Machine Learning Designs for Artificial
Man Xia Lee, Aye Sandar Moe1, Susheel Kumar Gunasekar, Kinjal
Zhiqiang Liu, Natalya Voloshchuk, Jin K. Montclare, Phyllis Frankl
and Lisa Hellerstein
Polytechnic Institute of NYU
Although, in vivo incorporation of unnatural amino acids can be used to improve protein
stability; there is a trade off. Higher stability of the protein may lead to loss in activity.
One way to improve function is to employ machine-learning algorithms to identify
proteins that have enhanced activity. Our target protein Tetrahymena GCN5 (tGCN5), a
member of the family of Histone Acetyltransferases (HAT), acetylates histones at
specific lysine residues, enabling transcriptional regulation. Experimental data have
shown an increase in stability of the protein but loss in activity with the incorporation of
ortho-fluorophenylalanine (oFF) into tGCN5. Using information from biochemical and
structural data, we identify 11 potential mutants that may lead to improve function. We
investigate the structure and function of the tGCN5 mutants in the conventional and
fluorinated contexts. Moreover, we seek to generate optimized variants bearing these
mutants with the help of machine learning algorithms.
Histone Acetyltransferases (HAT) are proteins that acetylate the lysine residue of
the histone proteins on the N-terminal tails, enabling transcriptional regulation (Figure 1
A) . When the positive charged lysine residue of the histone protein is acetylated, the
histone becomes neutralized and the negative charged DNA is more accessible for
Man Xia Lee and Aye Sandar Moe were supported by the CRAW Multidiciplinary Research
Opportunities for Women (M-ROW) program. Additional support was provided by the Othmer Institute,
transcription to occur . The HAT protein Tetrahymena GCN5 (tGCN5) is comprised
of a mixture of alpha-helices and beta-sheets  that catalyze the reaction involving the
transfer of the acetyl group from the acetyl-coenzyme A .
OH OH OH OH
H2N H2N H2N H2N
O O O O
F oFF pFF
Figure 1. A) Crystal structure of tGCN5: Nine
phenylalanine residues are shown in purple. B)
Structure of phenylalanine (F), ortho-
fluorophenylalanine (oFF), meta-fluorophenylalanine (mFF), and para-fluorophenylalanine (pFF).
Previously, Montclare and coworkers incorporated the fluorinated phenylalanine
(oFF, mFF and pFF) into tGCN5 in a residue specific fashion (Figure 1 A, B).
According to experimental data, in vivo incorporation of oFF has shown an increase in
thermal stability. Although tGCN5 bearing oFF displays improved thermal stability, there
is a decrease in activity. Based on biochemical data by numerous groups, we identified
15 residues that are important in the activity and stability of the protein [3-8] (Table 1,
Figure 2). With this set of mutants, we plan to create new variants with combined
mutations to improve protein function.
Table 1. Summary of mutations and their significance.
V 86 T Structurally similar
K 87 R Alignment analysis: conserved
F 90 Y Alignment analysis: conserved
V 98 A Important role in protein stability [6, 8]
I 99 V Important role in protein stability [6, 8]
L 100 I Important role in protein stability [6, 8]
I 107 V Important role in protein stability [6, 8]
F 112 R Alignment analysis: conserved
Q 114 L Important in raising the pKa for a more hydrophobic area[6, 7]
A 121 T Alignment analysis: conserved
A 130 S Alignment analysis: conserved
R 140 H Alignment analysis: conserved
K 144 H Important role in catalysis [6, 7]
F 145 L Important role in catalysis [6, 7]
Y 192 A Important role in catalysis [6, 7]
Figure 2. Structure of tGCN5 with mutations highlighted in green are the conserved residues , orange
are residues that are critical for catalysis , red residues are important for protein stability [6, 7], and blue
residue is an isoteric change.
To reduce the time and cost investigating a combination of all 15 residues
mentioned in Table 1 for a more active tGCN5, we based our design on the theory of
Design of Experiments. The Placket Burman design is widely used to generate a set of
manageable experiments . Because some of the mutations were adjacent to each other
Table 2, we chose to combine those adjacent mutations and designated them as a single
mutation. Using the Placket Burman design, we produced twelve variants bearing five to
eleven mutations to test (Table 2).
Table 2. Placket-Burman Design. The mutant(s) represented X1-X11. The ones with only single mutation are X2,
X4-X9 and X11. Those that consisted of two mutations are X1 and X10. Only X3 contained three mutants.
