the ligand only and thus reduces the size of conformation space. The method’s performance is evaluated using the 55 newly added targets in Protein–Protein Docking Benchmark v 5 and is found to be successful for around 47% of the targets.

scholarly journals ResDock: Protein-Protein Docking Using Shape Complementarity of Surface Residues

2021 ◽  
Author(s):  
Sharon Sunny ◽  
Jayaraj PB
Keyword(s):  
Ab Initio ◽  
Protein Complex ◽  
Structure Prediction ◽  
Complex Structure ◽  
Scoring Function ◽  
Protein Docking ◽  
New Method ◽  
Protein Surfaces ◽  
Shape Complementarity ◽  
Conformation Space

ResDock is a new method to improve the performance of protein-protein complex structure prediction. It utilizes shape complementarity of the protein surfaces to generate the conformation space. The use of an appropriate scoring function helps to select the feasible structures. An interplay between pose generation phase and scoring phase enhance the performance of the proposed ab initio technique. <br>

scholarly journals ResDock: Protein-Protein Docking Using Shape Complementarity of Surface Residues

2021 ◽  
Author(s):  
Sharon Sunny ◽  
Jayaraj PB
Keyword(s):  
Ab Initio ◽  
Protein Complex ◽  
Structure Prediction ◽  
Complex Structure ◽  
Scoring Function ◽  
Protein Docking ◽  
New Method ◽  
Protein Surfaces ◽  
Shape Complementarity ◽  
Conformation Space

ResDock is a new method to improve the performance of protein-protein complex structure prediction. It utilizes shape complementarity of the protein surfaces to generate the conformation space. The use of an appropriate scoring function helps to select the feasible structures. An interplay between pose generation phase and scoring phase enhance the performance of the proposed ab initio technique. <br>

scholarly journals Protein-protein docking with large-scale backbone flexibility

2021 ◽  
Author(s):  
Mateusz Kurcinski ◽  
Sebastian Kmiecik ◽  
Mateusz Zalewski ◽  
Andrzej Kolinski
Keyword(s):  
Binding Site ◽  
Structure Prediction ◽  
Large Scale ◽  
Protein Complexes ◽  
Complex Structure ◽  
Protein Docking ◽  
Coarse Grained ◽  
Backbone Flexibility ◽  
Docking Simulations ◽  
Docking Protocol

AbstractStructure prediction of protein-protein complexes is one of the most critical challenges in computational structural biology. It is often difficult to predict the complex structure, even for relatively rigid proteins. Modeling significant structural flexibility in protein docking remains an unsolved problem. This work demonstrates a protein-protein docking protocol with enhanced sampling that accounts for large-scale backbone flexibility. The docking protocol starts from unbound x-ray structures and is not using any binding site information. In docking, one protein partner undergoes multiple fold rearrangements, rotations, and translations during docking simulations, while the other protein exhibits small backbone fluctuations. Including significant backbone flexibility during the search for the binding site has been made possible using the CABS coarse-grained protein model and Replica Exchange Monte Carlo dynamics. In our simulations, we obtained acceptable quality models for the set of 12 protein-protein complexes, while for selected cases, models were close to high accuracy.

scholarly journals Basin Hopping as a General and Versatile Optimization Framework for the Characterization of Biological Macromolecules

2012 ◽  
Vol 2012 ◽  
pp. 1-19 ◽  
Author(s):  
Brian Olson ◽  
Irina Hashmi ◽  
Kevin Molloy ◽  
Amarda Shehu
Keyword(s):  
Protein Structure ◽  
Structure Prediction ◽  
Optimization Problems ◽  
Protein Docking ◽  
Coarse Grained ◽  
Biological Macromolecules ◽  
Wide Range ◽  
Multiple Variables ◽  
Basin Hopping

