diff --git a/docs/.vitepress/dist/.DS_Store b/docs/.vitepress/dist/.DS_Store index e33624bd..e03ca1eb 100755 Binary files a/docs/.vitepress/dist/.DS_Store and b/docs/.vitepress/dist/.DS_Store differ diff --git a/docs/.vitepress/dist/404.html b/docs/.vitepress/dist/404.html index 4dc4b960..7e265b4c 100644 --- a/docs/.vitepress/dist/404.html +++ b/docs/.vitepress/dist/404.html @@ -8,14 +8,14 @@ - +
Nipah RBP DMS

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But if you don't change your direction, and if you keep looking, you may end up where you are heading.
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Built by Brendan Larsen and Jesse Bloom

- + \ No newline at end of file diff --git a/docs/.vitepress/dist/antibody_escape.html b/docs/.vitepress/dist/antibody_escape.html index d15a3edc..c0bf1c03 100644 --- a/docs/.vitepress/dist/antibody_escape.html +++ b/docs/.vitepress/dist/antibody_escape.html @@ -8,17 +8,17 @@ - + - - - + + + -
Nipah RBP DMS

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Antibody Escape ​

We determined the effects of RBP mutations on neutralization by different RBP-directed antibodies. Antibody selections were performed by incubating pseudovirus libraries with different concentrations of antibody, followed by infection of CHO-bEFNB3 cells to recover barcodes. Neutralization curves were fit on the DMS data with polyclonal.

Individual Antibody Selections ​

Individual antibody selection model fitting notebooks

LibB-230720-m102.4

LibA-230725-m102.4

LibA-231116-m102.4

LibA-230725-nAH1.3

LibB-230630-nAH1.3

LibB-230720-nAH1.3

LibA-231024-HENV26

LibB-230815-HENV26

LibB-230818-HENV26

LibB-230907-HENV26

LibB-230704-HENV32

LibB-230720-HENV32

LibA-230725-HENV32

LibB-230907-HENV32

LibA-230815-HENV103

LibB-230818-HENV103

LibB-230906-HENV103

LibA-230815-HENV117

LibB-230818-HENV117

LibB-230907-HENV117

Average Antibody Escape ​

Averaging antibody escape across libraries and replicate selections.

Average antibody escape notebooks

m102.4

nAH1.3

HENV-26

HENV-32

HENV-103

HENV-117

Antibody Escape Comprehensive Heatmaps ​

Additional control over filtering parameters. Users can adjust different parameters to filter the heatmap data. These provide more information and control compared to the final filtered heatmaps provided on the heatmaps page.

m102.4

HENV-117

HENV-26

HENV-32

HENV-103

nAH1.3

Neutralization Curves ​

Neutralization curves notebook

Neutralization of unmutated Nipah RBP/F pseudovirus by different anti-RBP antibodies.

Antibody Escape Validations ​

Antibody validation notebook

To validate the escape measurements from DMS, we generated single RBP mutant pseudoviruses and tested their neutralization by antibody nAH1.3.

Miscellaneous Figures ​

Escape at Nipah and Hendra polymorphisms and differences

Functional Effect of Antibody Escape Mutations ​

Effects of mutations on cell entry and antibody neutralization

Escape by Site ​

Line plot of average antibody escape at each site

Antibody Escape Analysis Notebook ​

Antibody analysis notebook

Raw Data ​

These data have not been filtered. They are the raw output from dms-vep-pipeline-3. For filtered .csv files, click here.

Individual antibody escape selection files

Averaged effects of RBP mutations on neutralization across replicate selections

antibody m102.4

antibody HENV-117

antibody HENV-26

antibody HENV-32

antibody HENV-103

antibody nAH1.3

Built by Brendan Larsen and Jesse Bloom

- +
Nipah RBP DMS

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Antibody Escape ​

We determined the effects of receptor binding protein mutations on antibody neutralization. Antibody selections were performed by incubating pseudovirus libraries with different concentrations of antibody, followed by infection of CHO cells expressing bat ephrin-B3. Neutralization curves were fit on the DMS data with polyclonal.

image

Schematic of antibody selections.

Individual Antibody Selections ​

Individual antibody selection model fitting notebooks

LibB-230720-m102.4

LibA-230725-m102.4

LibA-231116-m102.4

LibA-230725-nAH1.3

LibB-230630-nAH1.3

LibB-230720-nAH1.3

LibA-231024-HENV26

LibB-230815-HENV26

LibB-230818-HENV26

LibB-230907-HENV26

LibB-230704-HENV32

LibB-230720-HENV32

LibA-230725-HENV32

LibB-230907-HENV32

LibA-230815-HENV103

LibB-230818-HENV103

LibB-230906-HENV103

LibA-230815-HENV117

LibB-230818-HENV117

LibB-230907-HENV117

Average Antibody Escape ​

Averaging antibody escape across libraries and replicate selections.

Average antibody escape notebooks

m102.4

nAH1.3

HENV-26

HENV-32

HENV-103

HENV-117

Antibody Escape Comprehensive Heatmaps ​

Additional control over filtering parameters. Users can adjust different parameters to filter the heatmap data. These provide more information and control compared to the final filtered heatmaps provided on the heatmaps page.

m102.4

HENV-117

HENV-26

HENV-32

HENV-103

nAH1.3

Neutralization Curves ​

Neutralization curves notebook

Neutralization of unmutated Nipah RBP/F pseudovirus by different anti-RBP antibodies.

Antibody Escape Validations ​

To validate our deep mutational scanning measurements, we generated pseudoviruses expressing different receptor binding protein mutations. We tested whether neutralization by the antibody nAH1.3 correlated with our deep mutational scanning data.

Antibody validation notebook

To validate the escape measurements from DMS, we generated single RBP mutant pseudoviruses and tested their neutralization by antibody nAH1.3.

Miscellaneous Figures ​

Escape at Nipah and Hendra polymorphisms and differences

Functional Effect of Antibody Escape Mutations ​

Effects of mutations on cell entry and antibody neutralization

Escape by Site ​

Line plot of average antibody escape at each site

Antibody Escape Analysis Notebook ​

Antibody analysis notebook

Raw Data ​

These data have not been filtered. They are the raw output from dms-vep-pipeline-3. For filtered .csv files, click here.

Individual antibody escape selection files

Averaged effects of RBP mutations on neutralization across replicate selections

antibody m102.4

antibody HENV-117

antibody HENV-26

antibody HENV-32

antibody HENV-103

antibody nAH1.3

Built by Brendan Larsen and Jesse Bloom

+ \ No newline at end of file diff --git a/docs/.vitepress/dist/cell_entry.html b/docs/.vitepress/dist/cell_entry.html index 076e9555..ec6639c6 100644 --- a/docs/.vitepress/dist/cell_entry.html +++ b/docs/.vitepress/dist/cell_entry.html @@ -8,17 +8,17 @@ - + - - - + + + -
Nipah RBP DMS

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Cell Entry ​

To measure the effects of mutations on RBP-mediated cell entry, we performed 'functional selections' where the frequencies of barcodes were compared between pseudoviruses expressing either Nipah RBP/F or VSV-G. Notebooks below contain information about each step.

Global Epistasis Fitting ​

Individual cell entry selections were fit with multidms to decompose the effects of individual mutations using a global epistasis model.

