In Silico Prediction of Protein Adsorption Energy on Titanium Dioxide and Gold Nanoparticles
Abstract
:1. Introduction
2. Materials and Methods
2.1. Nanoparticles
2.2. Proteins
2.3. Protein Descriptors
2.4. Adsorption Free Energy Calculation
2.5. Adsorption Affinity Ranking
2.6. Prediction of Adsoprtion Energy from Protein and NP Descriptors
3. Results
3.1. Protein Adsorption Energies
3.2. Impact of Structural Error on Binding Energies
3.3. Prediction of Ranking by Binding Affinity
3.4. Metamodel of Adsorption
4. Discussion
5. Conclusions
Supplementary Materials
Author Contributions
Funding
Acknowledgments
Conflicts of Interest
Appendix A
Protein Descriptors
- Solvent-accessible surface area: This the surface area of a protein that can be accessed by solvents. We will assume that the quantity represents the geometric surface area of the protein. It makes sense that proteins with a larger surface area should be more reactive relative to equally massive proteins with a smaller surface area, as chemical reactions take place on the surface of objects. This predictor is calculated using the Shrake–Rupley algorithm. Place a sphere at the atom of every AA in the PDB. The radii of these spheres are the same as the radii derived in the Appendix section.
- Place points uniformly on the surface of each sphere i (a golden spiral approximation was used to achieve this uniform distribution in our implementation).
- For each sphere i, check all of its points to see if any of them lie within the volume of another sphere. If it does, remove that point from the surface of sphere i. After this process, there will be points remaining on the surface of each sphere, such that gives the fraction of the exposed area of the sphere relative to the total area of that sphere.
- The total surface area of the protein is given by the sum of the fractional surface areas contributed by each sphere to the total surface,
- Sphericity: How close a particle resembles a sphere. A highly spherical particle will have a value close to 1. The value is given by
- Surface Area per Mass: The area of the protein divided by its total mass.
- Amino Acid count on the surface: The number of each AA species that appears on the surface of the protein. Being ‘on the surface’ is determined by summing over the surface fractional values from Equation (A1)
- Amino Acid percentage on the surface: The percentage of each AA species that appears on the surface.
- Amino Acid Dayhoff statistic on the surface: The percentage of each AA species that appears on the surface, weighted by the Dayhoff statistic.
- Surface Charge: The amount of charge that appears on the surface of the protein.
- Tiny count on surface: The number of Alanine, Cysteine, Glycine, Serine, and Threonine amino acids on the protein’s surface.
- Small count on surface: The number of Alanine, Cysteine, Aspartic Acid, Glycine, Asparagine, Proline, Serine, Tyrosine, and Valine amino acids on the protein’s surface.
- Aliphatic count on surface: The number of Alanine, Isoleucine, Leucine, and Valine amino acids on the protein’s surface.
- Aromatic count on surface: The number of Phenylalanine, Histidine, Tryptophan, and Tyrosine amino acids on the protein’s surface.
- Non-Polar count on surface: The number of Alanine, Cysteine, Phenylalanine, Glycine, Isoleucine, Leucine, Methionine, Proline, Valine, Tryptophan, and Tyrosine amino acids on the protein’s surface.
- Polar count on surface: The number of Aspartic Acid, Histidine, Lysine, Asparagine, Glutamine, Arginine, Serine, and Threonine amino acids on the protein’s surface.
- Charged count on surface: The number of Aspartic Acid, Glutamic Acid, Histidine, Lysine, and Arginine amino acids on the protein’s surface.
- Basic count on surface: The number of Histidine, Lysine and Arginine amino acids on the protein’s surface (positively charged amino acids).
- Acidic count on surface: The number of Aspartic Acid and Glutamic Acid amino acids on the protein’s surface (negatively charged amino acids).
