Timo Greiner, Sebastian Bolduan, Brigitte Hertel, Christine Groß, Kay Hama-cher, Ulrich Schubert, Anna Moroni and Gerhard Thiel (2016). Ion Channel Activity of Vpu Proteins Is Conserved throughout Evolution of HIV-1 and SIV, Viruses, 8(12):325. doi:10.3390/v8120325
a.1 supporting information to chapter 2
Figure A.1:kmeans clustering of the 13Å null model with maximal within-cluster sum of squares (withinss) as a function of number of clusters with the red arrow highlighting the drastic drop using four instead of three clusters.
A.1 supporting information to chapter2 99
binding loop
active site
flap helix
A B
C D
Figure A.2: Lowest frequency normal mode of FsC in one direction (A + C) and in the opposite direction (B + D) in top and side view (right angle to each other).
In contrast to the "breath-like" motions after perturbation of S120by substrate binding/cleavage, the normal mode loop motions are in a parallel manner (both loops move to the left or both loops move to the right).
r
Figure A.3: Comparison of NMR data with RMSF values of the wildtype simulation with Pearson’s r = 0.721, p-value= 0.00043. H188 is labeled in red. δshift values are obtained from Promperset al. [158]
A.1 supporting information to chapter2 101 For secondary structure quantification we used standardized secondary struc-ture assignment, Define Secondary Structure of Proteins (short: DSSP). DSSP begins by identifying the intra-backbone hydrogen bonds of the protein using a purely electrostatic definition [92]. We computed DSSP for each frame and com-puted the mean occurrence of secondary stucture elements of the complete pro-duction run of the simulation usingR [161] andbio3d[62].
We measured the following secondary structural states derived fromDSSP (Fig-ureA.4D):
B = residue in isolatedβ-bridge,
E = extended strand, participates inβladder, G =3-helix (310helix),
H =α-helix,
I =5helix (π-helix), S = bend,
T = hydrogen bonded turn, U = loop region.
Furthermore, to see that the protein does not unfold during the simulation time, we plotted the percentages of α-helices, residues in isolated β-bridges, and bends over time (FigureA.4A-C).
Figure A.4: Secondary structure analysis for the MD simulations with0% and 2% EG in the solvent. (A-C) Main secondary structure elements over time. The curves were smoothed using the LOESS [33] algorithm. (D) Average percentages of all observed secondary structure elements.
A.1 supporting information to chapter2 103
0 2 4 6
0 10 20 30
r [ A° ]
g(r)
EG Conc. 2% 3% 5% 10% 20%
A
0 2 4 6
0 10 20 30
r [ A° ]
g(r)
EtOH 5% MeOH 5%
B
Figure A.5: (A) Radial distribution function (RDF) of protein Cα to EG O for the simulations with different concentrations of EG in the solvent. For all EG concentrations we see a peak around5-7Å which is the first coordination shell of an amino acid followed by a second peak at around 10 Å. With further distances the radial distribution strongly decreases. These results indicate accumulation of EG near theFsC surface while the amount of EG in the bulk is minor. (B) RDF af protein Cαto EtOH O or MeOH O, respectively, for the simulations with other alcohols in the solvent. For both alcohols we see a tiny peak in the first coordination shell but in contrast to EG most of the EtOH and MeOH molecules are located in the bulk far away from the protein surface.
Figure A.6: Comparison of RMSD (A) and RMSF (B) for5% EG,5% EtOH, and5% MeOH in the solvent. To see the effect of the different alcohols in the solvent, the corresponding RMSD and RMSF without any alcohol (0% EG) is also plotted.
The curves were smoothed using the LOESS[33] algorithm.
100 101 102 103
102 103 104 105
Time [ps]
r2 (τ)[nm2 ]
EtOH 5% MeOH 5% EG 5% EG 0%
A
0.00 0.25 0.50 0.75 1.00
−1.0 −0.5 0.0 0.5 1.0
Qi
P(Qi)
EtOH 5% MeOH 5% EG 5% EG 0%
B
Figure A.7: Characterization of the solvent for the simulations with5% EtOH,5% MeOH, and5% EG in comparison to the simulation without any alcohol added to the solvent. (A) Double logarithmic representation of MSD of the water molecules as indirect measure of the viscosity. The constant increase of the MSD during the simulation describes normal diffusion. While the MSD of the simulation with EG is slightly decreased compared to the simulation with water only, the MSD for the simulation with MeOH is increased and the MSD of the simulation with EtOH behaves as with water only. (B) Tetrahedral order parameter Qi to quantify the ability of water molecules to form hydrogen bonds to adjacent water molecules. The distribution of Qi is right shifted for EG and MeOH while EtOH does not significantly influence the hydrogen bonding network of the water molecules.
