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PreprintCited by 0Protein Structure and DynamicsMonoclonal and Polyclonal Antibodies Researchvaccines and immunoinformatics approaches

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Deciphering GB1’s Single Mutational Landscape: Insights from MuMi Analysis

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Guclu, Tandac F., Atilgan, Ali Rana, Atilgan, Canan·2024·The Journal of Physical Chemistry B
DOI 10.1101/2024.06.01.596930PMC PMC11671028

Preprint — published as 10.1021/acs.jpcb.4c04916 · Paper page

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Abstract

Mutational changes that affect the binding of the C2 fragment of Streptococcal protein G (GB1) to the Fc domain of human IgG (IgG-Fc) have been extensively studied using deep mutational scanning (DMS), and the binding affinity of all single mutations has been measured experimentally in the literature. To investigate the underlying molecular basis, we perform in-silico mutational scanning for all possible single mutations, along with 2-µs-long molecular dynamics (WT-MD) of the wild-type (WT) GB1 in both unbound and IgG-Fc bound forms. We compute the hydrogen bonds between GB1 and IgG-Fc in WT-MD to identify the dominant hydrogen bonds for binding, which we then assess in conformations produced by Mutation and Minimization (MuMi) to explain the fitness landscape of GB1 and IgG-Fc binding. Furthermore, we analyze MuMi and WT-MD to investigate the dynamics of binding, focusing on the relative solvent accessibility (RSA) of residues and the probability of residues being located at the binding interface. With these analyses, we explain the interactions between GB1 and IgG-Fc and display the structural features of binding. Our findings pave the way for improved predictive accuracy in protein stability and interaction studies, which are crucial for advancements in drug design and synthetic biology.

