Preloader

Unifying structural descriptors for biological and bioinspired nanoscale complexes

  • Morrison, J. L., Breitling, R., Higham, D. J. & Gilbert, D. R. A lock-and-key model for protein–protein interactions. Bioinformatics 22, 2012–2019 (2006).

    Article 

    Google Scholar 

  • Baspinar, A., Cukuroglu, E., Nussinov, R., Keskin, O. & Gursoy, A. PRISM: a web server and repository for prediction of protein–protein interactions and modeling their 3D complexes. Nucleic Acids Res. 42, W285 (2014).

    Article 

    Google Scholar 

  • Murakami, Y. & Mizuguchi, K. Applying the naïve Bayes classifier with kernel density estimation to the prediction of protein–protein interaction sites. Bioinformatics 26, 1841–1848 (2010).

    Article 

    Google Scholar 

  • Gainza, P. et al. Deciphering interaction fingerprints from protein molecular surfaces using geometric deep learning. Nat. Methods 17, 184–192 (2020).

    Article 

    Google Scholar 

  • Montoya, M. A PrePPI way to make predictions. Nat. Struct. Mol. Biol. 19, 1067 (2012).

    Article 

    Google Scholar 

  • Northey, T. C., Bareši, A. & Martin, A. C. R. IntPred: a structure-based predictor of protein–protein interaction sites. Bioinformatics 34, 223–229 (2018).

    Article 

    Google Scholar 

  • Baranwal, M. et al. Struct2Graph: a graph attention network for structure based predictions of protein–protein interactions. Preprint at bioRxiv https://doi.org/10.1101/2020.09.17.301200 (2020).

  • Chen, K.-H., Wang, T.-F. & Hu, Y.-J. Protein–protein interaction prediction using a hybrid feature representation and a stacked generalization scheme. BMC Bioinformatics 20, 308 (2019).

    Article 

    Google Scholar 

  • Sarkar, D. & Saha, S. Machine-learning techniques for the prediction of protein–protein interactions. J. Biosci. 44, 104 (2019).

    Article 

    Google Scholar 

  • Wang, Y. et al. Predicting protein interactions using a deep learning method-stacked sparse autoencoder combined with a probabilistic classification vector machine. Complexity 2018, 4216813 (2018).

    Google Scholar 

  • Kotov, N. A. Inorganic nanoparticles as protein mimics. Science 330, 188–189 (2010).

    Article 

    Google Scholar 

  • Pinals, R. L., Chio, L., Ledesma, F. & Landry, M. P. Engineering at the nano–bio interface: harnessing the protein corona towards nanoparticle design and function. Analyst 145, 5090–5112 (2020).

    Article 

    Google Scholar 

  • Govan, J. & Gun’ko, Y. K. Recent progress in chiral inorganic nanostructures. Nanoscience 3, 1–30 (2016).

    Article 

    Google Scholar 

  • Weininger, D. SMILES, a chemical language and information system. 1. Introduction to methodology and encoding rules. J. Chem. Inf. Model. 28, 31–36 (1988).

    Article 

    Google Scholar 

  • Xu, L. et al. Enantiomer-dependent immunological response to chiral nanoparticles. Nature 601, 366–373 (2022).

    Article 

    Google Scholar 

  • Cha, S.-H. et al. Shape-dependent biomimetic inhibition of enzyme by nanoparticles and their antibacterial activity. ACS Nano 9, 9097–9105 (2015).

    Article 

    Google Scholar 

  • Ravikumar, K. M., Huang, W. & Yang, S. Coarse-grained simulations of protein–protein association: an energy landscape perspective. Biophys. J. 103, 837–845 (2012).

    Article 

    Google Scholar 

  • Kmiecik, S. et al. Coarse-grained protein models and their applications. Chem. Rev. 116, 7898–7936 (2016).

    Article 

    Google Scholar 

  • Wang, Y. et al. Anti-biofilm activity of graphene quantum dots via self-assembly with bacterial amyloid proteins. ACS Nano 13, 4278–4289 (2019).

    Article 

    Google Scholar 

  • Acosta-Tapia, N., Galindo, J. F. & Baldiris, R. Insights into the effect of Lowe syndrome-causing mutation p.Asn591Lys of OCRL-1 through protein–protein interaction networks and molecular dynamics simulations. J. Chem. Inf. Model. 60, 1019–1027 (2020).

    Article 

    Google Scholar 

  • Verma, M. K. & Shakya, S. LRP-1 mediated endocytosis of EFE across the blood–brain barrier; protein–protein interaction and molecular dynamics analysis. Int. J. Pept. Res. Ther. 27, 71–81 (2021).

    Article 

    Google Scholar 

  • Li, Z. L. & Buck, M. Modified potential functions result in enhanced predictions of a protein complex by all-atom molecular dynamics simulations, confirming a stepwise association process for native protein–protein interactions. J. Chem. Theory Comput. 15, 4318–4331 (2019).

