M ACHINE L EARNING TO H UNT FOR P HAGE P ROTEINS TO C ATCH Klebsiella
A BSTRACT
Antimicrobial resistance (AMR) has been declared a global threat by the World
Health Organization.
Development of novel and effective therapies against microbes is an active research area of ever-growing importance.
One of the leading threats are Klebsiella species, which cause virulent AMR infections with high death rates, particularly in hospital settings.
Klebsiella species are particularly problematic because they produce a thick sticky polysaccharide capsule that protects them from antimicrobials and allows them to build highly resistant biofilms - defensive layers of cells.
A natural solution to eradicate Klebsiella capsules and biofilms are depolymerase proteins that can target and neutralize polysaccharide capsules of specific Klebsiella species, often found in bacteriophages.
However, machine learning guided discovery of depolymerase proteins in such phages is an unexplored area.
In this work, we use machine learning to help identify proteins in phage proteomes that can act as depolymerases against Klebsiella .
Specifically, we utilize a dataset of phages, containing depolymerase proteins, that can target and neutralize polysaccharide capsules of specific Klebsiella species.
We train a ranking model to rank proteins in an input phage proteome based on their predicted ability to act as a depolymerase.
We use a non-redundant validation protocol to evaluate the predictive accuracy of the proposed model.
Our analysis shows that for all test proteomes containing at least one depolymerase, the depolymerase protein was ranked within the top scoring 5% of proteins.
We expect that the proposed approach (called DepoRanker) will be useful in accelerating the discovery of such antibacterial proteins in the wet lab
I NTRODUCTION
Antibiotic or antimicrobial resistance (AMR) represents a current threat to global health and security, making routine surgery and bacterial infections risky.
Of particular concern are multidrug-resistant
Enterobacteriaceae infections that are becoming increasingly difficult to treat with existing antibiotics available in clinical settings [6].
The ability of the Enterobacteriaceae to gain and transfer an increasing number of antimicrobial-resistance genes, particularly in health-care settings represents a critical threat to human health [20].
The World Health Organisation has named the Enterobacteriaceae Klebsiella as a priority pathogen that requires the urgent development of new antimicrobials due to high levels of resistance [25].
Another treatment for AMR infections is phage therapy: the use of bacteriophages (phages for short), to kill bacterial pathogens in a targeted manner [5].
Phages are natural killers of bacteria and phage therapy is emerging as a potential weapon against multi-drug resistant bacterial infections [4].
The commercial development of phages faces a number of challenges, including (but not limited to) small host range compared to antibiotics, potential adverse reactions in patients, toxins co-purified with phages and proteins of unknown functions encoded by phages.
Ultimately, these hurdles can be overcome with an improved understanding and characterisation of phages.
Phages have very narrow host ranges, which is usually limited to one bacterial genera, species or subspecies that they can infect [12].
The host range of Klebsiella phages is further restricted by the wide range of polysaccharide capsules their hosts express, making the host bacteria intrinsically resistant to both antibiotic treatment and phage infections.
However, some Klebsiella phages have been shown to be specific to particular host capsule types.
This is frequently linked to phage encoded sugar-degrading enzymes called depolymerases that target specific capsule types [23, 11].
A small number of depolymerases have previously been characterised in wet lab studies, but the process is very time consuming [9, 16, 21, 13].
Klebsiella phage depolymerases are difficult to identify using standard annotation pipelines based on homology searches, such as BLAST.
Klebsiella phage annotations usually identify at least one putative “tail-fibre” or “tail-spike” protein.
Structural predictions suggest these proteins have betahelical structures, a common protein architecture of both tail-spike proteins and the depolymerase enzymes that are suggested to have evolved from these [24, 14].
This allows us to carry out more targeted homology searches to identify putative depolymerase genes.
However, we have identified a number of lytic phages in our collection that display depolymerase-like activity in wet lab experiments, namely the formation of semi-translucent halos around a clear phage plaque in plaque assays on solid agar medium, but lack identifiable depolymerase genes.
We suggest that this is either because conventional depolymerases are not essential for the phages to permeate the capsule layer, or because of short-comings in the sequence-based annotation of phage genomes [17, 18].
Here, for the first time, we propose the use of a machine learning approach to improve on the standard bioinformatics pipelines to solve the problem of depolymerase identification as no such methods are currently available.