Individual functional selection global epistasis model fitting notebooks

LibA-230725-CHO-bEFNB3

LibA-230815-CHO-bEFNB3

LibA-230818-CHO-bEFNB3

LibA-230825-CHO-bEFNB3

LibA-230916-CHO-bEFNB3

LibA-231006-CHO-bEFNB3

LibA-231019-CHO-bEFNB3_1

LibA-231019-CHO-bEFNB3_2

LibA-231019-CHO-bEFNB2_1

LibA-231024-CHO-bEFNB3

LibA-231024-CHO-bEFNB2

LibA-231112-CHO-bEFNB3_1

LibA-231112-CHO-bEFNB3_2

LibA-231112-CHO-bEFNB2

LibA-231207-CHO-bEFNB2_1

LibA-231207-CHO-bEFNB2_2

LibA-231207-CHO-bEFNB2_3

LibA-231207-CHO-bEFNB2_4

LibA-231207-CHO-bEFNB2_5

LibA-231222-CHO-bEFNB2

LibB-230630-CHO-C6-nac

LibB-230720-CHO-bEFNB3

LibB-230731-CHO-BA6-nac

LibB-230815-CHO-bEFNB3

LibB-230818-CHO-bEFNB3

LibB-230906-CHO-EFNB3-C6_diffVSV

LibB-230907-CHO-EFNB3-C6-nac_diffVSV

LibB-231105-CHO-EFNB2-BA6-nac_diffVSV

LibB-231108-CHO-EFNB2-BA6-nac_diff_VSV

LibB-231108-CHO-EFNB3-C6-nac_diff_VSV

LibB-231112-CHO-bEFNB2

LibB-231112-CHO-EFNB2-BA6-1

LibB-231112-CHO-EFNB2-BA6-2

LibB-231112-CHO-EFNB3-C6-1

LibB-231112-CHO-EFNB3-C6-2

LibB-231116-CHO-bEFNB3

LibB-231116-CHO-bEFNB2

LibB-231116-CHO-BA6_nac_diff_VSV

LibB-231222-CHO-EFNB2-BA6_diffVSV

LibB-231222-CHO-EFNB2-BA6-nac_diffVSV

Averaging Cell Entry ​

Individual functional selections were averaged between libraries and replicates below.

Notebook averaging the effects of cell entry in CHO-bEFNB2 cells

Notebook averaging the effects of cell entry in CHO-bEFNB3 cells

Comprehensive Cell Entry Heatmaps ​

Additional control over filtering parameters. Users can adjust different parameters to filter the heatmap data. These provide more information and control compared to the final filtered heatmaps provided on the heatmaps page.

CHO-bEFNB2 cell entry heatmap

CHO-bEFNB3 cell entry heatmap

Functional Scores ​

Notebook analyzing the distribution of functional scores for all individual selections.

Functional scores notebook

Analyze Data ​

Notebook analyzing cell entry from filtered data. Make figures for manuscript using python and altair.

Cell entry analysis notebook

Cell Entry Figures ​

TIP

Plots below are interactive. Hover over points to see more information. Click arrow box to view altair plots in separate page.

Cell entry of different RBP regions

Site-averaged Effects of Mutations on Cell Entry ​

Sites in RBP neck and contact sites (ranked from least constrained to most)

Cell Entry Correlations ​

Correlation between site-averaged effects of mutations on cell entry

Correlation between effects of all mutations on cell entry

Cell Entry Validations ​

Cell entry validation notebook

We validated the DMS cell entry measurements by making individual RBP mutants, expressing them on pseudovirus particles, and measuring luciferase following infection.

Raw Data ​

These data have not been filtered. They are the raw output from dms-vep-pipeline-3. For filtered .csv files, click here

Individual cell entry files

Averaged effects of RBP mutations on entry in CHO-bEFNB2 cells across replicates

Averaged effects of RBP mutations on entry in CHO-bEFNB3 cells across replicates

Built by Brendan Larsen and Jesse Bloom

- +
Nipah RBP DMS

Appearance

Cell Entry ​

To measure the effects of mutations on cell entry mediated by the receptor binding protein, we conducted 'functional selections'. In these selections, we compared the frequencies of specific barcodes between two groups of pseudoviruses: those expressing Nipah RBP/F and those expressing VSV-G. Notebooks below contain information about each step.

image

Schematic of functional selections using VSV-G entry as a control for library composition.

Global Epistasis Fitting ​

Individual cell entry selections were fit with multidms to decompose the effects of individual mutations using a global epistasis model.

Individual functional selection global epistasis model fitting notebooks

LibA-230725-CHO-bEFNB3

LibA-230815-CHO-bEFNB3

LibA-230818-CHO-bEFNB3

LibA-230825-CHO-bEFNB3

LibA-230916-CHO-bEFNB3

LibA-231006-CHO-bEFNB3

LibA-231019-CHO-bEFNB3_1

LibA-231019-CHO-bEFNB3_2

LibA-231019-CHO-bEFNB2_1

LibA-231024-CHO-bEFNB3

LibA-231024-CHO-bEFNB2

LibA-231112-CHO-bEFNB3_1

LibA-231112-CHO-bEFNB3_2

LibA-231112-CHO-bEFNB2

LibA-231207-CHO-bEFNB2_1

LibA-231207-CHO-bEFNB2_2

LibA-231207-CHO-bEFNB2_3

LibA-231207-CHO-bEFNB2_4

LibA-231207-CHO-bEFNB2_5

LibA-231222-CHO-bEFNB2

LibB-230630-CHO-C6-nac

LibB-230720-CHO-bEFNB3

LibB-230731-CHO-BA6-nac

LibB-230815-CHO-bEFNB3

LibB-230818-CHO-bEFNB3

LibB-230906-CHO-EFNB3-C6_diffVSV

LibB-230907-CHO-EFNB3-C6-nac_diffVSV

LibB-231105-CHO-EFNB2-BA6-nac_diffVSV

LibB-231108-CHO-EFNB2-BA6-nac_diff_VSV

LibB-231108-CHO-EFNB3-C6-nac_diff_VSV

LibB-231112-CHO-bEFNB2

LibB-231112-CHO-EFNB2-BA6-1

LibB-231112-CHO-EFNB2-BA6-2

LibB-231112-CHO-EFNB3-C6-1

LibB-231112-CHO-EFNB3-C6-2

LibB-231116-CHO-bEFNB3

LibB-231116-CHO-bEFNB2

LibB-231116-CHO-BA6_nac_diff_VSV

LibB-231222-CHO-EFNB2-BA6_diffVSV

LibB-231222-CHO-EFNB2-BA6-nac_diffVSV

Averaging Cell Entry ​

Individual functional selections were averaged between libraries and replicates below.

Notebook averaging the effects of cell entry in CHO-bEFNB2 cells

Notebook averaging the effects of cell entry in CHO-bEFNB3 cells

Comprehensive Cell Entry Heatmaps ​

Additional control over filtering parameters. Users can adjust different parameters to filter the heatmap data. These provide more information and control compared to the final filtered heatmaps provided on the heatmaps page.

CHO-bEFNB2 cell entry heatmap

CHO-bEFNB3 cell entry heatmap

Functional Scores ​

Notebook analyzing the distribution of functional scores for all individual selections.

Functional scores notebook

Analyze Data ​

Notebook analyzing cell entry from filtered data. Make figures for manuscript using python and altair.

Cell entry analysis notebook

Cell Entry Figures ​

TIP

Plots below are interactive. Hover over points to see more information. Click arrow box to view altair plots in separate page.

Cell entry of different RBP regions

Site-averaged Effects of Mutations on Cell Entry ​

Sites in RBP neck and contact sites (ranked from least constrained to most)

Cell Entry Correlations ​

Comparison of average effects of mutations on entry in cells expressing bat ephrin-B2 or bat ephrin-B3. Some mutations are much more tolerated for cell entry in bat ephrin-B2 cells, especially at sites near the receptor-binding interface.

Correlation between site-averaged effects of mutations on cell entry

Correlation between effects of all mutations on cell entry

Cell Entry Validations ​

To validate our deep mutational scanning measurements, we produced lentiviruses with individual mutations that spanned a range of effects. Cell entry validation notebook

We validated the DMS cell entry measurements by making individual RBP mutants, expressing them on pseudovirus particles, and measuring luciferase following infection.

