Back

Constraint Network Analysis of Global and Local Rigidity in Wild-Type EGFR: Apo vs Gefitinib-Bound States

Bhattacharjee, K.; Ghosh, A.

2026-01-22 biophysics
10.64898/2026.01.19.700489 bioRxiv
Show abstract

The mechanical rigidity-flexibility architecture of protein kinases play a critical role in regulating conformational stability and inhibitor response, yet remains insufficiently quantified for Epidermal Growth Factor Receptor (EGFR). Here, we apply Constraint Network Analysis (CNA) to systematically characterize the global and local mechanical properties of wild-type EGFR in its apo state and when bound to the ATP-competitive inhibitor gefitinib. Analysis of multiple global rigidity indices reveal a well-defined rigidity percolation transition in apo EGFR at an energy cutoff of approximately -2.0 kcal mol-1, indicative of an intrinsically stable mechanical framework. Gefitinib binding shifts this transition slightly to higher energies and sharpens the percolation behavior, accompanied by enhanced long-range mechanical coupling, increased rigidity order parameters, and reduced configurational entropy. Importantly, residue-level rigidity and percolation profiles remain largely conserved between the two states, demonstrating that ligand binding does not induce large-scale reorganization of the EGFR mechanical network. Instead, inhibition arises from subtle, localized rigidification within functionally relevant regions, consistent with stabilization of an inactive conformational ensemble. Collectively, this work establishes the first CNA-based mechanical reference state for wild-type EGFR and underscores the utility of network rigidity analysis for resolving ligand-induced effects that are structurally subtle yet mechanistically significant. This framework provides a quantitative baseline for future studies of oncogenic mutations and drug-resistant EGFR variants.

Matching journals

The top 6 journals account for 50% of the predicted probability mass.

1
Journal of Molecular Biology
232 papers in training set
Top 0.1%
11.6%
2
Physical Biology
46 papers in training set
Top 0.1%
9.5%
3
Biophysical Journal
631 papers in training set
Top 0.9%
9.5%
4
Journal of Chemical Information and Modeling
238 papers in training set
Top 0.7%
7.7%
5
Computational and Structural Biotechnology Journal
242 papers in training set
Top 0.3%
6.6%
6
PLOS Computational Biology
1863 papers in training set
Top 5%
6.6%
50% of probability mass above
7
The Journal of Physical Chemistry B
167 papers in training set
Top 0.4%
5.4%
8
Biomolecules
100 papers in training set
Top 0.2%
4.7%
9
International Journal of Molecular Sciences
494 papers in training set
Top 2%
4.3%
10
Frontiers in Molecular Biosciences
102 papers in training set
Top 0.2%
3.2%
11
Scientific Reports
3612 papers in training set
Top 36%
3.2%
12
The Journal of Physical Chemistry Letters
63 papers in training set
Top 0.2%
3.2%
13
Journal of The Royal Society Interface
235 papers in training set
Top 2%
2.3%
14
PLOS ONE
5266 papers in training set
Top 49%
1.7%
15
Protein Science
246 papers in training set
Top 3%
1.4%
16
Proceedings of the National Academy of Sciences
2444 papers in training set
Top 32%
1.4%
17
Communications Biology
993 papers in training set
Top 27%
1.0%
18
Journal of Chemical Theory and Computation
140 papers in training set
Top 1%
0.9%
19
Journal of Computational Chemistry
13 papers in training set
Top 0.3%
0.8%
20
ACS Omega
105 papers in training set
Top 4%
0.8%
21
Physical Chemistry Chemical Physics
36 papers in training set
Top 0.6%
0.8%
22
eLife
5828 papers in training set
Top 70%
0.6%
23
Biochemical Journal
91 papers in training set
Top 2%
0.6%
24
Biophysics and Physicobiology
11 papers in training set
Top 0.2%
0.6%