Abstract
Protein–protein interaction (PPI) is critical for several biological functions in living cells through the formation of an interface. Therefore, it is of interest to characterize protein–protein interfaces using an updated non-redundant structural dataset of 2557 homo (identical subunits) and 393 hetero (different subunits) dimer protein complexes determined by X-ray crystallography. We analyzed the interfaces using van der Waals (vdW), hydrogen bonding and electrostatic energies. Results show that on average homo and hetero interfaces are similar. Hence, we further grouped the 2950 interfaces based on percentage vdW to total energies into dominant (≥60%) and sub-dominant (<60%) vdW interfaces. Majority (92%) of interfaces have dominant vdW energy with large interface size (146 ± 87 (homo) and 137 ± 76 (hetero) residues) and interface area (1622 ± 1135 Å2 (homo) and 1579 ± 1060 Å2 (hetero)). However, a proportion (8%) of interfaces have sub-dominant vdW energy with small interface size (85 ± 46 (homo) and 88 ± 36 (hetero) residues) and interface area (823 ± 538 Å2 (homo) and 881 ± 377 Å2 (hetero)). It is found that large interfaces have two-fold more interface area and interface size than small interfaces with increasing hydrogen bonding energy to interface size. However, small interfaces have three-fold more electrostatics energy than large interfaces with increasing electrostatics to interface size. Thus, 8% of complexes having small interfaces with limited interface area and sub-dominant vdW energy are rich in electrostatics. It is interesting to observe that complexes having small interfaces are often associated with regulatory function. Hence, the observed structural features with known molecular function provide insights for the better understanding of PPI.
Communicated by Ramaswamy H. Sarma
Acknowledgement
We wish to express our sincere appreciation to several members of Biomedical Informatics (P) Ltd, Molecular Biophysics Unit, Indian Institute of Science (IISc), Centre for Biotechnology, Anna University, Bioinformatics Centre, National University of Singapore (NUS) and School of Mechanical Engineering, Nanyang Technological University (NTU) for extensive discussion on the subject of this article over the last three decades (1994–2019). We thank Dr. J. Jeyakanthan from Allagappa University for useful discussion on the subject of this article at VIT University during the doctoral committee meeting for Christina Nilofer. We also thank VIT University and National Centre for Biological Sciences (NCBS) for structural and administrative support related to this work. Christina Nilofer thanks Biomedical Informatics (P) Ltd for all support rendered to her during this period. We thank Vikas Tiwari for helping with hotspot residue analysis. We also thank Thiruvengadam Kothai and Peter Pushparaj for critical comments and corrections on the manuscript. PK thanks Ramanathan Sowdhamini for her suggestions, comments, support, generosity and discussion on the subject of the manuscript. P. Kangueane values Baddrireddi Subhadra Lakshmi (BSL) for her silent yet solid and sound support on the subject of this script since the start in 1994. We thank the reviewers for their critical comments on the subject of this manuscript which helped us to present it to the context.
Disclosure statement
The authors declare that there is no financial conflict of interest.