Prediction of Protein-Protein Interface Residues Using Sequence Neighborhood and Surface Properties

2006 ◽  
pp. 660-666
Author(s):  
Yasir Arafat ◽  
Joarder Kamruzzaman ◽  
Gour Karmakar
Keyword(s):  
Surface Properties ◽  
Protein Interface ◽  
Interface Residues

Protein Interface Residues Recognition Using Granular Computing Theory

2010 ◽  
pp. 727-734
Author(s):  
Jiaxing Cheng ◽  
Xiuquan Du ◽  
Jiehua Cheng
Keyword(s):  
Granular Computing ◽  
Protein Interface ◽  
Interface Residues

scholarly journals PIPENN: Protein Interface Prediction with an Ensemble of Neural Nets

2021 ◽  
Author(s):  
Bas Stringer ◽  
Hans De Ferrante ◽  
Sanne Abeln ◽  
Jaap Heringa ◽  
K. Anton A. Feenstra ◽  
...  
Keyword(s):  
Small Molecules ◽  
Small Molecule ◽  
Protein Interaction ◽  
Receptor Activation ◽  
Neural Nets ◽  
Data Sets ◽  
Protein Interface ◽  
Protein Protein Interaction ◽  
Independent Test ◽  
Interface Residues

Motivation: Protein interactions play an essential role in many biological and cellular processes, such as protein—protein interaction (PPI) in signaling pathways, binding to DNA in transcription, and binding to small molecules in receptor activation or enzymatic activity. Experimental identification of protein binding interface residues is a time-consuming, costly, and challenging task. Several machine learning and other computational approaches exist which predict such interface residues. Here we explore if Deep Learning (DL) can be used effectively for this prediction task, and which learning strategies and architectures may be most efficient. We introduce seven DL architectures that are applied to eleven independent test sets, focused on the residues involved in PPI interfaces and in binding RNA/DNA and small molecule ligands. Results: We constructed a large data set dubbed BioDL, comprising protein-protein interaction data from the PDB and protein-ligand interactions (DNA, RNA and small molecules) from the BioLip database. Additionally, we reused our existing curated homo- and heteromeric PPI data sets. We performed several experiments to assess the impact of different data features, spatial forms, encoding schemes, network initializations, loss functions, regularization mechanisms, and activation functions on the performance of the predictors. Benchmarking the resulting DL models with an independent test set (ZK448) shows no single DL architecture performs best on all instances, but that an ensemble of DL architectures consistently achieves peak prediction performance. Our PIPENN's ensemble predictor outperforms current state-of-the-art sequence-based protein interface predictors on all interaction types, achieving AUCs of 0.718 (protein—protein), 0.823 (protein—nucleotide) and 0.842 (protein—small molecule) respectively. Availability: Source code and data sets at https://github.com/ibivu/

scholarly journals Multiple dimensional space for protein interface residue characterization

2016 ◽  
Vol 4 (1) ◽  
Author(s):  
Tingyi Cao ◽  
Yongxiao Yang ◽  
Xinqi Gong
Keyword(s):  
Life Science ◽  
Science Studies ◽  
Dimensional Space ◽  
Excellent Result ◽  
Interface Residue ◽  
Protein Interface ◽  
Biological Functions ◽  
Train Of Thought ◽  
Interface Residues

AbstractProteins interact to perform biological functions through specific interface residues. Correctly understanding the mechanisms of interface recognition and prediction are important for many aspects of life science studies. Here, we report a novel architecture to study protein interface residues. In our method, multiple dimensional space was built on some meaningful features. Then we divided the space and put all the surface residues into the regions according to their features’ values. Interestingly, interface residues were found to prefer some grids clustered together. We obtained excellent result on a public and verified data benchmark. Our approach not only opens up a new train of thought for interface residue prediction, but also will help to understand proteins interaction more deeply.

scholarly journals A two-stage classifier for identification of protein-protein interface residues

