scholarly journals On the analysis of protein-protein interactions via knowledge-based potentials for the prediction of protein-protein docking

2011 ◽  
Vol 20 (3) ◽  
pp. 529-541 ◽  
Author(s):  
Elisenda Feliu ◽  
Patrick Aloy ◽  
Baldo Oliva
Keyword(s):  
Protein Interactions ◽  
Protein Docking ◽  
Protein Protein Interactions ◽  
Knowledge Based

scholarly journals A knowledge–based scoring function to assess the stability of quaternary protein assemblies

2019 ◽  
Author(s):  
Abhilesh S. Dhawanjewar ◽  
Ankit Roy ◽  
M.S. Madhusudhan
Keyword(s):  
Protein Interactions ◽  
Binary Classification ◽  
Scoring Function ◽  
Protein Docking ◽  
Scoring Functions ◽  
Protein Protein Interactions ◽  
Residue Contact ◽  
Knowledge Based ◽  
Cellular Biochemistry ◽  
The Stability

AbstractMotivationElucidation of protein-protein interactions is a necessary step towards understanding the complete repertoire of cellular biochemistry. Given the enormity of the problem, the expenses and limitations of experimental methods, it is imperative that this problem is tackled computationally. In silico predictions of protein interactions entail sampling different conformations of the purported complex and then scoring these to assess for interaction viability. In this study we have devised a new scheme for scoring protein-protein interactions.ResultsOur method, PIZSA (Protein Interaction Z Score Assessment) is a binary classification scheme for identification of stable protein quaternary assemblies (binders/non-binders) based on statistical potentials. The scoring scheme incorporates residue-residue contact preference on the interface with per residue-pair atomic contributions and accounts for clashes. PIZSA can accurately discriminate between native and non-native structural conformations from protein docking experiments and outperform other recently published scoring functions, demonstrated through testing on a benchmark set and the CAPRI Score_set. Though not explicitly trained for this purpose, PIZSA potentials can identify spurious interactions that are artefacts of the crystallization process.AvailabilityPIZSA is implemented as awebserverat http://cospi.iiserpune.ac.in/pizsa/[email protected]

ATRIPPI: AN ATOM-RESIDUE PREFERENCE SCORING FUNCTION FOR PROTEIN–PROTEIN INTERACTIONS

2010 ◽  
Vol 19 (03) ◽  
pp. 251-266 ◽  
Author(s):  
KANG-PING LIU ◽  
KAI-CHENG HSU ◽  
JHANG-WEI HUANG ◽  
LU-SHIAN CHANG ◽  
JINN-MOON YANG
Keyword(s):  
Protein Interactions ◽  
Electrostatic Interactions ◽  
Specific Interaction ◽  
Scoring Function ◽  
Protein Docking ◽  
Protein Protein Interactions ◽  
Disulfide Bonding ◽  
Knowledge Based ◽  
Aromatic Interactions ◽  
Protein Interfaces

We present an ATRIPPI model for analyzing protein–protein interactions. This model is a 167-atom-type and residue-specific interaction preferences with distance bins derived from 641 co-crystallized protein–protein interfaces. The ATRIPPI model is able to yield physical meanings of hydrogen bonding, disulfide bonding, electrostatic interactions, van der Waals and aromatic–aromatic interactions. We applied this model to identify the native states and near-native complex structures on 17 bound and 17 unbound complexes from thousands of decoy structures. On average, 77.5% structures (155 structures) of top rank 200 structures are closed to the native structure. These results suggest that the ATRIPPI model is able to keep the advantages of both atom–atom and residue–residue interactions and is a potential knowledge-based scoring function for protein–protein docking methods. We believe that our model is robust and provides biological meanings to support protein–protein interactions.

scholarly journals A knowledge-based scoring function to assess quaternary associations of proteins

2020 ◽  
Vol 36 (12) ◽  
pp. 3739-3748
Author(s):  
Abhilesh S Dhawanjewar ◽  
Ankit A Roy ◽  
Mallur S Madhusudhan
Keyword(s):  
Protein Interactions ◽  
Statistical Physics ◽  
Binary Classification ◽  
Scoring Function ◽  
Protein Docking ◽  
Supplementary Information ◽  
Scoring Functions ◽  
Biological Interactions ◽  
Protein Protein Interactions ◽  
Knowledge Based

