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Predicting the effect that a mutation would have on protein function is the overall topic . Only hypothesis and introduction will be writtenMake your Mutant Lab BSC3466L
Lab 2 – Developing a Hypothesis for Your Mutant Design
In this activity, each team of students will design a mutant enzyme by replacing one wild type
amino acid for another amino acid of your choosing. Based on your mutation’s structural
features, a comparison to previously published data, and taking an evolutionary context into
account, you will predict the function of your mutant enzyme.
Learning Objectives
● Use available data and conceptual understanding of protein structure to select amino acid
substitutions in the BglB protein for subsequent analysis and build hypotheses relating
these amino acid substitutions to catalytic activity, substrate binding, and thermodynamic
stability of BglB.
● Design oligonucleotides that will be used to introduce selected amino acid substitutions
into the BglB protein.
Getting started
When you are creating a mutant, you should be aware of the following:
Mutants should be named by standard convention, e.g., H243W. This translates as: “at amino
acid #243, the wild type histidine is changed to tryptophan.”
Verify that your selected mutant has not been previously characterized, if that is what you want.
Alternatively, you can choose to repeat a previous one. You can find mutants that have previously
been characterized by browsing the D2D CURE website (“Control/Command” + “F”). The D2D
CURE website will allow you to see expression data results that you can consider when you
compare your intended mutation to previously done work. The D2D CURE website is a work in
progress but contains most of tested mutants yet. So, while novelty is always exciting, do not feel
too much pressure to make a novel mutant. Find something you are really interested and that
you want to test. We definitely see the value in replication, especially across institutions. If there
is a mutation that you are very excited about and you want to test it, you are free to do so.
Mutants uploaded to the D2D CURE website:
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Developing a Hypothesis: A Three-Pronged Approach
When developing a hypothesis, three approaches that can be considered are:
1. The comparative approach
2. The theoretical approach
3. Evolutionary approach
These approaches are not mutually exclusive (nor are they the only approaches that exist). You
are encouraged to consider all approaches by developing different hypotheses using each
approach and later comparing them. Optimally, your comparison will generate ideas that are in
agreement, thereby increasing the robustness of your hypothesis. If these approaches produce
hypotheses that are in conflict, further consideration is needed.
The evolutionary approach take-home assignment was intended to provide you with a “big
picture” of BglB’s history and how it varies across species.
As you go through the different approaches, you will be presented with different questions that
you should be thinking about. Allow these questions to guide you as you design your mutant. You
may change directions a few times, and that is okay.
The Comparative Approach
The comparative approach allows you to assess your mutant design in relation to published
findings. Carlin et al. (2017), linked below, reported the characterization on over 100 mutants
(Figure 2).
Carlin, D. A., Caster, R. W., Wang, X., Betzenderfer, S. A., Chen, C. X., Duong, V. M., … & Kim, N.
(2016). Kinetic characterization of 100 glycoside hydrolase mutants enables the discovery of
structural features correlated with kinetic constants. PloS one, 11(1), e0147596.
You may also refer to the D2D network website, https://d2dcure.com/data/?protein=BglB, which
reports characterization data generated by other students working on this project.
In reviewing the published data compared to your mutant design, consider the following:
● Are you making changes at a residue site that’s been mutated before?
● Do you see the mutation you are considering making (e.g, E → A) done at a different site?
Perhaps at various sites? If at various sites, how do the expression and kinetics data
compare for the same amino acid replacement made at these different sites? How can
any of these data inform the prediction you make about the effect of your mutation?
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Notes on interpreting Figure 2 from Carlin et al. (2017) and D2D CURE.
1. Expression—did this mutant produce a detectable quantity of our BglB enzyme at all?
This data is based on the quantification of protein at A280 and SDS-PAGE analyses. If the
protein does not express, you cannot collect data on the effects of the mutation on the
enzyme’s kinetics or thermostability.
2. TM: This is a measure of the enzyme’s thermostability (how well it can withstand
increasing temperatures). TM is the temperature at which half of the enzymes were
denatured. TM compares how the mutant performed compared to the wild type. Thus,
more negative values indicate reduced thermostability, relative to wild type.
3. Kcat/KM: Kcat, the catalytic constant (or turnover number), describes how many
molecules of substrate can be converted to product each second per single active site of
enzyme. Higher values of Kcat are favorable. KM is the Michaelis constant which represents
how much substrate is needed to get to half of the maximum velocity (V max/2) of the
reaction (related to the affinity of the enzyme for the substrate). Lower KM means less
substrate is needed to reach a certain reaction rate. The ratio Kcat/KM therefore indicates
the catalytic efficiency. Kcat/KM compares the catalytic efficiency of a mutant to that of
the wild type. Thus, more negative values indicate reduced catalytic efficiency, relative to
wild type.