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Figure 1 C2 fragment of Streptococcal protein G (GB1, in black/red) partnered with the FC domain of human IgG (IgG-Fc, in blue). (a) WT form of the complex is employed for visualization and analysis by using minimized 1FCC PDB-coded structure. GB1 and IgG-Fc have 56 and 206 residues, respectively. Binding region of GB1 (residues that have at least one heavy atom within 5 Å of IgG-Fc) is shown in red. (b) Sequence of GB1 is illustrated by signifying secondary structure (yellow patterns) and charged residues (flags above positions). The amino acid differences between the sequences of 1PGA (unbound GB1) and 1FCC (bound form) are noted for residue positions 1, 2, 6, and 7. Accompanying RSA is calculated for unbound/bound forms of GB1 after minimization in water. Residues at the binding region are indicated by red dashed lines. The solvent accessibility of residues 24, 25, 27, 28, 31, 32, 35, 40, and 43 decreases after the binding, while a slight increase occurs for residues 19, 41, 47, and 48. There are 9 hydrogen bonds between GB1 and its partner in minimized WT. Residues 27 and 28 have two hydrogen bonds per position, and residues 35, 39, 40, 42, and 43 have one each.
Figure 1 C2 fragment of Streptococcal protein G (GB1, in black/red) partnered with the FC domain of human IgG (IgG-Fc, in blue). (a) WT form of the complex is employed for visualization and analysis by using minimized 1FCC PDB-coded structure. GB1 and IgG-Fc have 56 and 206 residues, respectively. Binding region of GB1 (residues that have at least one heavy atom within 5 Å of IgG-Fc) is shown in red. (b) Sequence of GB1 is illustrated by signifying secondary structure (yellow patterns) and charged residues (flags above positions). The amino acid differences between the sequences of 1PGA (unbound GB1) and 1FCC (bound form) are noted for residue positions 1, 2, 6, and 7. Accompanying RSA is calculated for unbound/bound forms of GB1 after minimization in water. Residues at the binding region are indicated by red dashed lines. The solvent accessibility of residues 24, 25, 27, 28, 31, 32, 35, 40, and 43 decreases after the binding, while a slight increase occurs for residues 19, 41, 47, and 48. There are 9 hydrogen bonds between GB1 and its partner in minimized WT. Residues 27 and 28 have two hydrogen bonds per position, and residues 35, 39, 40, 42, and 43 have one each.CC BY · PubMed Central
Figure 2 Key distances from WT-MD simulations; two independent 1 μs runs for each of the unbound and bound forms of GB1. The salt bridge distances are calculated based on center mass of both oxygen and nitrogen atoms. (a) E27–K31 distance in the apo structure is highly variable (black) but is completely stabilized upon binding (blue). (b) The coordination of salt bridges occurs between residues E27 and K28 of GB1 and residues I253, S254, E380, and E382 of IgG-Fc. Here, K31 stabilizes E27 with a salt bridge, leading to successful binding. The annotated residues are shown in licorice, and the salt bridges are depicted as dashed pink lines. (c) Stabilization at the interface is due to dynamical interactions of K28 from GB1 with E380 and E382 from IgG-Fc. (d) The network of interactions at the GB1 and IgG-Fc (see also Table 2).
Figure 2 Key distances from WT-MD simulations; two independent 1 μs runs for each of the unbound and bound forms of GB1. The salt bridge distances are calculated based on center mass of both oxygen and nitrogen atoms. (a) E27–K31 distance in the apo structure is highly variable (black) but is completely stabilized upon binding (blue). (b) The coordination of salt bridges occurs between residues E27 and K28 of GB1 and residues I253, S254, E380, and E382 of IgG-Fc. Here, K31 stabilizes E27 with a salt bridge, leading to successful binding. The annotated residues are shown in licorice, and the salt bridges are depicted as dashed pink lines. (c) Stabilization at the interface is due to dynamical interactions of K28 from GB1 with E380 and E382 from IgG-Fc. (d) The network of interactions at the GB1 and IgG-Fc (see also Table 2).CC BY · PubMed Central
Figure 3 Effect of minimization for E27V, K31L, and W43K mutants. GB1 and IgG-Fc are shown in black and blue. Mutated residues are shown as licorice with atom coloring. Initial (mutated WT crystal structure, 1fcc PDB-coded) structure is illustrated as transparent; on the other hand, final minimized structures are illustrated in opaque colors. Heavy atom RMSD between the initial and final structures is 1.15, 1.07, and 1.11 Å for the respective MuMi replacements.
Figure 3 Effect of minimization for E27V, K31L, and W43K mutants. GB1 and IgG-Fc are shown in black and blue. Mutated residues are shown as licorice with atom coloring. Initial (mutated WT crystal structure, 1fcc PDB-coded) structure is illustrated as transparent; on the other hand, final minimized structures are illustrated in opaque colors. Heavy atom RMSD between the initial and final structures is 1.15, 1.07, and 1.11 Å for the respective MuMi replacements.CC BY · PubMed Central
Figure 4 (a) Heatmap of number of WT intermolecular dominant hydrogen bonds for all MuMi-produced conformations. (b) The number of WT intermolecular dominant hydrogen bonds compared with the experimental binding energies is shown on the right. The experimental binding energies are capped at 9 kcal/mol and any mutation exceeding this amount of destabilization is reported through this value.18
Figure 4 (a) Heatmap of number of WT intermolecular dominant hydrogen bonds for all MuMi-produced conformations. (b) The number of WT intermolecular dominant hydrogen bonds compared with the experimental binding energies is shown on the right. The experimental binding energies are capped at 9 kcal/mol and any mutation exceeding this amount of destabilization is reported through this value.18CC BY · PubMed Central
Figure 5 Assessment of GB1 binding by using MuMi and MD simulations. (a) Probability of residues being located at the binding region for mutations (MuMi) and MD simulations. Residues 1–19 are not shown due to insignificant probabilities. In MD results, residues 21, 25, 39, and 54 have higher propensity to stay at the binding region compared to MuMi, while residue 42 has lower probability. (b) PDFs of hydrogens bonds established between GB1 and IgG-Fc for residues listed in Table 2, calculated for MuMi and MD simulations. Residues tend to have a lower number of hydrogen bonds in MD simulations in contrast to MuMi. (c) PDFs of RSA are calculated for unbound/bound forms for the same residue set (see Figures S6 and S7 for PDFs of the complete set of residues of unbound/bound for MuMi and WT-MD).
Figure 5 Assessment of GB1 binding by using MuMi and MD simulations. (a) Probability of residues being located at the binding region for mutations (MuMi) and MD simulations. Residues 1–19 are not shown due to insignificant probabilities. In MD results, residues 21, 25, 39, and 54 have higher propensity to stay at the binding region compared to MuMi, while residue 42 has lower probability. (b) PDFs of hydrogens bonds established between GB1 and IgG-Fc for residues listed in Table 2, calculated for MuMi and MD simulations. Residues tend to have a lower number of hydrogen bonds in MD simulations in contrast to MuMi. (c) PDFs of RSA are calculated for unbound/bound forms for the same residue set (see Figures S6 and S7 for PDFs of the complete set of residues of unbound/bound for MuMi and WT-MD).CC BY · PubMed Central
Figure 6 Comparison of ⟨RSA⟩ of unbound/bound form for MuMi, MuMi-Dyn, and WT-MD simulations. Allosteric residues and binding residues are indicated with orange and red, and R2 values are written on graphs. (a, b) The ⟨RSA⟩ and σ values of MuMi and MD simulations for unbound/bound. (c, d) The ⟨RSA⟩ and σ values of MuMi and MuMi-Dyn for unbound/bound. (e, f) The ⟨RSA⟩ and σ values of WT-MD and MuMi-Dyn simulations for unbound/bound (see Figures S6, S7, and S10 for PDFs of all residues by MuMi, MuMi-Dyn, and WT-MD simulations).
Figure 6 Comparison of ⟨RSA⟩ of unbound/bound form for MuMi, MuMi-Dyn, and WT-MD simulations. Allosteric residues and binding residues are indicated with orange and red, and R2 values are written on graphs. (a, b) The ⟨RSA⟩ and σ values of MuMi and MD simulations for unbound/bound. (c, d) The ⟨RSA⟩ and σ values of MuMi and MuMi-Dyn for unbound/bound. (e, f) The ⟨RSA⟩ and σ values of WT-MD and MuMi-Dyn simulations for unbound/bound (see Figures S6, S7, and S10 for PDFs of all residues by MuMi, MuMi-Dyn, and WT-MD simulations).CC BY · PubMed Central