Raw Data ​

These data have not been filtered. They are the raw output from dms-vep-pipeline-3. For filtered .csv files, click here

Individual cell entry files

Averaged effects of RBP mutations on entry in CHO-bEFNB2 cells across replicates

Averaged effects of RBP mutations on entry in CHO-bEFNB3 cells across replicates

Built by Brendan Larsen and Jesse Bloom

+ \ No newline at end of file diff --git a/docs/.vitepress/dist/hashmap.json b/docs/.vitepress/dist/hashmap.json index 4d150396..2e8793b4 100644 --- a/docs/.vitepress/dist/hashmap.json +++ b/docs/.vitepress/dist/hashmap.json @@ -1 +1 @@ -{"index.md":"Dykd9Nku","interactive.md":"DPEN0iki","pipeline_information.md":"BGHUoDcX","antibody_escape.md":"BV6AVDKe","heatmaps.md":"DTP-hRMN","cell_entry.md":"BrmqhwMi","receptor_binding.md":"CQ30tqj1"} +{"heatmaps.md":"C4mrizPX","index.md":"BJPGomlH","interactive.md":"BrWcZFR1","antibody_escape.md":"DNIudxV8","cell_entry.md":"D496DCbu","receptor_binding.md":"B_Ts74Pk","pipeline_information.md":"Dwv2BOHq"} diff --git a/docs/.vitepress/dist/heatmaps.html b/docs/.vitepress/dist/heatmaps.html index 529be508..886f376c 100644 --- a/docs/.vitepress/dist/heatmaps.html +++ b/docs/.vitepress/dist/heatmaps.html @@ -8,17 +8,17 @@ - + - - - + + + -
Nipah RBP DMS

Appearance

Heatmaps ​

Heatmaps represent one of the best ways to explore deep mutational scanning data. This page contains links to various heatmaps, which show the effects of mutations on three different phenotypes: cell entry, receptor binding, and antibody escape.

TIP

Hover over the heatmaps to see more information about each mutation

Entry Heatmaps ​

Effects of RBP mutations on entry in CHO-bEFNB2 Cells

Effects of RBP mutations on entry in CHO-bEFNB3 Cells

Binding Heatmaps ​

INFO

Mutations with low cell entry scores are masked in dark gray.

bEFNB2 Binding Heatmap

bEFNB3 Binding Heatmap

Antibody Escape Heatmaps ​

INFO

Mutations with low cell entry scores are masked in dark gray. If protein structure is available, distance in angstroms to the closest antibody residue is shown.

m102.4 Antibody Escape

HENV-117 Antibody Escape

HENV-26 Antibody Escape

HENV-103 Antibody Escape

HENV-32 Antibody Escape

nAH1.3 Antibody Escape

Heatmaps of Specific RBP Regions ​

TIP

Click arrow in upper right to view full-sized plots

Effects of mutations on cell entry and binding at receptor contact sites. Receptor contact sites are less constrained for entry in CHO-bEFNB2 cells than CHO-bEFNB3 cells, likely due to ~25-fold higher receptor affinity of RBP to EFNB2 versus EFNB3.

Effects of mutations on cell entry and binding at glycosylation sites ​

Nipah RBP has six sites that are glycosylated. One in the neck (site 159) and five in the head. Here are the effects of mutations on entry and binding.

Effects of mutations on cell entry and binding at polymorphic Nipah sites ​

These sites are polymorphic in Nipah sequences. Most of these sites tolerate multiple mutations.

Effects of mutations on cell entry, organized by type of the unmutated amino acid residue ​

The effects of mutations organized by the unmutated amino acid type. Strong preference for certain amino acids can be seen in certain regions. For example, portions of the stalk only tolerate hydrophobic residues (see sites 101-160 below).

Notebooks ​

Notebook that makes all heatmaps from filtered DMS data. Make figures for manuscript using python and altair.

Heatmap notebook

Built by Brendan Larsen and Jesse Bloom

- +
Nipah RBP DMS

Appearance

Heatmaps ​

Heatmaps represent one of the best ways to explore deep mutational scanning data. This page contains links to various heatmaps, which show the effects of mutations on three different phenotypes: cell entry, receptor binding, and antibody escape.

TIP

Hover over the heatmaps to see more information about each mutation. An 'X' represents the amino acid found at that site in the unmutated Nipah Malaysia sequence.

Entry Heatmaps ​

Effects of all mutations on entry in CHO cells expressing different bat receptors.

Entry with bat ephrin-B2

Entry with bat ephrin-B3

Binding Heatmaps ​

INFO

Mutations with low cell entry scores are masked in dark gray.

Binding to bat ephrin-B2 (monomeric)

Binding to bat ephrin-B3 (dimeric)

Antibody Escape Heatmaps ​

INFO

Mutations with low cell entry scores are masked in dark gray. If protein structure is available, distance in angstroms to the closest antibody residue is shown.

m102.4 Antibody Escape

HENV-117 Antibody Escape

HENV-26 Antibody Escape

HENV-103 Antibody Escape

HENV-32 Antibody Escape

nAH1.3 Antibody Escape

Heatmaps of Specific Receptor Binding Protein Regions ​

TIP

Click arrow in upper right to view full-sized plots

Contact sites ​

Effects of mutations on cell entry and binding at receptor contact sites. Receptor contact sites are less constrained for entry in CHO cells expressing bat ephrin-B2 than CHO cells expressing bat ephrin-B3. This is likely due to ~25-fold higher receptor affinity of the receptor binding protein to ephrin-B2 versus ephrin-B3.

Effects of mutations on cell entry and binding at glycosylation sites ​

Nipah RBP has six sites that are glycosylated. One in the neck (site 159) and five in the head. Here are the effects of mutations on entry and binding.

Effects of mutations on cell entry and binding at polymorphic Nipah sites ​

These sites are polymorphic in Nipah sequences. Most of these sites tolerate multiple mutations.

Effects of mutations on cell entry, organized by type of the unmutated amino acid residue ​

The effects of mutations organized by the unmutated amino acid type. Strong preference for certain amino acids can be seen in certain regions. For example, portions of the stalk only tolerate hydrophobic residues (see sites 101-160 below).

Notebooks ​

Notebook that makes all heatmaps from filtered DMS data. Make figures for manuscript using python and altair.

Heatmap notebook

Built by Brendan Larsen and Jesse Bloom

+ \ No newline at end of file diff --git a/docs/.vitepress/dist/index.html b/docs/.vitepress/dist/index.html index a8c2656c..3067f910 100644 --- a/docs/.vitepress/dist/index.html +++ b/docs/.vitepress/dist/index.html @@ -8,17 +8,17 @@ - + - - - + + + -
Nipah RBP DMS

Appearance

Nipah virus deep mutational scanning

Collection of data, figures, and information for the Nipah virus receptor binding protein deep mutational scanning project

Pipeline information and data processing

Analysis of Pacbio and Illumina sequencing data produced from deep mutational scanning experiments

Cell entry

Analysis of functional selections for assessing how RBP mutations impact cell entry

Receptor binding

Analysis of soluble receptor selections for measuring how mutations affect binding to host receptors

Antibody escape

Analysis of antibody neutralization data for finding sites of escape in the RBP

Heatmaps

Links to heatmaps showing effects of mutations on cell entry, receptor binding, and antibody escape

Interactive figures

Explore DMS data on Nipah RBP with interactive figures

About ​

This website contains links and information for the Nipah virus receptor binding protein deep mutational scanning project. Look through Jupyter notebooks used in analyses, explore interactive charts, or download raw data. To view more information on the code used to analyze these data and generate the website, check out our GitHub repo. Interactive charts made with Altair. Embedding of Altair plots was done with custom javascript code from dms-vep. All work was done in the Bloom Lab, part of Fred Hutch Cancer Center. To access the old version of the homepage built by dms-vep-pipeline-3, click here.