Abstract Motivation The elucidation of all inter-protein interactions would significantly enhance our knowledge of cellular processes at a molecular level. Given the enormity of the problem, the expenses and limitations of experimental methods, it is imperative that this problem is tackled computationally. In silico predictions of protein interactions entail sampling different conformations of the purported complex and then scoring these to assess for interaction viability. In this study, we have devised a new scheme for scoring protein–protein interactions. Results Our method, PIZSA (Protein Interaction Z-Score Assessment), is a binary classification scheme for identification of native protein quaternary assemblies (binders/nonbinders) based on statistical potentials. The scoring scheme incorporates residue–residue contact preference on the interface with per residue-pair atomic contributions and accounts for clashes. PIZSA can accurately discriminate between native and non-native structural conformations from protein docking experiments and outperform other contact-based potential scoring functions. The method has been extensively benchmarked and is among the top 6 methods, outperforming 31 other statistical, physics based and machine learning scoring schemes. The PIZSA potentials can also distinguish crystallization artifacts from biological interactions. Availability and implementation PIZSA is implemented as a web server at http://cospi.iiserpune.ac.in/pizsa and can be downloaded as a standalone package from http://cospi.iiserpune.ac.in/pizsa/Download/Download.html. Supplementary information Supplementary data are available at Bioinformatics online.

Decoding Protein-protein Interactions: An Overview

2020 ◽  
Vol 20 (10) ◽  
pp. 855-882
Author(s):  
Olivia Slater ◽  
Bethany Miller ◽  
Maria Kontoyianni
Keyword(s):  
Drug Discovery ◽  
Protein Interactions ◽  
Drug Repurposing ◽  
Protein Docking ◽  
Target Space ◽  
Protein Protein Interactions ◽  
X Ray Crystallography ◽  
Protein Protein Interaction ◽  
Interaction Sites ◽  
Long Time

Drug discovery has focused on the paradigm “one drug, one target” for a long time. However, small molecules can act at multiple macromolecular targets, which serves as the basis for drug repurposing. In an effort to expand the target space, and given advances in X-ray crystallography, protein-protein interactions have become an emerging focus area of drug discovery enterprises. Proteins interact with other biomolecules and it is this intricate network of interactions that determines the behavior of the system and its biological processes. In this review, we briefly discuss networks in disease, followed by computational methods for protein-protein complex prediction. Computational methodologies and techniques employed towards objectives such as protein-protein docking, protein-protein interactions, and interface predictions are described extensively. Docking aims at producing a complex between proteins, while interface predictions identify a subset of residues on one protein that could interact with a partner, and protein-protein interaction sites address whether two proteins interact. In addition, approaches to predict hot spots and binding sites are presented along with a representative example of our internal project on the chemokine CXC receptor 3 B-isoform and predictive modeling with IP10 and PF4.

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

2017 ◽  
Vol 114 (9) ◽  
pp. 2224-2229 ◽  
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.

scholarly journals Protein-protein docking using learned three-dimensional representations

2019 ◽  
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.

scholarly journals Pushing the accuracy limit of shape complementarity for protein-protein docking

2019 ◽  
Vol 20 (S25) ◽  
Author(s):  
Yumeng Yan ◽  
Sheng-You Huang
Keyword(s):  
Success Rate ◽  
Protein Interactions ◽  
Shape Representation ◽  
Scoring Function ◽  
Protein Docking ◽  
Protein Protein Interactions ◽  
Second Best ◽  
Shape Complementarity ◽  
Docking Program ◽  
Docking Approach

Abstract Background Protein-protein docking is a valuable computational approach for investigating protein-protein interactions. Shape complementarity is the most basic component of a scoring function and plays an important role in protein-protein docking. Despite significant progresses, shape representation remains an open question in the development of protein-protein docking algorithms, especially for grid-based docking approaches. Results We have proposed a new pairwise shape-based scoring function (LSC) for protein-protein docking which adopts an exponential form to take into account long-range interactions between protein atoms. The LSC scoring function was incorporated into our FFT-based docking program and evaluated for both bound and unbound docking on the protein docking benchmark 4.0. It was shown that our LSC achieved a significantly better performance than four other similar docking methods, ZDOCK 2.1, MolFit/G, GRAMM, and FTDock/G, in both success rate and number of hits. When considering the top 10 predictions, LSC obtained a success rate of 51.71% and 6.82% for bound and unbound docking, respectively, compared to 42.61% and 4.55% for the second-best program ZDOCK 2.1. LSC also yielded an average of 8.38 and 3.94 hits per complex in the top 1000 predictions for bound and unbound docking, respectively, followed by 6.38 and 2.96 hits for the second-best ZDOCK 2.1. Conclusions The present LSC method will not only provide an initial-stage docking approach for post-docking processes but also have a general implementation for accurate representation of other energy terms on grids in protein-protein docking. The software has been implemented in our HDOCK web server at http://hdock.phys.hust.edu.cn/.

