The In Silico Prediction of Hotspot Residues that Contribute to the Structural Stability of Subunit Interfaces of a Picornavirus Capsid
Abstract
:1. Introduction
2. Materials and Methods
2.1. Preparation of TMEV Subcomplexes
2.2. Hotspot Prediction
2.3. Prediction of Interacting Residues
2.4. Residue Conservation
3. Results
3.1. Analysis of the Residues that Contribute to the Intraprotomer Interfaces of TMEV GDVII
3.1.1. Predicted Hotspots at the Intraprotomer Interfaces
3.1.2. Intraprotomer Hotspot Residues in TMEV that are Conserved with Residues in Other Viruses of the Family
3.2. Analysis of the Residues that Contribute to the Interprotomer Interfaces of TMEV GDVII
3.2.1. Predicted Hotspot Residues at the Interprotomer Interfaces
3.2.2. Interprotomer Hotspot Residues in TMEV that are Conserved with Residues in Other Viruses of the Family
3.3. Analysis of the Residues that Contribute to the Interpentamer Interfaces of TMEV GDVII
3.3.1. Predicted Hotspot Residues at the Interpentamer Interfaces
3.3.2. Interpentamer Hotspot Residues in TMEV that are Conserved with Residues in Other Viruses of the Family
4. Discussion
5. Conclusions
Supplementary Materials
Author Contributions
Funding
Conflicts of Interest
References
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Web Server/Database | Strategy | Technique | Ref. |
---|---|---|---|
ROBETTA http://www.robetta.org/alascansubmit.jsp | Energy-based: | Computational alanine scanning: Interfacial residues are individually substituted to alanine. The Δ binding free energy of the complex and Δ protein stability are calculated. Hotspots are defined as residues that increase the binding free energy ≥1 kcal/mol following substitution to alanine. | [23] |
Free energy function. | |||
PPCheck http://caps.ncbs.res.in/ppcheck/ | Energy- and feature-based: | Energy scoring scheme: Calculates and imparts pseudoenergies to noncovalent interactions in protein–protein interfaces. These energies are weighted with features to identify hotspot residues. | [40] |
Energy scoring, extent of spatial residue interaction, extent of energy contribution. | |||
PredHS http://predhs.denglab.org/ | Feature- and energy-based: | Machine Learning: Euclidian and Voronoi neighbourhoods are used to weight features and generate individual residue scores. Hotspots are defined as residues with scores >0. | [41] |
38 sequence-, structure- and energy-based features. | |||
KFC2 https://mitchell-web.ornl.gov/KFC_Server/upload.php | Feature-based: | Machine Learning: Various features are assessed by two models built using support vector machines. Residues which are detected as hotspots are highlighted. | [42,43] |
Residue size, packing density, solvent accessibility, hydrophobicity, flexibility. | |||
HotRegion/Hotpoint http://prism.ccbb.ku.edu.tr/hotregion | Feature-based: | Machine Learning: HotPoint assesses features using an empirical model and defines a residue as a hotspot if ASA values are ≤20% and contact potential values ≥18.0. HotRegion creates a network of these hotspots and highlights those which form contacts. | [44] |
Solvent accessibility (ASA) and residue contact potential/known residue pair energies. |
Web Server | Technique | Interactions and Cutoff Distances | Ref. |
---|---|---|---|
jsPISA http://www.ccp4.ac.uk/pisa | Uses seven parameters to identify interfaces. Solvation energy, binding energy, hydrophobic p-value, number and type of contacts are then determined. | hydrogen bonds; salt bridges; disulphide bonds | [46] |
PIC http://pic.mbu.iisc.ernet.in/ | Predicts residue–residue interactions at protein–protein interfaces from the atomic coordinates of the input complex using standard widely accepted criteria. | hydrogen bonds; hydrophobic interactions (5 Å); cation-pi and ionic interactions (6 Å); disulphide bonds and aromatic–aromatic interactions bonds (4.5–7 Å); aromatic–sulphur interactions (5.3 Å) | [45] |
Hotspot Residue | Known Function | Ref. |
---|---|---|
VP1: R94, W95 & V96 VP2: F176 & M178 | Reside on VP1 loop II and VP2 Puff A. Interactions between these residues are predicted to increase the stability of these loops. The substitution of A101 within the VP1 loop to tryptophan disrupted interactions between these residues and reduced virus yield and persistence. | [49,50] |