X1 X2 X3 X4 X5 X6 X7 X8 X9 X10 X11
Seq# 86 87 90 98 99 100 107 112 114 121 130 140 144 145 192
V K F V I L I F Q A A R K F Y
1 - - Y - - - V R L - - - H L -
2 - - - A V I - R L T - - - - A
3 T R - - - - V - L T S - - - -
4 - - Y - - - - R - T S H - - -
5 T R - - - - - R - - S - H L A
6 - - Y - - - - - L - - H - - A
7 - - - - - - V - - T - H H L A
8 - - Y A V I - - - T - - H L -
9 - - - A V I - - L - S H H L -
10 - - Y A V I V - - - S - - - A
11 - - - A V I V R - - - - - - -
12 T R Y A V I V R L T S H H L A
To identify which variants to test next, we intend to employ machine learning
algorithms in our protein design. Using genetic engineering techniques, we are
generating the protein variants and measuring the activities relative to the starting wild-
type tGCN5 and with the incorporation of oFF.
Machine-learning algorithms can be employed to predict the next set of variants
with an improved combination of substitutions . By this approach, we hope to isolate
artificial tGCN5 variants with improved activity for the target histone peptide while
maintaining improved stability.
Active learning is a type of supervised learning technique where the classifier is
built by iteratively choosing the most informative data from a superset of unlabelled data.
This type of learning method is useful for experiments where data is expensive. Based on
the available data, a classifier is built. New data points are then chosen based on this
classifier. The chosen data points are then added to the training file to build another
classifier which is expected to be better than the previous one. We explored uses three
active learning methods discussed by Danziger et al., minimal marginal hyperplane,
maximal marginal hyperplane, and maximum curiosity.
Minimal Marginal Hyperplane 
Minimal Marginal Hyperplane chooses the next data point by the data point’s
proximity to the decision boundary. The assumption here is that the points that are
closest to the decision boundary are those the most informative data. Therefore, the
classifier expects to achieve the desired learning accuracy faster by making use of this
close, unclassified data.
Maximal Marginal Hyperplane 
Maximal Marginal Hyperplane is similar to Minimal Marginal Hyperplane,
except that the next furthest point from the hyperplane is chosen to be the next data point.
Maximum Curiosity 
Maximum Curiosity chooses the data point by giving each point a score and then
picking the point which has the highest score. The formula to calculate the score of each
data point is
(tpt • tnt ) − ( fpt • fpn )
( tpt + fpt )(tpt + fnt )( tnt + fpt )(tnt + fnt )
This method assumes each data point to be active and then calculate the score.
Then, it takes the same data point and assumes it to be inactive and then calculate the
score. The higher score among the two was chosen.
Results and Discussion
Comparison of Active Learning Techniques
In order to determine the best active learning technique to use selecting tGcN5
variants, we compared the active learning techniques on a similar data set from a project
by Liao et al . Figure 3 shows the overall experiment design. Generally, the more
the data, the more accurate the classifier will be. The active learning methods are
intended to help gain the highest accuracy quicker. We generated two different initial
training sets. We recorded the accuracy of the classifier as more data have been added.
The following graph shows the accuracy level obtained by each method as more data
points are added.
Training Weka Classifier Active
Label the new point
Figure 3. Choosing the next data point using active learning
Comparison ( Run 2)
100 Min HP
accuracy 70 MaxHP
0 20 40 sizes 60 80 100
Figure 4: Comparison of data points chosen using active learning methods and random selection on the
To make sure that we did not have a biased initial training set, another training set was
chosen to be the starting set and the active learning methods were run again.
0 20 40 sizes 60 80 100
Figure 5: Comparison of data points chosen using active learning methods and random
selection on the second run
The two different seed training file gives different accuracy value to start of with. In
figure 4, the classifier improves its accuracy quickly. It was also shown that using active
learning methods is actually better than random selection of data. For the second initial
training file, the difference between random data selection and active learning methods is
not significant. Among the three methods that have been tested, maximum curiosity
seems to improve the classifier faster than the other two methods. When the
experimental data on tGCn5 are available, we plan to use an active learning method to
select additional protein variants.
PCR amplification of each fragment
In order to generate the designed variants bearing multiple mutations, we had to
assemble the fragments bearing mutations. By using the primers containing the
mutations, we were able to generate the fragments with the mutation (s) using PCR
assembly . PCR allowed the primers to anneal to the template DNA (tGCN5 gene)
and amplify a fragment of the tGCN5 sequence. After amplifying all the fragments, we
ran another PCR to anneal the individual fragments to each other to generate a full-length
variant bearing the set of mutation, an example of sequence 10 shown in Figure 5.
Sequence 8, 9, and 11 were also generated shown in Figure 6. The full-length variant
will be restricted with the enzymes, Hind III and Bam HI, and cloned into the vector
pQE30. Once we have our new construct, we will proceed to protein expression and do
Figure 5: PCR amplified, example, variant 10 ( (ladder), mutant 1 (~150 bp), mutant 2 (~54 bp), mutant 3
(~48 bp), mutant 4 (~90 bp), mutant 5a and 5b (~212 bp), mutant 6 (~100 bp)) on a 2% DNA gel (left).
The fragments are annealed and amplified (right).
L I----------------8-----------I I--------------9-----------I I------------11---------I
8 8 8 9 9 9 11 11 11
Figure 6: PCR alignment of sequence 8, sequence 9, and sequence 11.