The Theoretical Approach
The theoretical approach uses data generated by the Foldit Modeling functions to evaluate the
mutant’s local interactions that are summarized in the local score. You can see the local score of
any residue by selecting it and hitting “Tab”. Consider the following:
● How do the properties (e.g., hydrophobic/hydrophilic, large/small, etc.) of your mutant
amino acid interact with the other local amino acids? Are these interactions different
from those of the wild type amino acid? How about any broken or new hydrogen bonds?
● What local scores are produced by your mutant amino acid? How do they compare to the
local scores observed in the wild type?
● How might these modeled interactions deter or enhance substrate binding? Can you
provide an explanation for this prediction?
Notes on interpreting the local score in Foldit
1. In the same manner that a lower (more negative) Foldit Energy is indicative of higher
stability, a lower local score (both overall and for specific factors) is considered more
favorable from an energy-based perspective. There are different factors that all
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contribute to the overall score, detailed below. If the score for any of these is 0 (zero), it
will not be displayed.
2. Clashing: Clashes occur when atoms are spaced too closely together, resulting in their
electron clouds repelling each other and destabilization of the protein.
3. Packing: A measure of the extent that atoms are surrounded by other atoms. Poorly
packed proteins will have voids (empty spaces), which are energetically unfavorable.
4. Hiding: Based on hydrophobicity. Hydrophobic side chains should point inward and
hydrophilic side chains should point outward.
5. Backbone: Reflection of how the backbone is modeled, affected by the dihedral angles.
6. Sidechain: Compares the shape of the sidechain to the shape of the backbone. This is an
exception where a more negative score is worse, because it indicates that a configuration
is rare and thus less likely to occur in nature.
7. Disulfides: Depicts quality of a disulfide bridge, shown only for cysteines.
8. Ideality: A measure of how closely bond lengths, angles, and dihedral angles are to ideal
9. Bonding: Based on hydrogen bonds between the residue and others.
10. Pairwise: Reflects the electrostatic energy between charged but unbonded atoms within
a certain vicinity of one another.
At the end of the manual, you can find the structure of all amino acids as well as the Venn diagram
of their physicochemical properties.
Notes and Helpful Tips: Interpreting Total Protein Scores
When considering changes in the energy score, the variations in energy described below refer to
the change in energy score between the wild type and the mutation you just modeled.
● A total energy that is more negative, i.e., < 0 (relative to the wildtype), represents an increase in stability. ● A total energy change that is more positive, i.e., between 0 and +2 (relative to the wildtype), is okay if the clash score (fa_rep) is < 1. ● A total energy between +2 and +5 is iffy (in terms of likelihood of protein expression); however, it can work if you think the Rosetta picture of the protein is missing an enabling feature, such as increased formation of hydrogen bonds. ● A total energy between +5 and +10 is unlikely to express the protein. ● Energies >+10 are very unlikely to be expressed, but this data can also be important.
Do not worry about the total protein score too much as long as it is no more than 5 Rosetta
Energy Units higher than wild type. If the score is more than 5 Rosetta Energy Units higher than
wild type, you may have reason to worry but that does not always have to be the case. Use your
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knowledge about the physicochemical properties of amino acids, the interactions/bonds they
can form, the local structure of the area you are working in, and the level of conservation at an
evolutionary level to guide you in making your decision. You may design a mutant that has an
energy score >+10 than that of the wild type and all of the information that you have access to
can indicate to you that this protein should express (perhaps it’s still quite similar to the wild
type). For example, we created the mutation V275I with a final energy of -1065.855. It expressed
and we were able to get functional data from the mutant. So use your instincts as a budding
biochemist! Don’t be afraid to give something a try as long as you can back it up with a good
The Evolutionary Approach
The evolutionary approach takes homologous sequences and compares them to one another.
The degree of conservation or variability at a site can provide meaningful insight about a residue’s
role in maintaining local or global structural contacts and overall protein function. Oftentimes, a
residue that is conserved across many species can be assumed to be functionally relevant due to
the selective pressures that are preventing mutations at that site. Sites with lower selective
pressures are able to accumulate mutations more freely if function is not significantly impaired
(no negative functional consequence = the mutation cannot be selected against). Consider the
● Which residues are conserved? Which residues are more variable? Where are they found
in the protein structure? How might the functional consequences at a conserved site
differ from the functional consequences at a variable site?
● Are there sites that fluctuate between only a few amino acids (e.g. always valine or
isoleucine)? If so, do those residues have any physicochemical properties in common (i.e.,
the amino acid varies but the property is conserved)? Alternatively, do those residues
display different physicochemical properties?
Developing a Hypothesis: Putting it all Together
After considering the many approaches that have been presented to you. You are now ready to
construct your hypothesis. A hypothesis differs from a prediction. With a prediction, you are
stating what you expected to observe at the end of an experiment. A prediction becomes a
hypothesis when you provide a potential explanation or mechanism to justify why you expect to
observe specific results. As such, hypotheses incorporate previously made observations and
known data. You are not only addressing what you think will be the functional consequence of
the mutation you are choosing to engineer, but why you think that will be the case.
