DrugScorePPI for scoring protein-protein interactions: improving a knowledge-based scoring function by atomtype-based QSAR

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]

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

International Journal of Artificial Intelligence Tools ◽

10.1142/s0218213010000169 ◽

2010 ◽

Vol 19 (03) ◽

pp. 251-266 ◽

Cited By ~ 1

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.

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

Bioinformatics ◽

10.1093/bioinformatics/btaa207 ◽

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.

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

BMC Bioinformatics ◽

10.1186/s12859-019-3270-y ◽

2019 ◽

Vol 20 (S25) ◽

Cited By ~ 8

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/.

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Feature Design for Protein Interface hotspots using KFC2 and Rosetta

10.1101/514372 ◽

2019 ◽

Author(s):

Franziska Seeger ◽

Anna Little ◽

Yang Chen ◽

Tina Woolf ◽

Haiyan Cheng ◽

...

Keyword(s):

Protein Interaction ◽

Protein Interactions ◽

Scoring Function ◽

Selection Strategy ◽

Support Vector ◽

Protein Protein Interactions ◽

Forward Selection ◽

Protein Protein Interaction ◽

Combined Features ◽

Improved Performance

AbstractProtein-protein interactions regulate many essential biological processes and play an important role in health and disease. The process of experimentally charac-terizing protein residues that contribute the most to protein-protein interaction affin-ity and specificity is laborious. Thus, developing models that accurately characterize hotspots at protein-protein interfaces provides important information about how to inhibit therapeutically relevant protein-protein interactions. During the course of the ICERM WiSDM workshop 2017, we combined the KFC2a protein-protein interaction hotspot prediction features with Rosetta scoring function terms and interface filter metrics. A 2-way and 3-way forward selection strategy was employed to train support vector machine classifiers, as was a reverse feature elimination strategy. From these results, we identified subsets of KFC2a and Rosetta combined features that show improved performance over KFC2a features alone.

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Prediction and validation of protein–protein interactors from genome-wide DNA-binding data using a knowledge-based machine-learning approach

Open Biology ◽

10.1098/rsob.160183 ◽

2016 ◽

Vol 6 (9) ◽

pp. 160183 ◽

Cited By ~ 9

Author(s):

Ashley J. Waardenberg ◽

Bernou Homan ◽

Stephanie Mohamed ◽

Richard P. Harvey ◽

Romaric Bouveret

Keyword(s):

Machine Learning ◽

Dna Binding ◽

Protein Interactions ◽

Heart Development ◽

Machine Learning Algorithms ◽

Protein Protein Interactions ◽

Knowledge Based ◽

Genome Wide ◽

Binding Data ◽

Gene Regulatory

The ability to accurately predict the DNA targets and interacting cofactors of transcriptional regulators from genome-wide data can significantly advance our understanding of gene regulatory networks. NKX2-5 is a homeodomain transcription factor that sits high in the cardiac gene regulatory network and is essential for normal heart development. We previously identified genomic targets for NKX2-5 in mouse HL-1 atrial cardiomyocytes using DNA-adenine methyltransferase identification (DamID). Here, we apply machine learning algorithms and propose a knowledge-based feature selection method for predicting NKX2-5 protein : protein interactions based on motif grammar in genome-wide DNA-binding data. We assessed model performance using leave-one-out cross-validation and a completely independent DamID experiment performed with replicates. In addition to identifying previously described NKX2-5-interacting proteins, including GATA, HAND and TBX family members, a number of novel interactors were identified, with direct protein : protein interactions between NKX2-5 and retinoid X receptor (RXR), paired-related homeobox (PRRX) and Ikaros zinc fingers (IKZF) validated using the yeast two-hybrid assay. We also found that the interaction of RXRα with NKX2-5 mutations found in congenital heart disease (Q187H, R189G and R190H) was altered. These findings highlight an intuitive approach to accessing protein–protein interaction information of transcription factors in DNA-binding experiments.

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