    Article 

    Google Scholar 

  • Liu, Y. et al. A compact biosensor for binding kinetics analysis of protein–protein interaction. IEEE Sens. J. 19, 11955–11960 (2019).

    Article 

    Google Scholar 

  • Moscetti, I., Cannistraro, S. & Bizzarri, A. R. Surface plasmon resonance sensing of biorecognition interactions within the tumor suppressor P53 network. Sensors https://doi.org/10.3390/s17112680 (2017).

  • Verboven, C. et al. Actin-DBP: the perfect structural fit? Acta Crystallogr. D 59, 263–273 (2003).

    Article 

    Google Scholar 

  • Dolinsky, T. J. et al. PDB2PQR: expanding and upgrading automated preparation of biomolecular structures for molecular simulations. Nucleic Acids Res. 35, 522–525 (2007).

    Article 

    Google Scholar 

  • Kawabata, T. Detection of multiscale pockets on protein surfaces using mathematical morphology. Proteins 78, 1195–1211 (2010).

    Article 

    Google Scholar 

  • Osipov, M. A., Pickup, B. T. & Dunmur, D. A. A new twist to molecular chirality: intrinsic chirality indices. Mol. Phys. 84, 1193–1206 (1995).

    Article 

    Google Scholar 

  • May, A. et al. Coarse-grained versus atomistic simulations: realistic interaction free energies for real proteins. Bioinformatics 30, 326–334 (2014).

    Article 

    Google Scholar 

  • Vishveshwara, S., Brinda, K. V. & Kannan, N. Protein structure: insights from graph theory. J. Theor. Comput. Chem. 1, 187–211 (2002).

    Article 

    Google Scholar 

  • Bahar, I., Atilgan, A. R. & Erman, B. Direct evaluation of thermal fluctuations in proteins using a single-parameter harmonic potential. Fold. Des. 2, 173–181 (1997).

    Article 

    Google Scholar 

  • Haliloglu, T., Bahar, I. & Erman, B. Gaussian dynamics of folded proteins. Phys. Rev. Lett. 79, 3090–3093 (1997).

    Article 

    Google Scholar 

  • Levy, E. D., Pereira-Leal, J. B., Chothia, C. & Teichmann, S. A. 3D complex: a structural classification of protein complexes. PLoS Comput. Biol. 2, 1395–1406 (2006).

    Article 

    Google Scholar 

  • Gavin, A. C. et al. Proteome survey reveals modularity of the yeast cell machinery. Nature 440, 631–636 (2006).

    Article 

    Google Scholar 

  • Ye, Q., West, A. M. V., Silletti, S. & Corbett, K. D. Architecture and self-assembly of the SARS-CoV-2 nucleocapsid protein. Protein Sci. 29, 1890–1901 (2020).

    Article 

    Google Scholar 

  • Romei, M. G., Lin, C., Mathews, I. I. & Boxer, S. G. Electrostatic control of photoisomerization pathways in proteins. Science 367, 76–79 (2020).

    Article 

    Google Scholar 

  • Sachpatzidis, A. et al. Crystallographic studies of phosphonate-based α-reaction transition-state analogues complexed to tryptophan synthase. Biochemistry 38, 12665–12674 (1999).

    Article 

    Google Scholar 

  • Ju, J., Regmi, S., Fu, A., Lim, S. & Liu, Q. Graphene quantum dot based charge-reversal nanomaterial for nucleus-targeted drug delivery and efficiency controllable photodynamic therapy. J. Biophoton. 12, e201800367 (2019).

    Article 

    Google Scholar 

  • Ahmed, K. B. A., Raman, T. & Veerappan, A. Future prospects of antibacterial metal nanoparticles as enzyme inhibitor. Mater. Sci. Eng. C 68, 939–947 (2016).

    Article 

    Google Scholar 

  • Unal, M. A. et al. Graphene oxide nanosheets interact and interfere with SARS-CoV-2 surface proteins and cell receptors to inhibit infectivity. Small 17, 2101483 (2021).

    Article 

    Google Scholar 

  • Blanco-López, M. C. & Rivas, M. Nanoparticles for bioanalysis. Anal. Bioanal. Chem. 411, 1789–1790 (2019).

    Article 

    Google Scholar 

  • Ma, W. et al. Attomolar DNA detection with chiral nanorod assemblies. Nat. Commun. 4, 2689 (2013).

    Article 

    Google Scholar 

  • Kagan, V. E. et al. Carbon nanotubes degraded by neutrophil myeloperoxidase induce less pulmonary inflammation. Nat. Nanotechnol. 5, 354–359 (2010).