Since its introduction, the basin hopping (BH) framework has proven useful for hard nonlinear optimization problems with multiple variables and modalities. Applications span a wide range, from packing problems in geometry to characterization of molecular states in statistical physics. BH is seeing a reemergence in computational structural biology due to its ability to obtain a coarse-grained representation of the protein energy surface in terms of local minima. In this paper, we show that the BH framework is general and versatile, allowing to address problems related to the characterization of protein structure, assembly, and motion due to its fundamental ability to sample minima in a high-dimensional variable space. We show how specific implementations of the main components in BH yield algorithmic realizations that attain state-of-the-art results in the context of ab initio protein structure prediction and rigid protein-protein docking. We also show that BH can map intermediate minima related with motions connecting diverse stable functionally relevant states in a protein molecule, thus serving as a first step towards the characterization of transition trajectories connecting these states.

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.

scholarly journals Integrating cross-linking experiments with ab initio protein-protein docking

2018 ◽  
Author(s):  
Thom Vreven ◽  
Devin K. Schweppe ◽  
Juan D. Chavez ◽  
Chad R. Weisbrod ◽  
Sayaka Shibata ◽  
...  
Keyword(s):  
Experimental Data ◽  
Ab Initio ◽  
Structure Prediction ◽  
Complex Structure ◽  
Protein Docking ◽  
Cross Linking ◽  
Test Cases ◽  
List Type ◽  
Cross Links ◽  
Chemical Cross Linking

ABSTRACTAb initio protein-protein docking algorithms often rely on experimental data to identify the most likely complex structure. We integrated protein-protein docking with the experimental data of chemical cross-linking followed by mass spectrometry. We tested our approach using 12 cases that resulted from an exhaustive search of the Protein Data Bank for protein complexes with cross-links identified in our experiments. We implemented cross-links as constraints based on Euclidean distance or void-volume distance. For most test cases the rank of the top-scoring near-native prediction was improved by at least two fold compared with docking without the cross-link information, and the success rates for the top 5 and top 10 predictions doubled. Our results demonstrate the delicate balance between retaining correct predictions and eliminating false positives. Several test cases had multiple components with distinct interfaces, and we present an approach for assigning cross-links to the interfaces. Employing the symmetry information for these cases further improved the performance of complex structure prediction.HighlightsIncorporating low-resolution cross-linking experimental data in protein-protein docking algorithms improves performance more than two fold.Integration of protein-protein docking with chemical cross-linking reveals information on the configuration of higher order complexes.Symmetry analysis of protein-protein docking results improves the predictions of multimeric complex structures

scholarly journals Protein docking model evaluation by 3D deep convolutional neural networks

2019 ◽  
Vol 36 (7) ◽  
pp. 2113-2118 ◽  
Author(s):  
Xiao Wang ◽  
Genki Terashi ◽  
Charles W Christoffer ◽  
Mengmeng Zhu ◽  
Daisuke Kihara
Keyword(s):  
Neural Network ◽  
Structure Prediction ◽  
Deep Neural Network ◽  
Molecular Mechanisms ◽  
Complex Structure ◽  
Protein Docking ◽  
Supplementary Information ◽  
Atomic Interaction ◽  
Deep Convolutional Neural Networks ◽  
Docking Model

Abstract Motivation Many important cellular processes involve physical interactions of proteins. Therefore, determining protein quaternary structures provide critical insights for understanding molecular mechanisms of functions of the complexes. To complement experimental methods, many computational methods have been developed to predict structures of protein complexes. One of the challenges in computational protein complex structure prediction is to identify near-native models from a large pool of generated models. Results We developed a convolutional deep neural network-based approach named DOcking decoy selection with Voxel-based deep neural nEtwork (DOVE) for evaluating protein docking models. To evaluate a protein docking model, DOVE scans the protein–protein interface of the model with a 3D voxel and considers atomic interaction types and their energetic contributions as input features applied to the neural network. The deep learning models were trained and validated on docking models available in the ZDock and DockGround databases. Among the different combinations of features tested, almost all outperformed existing scoring functions. Availability and implementation Codes available at http://github.com/kiharalab/DOVE, http://kiharalab.org/dove/. Supplementary information Supplementary data are available at Bioinformatics online.