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Research works printed but not linked: 40

  1. 2014, Deep mutational scanning: a new style of protein science, Nature Methods, 11, 801, 10.1038/nmeth.3027 · DOI · Paper page
  2. 10.1128/JB.00070-11 · DOI
  3. 10.1016/j.tibtech.2011.04.003 · DOI
  4. 2023, Accurate proteome-wide missense variant effect prediction with AlphaMissense, Science, 381, 1303
  5. Diaz, D. J. , Kulikova, A. V. , Ellington, A. D. & Wilke, C. O . Using machine learning to predict the effects and consequences of mutations in proteins. Current Opinion in Structural Biology 78, (2023). · DOI
  6. 2023, Discovering functionally important sites in proteins, Nature Communications, 14, 4175, 10.1038/s41467-023-39909-0 · DOI
  7. 2024, ProSTAGE: Predicting Effects of Mutations on Protein Stability by Using Protein Embeddings and Graph Convolutional Networks, Journal of Chemical Information and Modeling, 64, 340, 10.1021/acs.jcim.3c01697 · DOI
  8. Blaabjerg, L. M. et al. Rapid protein stability prediction using deep learning representations. eLife 12, (2023). · DOI · Paper page
  9. 2018, Biophysical inference of epistasis and the effects of mutations on protein stability and function, Molecular Biology and Evolution, 35, 2345, 10.1093/molbev/msy141 · DOI
  10. 10.1016/j.jmb.2016.12.007 · DOI
  11. 10.1101/2024.03.15.585128 · DOI
  12. 10.1038/s41586-022-04586-4 · DOI
  13. Dewachter, L. , Brooks, F. D. , Wolff, M. H. , White, A. R. & Moore, M. J . Deep mutational scanning of essential bacterial proteins can guide antibiotic development. Nature Communications 14, (2023). · DOI · Paper page
  14. 2021, Climbing Up and Down Binding Landscapes through Deep Mutational Scanning of Three Homologous Protein–Protein Complexes, J. Am. Chem. Soc, 143, 17261, 10.1021/jacs.1c08707 · DOI
  15. 2021, Structurally Distributed Surface Sites Tune Allosteric Regulation, Protein Science, 30, 133
  16. 10.1016/j.cub.2014.09.072 · DOI
  17. Wu, N. C. , Dai, L. , Olson, C. A. , Lloyd-Smith, J. O. & Sun, R . Adaptation in protein fitness landscapes is facilitated by indirect paths. eLife 5, (2016). · DOI
  18. 10.1073/pnas.1903888116 · DOI
  19. 2023, Mega-scale experimental analysis of protein folding stability in biology and design, Nature, 620, 434, 10.1038/s41586-023-06328-6 · DOI · Paper page
  20. 10.1038/s41588-019-0431-x · DOI
  21. 10.1038/s41588-019-0432-9 · DOI
  22. 10.1016/j.sbi.2021.11.001 · DOI
  23. 2001, Elucidating the structural mechanisms for biological activity of the chemokine family, Proteins: Structure, Function, and Bioinformatics, 43, 150, 10.1002/1097-0134(20010501)43:2<150::AID-PROT1027>3.0.CO;2-M · DOI
  24. 1996, New conformational search method based on local torsional deformations for cyclic molecules, loops in proteins, and dense polymer systems, Journal of Chemical Physics, 105, 7868, 10.1063/1.472607 · DOI
  25. 1997, Efficiency of the local torsional deformations method for identifying the stable structures of cyclic molecules, Journal of Physical Chemistry A, 101, 2185, 10.1021/jp9627537 · DOI
  26. 2015, In silico mutational studies of Hsp70 disclose sites with distinct functional attributes, Proteins: Structure, Function and Bioinformatics, 83, 2077, 10.1002/prot.24925 · DOI
  27. 10.1038/S41592-022-01488-1 · DOI · Paper page
  28. 10.1093/nar/28.1.235 · DOI · Paper page
  29. 10.1016/0263-7855(96)00018-5 · DOI · Paper page
  30. 2021, ProDy 2.0: increased scale and scope after 10 years of protein dynamics modelling with Python, Bioinformatics, 37, 3657, 10.1093/bioinformatics/btab187 · DOI
  31. 1999, NAMD2: greater scalability for parallel molecular dynamics, J Comput Phys, 151, 283, 10.1006/jcph.1999.6201 · DOI
  32. 2012, Optimization of the Additive CHARMM All-Atom Protein Force Field Targeting Improved Sampling of the Backbone ϕ, ψ and Side-Chain χ1 and χ2 Dihedral Angles, Journal of Chemical Theory and Computation, 8, 3257, 10.1021/ct300400x · DOI
  33. 10.1038/nbt.3988 · DOI · Paper page
  34. Mitternacht, S . FreeSASA: An open source C library for solvent accessible surface area calculations [version 1; peer review: 2 approved]. F1000Research vol. 5 (2016). · DOI
  35. 2016, High-throughput identification of protein mutant stability computed from a double mutant fitness landscape, Protein Science, 25, 530, 10.1002/pro.2840 · DOI
  36. 10.1002/prot.1124 · DOI
  37. 10.1002/prot.26688 · DOI · Paper page
  38. 2022, Ligand binding remodels protein side-chain conformational heterogeneity, Elife, 11, e74114, 10.7554/eLife.74114 · DOI
  39. 2021, N-Terminus of the Third PDZ Domain of PSD-95 Orchestrates Allosteric Communication for Selective Ligand Binding, Journal of Chemical Information and Modeling, 61, 347, 10.1021/acs.jcim.0c01079 · DOI
  40. 2021, Dynamic Community Composition Unravels Allosteric Communication in PDZ3, Journal of Physical Chemistry B, 125, 2266, 10.1021/acs.jpcb.0c11604 · DOI