IN SILICO SCREENING OF PROTEIN–PROTEIN INTERACTIONS WITH ALL-TO-ALL RIGID DOCKING AND CLUSTERING: AN APPLICATION TO PATHWAY ANALYSIS

2009 ◽  
Vol 07 (06) ◽  
pp. 991-1012 ◽  
Author(s):  
YURI MATSUZAKI ◽  
YUSUKE MATSUZAKI ◽  
TOSHIYUKI SATO ◽  
YUTAKA AKIYAMA
Keyword(s):  
Pathway Analysis ◽  
Structure Data ◽  
Protein Interactions ◽  
Tertiary Structure ◽  
Protein Structures ◽  
Protein Docking ◽  
Protein Protein Interactions ◽  
Benchmark Data ◽  
Computational Screening ◽  
F Measure

We propose a computational screening system of protein–protein interactions using tertiary structure data. Our system combines all-to-all protein docking and clustering to find interacting protein pairs. We tuned our prediction system by applying various parameters and clustering algorithms and succeeded in outperforming previous methods. This method was also applied to a biological pathway estimation problem to show its use in network level analysis. The structural data were collected from the Protein Data Bank, PDB. Then all-to-all docking among target protein structures was conducted using a conventional protein–protein docking software package, ZDOCK. The highest-ranked 2000 decoys were clustered based on structural similarity among the predicted docking forms. The features of generated clusters were analyzed to estimate the biological relevance of protein–protein interactions. Our system achieves a best F-measure value of 0.43 when applied to a subset of general protein–protein docking benchmark data. The same system was applied to protein data in a bacterial chemotaxis pathway, utilizing essentially the same parameter set as the benchmark data. We obtained 0.45 for the F-measure value. The proposed approach to computational PPI detection is a promising methodology for mediating between structural studies and systems biology by utilizing cumulative protein structure data for pathway analysis.

scholarly journals Elucidation of transient protein-protein interactions within carrier protein-dependent biosynthesis

2020 ◽  
Author(s):  
Michael Burkart ◽  
Thomas Bartholow ◽  
Terra Sztain ◽  
Ashay Patel ◽  
D Lee ◽  
...  
Keyword(s):  
Fatty Acid ◽  
Protein Interactions ◽  
Fatty Acid Biosynthesis ◽  
De Novo ◽  
Acyl Carrier Protein ◽  
Protein Docking ◽  
Carrier Protein ◽  
Protein Protein Interactions ◽  
Acid Biosynthesis ◽  
E Coli

Abstract Fatty acid biosynthesis (FAB) is an essential and highly conserved metabolic pathway. In bacteria, this process is mediated by an elaborate network of protein•protein interactions (PPIs) involving a small, dynamic acyl carrier protein that interacts with dozens of other partner proteins (PPs). These PPIs have remained poorly characterized due to their dynamic and transient nature. Using a combination of solution-phase NMR spectroscopy and protein-protein docking simulations, we report a comprehensive residue-by-residue comparison of the PPIs formed during FAB in Escherichia coli. This work reveals the molecular basis of six discrete binding events responsible for E. coli FAB and offers insights into a method to characterize these events and those in related carrier protein-dependent pathways. ONE SENTENCE SUMMARY: Through a combination of structural and computational analysis, a comparative evaluation of protein-protein interactions in de novo fatty acid biosynthesis in E. coli is performed.

Computational Approaches for Elucidating Protein-Protein Interactions in Cation Channel Signaling

2020 ◽  
Vol 21 (2) ◽  
pp. 179-192
Author(s):  
Baichun Hu ◽  
Xiaoming Zheng ◽  
Ying Wang ◽  
Jian Wang ◽  
Fengjiao Zhang
Keyword(s):  
Molecular Dynamics ◽  
Molecular Dynamics Simulation ◽  
Homology Modeling ◽  
Protein Interactions ◽  
Protein Docking ◽  
Cation Channel ◽  
Dynamics Simulation ◽  
Protein Protein Interactions ◽  
Molecular Graphics ◽  
Computational Approaches

Background: The lipid bilayer of the plasma membrane is impermeable to ions, yet changes in the flux of ions across the cell membrane are critical regulatory events in cells. Because of their regulatory roles in a range of physiological processes, such as electrical signaling in muscles and neurons, to name a few, these proteins are one of the most important drug targets. Objective: This review mainly focused on the computational approaches for elucidating proteinprotein interactions in cation channel signaling. Discussion: Due to continuously advanced facilities and technologies in computer sciences, the physical contacts of macromolecules of channel structures have been virtually visualized. Indeed, techniques like protein-protein docking, homology modeling, and molecular dynamics simulation are valuable tools for predicting the protein complex and refining channels with unreleased structures. Undoubtedly, these approaches will greatly expand the cation channel signaling research, thereby speeding up structure-based drug design and discovery. Conclusion: We introduced a series of valuable computational tools for elucidating protein-protein interactions in cation channel signaling, including molecular graphics, protein-protein docking, homology modeling, and molecular dynamics simulation.

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