Scientific Details ​

Nipah virus is a bat-borne paramyxovirus that occassionally spills over into humans in SE Asia, causing fatal infections. The Nipah virus receptor binding protein is responsible for binding to host receptors (ephrin-B2 and -B3) on the cell surface. Following receptor binding, RBP triggers the fusion (F) protein, which undergoes irreversible conformational changes to fuse the host and viral membranes. Here are the molecular structures of the receptor binding and fusion proteins:

Nipah virus receptor binding protein on left, fusion protein on right. Colors show individual monomers.

Given the spillover risk posed by Nipah, we sought to understand how mutations affect different phenotypes of the receptor binding protein. Specifically, we measured the effects of mutations on three different phenotypes: cell entry, receptor binding, and antibody escape. We utilized a recently developed lentivirus-based platform to perform the deep mutational scanning experiments. By applying different selection conditions on pseudovirus libraries, followed by deep sequencing to recover barcode frequencies, we were able to map the effects of thousands of mutations on the Nipah virus receptor binding protein. These data will help us understand functional constraints, and the possibility of escape from neutralizing antibodies.

Biosafety ​

All experiments were performed with non-replicative lentiviral-based pseudoviruses in a biosafety-level 2 laboratory by trained personnel. Pseudotyping is a method where viral entry proteins are expressed in combination with a viral vector from a different virus. By only expressing the Nipah receptor binding and fusion proteins on the surface of lentiviral particles, we can safely perform deep mutational scanning experiments without modifying authentic virus. Essential lentiviral genes, such as gag/pol, rev, and tat, are not encoded by the lentiviral vector. These genes are instead introduced by transfecting cells with three separate plasmids. This ensures the pseudotyped lentiviruses cannot replicate outside of a cell culture system where these plasmids are co-transfected. Finally, to limit the information hazards associated with identifying human-specific adaptive mutations, we used ephrin-B2 and ephrin-B3 orthologs from the bat species Pteropus alecto for all assays.

Built by Brendan Larsen and Jesse Bloom

- +
Nipah RBP DMS

Appearance

Nipah virus deep mutational scanning

Collection of data, figures, and information for the Nipah virus receptor binding protein deep mutational scanning project

Pipeline information and data processing

Analysis of Pacbio and Illumina sequencing data produced from deep mutational scanning experiments

Cell entry

Analysis of functional selections for assessing how mutations to the receptor binding protein impact cell entry

Receptor binding

Analysis of soluble receptor selections for measuring how mutations affect binding to host receptors

Antibody escape

Analysis of antibody neutralization data for finding sites of escape in the receptor binding protein.

Heatmaps

Links to heatmaps showing effects of mutations on cell entry, receptor binding, and antibody escape

Interactive figures

Explore deep mutational scanning data on the Nipah virus receptor binding protein with interactive figures

About ​

Nipah virus receptor binding protein

Receptor binding protein bound to ephrin-B2.

This website contains links and information for the Nipah virus receptor binding protein deep mutational scanning project. Look through Jupyter notebooks used in analyses, explore interactive charts, or download raw data. To view more information on the code used to analyze these data and generate the website, check out our GitHub repo. Interactive charts made with Altair. Embedding of Altair plots was done with custom javascript code from dms-vep. All work was done in the Bloom Lab, part of Fred Hutch Cancer Center. To access the old version of the homepage built by dms-vep-pipeline-3, click here.

Scientific Details ​

Nipah virus is a bat-borne paramyxovirus that occassionally spills over into humans in SE Asia, causing fatal infections. Nipah virus relies on the coordination of two different viral entry proteins to enter cells: the receptor binding and fusion protein. The Nipah virus receptor binding protein is responsible for binding to host receptors (ephrin-B2 and -B3) on the cell surface. Following receptor binding, the receptor binding protein triggers the fusion protein, which undergoes irreversible conformational changes to fuse the host and viral membranes. Here are the molecular structures of the receptor binding and fusion proteins:

Nipah virus receptor binding protein on left, fusion protein on right. Colors show individual monomers.

Given the spillover risk posed by Nipah, we sought to understand how mutations affect different phenotypes of the receptor binding protein. Specifically, we measured the effects of mutations on three different phenotypes: cell entry, receptor binding, and antibody escape. We utilized a recently developed lentivirus-based platform to perform the deep mutational scanning experiments. By applying different selection conditions on pseudovirus libraries, followed by deep sequencing to recover barcode frequencies, we were able to map the effects of thousands of mutations on the Nipah virus receptor binding protein. These data will help us understand functional constraints, and the possibility of escape from neutralizing antibodies.

Biosafety ​

All experiments were performed with non-replicative lentiviral-based pseudoviruses in a biosafety-level 2 laboratory by trained personnel. Pseudotyping is a method where viral entry proteins are expressed in combination with a viral vector from a different virus. By only expressing the Nipah receptor binding and fusion proteins on the surface of lentiviral particles, we can safely perform deep mutational scanning experiments without modifying authentic virus. Essential lentiviral genes, such as gag/pol, rev, and tat, are not encoded by the lentiviral vector. These genes are instead introduced by transfecting cells with three separate plasmids. This ensures the pseudotyped lentiviruses cannot replicate outside of a cell culture system where these plasmids are co-transfected. Finally, to limit the information hazards associated with identifying human-specific adaptive mutations, we used ephrin-B2 and ephrin-B3 orthologs from the bat species Pteropus alecto for all assays.

Built by Brendan Larsen and Jesse Bloom

+ \ No newline at end of file diff --git a/docs/.vitepress/dist/interactive.html b/docs/.vitepress/dist/interactive.html index 9234a25b..ec890052 100644 --- a/docs/.vitepress/dist/interactive.html +++ b/docs/.vitepress/dist/interactive.html @@ -8,17 +8,17 @@ - + - - - + + + -
Nipah RBP DMS

Appearance

Interactive Figures ​

Explore Nipah virus RBP deep mutational scanning data with interactive charts.

To explore heatmaps

TIP

Click white square in the upper right of each plot to view full-sized versions.

Cell Entry ​


Correlations ​

Correlations by Site ​


Notebooks ​

Link to notebooks showing how interactive figures were made:

Interactive figures notebook

Built by Brendan Larsen and Jesse Bloom

- +
Nipah RBP DMS

Appearance

Interactive Figures ​

Explore Nipah virus receptor binding protein deep mutational scanning data with interactive charts.

Click here to explore interactive heatmaps instead.

TIP

Click white square in the upper right of each plot to view full-sized versions.

Correlations by Site ​


Cell Entry ​


Correlations ​

Notebooks ​

Link to notebooks showing how interactive figures were made:

Interactive figures notebook

Built by Brendan Larsen and Jesse Bloom

+ \ No newline at end of file diff --git a/docs/.vitepress/dist/pipeline_information.html b/docs/.vitepress/dist/pipeline_information.html index 59f6149c..c6170c0d 100644 --- a/docs/.vitepress/dist/pipeline_information.html +++ b/docs/.vitepress/dist/pipeline_information.html @@ -8,19 +8,19 @@ - + - - - + + + -
Nipah RBP DMS

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Pipeline Information ​

To link barcodes to specific variants present in each RBP sequence, we performed Pacbio sequencing on variant libraries. Full-length RBP consensus sequences were made to determine which mutations were present in each library. DMS data were analyzed with dms-vep-pipeline-3. This pipeline utilizes the alignparse package. The general steps are listed below.