VP1: W202, W206 & F215 VP2: Y135 & E146 | W202-Y135 form a strong hydrophobic core which stabilizes the TMEV VP1 foot-and-mouth-disease virus (FMDV) loop. E146 on VP2 puff B forms hydrophobic interactions with a non-hotspot on the FMDV loop of VP1. These interactions allow TMEV, unlike other cardioviruses, to remain stable under a broad range of pH conditions. | [49] |
VP1: F254 VP3: R100 | Reside in receptor binding site and were predicted to be involved in binding to the TMEV co-receptor heparan sulphate. | [38] |
VP1: V245, F246, R249, L252, F253, F254, W256, T260 & I272 | Form part of the VP1 C-terminal loop located over the receptor binding site. | [38] |
Hotspot Residue | Corresponding Residues in Related Viruses with Known Function(s) | Ref. |
---|---|---|
VP1: K241 & R249 | Correspond to residues K256 and R264 in enterovirus 71 (EV-71), which have been shown to be necessary for virus replication. In vitro substitution of either residue to alanine was lethal as virus could not be recovered. | [20] |
VP1: Y124, N204, F246 & R249 | Conserved with energetically important residues Y128, D206, W261, and R264 (VP1) that form part of the intraprotomer interfaces of EV-71 which were found in a conserved motif within the enteroviruses. | [36] |
VP1: K241 | Corresponds to residue R202 in the VP1 protein of human parechovirus 3 (HPeV-3) that is known to be involved in interactions with the viral genome. | [51] |
Hotspot Residue | Known Function | Ref. |
---|---|---|
VP1: P153 VP3: I181 | These residues are exposed at the bottom of the putative receptor binding site in TMEV. P153 was previously shown to be critical for binding the unknown glycoprotein receptor. | [52] |
Hotspot Residue | Corresponding Residues in Related Viruses with Known Role(s) | Ref. |
---|---|---|
VP1: P153, VP3: N103, Q104, Q173, I181 & M222 | Conserved with residues in Saffold virus 3 (SAFV-3) that undergo conformational transitions to form a pore at the protomer–protomer interface in the expanded particle, which is thought to be involved in RNA release. | [53] |
VP3: S16 | Corresponds to residue T47 in the VP3 protein of HPeV-3 that is known to make contacts with the RNA genome. | [51] |
Hotspot Residue | Corresponding Residues in Related Viruses with Known Function(s) | Ref. |
---|---|---|
VP2: N25, R61, Y62 (W in SVV-1), Y63, T64, V95 (A in SVV-1), R102, N117, S240 (T in SVV-1) VP3: M144, Y148, I150, D152, L153 (I in SVV-1), T194, T197 | Conserved with residues at the pentamer interfaces of the Seneca Valley virus 1 (SVV-1) mature capsid that are predicted to form interactions across the interface. | [54] |
VP2: R61 VP3: K124, D152 & T194 | Correspond to residues R60 (VP2), R120, D148 and T190 (VP3) in FMDV, respectively, that were found to be important for virus growth. The in vitro substitution R60A was lethal as the virus could not be recovered. Residue substitutions R120A and D148A attenuated viral growth, yield and plaque size. The substitution of T194A could only be recovered after genotypic reversion. | [11] |
VP2: Y63 | Corresponds to F62 in FMDV SAT2. Mutation of F62 to tyrosine, as seen in TMEV, increased the stability of the FMDV particle. | [55] |
VP2: R61 & T64 VP3: M144 & D152 | Conserved with energetically important residues within conserved interacting motifs at the two-fold axes of enteroviruses. | [36] |
VP3: M144, I150, D152 & L153 | Conserved with residues in human rhinovirus (HRV)-2 VP3 at the pentamer interface that become disordered during capsid uncoating and RNA release. | [56] |
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Upfold, N.; Ross, C.; Tastan Bishop, Ö.; Knox, C. The In Silico Prediction of Hotspot Residues that Contribute to the Structural Stability of Subunit Interfaces of a Picornavirus Capsid. Viruses 2020, 12, 387. https://doi.org/10.3390/v12040387
Upfold N, Ross C, Tastan Bishop Ö, Knox C. The In Silico Prediction of Hotspot Residues that Contribute to the Structural Stability of Subunit Interfaces of a Picornavirus Capsid. Viruses. 2020; 12(4):387. https://doi.org/10.3390/v12040387
Chicago/Turabian StyleUpfold, Nicole, Caroline Ross, Özlem Tastan Bishop, and Caroline Knox. 2020. "The In Silico Prediction of Hotspot Residues that Contribute to the Structural Stability of Subunit Interfaces of a Picornavirus Capsid" Viruses 12, no. 4: 387. https://doi.org/10.3390/v12040387