Protein expression of tGCN5 and single mutants of tGCN5
Protein expressions of wild-type tGCN5, F90Y, and A121T, gene in the plasmid
pQE30 were transformed in a phenylalanine auxotrophic strain AFIQ. The protein
expression was visualized on 12 % SDS PAGE Figure 7 A. The expressed proteins were
purified on a 1 mL cobalt gel slurry (TALON® Metal Affinity Resin) with increasing
concentration of imidazole shown on 12 % SDS PAGE Figure 7 B, C and D. From the
SDS PAGE, the largest fraction of pure protein appeared in elution 4 (E4) for wild-type
tGCN5 at 21 kDa (Figure 7 B). For F90Y and A121T, the largest fraction appeared in
elution 2 (E2) and 3 (E3) (Figure 7 C and D). In Figure 7 B, there were impurities
shown in E1-4 for F90Y which indicate that we need to optimize purification conditions.
The largest fractions were subjected to dialysis for the removal of imidazole for
-- WT-- -- F90Y -- -- A121T --
A L - + - + - +
B L E1 E2 E3 E4 E5
C L E1 E2 E3 E4 E5 D L E1 E2 E3 E4 E5
Figure 7 A) SDS PAGE gel result of overexpressed protein at 21 kDa: L (Ladder), pre-induction (-) and
overexpressed protein (+). SDS PAGE gel results of protein purification at 21 kDa: B) Wild-type tGCN5,
C) F90Y, D) A121T: L (Ladder), E1 (elution 1), L2 (elution 2), E3 (elution 3), E4 (elution 4), E5 (elution
Fluorescent assay of tGCN5 and mutants
Kinetic data for tGCN5 was determined using fluorometric assay which detects
the enzymatic production of coenzyme A (CoA) as tGCN5 transfers the acetyl group
from AcCoA to lysine on a peptide, H3p19. The fluorophore, 7-diethylamino-3-(4’-
maleinidylphenyl)-4-methylcoumarin (CPM), reacts with CoA generated in the
acetyltransferase reaction giving a strong fluorescent emission at 465 nm (excitation
wavelength is 365 nm) . 5.9 µM tGCN5 and tGCN5 mutants were tested with
different concentrations of H3p19 (1.2 mM, 0.6 mM, 0.3 mM. 0.15 mM, and 0.075 mM).
The Line-Weaver Burke equation generated from the fluorescent assay was used to
calculate Vmax, Kcat, and Km.
Based on the data (Figure 8 A, B, C and Table 3), A121T appeared to have the
highest turnover and specificity towards H3p19 compared to wild-type tGCN5 and F90Y.
Wild-type tGCN5 was tested in triplicate whereas the mutants were tested only once.
The Vmax, Kcat, and Km for wild-type tGCN5 were within the standard deviation. The
observed large standard deviation might be due to the fact that each trial we did for the
wild-type tGCN5 was performed at room temperature. We will need to repeat the
A Wild-type tGCN5
y = 121.57x + 0.35
0 0.05 0.1 0.15 0.2
10 y = 246.73x + 0.2883
100 y = 874.58x + 197.01
0 0.05 0.1 0.15 0.2 0 0.05 0.1 0.15 0.2
Figure 8. Fluorescence assay results of: A. wild-type tGCN5. B.F90Y. C. A121T.
Table 3: Line Weaver-Burke equation was used to determine kinetics of wild-type tGCN5 and mutants.
Vmax(mM/sec) Km (mM) Kcat (Sec-1) Kcat/Km (Sec -1mM-1)
0.018 ± 2.388 ± 3.068 ± 1.420 ±
WT tGCN5 0.025 3.321 4.226 0.247
F90Y 1.14E-06 0.001 0.001163 1.162786
A121T 0.0035 0.8558 3.5274 4.1217
Wild-type tGCN5, F90Y and A121T were tested for activities using fluorescent
assay. The experimental data showed that A121T exhibited better activity and highest
turnover than wild-type tGCN5 and F90Y. Moreover, the wild-type tGCN5 Vmax, Kcat,
and Km were successfully calculated. The samples will be tested further under ice for
each trial to confirm experimental data.
Site-directed mutagenesis on tGCN5 was carried out to create single mutations
shown in Table 2 (X1- X10). Eight mutations were confirmed by DNA sequencing. We
will repeat site-directed mutagenesis procedure for the other three (X3: V98A, I99V,
L100I, X1: V86T, K87R, and X2: F112R) and send it for sequencing. The confirmed
single mutations will be analyzed for stability or/and activity and compared to wild-type
Once we have activity results from the protein variants shown in Table 1 and
Table 2 with or without the incorporation of oFF, we will employ a machine-learning
algorithm to design a set of variants, which we hope will have improved activity. Our
machine learning experiments suggest that maximum curiosity will be the best active
learning technique to use. In future work, we plan to explore variants of the active
learning algorithms and different ways to model the feature space of the tGCN5 variants.
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