Charge-Transfer Knowledge Graph among Amino Acids Derived from High-Throughput Electronic Structure Calculations for...

Area of Science:

• Computational chemistry
• Biophysics
• Protein science

Background:

• Charge-transfer coupling is crucial in tight-binding methods.
• The anisotropic nature of charge-transfer couplings in proteins is significant due to complex biomolecular structures.

Purpose of the Study:

• To conduct the first large-scale quantitative assessment of charge-transfer preference in proteins.
• To analyze charge-transfer couplings across all 20x20 amino acid side-chain combinations.

Main Methods:

• Calculated charge-transfer couplings using data from thousands of high-quality protein complex structures.
• Generated a comprehensive database of charge-transfer couplings for millions of amino acid side-chain combinations.

Main Results:

• The charge-transfer database reveals distinct quantitative features of couplings among numerous amino acid side-chain combinations.
• The overall distribution indicates that a single average structure is insufficient to represent typical charge-transfer preferences in real proteins.

Conclusions:

• This work offers a new approach to understanding charge-transfer couplings in the context of big data and realistic protein distributions.
• Highlights the necessity of considering diverse structural data for accurate modeling of charge-transfer phenomena.