A.1 supporting information to chapter2 105 To determine whether the interactions between EG and the surface residues are of
hydrophobic or hydrophilic nature, we made surface density calculations based on Equations7-11with dset to7Å. The subsets of surface residues were grouped as follows:
active site residues:
41 42 81 84 119 120 121 150 175 177 183 184 185 186 188 remaining surface residues:
1 2 3 4 5 6 7 8 9 10 11 12 13 14 17 18 24 27 28 29 30 31 32 33 34 35 36 37 38 44 45 46 47 48 49 50 51 52 53 59 60 61 62 63 64 65 66 67 68 69 70 71 72 75 76 77 79 80 83 85 86 87 88 89 90 91 92 94 95 96 97 105 114 115 116 117 118 122 123 124 126 127 128 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 151 152 153 154 156 157 158 159 160 162 163 164 165 166 167 168 169 170 171 172 173 174 176 178 179 180 181 182 187 189 190 191 192 193 194 195 196 197
Figure A.8: Distributions of EG densities at surface residues in the ative site in comparison to the EG densities at remaining surface residues. For both surface subsets the densities are seperately plotted for hydrophilic (blue) and hydrophobic (transparent) residues. While the EG densities at the remaining surface are quite similar for hydrophobic and hydrophilic residues, the densities of EG in the active site are significantly higher at hydrophilic residues. This indicates that the EG accumulation in the active site is mainly based on hydrogen-bonding interactions.
A B
C D
binding loop
active site
flap helix
Figure A.9: LRT model (cutoff 7 Å) with repulsive (A+ C) and attractive (B + D) force vectors with different forces analogous to the model with13 Å in Figure 2.7.
Here forces from0(blue) to60(red) in arbitrary units were applied.
A.1 supporting information to chapter2 107
A B
C
C D
Figure A.10: LRT null model (cutoff7Å) with1,000force directions for the external force vector analogous to the model with13Å in Figure2.8.
13 Å 15 Å 17 Å 21 Å 13 Å + long-range
Figure A.11: Comparison of the displacements of anisotropic network models with dif-ferent cutoffs for connected residues to account for long-range interactions.
The displacements only differ in their magnitude, which shows that the13Å modell does not disregard possible deviating long-range interactions. The 13Å + long-range model was computed with decreasing interactions between residues with larger distances than13Å. Nevertheless, the displacement is the same as for the original13Å model.
a.2 supporting information to chapter 3
Figure A.12: Perturbation of the ANM from HCN1in the cAMP-bound form. The force is applied at the tip of the elbow from the red cluster. This perturbation intends to represent displacements of the cAMP-bound structure in response to cAMP release from the binding site. Note that the red cluster points in the opposite direction of the perturbation from the yellow cluster, which simulates cAMP binding. For clarity, the displacements are only shown for one subunit as in Figure3.2 of main text. Color coding of the “elbow” and the “shoulder” are the same as in Figure3.1of main text.
A.2 supporting information to chapter3 109
Figure A.13: Displacements of S1to S6helices of the TMPC. (a-f), seen in top and bottom view after perturbing the “elbow” of the C-Linker from the most realistic perturbation direction. The corresponding helix is highlighted and labeled in lime green and the displacement is visualized as yellow arrows. For clarity only one subunit (residue94-402) is shown.
Figure A.14: Elastic network model of curated cAMP-free HCN1structure with a distance cutoff of 13 Å for connected residues. The Cα atoms of each residue are reduced to spheres (coloring of the subunits as in Figure 3.1 b) and the connections between atoms are shown as red lines. The elbow domain of the gray subunit is highlighted in light blue and the corresponding shoulder domain in orange as in Figure3.1a of main text.
A.2 supporting information to chapter3 111
●
●
●
●
●
●
●
●
● ●
2 4 6 8 10
1011121314
number of clusters
log(maximum withinss)
Figure A.15:kmeansclustering of the LRT null model with maximal within-cluster sum of squares (withinss) as a function of number of clusters. The most drastic drop is from three to four clusters.
a.3 supporting information to chapter 4
Figure A.16: Transfection of HEK293T cells with Vpus from HIV-1. Exemplary images of HEK293T cells transiently transfected with bi-cistronic vector containing different Vpus (from left to right: VpuRBF168, VpuCK1.62, VpuYBF30) together with GFP. Images are overlays of bright field images revealing cell contours and fluorescent images showing positive GFP signal in individual cells.
Figure A.17: Alignment of Vpu orthologs from SIV with consensus sequence of Vpu from HIV-1. Consensus sequence (top lane) for Vpu protein was constructed from alignment of 6,497 HIV Vpu entries in PFAM database. The SIV sequences within the PFAM alignment were extracted realigned amongst each other, and processed the same way as the HIV sequences. This resulted in the above alignment of22SIV sequences.
