ArticleBiophysical journal2017
AESOP: A Python Library for Investigating Electrostatics in Protein Interactions.
Article in Biophysical journal, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- Mutational signatures in GATA3 transcription factor and its DNA binding domain that stimulate breast cancer and HDR syndrome.Scientific reports · 2021Article
- Recent advances in user-friendly computational tools to engineer protein function.Briefings in bioinformatics · 2021Review
- Role of Electrostatic Hotspots in the Selectivity of Complement Control Proteins Toward Human and Bovine Complement Inhibition.Frontiers in molecular biosciences · 2021Article
- Factor H-Inspired Design of Peptide Biomarkers of the Complement C3d Protein.ACS medicinal chemistry letters · 2020Article
- Interaction between γC87 and γR242 residues participates in energy coupling between catalysis and proton translocation in Escherichia coli ATP synthase.Biochimica et biophysica acta. Bioenergetics · 2019Article
- Cross-Species Analysis of Glycosaminoglycan Binding Proteins Reveals Some Animal Models Are "More Equal" than Others.Molecules (Basel, Switzerland) · 2019Review
- Molecular Mechanisms of Macular Degeneration Associated with the Complement Factor H Y402H Mutation.Biophysical journal · 2019Article
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Authors and funding
5 authors.
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Abstract
Electric fields often play a role in guiding the association of protein complexes. Such interactions can be further engineered to accelerate complex association, resulting in protein systems with increased productivity. This is especially true for enzymes where reaction rates are typically diffusion limited. To facilitate quantitative comparisons of electrostatics in protein families and to describe electrostatic contributions of individual amino acids, we previously developed a computational framework called AESOP. We now implement this computational tool in Python with increased usability and the capability of performing calculations in parallel. AESOP utilizes PDB2PQR and Adaptive Poisson-Boltzmann Solver to generate grid-based electrostatic potential files for protein structures provided by the end user. There are methods within AESOP for quantitatively comparing sets of grid-based electrostatic potentials in terms of similarity or generating ensembles of electrostatic potential files for a library of mutants to quantify the effects of perturbations in protein structure and protein-protein association.
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