Build Pacbio Sequences ​

PacBio consensus sequences notebook

Builds the Pacbio consensus sequence. Parameters used are:

max_minor_sub_frac=0.2
+    
Nipah RBP DMS
Appearance
Pipeline Information ​
The lentivirus-based deep mutational scanning platform relies on obtaining relative frequencies of different protein variants that enter cells after applying selection to the libraries. By comparing mutation frequencies before and after selections, we can determine the effects of mutations on different phenotypes.
Calculating the relative frequencies of thousands of variants is not trivial. We rely on two different sequencing technologies to obtain the necessary data.
  • PacBio long-read sequencing to link barcodes to specific mutations in the receptor binding protein.
  • Illumina short-read sequencing to obtain the relative frequencies of barcodes in each selection experiment.
image
Schematic of lentivirus vector used in deep mutational scanning experiments (top), along with sequencing strategy (bottom). 
Because PacBio sequencing is expensive and lower throughput, we only sequence the variant libraries with this technology once. Full-length consensus sequences of the receptor binding protein and associated barcodes are assembled, while discarding low-quality reads. From these assembled consensus sequences we build a codon-variant lookup table, enabling us to match barcodes to specific mutations in the receptor binding protein. All subsequent Illumina sequencing of selection experiments use this lookup table to estimate mutational effects from barcode sequencing data alone. Our generated pseudovirus libraries consist of 60,000 to 80,000 unique variants. Each unique variant is sequenced hundreds of times with Illumina to get accurate frequency measurements.
Most of these computationally intensive steps were analyzed with dms-vep-pipeline-3. This pipeline utilizes the alignparse package. Each step, along with the associated jupyter notebooks are listed below.
Build Pacbio Sequences ​
PacBio consensus sequences notebook
This notebook builds the Pacbio consensus sequences used to link specific mutations in the receptor binding protein with a unique 16 bp barcode. Parameters used are:
max_minor_sub_frac=0.2
 max_minor_indel_frac=0.2
-min_support=3
These parameters filter consensus sequences generated from Pacbio CCS sequencing and assembly. If an assembled RBP sequence has a mutation or indel in more than 20% of the reads, it will be discarded. Consensus sequences must have at least three reads to be included as variants.
With alignparse, reads were mapped to a reference sequence, and clipped based on parameters in this config file.
Analyze PacBio CCS Reads ​
Analyze Pacbio CCS reads notebook
Reports information about CCS read filtering.
Build Codon Variants Notebook ​
Build codon variants notebook
Builds the codon-variant table from PacBio consensus sequences that links barcodes and RBP mutations.
Link to codon-variant table .csv file
Illumina Variant Counts ​
Once the barcodes are linked to mutations in the codon-variant table, all sequencing data is generated with Illumina on a small sequence fragment to obtain the relative frequencies of barcodes in each selection experiment. The config file linked below specifies the parameters used for converting barcode counts to functional scores, which are used to estimate cell entry.
Analysis of variant counts notebook
Link to raw barcode count .csv files
Link to functional selection config file
Filtering Selection Data ​
Filtering notebook
The final cell entry, receptor binding, and antibody escape data were filtered based on parameters that are contained within the nipah_config.yaml file. More information about these parameters are listed in the notebook.
Filtered Data ​
These data have been filtered and are the best choice for anyone interested in analyzing the data themselves. For unfiltered raw .csv files of mutational effects on different phenotypes, go to individual pages to view and download.
Cell Entry ​
CHO-bEFNB2 entry filtered (.csv)
CHO-bEFNB3 entry filtered (.csv)
Receptor Binding ​
bEFNB2 monomeric binding filtered (.csv)
bEFNB3 dimeric binding filtered (.csv)
Antibody Escape ​
Antibody escape filtered (.csv)
Miscellaneous Notebooks ​
Notebook for finding correlations between libraries and making histogram of variants
Notebook for making a Nipah phylogeny
Notebook for making specific file formats (.defattr), to map site-averaged scores onto protein structures in Chimera
Notebook for calculating atomic distances between residues from a PDB file
Notebook for finding variable sites in Nipah or Henipavirus alignments
Built by Brendan Larsen and Jesse Bloom

-    
+min_support=3

These parameters filter consensus sequences generated from Pacbio CCS sequencing and assembly. If an assembled RBP sequence has a mutation or indel in more than 20% of the reads, it will be discarded. Consensus sequences must have at least three reads to be included as variants.

Using alignparse, reads were mapped to a reference sequence, and clipped based on parameters in this config file.

Analyze PacBio CCS Reads ​

Analyze Pacbio CCS reads notebook

Reports information about CCS read filtering.

Build Codon Variants Notebook ​

Build codon variants notebook

Builds the codon-variant table from PacBio consensus sequences that links barcodes and RBP mutations. Displays information about the number of mutations and variants present in each library.

Link to codon-variant table .csv file

Illumina Variant Counts ​

Once the barcodes are linked to mutations in the codon-variant table, all sequencing data is generated with Illumina on a small sequence fragment to obtain the relative frequencies of barcodes in each selection experiment. The config file linked below specifies the parameters used for converting barcode counts to functional scores, which are used to estimate cell entry.

Analysis of variant counts notebook

Link to raw barcode count .csv files

Link to functional selection config file

Filtering Selection Data ​

Filtering notebook

Once the effects of mutations on different phenotypes have been calculated, we perform a data filtering step to remove low confidence measurements. The filtering parameters are contained within the nipah_config.yaml file. More information about these parameters are listed in the notebook. In brief, we require mutations to be present in at least two barcodes, and have a low variance between independent library measurements.

Filtered Data ​

These data have been filtered and are the best choice for anyone interested in analyzing the data themselves. For unfiltered raw .csv files of mutational effects on different phenotypes, go to individual pages to view and download.

Cell Entry ​

CHO-bEFNB2 entry filtered (.csv)

CHO-bEFNB3 entry filtered (.csv)

Receptor Binding ​

bEFNB2 monomeric binding filtered (.csv)

bEFNB3 dimeric binding filtered (.csv)

Antibody Escape ​

Antibody escape filtered (.csv)

Miscellaneous Notebooks ​

Notebook for finding correlations between libraries and making histogram of variants

Notebook for making a Nipah phylogeny

Notebook for making specific file formats (.defattr), to map site-averaged scores onto protein structures in Chimera

Notebook for calculating atomic distances between residues from a PDB file

Notebook for finding variable sites in Nipah or Henipavirus alignments

Built by Brendan Larsen and Jesse Bloom

+ \ No newline at end of file diff --git a/docs/.vitepress/dist/receptor_binding.html b/docs/.vitepress/dist/receptor_binding.html index 01e67fb5..0bc6b2a7 100644 --- a/docs/.vitepress/dist/receptor_binding.html +++ b/docs/.vitepress/dist/receptor_binding.html @@ -8,17 +8,17 @@ - + - - - + + + -
Nipah RBP DMS

Appearance

Receptor Binding ​

To understand how mutations affect binding to ephrin receptors, we performed selections on our pseudovirus libraries with soluble bat EFNB2 and EFNB3. Neutralization of pseudovirus variants serves as a proxy for receptor binding. Neutralization curves were fit with polyclonal.