A C R O N Y M S
B
AA amino acid
ANM anisotropic network model ART antiretroviral therapy
BIT225 N-carbamimidoyl-5-(1-methyl-1H-pyrazol-4-yl)-2-naphthamide BST2 bone marrow stromal cell antigen2
cAMP cyclic adenosine monophosphate CD4 cluster of differentiation4
CK-2 casein kinase2
CNDB cyclic-nucleotide binding domain
CRISPR Clustered Regularly Interspaced Short Palindromic Repeats cryoEM cryo-electron microscopy
DCA direct coupling analysis
DMEM Dulbecco’s Modified Eagle Medium
DNA deoxyribonucleic acid
EG ethylene glycol
ENM elastic network model
ER endoplasmic reticulum
ERAD endoplasmic reticulum-associated protein degradation
FsC Fusarium solaniCutinase
GAFF general AMBER force field GFP green fluorescent protein
GNM Gaussian network model
HCN channel hyperpolarization-activated cyclic nucleotide-gated channel HEK293T cells human embryonic kidney293T cells
HEPES 4-(2-hydroxyethyl)-1-piperazineethanesulfonic acid HIV human immunodeficiency virus
HTS high-throughput screening
Ig immunoglobulin
I/V current/voltage
Kv channel voltage-dependent potassium channel LRT linear response theory
MC Monte-Carlo
MD molecular dynamics
MI Mutual Information
MM molecular mechanics
mRNA messenger ribonucleic acid MSA multiple sequence alignment
MSD mean square displacement
NGS next generation sequencing
NMA normal mode analysis
NMR nuclear magnetic resonance
PA polyamide
PBS phosphate-buffered saline
PDB protein data bank
PET polyethylene terephthalate
PME partical mesh Ewald
POPS parameter optimized surface calculator
QM quantum mechanics
RDF radial distribution function RMSD root mean square deviation RMSF root mean square fluctuation
RNA ribonucleic acid
SASA solvent accessible surface area
SD standard deviation
SDC surface density calculations SDS sodium dodecyl sulfate
acronyms 115
SIV simian immunodeficiency virus
TMD transmembrane domain
TMPC transmembrane portion of the channel
TPA terephthalic acid
VCP viral channel forming protein
Vpu viral protein u
VSD voltage-sensing domain
D A N K S A G U N G
C
An dieser Stelle möchte ich mich noch bei allen bedanken, die mich auf dem Weg hierher und während meiner Zeit als Doktorandin begleitet und unterstützt haben.
Zuallererst danke ich meinem Doktorvater Prof. Dr. Kay Hamacher für die Möglichkeit zur Promotion in seiner Arbeitsgruppe, die Bereitstellung der sehr spannenden Forschungsthemen und das mir entgegengebrachte Vertrauen. Ein besonderer Dank geht auch an meinen Zweitgutachter Prof. Dr. Gerhard Thiel für die vielen konstruktiven Gespräche und das jederzeit offene Ohr bei Fragen rund um Ionenkanäle sowie die Unterstützung beim Erstellen und Einreichen von Publikationen.
Ein großer Dank geht auch an Prof. Dr. Katja Schmitz für die Möglichkeit in Ihrem Labor auch experimentelle Untersuchungen zur Cutinase durchzuführen sowie die Möglichkeit in diesem Zusammenhang auch ein Forschungspraktikum und eine Masterarbeit betreuen zu können. Vielen Dank auch an meine Studentin Sabrinaund die mitbetreuende Doktorandin Julia für die tolle Zusammenarbeit auf diesem Thema.
Ein großes Dankeschön geht auch an alle aktuellen und ehemaligen Mitglieder der Arbeitsgruppe Computational Biology & Simulation für viele wertvolle Diskus-sionen und die super Unterstützung bei Fragen jeder Art. Danke anSvenfür die tolle Zusammenarbeit am Cutinase Thema und die vielen Tipps und Tricks fürs Programmieren. Danke an Ben für ein jederzeit offenes Ohr und wertvolle Hilfe rund um alle Fragen zu Mathe und Physik. Danke an Sebastian für seine Hilfe und die vielen wertvollen Tipps rund um alle Fragen zu Mathe und IT-Zeugs.
Danke anDanielfür die tolle Zusammenarbeit im Rahmen von iNAPO. Danke an alle AG Mitglieder, auch die, mit denen ich inhaltlich wenig zu tu hatte, für die tolle Arbeitsatmosphäre. Vielen Dank natürlich auch anCaroline für alle organi-satorischen Dinge und ihr jederzeit offenes Ohr. Danke auch anLauraundRobert aus unserer Nachbar-AG für den ein oder anderen Kaffee und schöne Pausen im botanischen Garten.
Ein großes Dankeschön gebührt natürlich auch meinen Freunden und meiner Familie, die für die nötige Abwechselung im Privaten gesorgt haben. Ein beson-derer Dank geht an meine Eltern, die die Grundlage für alles geschaffen und mich in allen Lebenslagen begleitet und unterstützt haben. Gleiches gilt fürmeine Großeltern, die immer für mich da waren und das Ende meiner Promotionszeit leider doch nicht mehr miterleben dürfen. Vielen Dank auch anmeine Schwester und ihren Freund, sowie an meine Schwiegereltern, meine Schwägerin, deren Mannund deren Kinderfür viele schöne Momente und ausreichend Ablenkung.
Zu guter Letzt danke ichmeinem Mannfür seine großartige Unterstützung, Liebe, Verständnis und Geduld, die er mir in den letzten Jahren entgegengebracht hat, sowieunserem Sohnfür das Versüßen meiner Promotionszeit und die vielseitige Ablenkung von der Arbeit.