Phage proteomes containing known depolymerases were used as learning and test datasets with characterised depolymerase proteins providing labels.
Using amino acid composition features, our method ranks whole phage proteomes by their ability to act as a depolymerase.
This was rigorously tested using non redundant data sets for cross validation and shows that known depolymerases rank highly in the proteomes.
M ATERIALS AND M ETHODS
We model the problem of predicting depolymerase proteins in a phage as a supervised machine learning problem in which the goal is to rank all proteins in a phage based on their ability to act as a depolymerase.
Below, we describe different steps in the development of the proposed method.
2. D ATA C OLLECTION AND P REPROCESSING
We collected information from previously published data consisting of depolymerase proteins in phages.
The set of all depolymerases in this data constituted the positive class of our dataset.
To get a negative dataset, we used the phage proteomes to which these proteins belonged.
All proteins were taken and any already in the positive class were removed with the remaining proteins used to create the negative dataset.
This created a final dataset which comprised of 24 phage proteomes with a total of 39 characterized depolymerase protein sequences and 2,601 non-depolymerase protein sequences.
2. M ACHINE L EARNING M ODEL 2.2. F EATURE E XTRACTION
In this work, a simple amino acid composition of a protein is used as its feature representation [26, 22, 1].
Since the alphabet for a protein sequence has length 20, this gives a 20-dimensional nonnormalised feature vector representation for a given protein sequence.
2.2. G RADIENT B OOSTING M ODEL
We have used a ranking model based on Extreme Gradient Boosting (XGBoost) [3].
XGBoost is a fast and effective learning algorithm and has been widely used in machine learning, including predicting AMP in bacteria [26] and predicting protein function [27].
In this paper we have developed a ranking implementation of XGBoost which operates on proteomes in a pairwise fashion.
The XGBoost ranking model is trained using multiple training proteomes to produce high scores for known depolymerases and low scores for non-depolymerases in each input phage.
In testing, the same approach is used for generation of a ranked list of proteins in the test phage based on predicted deopolymerase activity.
Stated formally, our objective is to obtain a prediction function from XGBoost, f ( x, θ ) , with learnable parameters θ for a protein sequence represented in terms of its feature vector x .
We require the model to learn the optimal parameters θ ∗ such that the score f
( x i , θ ∗ ) for positive examples x i ∈ P g (known depolymerases in phage g ) is higher than f ( x j , θ ∗ ) for x j ∈ N g (non-depolymerase from the same phage) across all training phages.
The hyper parameters of the learning model are selected through the cross-validation performance evaluation and the optimal results obtained are a learning rate of 0.1, a subsample of 0.
and max tree depth of 3.
The same mode has previously been utilized for the discovery of novel anti-CRISPR proteins [7].
As a baseline we have used a kernelized support vector machine (SVM) and BLAST.
For the BLAST baseline, all test proteins in a phage were searched against the dataset of known depolymerases and sorted based on their e-values to rank proteins based on their expected depolymerase activity.
2.2. P ERFORMANCE E VALUATION
To evaluate the performance of our machine learning model, we have performed non-redundant cross-validation on the data.
Since we do not want to train the model on depolymerases with a similar sequence to those in the test data, we use the non-redundant training sets gained by clustering the proteins from CD-HIT as the folds for cross validation.
All known depolymerases were clustered at a sequence threshold of 10%.
If a depolymerase has a similarity score greater than 10% then it may contain the same structure and function [2].
We used CD-HIT [10] to create non-redundant sets based on sequence similarity of the 39 known depolymerases.
The sets were then translated into sets of the phage proteomes to which the depolymerase belonged.
If a phage contained multiple depolymerases separated into different clusters, then this phage would appear in multiple training sets.
To ensure the non-redundancy of our training sets, in this scenario the sets were merged.
We set aside all proteomes in the fold to be used as a validation set and any proteomes not removed were used as the training set.
The XGBoost model is trained on all proteins in the training set and the prediction scores were computed on a proteome-by-proteome basis.
To report the prediction performance of the machine learning model, we have used Area under the Receiver Operating Characteristic Curve (AUROC) as well as Area under the Precision Recall Curve (AUPRC).
An optimal predictor will rank all depolymerases higher than all other proteins in a test proteome.
For the background context of this problem, we want to find depolymerase proteins in as few wet-lab experiments as possible.