Appendix B
Evaluation of the AA Radius
Amino Acid | n | [nm] | [nm] | [nm] | [] | [] |
---|---|---|---|---|---|---|
ALA | 1.606 | 0.50 | 0.27 | 0.32 | 71.161 | 7.869 |
ARG | 1.664 | 0.66 | 0.35 | 0.43 | 72.818 | 9.466 |
ASN | 1.691 | 0.57 | 0.30 | 0.36 | 73.556 | 10.197 |
ASP | 1.700 | 0.56 | 0.29 | 0.36 | 73.798 | 10.439 |
CYS | 1.685 | 0.55 | 0.29 | 0.35 | 73.394 | 10.035 |
GLN | 1.670 | 0.60 | 0.32 | 0.37 | 72.984 | 9.629 |
GLU | 1.655 | 0.59 | 0.32 | 0.38 | 72.567 | 9.221 |
GLY | 1.685 | 0.45 | 0.24 | 0.28 | 73.394 | 10.035 |
HIS | 1.700 | 0.45 | 0.25 | 0.30 | 73.798 | 10.439 |
ILE | 1.568 | 0.62 | 0.33 | 0.40 | 70.02 | 6.802 |
LEU | 1.565 | 0.62 | 0.33 | 0.40 | 69.928 | 6.717 |
LYS | 1.615 | 0.64 | 0.34 | 0.42 | 71.425 | 8.119 |
MET | 1.646 | 0.64 | 0.34 | 0.36 | 72.314 | 8.975 |
PHE | 1.682 | 0.64 | 0.35 | 0.33 | 73.312 | 9.954 |
PRO | 1.596 | 0.56 | 0.30 | 0.36 | 70.865 | 7.59 |
SER | 1.676 | 0.52 | 0.27 | 0.33 | 73.148 | 9.792 |
THR | 1.618 | 0.56 | 0.30 | 0.36 | 71.512 | 8.202 |
TRP | 1.754 | 0.68 | 0.37 | 0.45 | 75.204 | 11.869 |
TYR | 1.643 | 0.65 | 0.35 | 0.42 | 72.229 | 8.892 |
VAL | 1.571 | 0.59 | 0.31 | 0.38 | 70.112 | 6.887 |
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Material | Allotrope | Miller Index | ( nm) | , 1015 Hz |
---|---|---|---|---|
TiO2 | Anatase | 101 | 2.5287 | 1.50 |
Au | FCC | 100 | 0.4242 | 4.87 |
ID | Structure | E(R = 5 nm) [kBT] | E(R = 50 nm) [kBT] | E(R = 100 nm) [kBT] | E(R = 200 nm) [kBT] |
---|---|---|---|---|---|
1AX8 | PDB | −152.5 | −214.2 | −219.4 | −222.3 |
I-TASSER | −168.8 | −242.1 | −249.0 | −249.2 | |
1F5F | PDB | −130.7 | −174.1 | −179.1 | −180.6 |
I-TASSER | −135.6 | −175.3 | −180.3 | −182.3 | |
1GQV | PDB | −146.0 | −195.0 | −201.0 | −204.3 |
I-TASSER | −160.9 | −223.8 | −228.6 | −230.9 | |
1HPT | PDB | −145.6 | −158.1 | −159.6 | −161.5 |
I-TASSER | −171.5 | −178.8 | −179.1 | −180.2 | |
1HUP | PDB | −131.2 | −192.8 | −201.3 | −204.3 |
I-TASSER | −126.9 | −198.0 | −206.4 | −211.0 |
ID | Structure | E(R = 5 nm) [ kBT] | E(R = 50 nm) [kBT] | E(R = 100 nm) [kBT] | E(R = 200 nm) [kBT] |
---|---|---|---|---|---|
1AX8 | PDB | −13.3 | −15.0 | −15.1 | −15.1 |
I-TASSER | −9.4 | −11.6 | −11.8 | −11.9 | |
1F5F | PDB | −16.1 | −20.0 | −20.0 | −20.3 |
I-TASSER | −15.5 | −18.9 | −18.9 | −19.0 | |
1GQV | PDB | −2.9 | −6.3 | −6.7 | −6.8 |
I-TASSER | −4.3 | −9.2 | −9.6 | −9.7 | |
1HPT | PDB | −7.3 | −8.7 | −8.9 | −9.0 |
I-TASSER | −7.1 | −9.8 | −9.9 | −10.1 | |
1HUP | PDB | −11.7 | −16.8 | −16.6 | −16.4 |
I-TASSER | −11.7 | −14.1 | −14.6 | −14.6 |
Ranking | Au, 200 nm | TiO2, 200 nm | ||
---|---|---|---|---|
Experimental Structure | I-TASSER Structure | Experimental Structure | I-TASSER Structure | |
1 | 2NSM | 6NCO | 3GW3 | 6NCO |
2 | 3GW3 | 5O7D | 2QYQ | 3GW3 |
3 | 6NCO | 3GW3 | 4DOU | 5VHG |
4 | 2RHP | 3DHP | 5VC1 | 4DOU |
5 | 5O7D | 2RHP | 6JE7 | 6JE7 |
6 | 3DHP | 2NSM | 4GLP | 2FJ9 |
7 | 5EC3 | 1ZXQ | 1NUH | 9CA2 |
8 | 4YEQ | 4XAT | 6M8Z | 4NH9 |
9 | 4XAT | 4CYY | 1IMV | 5EC3 |
10 | 5VC1 | 1NUH | 6NCO | 6M8Z |
Material | 200 nm | 100 nm | 50 nm | 5 nm |
---|---|---|---|---|
Au | 0.61 | 0.62 | 0.64 | 0.48 |
TiO2 | 0.66 | 0.66 | 0.65 | 0.59 |
Material | 200 nm | 100 nm | 50 nm | 5 nm |
---|---|---|---|---|
Au | 0.68 | 0.67 | 0.69 | 0.47 |
TiO2 | 0.72 | 0.73 | 0.72 | 0.62 |
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Alsharif, S.A.; Power, D.; Rouse, I.; Lobaskin, V. In Silico Prediction of Protein Adsorption Energy on Titanium Dioxide and Gold Nanoparticles. Nanomaterials 2020, 10, 1967. https://doi.org/10.3390/nano10101967
Alsharif SA, Power D, Rouse I, Lobaskin V. In Silico Prediction of Protein Adsorption Energy on Titanium Dioxide and Gold Nanoparticles. Nanomaterials. 2020; 10(10):1967. https://doi.org/10.3390/nano10101967
Chicago/Turabian StyleAlsharif, Shada A., David Power, Ian Rouse, and Vladimir Lobaskin. 2020. "In Silico Prediction of Protein Adsorption Energy on Titanium Dioxide and Gold Nanoparticles" Nanomaterials 10, no. 10: 1967. https://doi.org/10.3390/nano10101967