Individual Receptor Binding Selections ​

Individual antibody selection model fitting notebooks

LibB-231108-bEFNB2-monomeric

LibA-231112-bEFNB2-monomeric

LibA-231207-bEFNB2-monomeric

LibA-231222-bEFNB2-monomeric

LibB-231222-bEFNB2-monomeric

LibA-230818-EFNB3-dimeric

LibA-230825-bEFNB3-dimeric

LibB-230907-bEFNB3-dimeric

Average Receptor Binding ​

These notebooks average effects of mutations on receptor binding across libraries and replicate selections.

bEFNB2-monomeric

bEFNB3-dimeric

Comprehensive Receptor Binding Heatmaps ​

Additional control over filtering parameters. Users can adjust different parameters to filter the heatmap data. These provide more information and control compared to the final filtered heatmaps provided on the heatmaps page.

bEFNB2-monomeric heatmap

bEFNB3-dimeric heatmap

Neutralization of Nipah pseudoviruses ​

Pseudoviruses expressing Nipah virus RBP/F are neutralized by soluble bat ephrins.

Ephrin neutralization of pseudoviruses expressing unmutated Nipah RBP/F

Binding Correlations ​

Effects of mutations on binding to bEFNB2 and bEFNB3, with mutations of interest highlighted

Interactive plot of bEFNB2 and bEFNB3 site-averaged binding correlations

Binding Validations ​

Binding Validation by BLI ​

BLI validations notebook

Correlation of biolayer interferometry affinity measurements with DMS

Binding Validation by Neutralization ​

Binding validations notebook

Neutralization of single RBP mutant pseudoviruses and correlation with DMS

Analysis Notebooks ​

Notebook analyzing receptor binding from filtered data. Make figures for manuscript using python and altair.

Binding analysis notebook

Raw Data ​

These data have not been filtered. They are the raw output from dms-vep-pipeline-3. For filtered .csv files, click here

Individual receptor binding selection files

Averaged effects of RBP mutations on bEFNB2 binding across replicates

Averaged effects of RBP mutations on bEFNB3 binding across replicates

Built by Brendan Larsen and Jesse Bloom

- +
Nipah RBP DMS

Appearance

Receptor Binding ​

To understand how mutations affect binding to ephrin receptors, we performed selections on our pseudovirus libraries with soluble bat ephrin-B2 and ephrin-B3. Neutralization of pseudovirus variants serves as a proxy for receptor binding. Neutralization curves were fit with polyclonal.

Pseudoviruses expressing Nipah virus receptor binding and fusion proteins are neutralized by soluble bat ephrins.

Ephrin neutralization of pseudoviruses expressing unmutated Nipah receptor binding and fusion proteins.

Individual Receptor Binding Selections ​

Individual antibody selection model fitting notebooks

LibB-231108-bEFNB2-monomeric

LibA-231112-bEFNB2-monomeric

LibA-231207-bEFNB2-monomeric

LibA-231222-bEFNB2-monomeric

LibB-231222-bEFNB2-monomeric

LibA-230818-EFNB3-dimeric

LibA-230825-bEFNB3-dimeric

LibB-230907-bEFNB3-dimeric

Average Receptor Binding ​

These notebooks average effects of mutations on receptor binding across libraries and replicate selections.

bEFNB2-monomeric

bEFNB3-dimeric

Comprehensive Receptor Binding Heatmaps ​

Additional control over filtering parameters. Users can adjust different parameters to filter the heatmap data. These provide more information and control compared to the final filtered heatmaps provided on the heatmaps page.

bEFNB2-monomeric heatmap

bEFNB3-dimeric heatmap

Binding Correlations ​

Effects of mutations on binding to bat ephrin-B2 and bat ephrin-B3, with mutations of interest highlighted

Binding Validations ​

Binding Validation by Biolayer Interferometry ​

To validate our DMS binding measurements, we tested binding affinity by biolayer interferometry. Notebook analyzing biolayer interferometry data and plotting correlations.

Correlation of biolayer interferometry affinity measurements with deep mutational scanning measurements

Binding Validation by Neutralization ​

We also tested neutralization of individual mutations expressed on pseudoviruses with soluble bat ephrin-B2 (monomeric) or bat ephrin-B3 (dimeric). Individual mutations affect neutralizing potency by soluble receptor. Binding validations notebook

Neutralization of receptor binding protein mutant pseudoviruses and correlation with deep mutational scanning.

Analysis Notebooks ​

Notebook analyzing receptor binding from filtered data. Make figures for manuscript using python and altair.

Binding analysis notebook

Raw Data ​

These data have not been filtered. They are the raw output from dms-vep-pipeline-3. For filtered .csv files, click here