Proteomic Analysis of Medicinal Plant Calotropis Gigantea by insilico Peptide Mass Fingerprinting

2020 ◽  
Vol 16 ◽  
Author(s):  
Saad Ur Rehman ◽  
Muhammad Rizwan ◽  
Sajid Khan ◽  
Azhar Mehmood ◽  
Anum Munir
Keyword(s):  
Medicinal Plant ◽  
Structure Prediction ◽  
3D Structure ◽  
Peptide Mass Fingerprinting ◽  
Protein Docking ◽  
Receptor Protein ◽  
Human Leukemia ◽  
Protein Database ◽  
Calotropis Gigantea ◽  
Peptide Mass

: Medicinal plants are the basic source of medicinal compounds traditionally used for the treatment of human diseases. Calotropis gigantea a medicinal plant belonging to the family of Apocynaceae in the plant kingdom and subfamily Asclepiadaceae usually bearing multiple medicinal properties to cure a variety of diseases. Background: The Peptide Mass Fingerprinting (PMF) identifies the proteins from a reference protein database by comparing the amino acid sequence that is previously stored in a database and identified. Method: The calculation of insilico peptide masses is done through the ExPASy PeptideMass and these masses are used to identify the peptides from MASCOT online server. Anticancer probability is calculated from the iACP server, docking of active peptides is done by CABS-dock the server. Objective: The purpose of the study is to identify the peptides having anti-cancerous properties by in-silico peptide mass fingerprinting. Results : The anti-cancerous peptides are identified with the MASCOT peptide mass fingerprinting server, the identified peptides are screened and only the anti-cancer are selected. De novo peptide structure prediction is used for 3D structure prediction by PEP-FOLD 3 server. The docking results confirm strong bonding with the interacting amino acids of the receptor protein of breast cancer BRCA1 which shows the best peptide binding to the Active chain, the human leukemia protein docking with peptides shows the accurate binding. Conclusion : These peptides are stable and functional and are the best way for the treatment of cancer and many other deadly diseases.

scholarly journals FOLDING ELASTIC TRANSMEMBRANE HELICES TO FIT IN A LOW-RESOLUTION IMAGE BY ELECTRON MICROSCOPY

2011 ◽  
Vol 09 (supp01) ◽  
pp. 37-50 ◽  
Author(s):  
YUTAKA UENO ◽  
KAZUNORI KAWASAKI ◽  
OSAMU SAITO ◽  
MASAFUMI ARAI ◽  
MAKIKO SUWA
Keyword(s):  
Membrane Proteins ◽  
Structure Prediction ◽  
Molecular Model ◽  
Imaging Techniques ◽  
Transmembrane Helix ◽  
Prediction Method ◽  
Molecular Shape ◽  
Coarse Grained ◽  
Transmembrane Helices ◽  
Low Resolution

Structure prediction of membrane proteins could be constrained and thereby improved by introducing data of the observed molecular shape. We studied a coarse-grained molecular model that relied on residue-based dummy atoms to fold the transmembrane helices of a protein in the observed molecular shape. Based on the inter-residue potential, the α-helices were folded to contact each other in a simulated annealing protocol to search optimized conformation. Fitting the model into a three-dimensional volume was tested for proteins with known structures and resulted in a fairly reasonable arrangement of helices. In addition, the constraint to the packing transmembrane helix with the two-dimensional region was tested and found to work as a very similar folding guide. The obtained models nicely represented α-helices with the desired slight bend. Our structure prediction method for membrane proteins well demonstrated reasonable folding results using a low-resolution structural constraint introduced from recent cell-surface imaging techniques.

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

2020 ◽  
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.

Sign in / Sign up

Export Citation Format

Share Document