2004 ◽  
Vol 20 (Suppl 1) ◽  
pp. i371-i378 ◽  
Author(s):  
C. Yan ◽  
D. Dobbs ◽  
V. Honavar
Keyword(s):  
Protein Interface ◽  
Two Stage ◽  
Interface Residues

scholarly journals Chemical Space Overlap with Critical Protein‐Protein Interface Residues in Commercial and Specialized Small‐Molecule Libraries

ChemMedChem ◽  
2018 ◽  
Author(s):  
Yubing Si ◽  
David Xu ◽  
Khuchtumur Bum-Erdene ◽  
Mona Ghozayel ◽  
Baocheng Yang ◽  
...  
Keyword(s):  
Small Molecule ◽  
Chemical Space ◽  
Protein Interface ◽  
Small Molecule Libraries ◽  
Interface Residues

scholarly journals Predicting protein-protein interface residues using local surface structural similarity

2012 ◽  
Vol 13 (1) ◽  
Author(s):  
Rafael A Jordan ◽  
Yasser EL-Manzalawy ◽  
Drena Dobbs ◽  
Vasant Honavar
Keyword(s):  
Structural Similarity ◽  
Protein Interface ◽  
Local Surface ◽  
Interface Residues

Biological and Surface Properties of C-Type Virus Particles Produced by Novikoff Ascites Hepatoma Cells

1977 ◽  
Vol 35 ◽  
pp. 388-389
Author(s):  
D.C. Hixson ◽  
J.C. Chan ◽  
J.M. Bowen ◽  
E.F. Walborg
Keyword(s):  
Surface Properties ◽  
Ricinus Communis ◽  
Rat Kidney ◽  
Leukemia Virus ◽  
Viral Envelope ◽  
Virus Particles ◽  
Chemically Induced ◽  
Sarcoma Virus ◽  
Hepatocarcinoma Cells ◽  
Type C

Several years ago Karasaki (1) reported the production of type C virus particles by Novikoff ascites hepatocarcinoma cells. More recently, Weinstein (2) has reported the presence of type C virus particles in cell cultures derived from transplantable and primary hepatocellular carcinomas. To date, the biological function of these virus and their significance in chemically induced hepatocarcinogenesis are unknown. The present studies were initiated to determine a possible role for type C virus particles in chemically induced hepatocarcinogenesis. This communication describes results of studies on the biological and surface properties of type C virus associated with Novikoff hepatocarcinoma cells.Ecotropic and xenotropic murine leukemia virus (MuLV) activity in ascitic fluid of Novikoff tumor-bearing rats was assayed in murine sarcoma virus transformed S+L- mouse cells and S+L- mink cells, respectively. The presence of sarcoma virus activity was assayed in non-virus-producing normal rat kidney (NRK) cells. Ferritin conjugates of concanavalin A (Fer-Con wheat germ agglutinin (Fer-WGA), and Ricinus communis agglutinins I and II (Fer-RCAI and Fer-RCAII) were used to probe the structure and topography of saccharide determinants present on the viral envelope.

Inelastic scattering at surfaces and interfaces

1986 ◽  
Vol 44 ◽  
pp. 392-393
Author(s):  
R. H. Ritchie ◽  
A. Howie
Keyword(s):  
Inelastic Scattering ◽  
Surface Properties ◽  
Correlation Energy ◽  
Condensed Matter ◽  
Charged Particles ◽  
Condensed Matter Physics ◽  
Optical Phonons ◽  
Exchange Correlation ◽  
Surfaces And Interfaces ◽  
Inelastic Interactions

An important part of condensed matter physics in recent years has involved detailed study of inelastic interactions between swift electrons and condensed matter surfaces. Here we will review some aspects of such interactions.Surface excitations have long been recognized as dominant in determining the exchange-correlation energy of charged particles outside the surface. Properties of surface and bulk polaritons, plasmons and optical phonons in plane-bounded and spherical systems will be discussed from the viewpoint of semiclassical and quantal dielectric theory. Plasmons at interfaces between dissimilar dielectrics and in superlattice configurations will also be considered.

Sign in / Sign up

Export Citation Format

Share Document