Individual receptor binding selection files

Averaged effects of RBP mutations on bEFNB2 binding across replicates

Averaged effects of RBP mutations on bEFNB3 binding across replicates

Built by Brendan Larsen and Jesse Bloom

+ \ No newline at end of file diff --git a/docs/antibody_escape.md b/docs/antibody_escape.md index a1046ed0..6bdf7a4a 100755 --- a/docs/antibody_escape.md +++ b/docs/antibody_escape.md @@ -1,6 +1,11 @@ # Antibody Escape -We determined the effects of RBP mutations on neutralization by different RBP-directed antibodies. Antibody selections were performed by incubating pseudovirus libraries with different concentrations of antibody, followed by infection of CHO-bEFNB3 cells to recover barcodes. Neutralization curves were fit on the DMS data with [`polyclonal`](https://github.com/jbloomlab/polyclonal){target="_self"}. +We determined the effects of receptor binding protein mutations on antibody neutralization. Antibody selections were performed by incubating pseudovirus libraries with different concentrations of antibody, followed by infection of CHO cells expressing bat ephrin-B3. Neutralization curves were fit on the DMS data with [`polyclonal`](https://github.com/jbloomlab/polyclonal){target="_self"}. + +![image](./public/images/antibody_selection_schematic.png) + +
Schematic of antibody selections.
+ ## Individual Antibody Selections ::: details Individual antibody selection model fitting notebooks @@ -82,11 +87,12 @@ Additional control over filtering parameters. Users can adjust different paramet Neutralization curves notebook
- +
## Antibody Escape Validations +To validate our deep mutational scanning measurements, we generated pseudoviruses expressing different receptor binding protein mutations. We tested whether neutralization by the antibody nAH1.3 correlated with our deep mutational scanning data. Antibody validation notebook @@ -103,7 +109,7 @@ Additional control over filtering parameters. Users can adjust different paramet ### Functional Effect of Antibody Escape Mutations
- +
### Escape by Site diff --git a/docs/cell_entry.md b/docs/cell_entry.md index 3d88fd50..1e8c896f 100755 --- a/docs/cell_entry.md +++ b/docs/cell_entry.md @@ -1,11 +1,14 @@ # Cell Entry -To measure the effects of mutations on RBP-mediated cell entry, we performed 'functional selections' where the frequencies of barcodes were compared between pseudoviruses expressing either Nipah RBP/F or VSV-G. Notebooks below contain information about each step. +To measure the effects of mutations on cell entry mediated by the receptor binding protein, we conducted 'functional selections'. In these selections, we compared the frequencies of specific barcodes between two groups of pseudoviruses: those expressing Nipah RBP/F and those expressing VSV-G. Notebooks below contain information about each step. +![image](./public/images/functional_selection_schematic.png) + +
Schematic of functional selections using VSV-G entry as a control for library composition.
## Global Epistasis Fitting -Individual cell entry selections were fit with [`multidms`](https://github.com/matsengrp/multidms){target="_self"} to decompose the effects of individual mutations using a global epistasis model. +Individual cell entry selections were fit with [`multidms`](https://github.com/matsengrp/multidms){target="_self"} to decompose the effects of individual mutations using a [global epistasis model](https://pubmed.ncbi.nlm.nih.gov/30037990/){target="_self"}. ::: details Individual functional selection global epistasis model fitting notebooks LibA-230725-CHO-bEFNB3 @@ -122,28 +125,28 @@ Click arrow box to view altair plots in separate page.
- +
### Site-averaged Effects of Mutations on Cell Entry
- +
### Cell Entry Correlations - +Comparison of average effects of mutations on entry in cells expressing bat ephrin-B2 or bat ephrin-B3. Some mutations are much more tolerated for cell entry in bat ephrin-B2 cells, especially at sites near the receptor-binding interface.
- +
- +
### Cell Entry Validations - +To validate our deep mutational scanning measurements, we produced lentiviruses with individual mutations that spanned a range of effects. Cell entry validation notebook diff --git a/docs/heatmaps.md b/docs/heatmaps.md index 976e684a..60fae99d 100755 --- a/docs/heatmaps.md +++ b/docs/heatmaps.md @@ -3,21 +3,23 @@ Heatmaps represent one of the best ways to explore deep mutational scanning data. This page contains links to various heatmaps, which show the effects of mutations on three different phenotypes: cell entry, receptor binding, and antibody escape. ::: tip -Hover over the heatmaps to see more information about each mutation +Hover over the heatmaps to see more information about each mutation. An 'X' represents the amino acid found at that site in the unmutated Nipah Malaysia sequence. ::: ## Entry Heatmaps -Effects of RBP mutations on entry in CHO-bEFNB2 Cells +Effects of all mutations on entry in CHO cells expressing different bat receptors. -Effects of RBP mutations on entry in CHO-bEFNB3 Cells +Entry with bat ephrin-B2 + +Entry with bat ephrin-B3 ## Binding Heatmaps ::: info Mutations with low cell entry scores are masked in dark gray. ::: -bEFNB2 Binding Heatmap +Binding to bat ephrin-B2 (monomeric) -bEFNB3 Binding Heatmap +Binding to bat ephrin-B3 (dimeric) ## Antibody Escape Heatmaps ::: info @@ -37,11 +39,13 @@ If protein structure is available, distance in angstroms to the closest antibody nAH1.3 Antibody Escape -## Heatmaps of Specific RBP Regions +## Heatmaps of Specific Receptor Binding Protein Regions ::: tip Click arrow in upper right to view full-sized plots ::: -Effects of mutations on cell entry and binding at receptor contact sites. Receptor contact sites are less constrained for entry in CHO-bEFNB2 cells than CHO-bEFNB3 cells, likely due to ~25-fold higher receptor affinity of RBP to EFNB2 versus EFNB3. + +### Contact sites +Effects of mutations on cell entry and binding at receptor contact sites. Receptor contact sites are less constrained for entry in CHO cells expressing bat ephrin-B2 than CHO cells expressing bat ephrin-B3. This is likely due to ~25-fold higher receptor affinity of the receptor binding protein to ephrin-B2 versus ephrin-B3. ### Effects of mutations on cell entry and binding at glycosylation sites diff --git a/docs/index.md b/docs/index.md index 7bad1d2b..198b66f9 100755 --- a/docs/index.md +++ b/docs/index.md @@ -12,7 +12,7 @@ features: link: /pipeline_information - title: Cell entry - details: Analysis of functional selections for assessing how RBP mutations impact cell entry + details: Analysis of functional selections for assessing how mutations to the receptor binding protein impact cell entry link: /cell_entry - title: Receptor binding @@ -20,7 +20,7 @@ features: link: /receptor_binding - title: Antibody escape - details: Analysis of antibody neutralization data for finding sites of escape in the RBP + details: Analysis of antibody neutralization data for finding sites of escape in the receptor binding protein. link: /antibody_escape - title: Heatmaps @@ -28,26 +28,32 @@ features: link: /heatmaps - title: Interactive figures - details: Explore DMS data on Nipah RBP with interactive figures + details: Explore deep mutational scanning data on the Nipah virus receptor binding protein with interactive figures link: /interactive --- ### About +
+ Nipah virus receptor binding protein +

Receptor binding protein bound to ephrin-B2.

+
This website contains links and information for the Nipah virus receptor binding protein deep mutational scanning project. Look through Jupyter notebooks used in analyses, explore [interactive charts](/interactive), or download raw [data](/pipeline_information#filtered-data). To view more information on the code used to analyze these data and generate the website, check out our [GitHub repo](https://github.com/dms-vep/Nipah_Malaysia_RBP_DMS){target="_self"}. Interactive charts made with [Altair](https://altair-viz.github.io){target="_self"}. Embedding of Altair plots was done with custom javascript code from [dms-vep](https://github.com/dms-vep/dms-vep.github.io){target="_self"}. All work was done in the [Bloom Lab](https://research.fredhutch.org/bloom/en.html){target="_self"}, part of [Fred Hutch Cancer Center](https://www.fredhutch.org/en.html){target="_self"}. To access the old version of the homepage built by `dms-vep-pipeline-3`, click [here](/index){target='_self'}. + + ### Scientific Details -Nipah virus is a bat-borne paramyxovirus that occassionally spills over into humans in SE Asia, causing fatal infections. The Nipah virus receptor binding protein is responsible for binding to host receptors (ephrin-B2 and -B3) on the cell surface. Following receptor binding, RBP triggers the fusion (F) protein, which undergoes irreversible conformational changes to fuse the host and viral membranes. Here are the molecular structures of the receptor binding and fusion proteins: +Nipah virus is a bat-borne paramyxovirus that occassionally spills over into humans in SE Asia, causing fatal infections. Nipah virus relies on the coordination of two different viral entry proteins to enter cells: the receptor binding and fusion protein. The Nipah virus receptor binding protein is responsible for binding to host receptors (ephrin-B2 and -B3) on the cell surface. Following receptor binding, the receptor binding protein triggers the fusion protein, which undergoes irreversible conformational changes to fuse the host and viral membranes. Here are the molecular structures of the receptor binding and fusion proteins:
-
-
Nipah virus receptor binding protein on left, fusion protein on right. Colors show individual monomers.
+
Nipah virus receptor binding protein on left, fusion protein on right. Colors show individual monomers.
Given the spillover risk posed by Nipah, we sought to understand how mutations affect different phenotypes of the receptor binding protein. Specifically, we measured the effects of mutations on three different phenotypes: cell entry, receptor binding, and antibody escape. We utilized a [recently developed lentivirus-based platform](https://pubmed.ncbi.nlm.nih.gov/36868218/){target='_self'} to perform the deep mutational scanning experiments. By applying different selection conditions on pseudovirus libraries, followed by deep sequencing to recover barcode frequencies, we were able to map the effects of thousands of mutations on the Nipah virus receptor binding protein. These data will help us understand functional constraints, and the possibility of escape from neutralizing antibodies. diff --git a/docs/interactive.md b/docs/interactive.md index 1b8217dd..15c5c132 100755 --- a/docs/interactive.md +++ b/docs/interactive.md @@ -1,14 +1,18 @@ # Interactive Figures -Explore Nipah virus RBP deep mutational scanning data with interactive charts. - -To explore [heatmaps](/heatmaps) +Explore Nipah virus receptor binding protein deep mutational scanning data with interactive charts. +Click here to explore interactive [heatmaps](/heatmaps) instead. ::: tip Click white square in the upper right of each plot to view full-sized versions. ::: +### Correlations by Site + +

+ + ## Cell Entry @@ -21,10 +25,6 @@ Click white square in the upper right of each plot to view full-sized versions. -### Correlations by Site - -