Metadata

Authors
Guclu, T. F., Atilgan, A. R., & Atilgan, C.
Affiliations
Sabancı Üniversitesi (TR)
Corresponding author
Guclu, Tandac F.
ORCID
Venue
The Journal of Physical Chemistry B
Publisher
openRxiv
Published
2024-06-03
Volume
128
Issue
33
Pages
7987-7996
ISSN
1520-5207 (ISSN-L)
DOI
10.1101/2024.06.01.596930
PMC
PMC11671028
OpenAlex
W4401425370
Cited by
0
References
2
FWCI
0.00
Type
journal article
Language
en
Open access
Licence
cc-by
Locations
  • The Journal of Physical Chemistry B (publishedVersion, cc-by)
  • bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by-nc)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • Sabanci University (acceptedVersion)
  • Sabanci University (submittedVersion)
Funders
Türkiye Bilimsel ve Teknolojik Araştırma Kurumu (122F149, 121Z329)
Topics
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Protein Structure and Dynamics
  • Life Sciences › Immunology and Microbiology › Immunology › Monoclonal and Polyclonal Antibodies Research
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › vaccines and immunoinformatics approaches
Keywords
streptococcal protein G, GB1, IgG Fc binding, deep mutational scanning, fitness landscape, hydrogen bonding, protein-protein binding, solvent accessibility, binding interface, protein stability, molecular dynamics
MeSH terms
Bacterial Proteins, Humans, Hydrogen Bonding, Immunoglobulin G, Immunoglobulin Fc Fragments, Mutation, Protein Binding, Molecular Dynamics Simulation

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CoChat. (2026). Citation check: Deciphering GB1’s Single Mutational Landscape: Insights from MuMi Analysis. https://app.cochat.ai/papers/10.1101/2024.06.01.596930
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Guclu, T. F., Atilgan, A. R., & Atilgan, C. (2024). Deciphering GB1’s Single Mutational Landscape: Insights from MuMi Analysis. The Journal of Physical Chemistry B. https://doi.org/10.1101/2024.06.01.596930