    Article 

    Google Scholar 

  • Pinals, R. L. et al. Quantitative protein corona composition and dynamics on carbon nanotubes in biological environments. Angew. Chem. Int. Ed. 59, 23668–23677 (2020).

    Article 

    Google Scholar 

  • Monopoli, M. P., Pitek, A. S., Lynch, I. & Dawson, K. A. Formation and characterization of the nanoparticle–protein corona. Methods Mol. Biol. 1025, 137–155 (2013).

    Article 

    Google Scholar 

  • Madathiparambil Visalakshan, R. et al. The influence of nanoparticle shape on protein corona formation. Small https://doi.org/10.1002/smll.202000285 (2020).

  • Faridi, A. et al. Graphene quantum dots rescue protein dysregulation of pancreatic β-cells exposed to human islet amyloid polypeptide. Nano Res. 12, 2827–2834 (2019).

    Article 

    Google Scholar 

  • Wang, M. et al. Graphene quantum dots against human IAPP aggregation and toxicity: in vivo. Nanoscale 10, 19995–20006 (2018).

    Article 

    Google Scholar 

  • Lin, W. et al. Control of protein orientation on gold nanoparticles. J. Phys. Chem. C 119, 21035–21043 (2015).

    Article 

    Google Scholar 

  • Ma, C. D., Wang, C., Acevedo-Vélez, C., Gellman, S. H. & Abbott, N. L. Modulation of hydrophobic interactions by proximally immobilized ions. Nature 517, 347–350 (2015).

    Article 

    Google Scholar 

  • Horovitz, A. Non-additivity in protein–protein interactions. J. Mol. Biol. 196, 733–735 (1987).

    Article 

    Google Scholar 

  • Batista, C. A. S. et al. Nonadditivity of nanoparticle interactions. Science 350, https://doi.org/10.1126/science.1242477 (2015).

  • Qiao, Y., Xiong, Y., Gao, H., Zhu, X. & Chen, P. Protein–protein interface hot spots prediction based on a hybrid feature selection strategy. BMC Bioinformatics 19, 14 (2018).

    Article 

    Google Scholar 

  • Kyte, J. & Doolittle, R. F. A simple method for displaying the hydropathic character of a protein. J. Mol. Biol. 157, 105–132 (1982).

    Article 

    Google Scholar 

  • Jumper, J. M., Faruk, N. F., Freed, K. F. & Sosnick, T. R. Accurate calculation of side chain packing and free energy with applications to protein molecular dynamics. PLoS Comput. Biol. 14, e1006342 (2018).

    Article 

    Google Scholar 

  • Chakrabarty, B., Naganathan, V., Garg, K., Agarwal, Y. & Parekh, N. NAPS update: network analysis of molecular dynamics data and protein–nucleic acid complexes. Nucleic Acids Res. 47, W462–W470 (2019).

    Article 

    Google Scholar 

  • Chakraborty, S., Venkatramani, R., Rao, B. J., Asgeirsson, B. & Dandekar, A. M. Protein structure quality assessment based on the distance profiles of consecutive backbone Cα atoms. F1000Res. 2, 1–12 (2013).

    Article 

    Google Scholar 

  • Brancolini, G. & Tozzini, V. Multiscale modeling of proteins interaction with functionalized nanoparticles. Curr. Opin. Colloid Interface Sci. 41, 66–73 (2019).

    Article 

    Google Scholar 

  • Hazarika, Z. & Jha, A. N. Computational analysis of the silver nanoparticle–human serum albumin complex. ACS Omega 5, 170–178 (2020).

    Article 

    Google Scholar 

  • Samal, A. et al. Comparative analysis of two siscretizations of Ricci curvature for complex networks. Sci. Rep. 8, 8650 (2018).

    Article 

    Google Scholar 

  • Eidi, M. & Jost, J. Ollivier Ricci curvature of directed hypergraphs. Sci. Rep. 10, 12466 (2020).

    Article 

    Google Scholar 

  • Yang, R. & Bogdan, P. Controlling the multifractal generating measures of complex networks. Sci. Rep. 10, 5541 (2020).

    Article 

    Google Scholar 

  • Xiao, X., Chen, H. & Bogdan, P. Deciphering the generating rules and functionalities of complex networks. Sci. Rep. 11, 22964 (2021).

    Article 

    Google Scholar 

  • Pedregosa, F. et al. Scikit-learn: machine learning in Python. J. Mach. Learn. Res. 12, 2825–2830 (2011).

    MathSciNet 
    MATH 

    Google Scholar 

  • Chen, T. & Guestrin, C. XGBoost: a scalable tree boosting system. In Proc. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining 785–794 (ACM, 2016).

  • Cha, M. et al. Unifying structural descriptors for biological and bioinspired nanoscale complexes [source code]. Code Ocean https://doi.org/10.24433/CO.7800040.V1 (2022).

  • Source link