To evaluate the predictive performance of the proposed models, we utilize a metric called the Rank of First Positive Prediction (RFPP).
RFPP is a biologist-centric performance metric which is based on the fact that a perfect machine learning model should give the highest score to the known depolymerases in a phage proteome and therefore rank them lower in a sorted list compared with non-depolymerases [19].
Therefore the perfect machine learning model would have an RFPP of 1 for each proteome.
RFPP is computed across different percentiles in the test set to get an idea of the overall performance on the test set.
R ESULTS 3. P RINCIPAL C OMPONENT A NALYSIS
To explore the relationship between the two classes in our data we performed Principal Component
Analysis (PCA) on our features [15].
We took the top 3 principal components to compare visually, (fig. 2) shows these in a 3D scatter plot.
There is a clear separation between the two classes in our data showing that machine learning will be able to accurately classify these in this feature space
3. N ON - REDUNDANT C ROSS -V ALIDATION P ERFORMANCE
To evaluate our machine learning model, we computed the receiver operating characteristic (ROC) curve which is a common analysis method in machine learning that demonstrates the discriminative ability of a prediction model [27, 8].
The area under the curve is the empirical metric which can be observed from this analysis method.
An AUC-ROC score of 1 would indicate that a classifier can perfectly separate the depolymerase and non-depolymerase proteins.
Fig.
3 shows the ROC curve plotted for our ranking model.
The AUC-ROC score for the proposed model is 0.99.
In comparison, a kernelized support vector machine (SVM) with the polynomial kernel and BLAST both give an AUROC of 0.94.
The precision recall curves plotted in fig.
3 show a similar trend with the proposed model giving an improved performance in comparison to BLAST (with AUC-PR of 0.37) and SVM (AUC-PR of 0.42).
3. R ANKING D EPOLYMERASES IN P HAGE P ROTEOMES
Fig.
4 shows the RFPP for all proteomes for different redundancy thresholds, and different models with a random predictor for an experimental control.
For a random ranking model which produces a random score for any given example, the median RFPP is 52.
So for each proteome tested during cross validation 50% would have a known depolymerase in the top 52 predictions by the model.
This is greatly improved upon by the proposed model and a kernelised SVM which both have median
RFPP of 1.
However, for the 95th percentile, our model shows an RFPP of 3 whereas for SVM this is 25.6.
Out of the 24 phage proteomes tested, the ranking model had an RFPP of 3 or lower for all proteomes, which is in the top 5% in their respective proteome.
Full results are shown in table 1.
This clearly shows the effectiveness of our ranking model for identifying depolymerases in phage proteomes.
To see the effect of increasing the similarity thresholds of training sets, we compare our results with our ranking model trained on a non-redundant dataset created from a 30% similarity threshold for depolymerase sequences.
As shown in (fig. 4), a more redundant set of data creates better results.
However, this is to be expected as the model will be trained on more similar depolymerases, we have opted to use the 10% threshold for the final model as it better represents the challenge of identifying depolymerases in proteomes independent of our dataset.
These ranking results also show that the proposed model gives better performance in comparison to BLAST and SVM baselines.
We have developed a machine learning model which was able to identify the characterised depolymerases from the test dataset to a good accuracy.
The principal component analysis shows that a number of genes that were not characterised depolymerases (non-depolymerase proteins) clustered with the depolymerases (fig. 2).
There are two possibilities for these non-depolymerase proteins; 1.
they are false positives, 2.
they are true depolymerases that were not characterised, present in the proteomes of phages that also contained a characterised depolymerase.
These will be further investigated in the future.
C ONCLUSIONS AND F UTURE W ORK
Any novel depolymerase groups identified from the machine learning approach will need to be structurally and biochemically validated to confirm their function.
Once a robust machine learning approach has been validated it will enable phage researchers to determine the usefulness of phages for therapeutics.
Phage depolymerases also have the added value of being applied as therapeutic enzymes in their own right which circumvents some of the obstacles to using phages as antimicrobials.
Enabling accurate identification of phage proteins will be critical for the future optimisation of phage therapeutics.
Phage Accession Number
Cluster
Proteome Size
BLAST
SVM
DepoRanker
NC
AB
MW MW
MF
OU OU
AB
KX
KU KU
GQ
MK
MT
KY
LC
JF
KY
KT
KY
[Table]