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To draft your hypothesis, answer the following questions with your partner, and use the answers
to formulate a coherent hypothesis which you will submit with the second part of the assignment
that was given out last lab. If done correctly, your hypothesis will be roughly a paragraph long.
● What are the physicochemical properties of the wild type amino acid that you are
mutating? What are the physicochemical properties of the amino acid you have selected
to replace the wild type? How do they compare to one another? Consider also the
physicochemical properties of residues that neighbor the one you are mutating.
● Where in the 3D structure of the protein is the amino acid that you are mutating located?
Is there anything significant about this region? Here, significance can be relative to protein
function as well as the goals you have for the mutation that you are engineering.
● Is your residue located in a secondary structure element? If so, which one?
○ NOTE: Methionine, Alanine, Leucine, Glutamic acid, and Lysine (“MALEK”) have
especially high alpha helical propensities. Prolines and glycines often disrupt alpha
helices. Tyrosine, Phenylalanine, Tryptophan (aromatics , “YFW”), and Threonine,
Valine, Isoleucine (beta-branched, “TRI”) have high beta sheet propensities.
Prolines may be found in beta sheets, but often in the edge strands (as opposed
to a middle strand).
● Does your mutation disrupt any previously existing bonds? Does it create any new bonds
or interactions of interest (hydrogen bonds, disulfide bridges, etc)?
● From the multiple sequence alignments you made, how conserved is the wild type amino
acid at the site you are mutating? Is there conservation at that same for any
physicochemical properties? How do the properties of your replacement amino acid
● How does the final Foldit energy score compare to the initial energy score?
● Is there experimental data for a mutant similar to yours? What do those results indicate?
● Based on all of the above components, how do you think your mutation will affect
catalytic efficiency? Enzyme turnover rate? Binding affinity? Thermal stability? Be sure to
provide possible explanations.
Foldit Figures
As you are designing your mutant in Foldit, there are certain figures that you may want to
generate and save (i.e. take a screenshot).
Before mutation:
1. A screenshot of the residue you have chosen to mutate (wild type). Make sure the residue
is selected (highlighted) and in clear view. You may want to try pressing Q or Shift + Q to
orient and zoom in on your selected residue (no pop out window).
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2. The same as above, but with the pop out window that contains additional information
about your selected residue. You may toggle this window by pressing Tab.
After mutation:
3. A screenshot of the residue after you’ve induced the mutation. Make sure the residue is
selected (highlighted) and in clear view. You may want to try pressing Q or Shift + Q to
orient and zoom in on your selected residue (no pop out window).
4. The same as above, but with the pop out window that contains additional information
about your selected residue. You may toggle this window by pressing Tab.
You may or may not want to use couple of these figures in your poster at the end of the semester.
Therefore, you may choose to organize these screenshots in a PowerPoint or Word document.
Your figures should be accompanied by a figure legend (caption) that describes what is being
shown in the figure. You may choose to write 4 different figure legends, or you may choose to
combine your 4 figures into one larger figure with components neatly labeled a, b, c, d. Imagine
that you are trying to put something together for publication. This is a good opportunity to
practice presenting your data the way a scientist would, since you are, in fact, now a scientist.
For the legend, you may want to include the following information:
● What protein is this? From what organism? What’s the PDB id?
● What residue position is being shown/highlighted?
● What is the wild type amino acid? What is the mutated amino acid?
● What is the initial vs final energy?
● What do the different colors represent?
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The Oligo Order Process
“Oligo” is short for “oligomer,” which is a technical term for a relatively short chain of polymers,
such as nucleotides, saccharides, or peptides. In our case, the oligo will contain 33 nucleotides; it
will span the region that codes for 11 amino acids, where the middle amino acid is an amino acid
that will be changed.
To mutate a protein, we will change the DNA sequence that codes for it (gene). The DNA oligo
we will be ordering in this step encodes for the mutation you designed in the BglB gene.
We order oligos through the biotech company, IDT technologies. This protocol will walk you
through completing the order. Students can assist with sections (1) Identifying Oligo Sequences
and (2) Organizing Mutant Names and Oligo Sequences.
Obtaining Oligo Sequences with D2D CURE Application
First, you will need to find the oligo sequence that encodes for your mutation. To do this, you will
perform a quick search of an oligo database from the D2D Cure website.
Navigate to the D2D Cure page: Oligo Search
Type in the name of the enzyme variant using the format A123G. Remember that the first letter
is the original amino acid residue at the given position, and the final letter is the new residue.
Click “Search.” The website will provide a codon-optimized DNA 33-mer sequence to use as a
primer, which you can copy and paste as needed. (This 33-mer codes for an 11-mer peptide with
the modified residue at the center.)
Students should submit their sequence information on the Google Form emailed by your
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