- ## Notebooks diff --git a/docs/pipeline_information.md b/docs/pipeline_information.md index bdb76a89..ae8c46af 100755 --- a/docs/pipeline_information.md +++ b/docs/pipeline_information.md @@ -1,12 +1,24 @@ # Pipeline Information -To link barcodes to specific variants present in each RBP sequence, we performed Pacbio sequencing on variant libraries. Full-length RBP consensus sequences were made to determine which mutations were present in each library. DMS data were analyzed with [`dms-vep-pipeline-3`](https://github.com/dms-vep/dms-vep-pipeline-3){target="_self"}. This pipeline utilizes the [`alignparse`](https://jbloomlab.github.io/alignparse/){target="_self"} package. The general steps are listed below. +The lentivirus-based deep mutational scanning platform relies on obtaining relative frequencies of different protein variants that enter cells after applying selection to the libraries. By comparing mutation frequencies before and after selections, we can determine the effects of mutations on different phenotypes. + +Calculating the relative frequencies of thousands of variants is not trivial. We rely on two different sequencing technologies to obtain the necessary data. +- PacBio long-read sequencing to link barcodes to specific mutations in the receptor binding protein. +- Illumina short-read sequencing to obtain the relative frequencies of barcodes in each selection experiment. + +![image](./public/images/library_schematic.png) + +
Schematic of lentivirus vector used in deep mutational scanning experiments (top), along with sequencing strategy (bottom).
+ +Because PacBio sequencing is expensive and lower throughput, we only sequence the variant libraries with this technology once. Full-length consensus sequences of the receptor binding protein and associated barcodes are assembled, while discarding low-quality reads. From these assembled consensus sequences we build a codon-variant lookup table, enabling us to match barcodes to specific mutations in the receptor binding protein. All subsequent Illumina sequencing of selection experiments use this lookup table to estimate mutational effects from barcode sequencing data alone. Our generated pseudovirus libraries consist of 60,000 to 80,000 unique variants. Each unique variant is sequenced hundreds of times with Illumina to get accurate frequency measurements. + +Most of these computationally intensive steps were analyzed with [`dms-vep-pipeline-3`](https://github.com/dms-vep/dms-vep-pipeline-3){target="_self"}. This pipeline utilizes the [`alignparse`](https://jbloomlab.github.io/alignparse/){target="_self"} package. Each step, along with the associated jupyter notebooks are listed below. ## Build Pacbio Sequences PacBio consensus sequences notebook -Builds the Pacbio consensus sequence. [Parameters](https://github.com/dms-vep/Nipah_Malaysia_RBP_DMS/blob/master/config.yaml){target="_self"} used are: +This notebook builds the Pacbio consensus sequences used to link specific mutations in the receptor binding protein with a unique 16 bp barcode. [Parameters](https://github.com/dms-vep/Nipah_Malaysia_RBP_DMS/blob/master/config.yaml){target="_self"} used are: ``` max_minor_sub_frac=0.2 max_minor_indel_frac=0.2 @@ -15,7 +27,7 @@ min_support=3 These parameters filter consensus sequences generated from Pacbio CCS sequencing and assembly. If an assembled RBP sequence has a mutation or indel in more than 20% of the reads, it will be discarded. Consensus sequences must have at least three reads to be included as variants. -With [alignparse](https://jbloomlab.github.io/alignparse/){target="_self"}, reads were mapped to a [reference sequence](https://github.com/dms-vep/Nipah_Malaysia_RBP_DMS/blob/master/data/PacBio_amplicon.gb){target="_self"}, and clipped based on parameters in this [config](https://github.com/dms-vep/Nipah_Malaysia_RBP_DMS/blob/master/data/PacBio_feature_parse_specs.yaml){target="_self"} file. +Using [alignparse](https://jbloomlab.github.io/alignparse/){target="_self"}, reads were mapped to a [reference sequence](https://github.com/dms-vep/Nipah_Malaysia_RBP_DMS/blob/master/data/PacBio_amplicon.gb){target="_self"}, and clipped based on parameters in this [config](https://github.com/dms-vep/Nipah_Malaysia_RBP_DMS/blob/master/data/PacBio_feature_parse_specs.yaml){target="_self"} file. ## Analyze PacBio CCS Reads @@ -28,7 +40,7 @@ Reports information about CCS read filtering. Build codon variants notebook -Builds the codon-variant table from PacBio consensus sequences that links barcodes and RBP mutations. +Builds the codon-variant table from PacBio consensus sequences that links barcodes and RBP mutations. Displays information about the number of mutations and variants present in each library. [Link to codon-variant table .csv file](https://github.com/dms-vep/Nipah_Malaysia_RBP_DMS/blob/master/results/variants/codon_variants.csv){target="_self"} @@ -46,7 +58,7 @@ Once the barcodes are linked to mutations in the codon-variant table, all sequen Filtering notebook -The final cell entry, receptor binding, and antibody escape data were filtered based on parameters that are contained within the [nipah_config.yaml](https://github.com/dms-vep/Nipah_Malaysia_RBP_DMS/blob/master/nipah_config.yaml){target="_self"} file. More information about these parameters are listed in the notebook. +Once the effects of mutations on different phenotypes have been calculated, we perform a data filtering step to remove low confidence measurements. The filtering parameters are contained within the [nipah_config.yaml](https://github.com/dms-vep/Nipah_Malaysia_RBP_DMS/blob/master/nipah_config.yaml){target="_self"} file. More information about these parameters are listed in the notebook. In brief, we require mutations to be present in at least two barcodes, and have a low variance between independent library measurements. ## Filtered Data ::: warning These data have been filtered and are the best choice for anyone interested in analyzing the data themselves. For unfiltered raw `.csv` files of mutational effects on different phenotypes, go to individual pages to view and download. diff --git a/docs/public/images/antibody_selection_schematic.png b/docs/public/images/antibody_selection_schematic.png new file mode 100755 index 00000000..963788b0 Binary files /dev/null and b/docs/public/images/antibody_selection_schematic.png differ diff --git a/docs/public/images/functional_selection_schematic.png b/docs/public/images/functional_selection_schematic.png new file mode 100755 index 00000000..c56fc749 Binary files /dev/null and b/docs/public/images/functional_selection_schematic.png differ diff --git a/docs/public/images/library_schematic.png b/docs/public/images/library_schematic.png new file mode 100755 index 00000000..ac213bec Binary files /dev/null and b/docs/public/images/library_schematic.png differ diff --git a/docs/receptor_binding.md b/docs/receptor_binding.md index 484f8585..c731f31c 100755 --- a/docs/receptor_binding.md +++ b/docs/receptor_binding.md @@ -1,6 +1,12 @@ # Receptor Binding -To understand how mutations affect binding to ephrin receptors, we performed selections on our pseudovirus libraries with soluble bat EFNB2 and EFNB3. Neutralization of pseudovirus variants serves as a proxy for receptor binding. Neutralization curves were fit with [`polyclonal`](https://github.com/jbloomlab/polyclonal){target="_self"}. +To understand how mutations affect binding to ephrin receptors, we performed selections on our pseudovirus libraries with soluble bat ephrin-B2 and ephrin-B3. Neutralization of pseudovirus variants serves as a proxy for receptor binding. Neutralization curves were fit with [`polyclonal`](https://github.com/jbloomlab/polyclonal){target="_self"}. + +Pseudoviruses expressing Nipah virus receptor binding and fusion proteins are neutralized by soluble bat ephrins. + +
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## Individual Receptor Binding Selections @@ -39,36 +45,31 @@ Additional control over filtering parameters. Users can adjust different paramet -## Neutralization of Nipah pseudoviruses -Pseudoviruses expressing Nipah virus RBP/F are neutralized by soluble bat ephrins. -
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## Binding Correlations -
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+ ## Binding Validations -### Binding Validation by BLI -BLI validations notebook +### Binding Validation by Biolayer Interferometry +To validate our DMS binding measurements, we tested binding affinity by biolayer interferometry. +Notebook analyzing biolayer interferometry data and plotting correlations. -
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### Binding Validation by Neutralization +We also tested neutralization of individual mutations expressed on pseudoviruses with soluble bat ephrin-B2 (monomeric) or bat ephrin-B3 (dimeric). Individual mutations affect neutralizing potency by soluble receptor. Binding validations notebook -
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