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Quantitative Biology

ISSN 2095-4689

ISSN 2095-4697(Online)

CN 10-1028/TM

邮发代号 80-971

Quantitative Biology  2018, Vol. 6 Issue (2): 163-174   https://doi.org/10.1007/s40484-018-0138-5
  本期目录
Geometric and amino acid type determinants for protein-protein interaction interfaces
Yongxiao Yang1, Wei Wang2, Yuan Lou1, Jianxin Yin2, Xinqi Gong1()
1. Institute for Mathematical Sciences, Renmin University of China, Beijing 100872, China
2. School of Statistics, Renmin University of China, Beijing 100872, China
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Abstract

Background: Protein-protein interactions are essential to many biological processes. The binding site information of protein-protein complexes is extremely useful to obtain their structures from biochemical experiments. Geometric description of protein structures is the precondition of protein binding site prediction and protein-protein interaction analysis. The previous description of protein surface residues is incomplete, and little attention are paid to the implication of residue types for binding site prediction.

Methods: Here, we found three new geometric features to characterize protein surface residues which are very effective for protein-protein interface residue prediction. The new features and several commonly used descriptors were employed to train millions of residue type-nonspecific or specific protein binding site predictors.

Results: The amino acid type-specific predictors are superior to the models without distinction of amino acid types. The performances of the best predictors are much better than those of the sophisticated methods developed before.

Conclusions: The results demonstrate that the geometric properties and amino acid types are very likely to determine if a protein surface residue would become an interface one when the protein binds to its partner.

Key wordsprotein-protein interaction    protein-protein complex interface    geometry feature    residue type    binding site
收稿日期: 2017-09-11      出版日期: 2018-06-11
Corresponding Author(s): Xinqi Gong   
 引用本文:   
. [J]. Quantitative Biology, 2018, 6(2): 163-174.
Yongxiao Yang, Wei Wang, Yuan Lou, Jianxin Yin, Xinqi Gong. Geometric and amino acid type determinants for protein-protein interaction interfaces. Quant. Biol., 2018, 6(2): 163-174.
 链接本文:  
https://academic.hep.com.cn/qb/CN/10.1007/s40484-018-0138-5
https://academic.hep.com.cn/qb/CN/Y2018/V6/I2/163
Fig.1  
Fig.2  
Fig.3  
NRSR MPPM (%) FC
1 52.6 absEA, relEA, EC, IC, pKa2
2 69.6 relEA, EC, EV, IC, H1
3 79.4 absEA, relEA, EC, EV, IC, H1
4 87.2 EC, IC, H2, pKa1
5 92.0 absEA, IC, H1, pKa1, pKa2
6 95.2 absEA, EV, IC, pKa2
7 97.2 EC, IC, pKa1
8 97.8 EV, IC, H1, H2, pKa1, pKa2
9 99.0 relEA, EC, EV, IC, H1, pKa1, pKa2
10 100 EV, IC, H1, H2, pKa1
10 100 relEA, EC, IC, H1
10 100 absEA, EV, IC, pKa2
10 100 absEA, relEA, EC, IC, H2, pKa2
10 100 absEA, relEA, EC, EV, IC, H1, pKa1
Tab.1  
AAT NM FC MNRSR
TRP 1302000 absEA, relEA, EC, IC 1
MET 1302000 relEA, IC 2
CYS 2646000 absEA, relEA, EV, IC, pKa1 2
HIS 2646000 absEA, relEA, EC, IC 3
PHE 1302000 relEA, EC, EV, IC 5
TYR 2646000 relEA, EC, IC, pKa1 5
ASN 1302000 absEA, EV, IC 5
GLN 1302000 relEA, EC 5
GLY 1302000 relEA, EC, IC 6
LEU 1302000 absEA, EC, EV, IC 6
PRO 1302000 absEA, EC, EV, IC 6
ARG 2646000 absEA, relEA, EC, IC, pKa1 6
ALA 1302000 absEA, relEA, EC, EV, IC 7
VAL 1302000 relEA, EC, IC 7
GLU 2646000 absEA, EC, EV 7
THR 1302000 absEA, EC, IC 7
ASP 2646000 relEA, EC, EV, IC 8
SER 1302000 absEA, EC, EV, IC 8
ILE 1302000 relEA, EC, EV, IC 2
LYS 2646000 absEA, EC, pKa1 9
Tab.2  
AAT MV SD
TRP 0.740691 0.026848
MET 0.709371 0.000889
LEU 0.691242 0.020786
PHE 0.681853 0.004221
CYS 0.673883 0.001095
VAL 0.655965 0.020019
ARG 0.643709 0.010168
HIS 0.623985 0.006025
ILE 0.619012 0.024085
GLY 0.617001 0.021070
TYR 0.605156 0.030080
PRO 0.603635 0.049412
GLN 0.598281 0.003730
LYS 0.591322 0.091578
SER 0.585913 0.002099
ASN 0.576256 0.020634
GLU 0.569275 0.087888
THR 0.558572 0.060780
MET 0.557027 0.048983
ASP 0.538908 0.046054
Tab.3  
NRSR MPPM (%)
1 33.9
2 47.8
3 78.4
4 86.7
5 91.6
6 94.1
7 97.5
8 99.2
9 100
Tab.4  
Fig.4  
Fig.5  
Fig.6  
Fig.7  
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