ProteoKnight: Convolution-based phage virion protein classification and uncertainty analysis

arXiv:2508.07345 · cs.LG, cs.AI · Submitted 2026-08-15 · Read on arXiv

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Introduction to the show: ident: AI Radio. Generated commentary on the latest Artificial Intelligence papers.

Tom: Next we'll be talking about the paper "ProteoKnight: Convolution-based Phage Virion Protein Classification and Uncertainty Analysis".

Jane: The paper was written by Samiha Afaf Neha, Md. Ishrak Khan and Abir Ahammed Bhuiyan from BRAC University.

Tom: Stay tuned as we take you through the paper and discuss its implications.

Title: Tom: Welcome back to the show, everyone. Today we're looking at a fresh arXiv preprint called "ProteoKnight: Convolution-based phage virion protein classification and uncertainty analysis." Jane, I have to say, the title alone got me curious — phage virion proteins, that's a mouthful.

Jane: It really is, Tom, but the idea is simpler than it sounds. Bacteriophages are viruses that infect bacteria, and their structural proteins are what make up their outer shell. Knowing whether a protein is one of those structural pieces or something else matters a lot for phage therapy and drug development.

Tom: So the paper is basically trying to build a better way to classify these proteins automatically, right?

Jane: Exactly. Instead of running expensive lab experiments, they want to look at the amino acid sequence and predict whether it's a phage virion protein or not. The clever part is how they turn text sequences into images so a convolutional neural network can process them.

Tom: And that's where the name ProteoKnight comes from. They adapted something called the DNA-walk algorithm, which was originally used for DNA sequences, and made it work for protein sequences. Each amino acid gets a color and a direction, and the sequence becomes a trail of dots on a grid.

Jane: It's like drawing a map of the protein. The order and position of each amino acid matters, so the spatial information is preserved. That's actually a big deal because the only previous image-based method for this task, frequency chaos game representation, tended to lose that spatial structure.

Tom: And the authors from BRAC University in Bangladesh are claiming competitive results — around ninety percent accuracy on binary classification. That's on par with the state of the art, but with a much simpler encoding.

Jane: Right, and they also did something nobody had done before in this field. They looked at prediction uncertainty, not just accuracy. That's what the second half of the title is about.

Tom: So we're not just asking "is this protein a virion protein?" but also "how confident is the model when it says yes?"

Jane: Precisely. That's a crucial question for real-world use, because if the model is uncertain, you might want a human to double-check before making decisions based on that prediction.

Tom: I love that they're thinking beyond raw accuracy. But I'm curious how they actually measure that uncertainty and what they found. That's coming up in the next segment.

Summary: Tom: So Jane, we've established what ProteoKnight does at a high level. Now let's dig into the actual results. The paper reports about ninety point eight percent accuracy for binary classification — that's distinguishing phage virion proteins from non-virion proteins.

Jane: And that's competitive with the best existing tools like DeePVP and PhaVIP. But what impressed me is the consistency across metrics. They got precision around ninety-one percent, recall around ninety-one percent, and an F1-score near ninety percent. Some older methods had high accuracy but much lower recall or precision, so the balance here is notable.

Tom: They used pre-trained convolutional neural networks, specifically GoogLeNet, which has only about five point five million parameters. That's tiny compared to modern transformers, yet it performed really well.

Jane: That's a key point. They didn't train a model from scratch. They took a network already trained on ImageNet and fine-tuned it on their encoded protein images. That saves a ton of compute and training time.

Tom: And they compared several architectures — EfficientNet, MobileNet, GoogLeNet — and GoogLeNet won on the trade-off between performance and efficiency. EfficientNet was slightly better in accuracy, but it has four times more parameters.

Jane: Now, the multiclass task was a different story. They tried classifying proteins into eight subclasses like capsid, tail fiber, baseplate, and so on. There the accuracy dropped to around seventy-six percent. The authors admit that's not great.

Tom: Why the big drop?

Jane: They think it's because of point overlaps in the encoding. When two amino acids land on the same pixel, information gets lost. In binary classification, that loss is tolerable. But when you need to distinguish between eight similar classes, every bit of detail matters.

Tom: So the encoding works well for a coarse question but struggles with fine-grained distinctions.

Jane: Exactly. And that's an honest limitation to report. They're not overselling their method.

Tom: I also noticed they used a benchmark dataset from December two thousand twenty-two with over thirty-five thousand virion proteins and nearly forty-seven thousand non-virion proteins. That's a solid foundation for training.

Jane: It is. And they were careful about data quality — they removed low-confidence labels and used CD-hit to cluster sequences at ninety percent similarity so the dataset wasn't redundant.

Tom: So the summary is: great binary performance, decent but not stellar multiclass performance, and a novel encoding that preserves spatial information. What's next?

Jane: Next is the part that really sets this paper apart — the uncertainty analysis. That's where they go beyond just reporting accuracy and actually probe how reliable the model is.

Improvements: Tom: Jane, you teased the uncertainty analysis at the end of the last segment. Let's get into it now, because that's genuinely new territory for phage protein classification.

Jane: It is. The authors used Monte Carlo Dropout. The idea is simple: during testing, you keep the dropout layers active, so each time you run the same input through the model, a slightly different set of neurons gets dropped. You do this many times and look at the spread of predictions.

Tom: So if the model gives the same answer every time, it's confident. If the answers bounce around, it's uncertain.

Jane: Exactly. They ran one hundred stochastic forward passes per sequence and measured the variance and entropy of the predictions. Then they split the data by class and by sequence length to see where uncertainty was highest.

Tom: And what did they find?

Jane: The model was more confident on non-virion proteins than on virion proteins. That might be because there were more training examples for non-virion proteins, or because the virion sequences are more diverse.

Tom: What about sequence length?

Jane: Shorter sequences gave more confident predictions than longer ones. That makes sense given the encoding — longer sequences have more chances for dots to overlap, which muddies the image.

Tom: So the model is less reliable exactly when the protein is long and complex?

Jane: Right. And that's a practical insight. If you're screening a long protein sequence, you might want to treat the model's prediction with extra caution.

Tom: They also checked entropy for high-variance versus low-variance samples. The pattern held — high-variance samples had higher entropy, meaning the model was genuinely unsure, not just noisy.

Jane: And they validated this across different dropout rates — zero point one, zero point two, zero point three — and the pattern stayed consistent. That's good experimental hygiene.

Tom: Now, Lu, you're the researcher here. What do you make of this uncertainty work?

Lu: I think it's a step toward making these tools usable in practice. In medicine or drug design, you can't just take a model's word for it. Knowing when the model is uncertain lets you route those cases to human experts or additional tests. This paper gives a template for how to do that in protein classification.

Tom: So it's not just about the encoding — it's about building trust in the prediction pipeline.

Lu: Exactly. And I'd love to see this extended to multiclass classification, because that's where uncertainty matters most. If the model can't tell a tail fiber from a baseplate, you need to know that before you annotate a genome.

Tom: That's a great point. The authors themselves say multiclass needs work, so maybe uncertainty analysis there would be even more valuable.

Jane: And that's a natural lead into the conclusion — what does this mean for the field, and where does it go next?

Conclusion: Tom: Alright, let's wrap up our discussion of ProteoKnight: Convolution-based phage virion protein classification and uncertainty analysis. Jane, give us the one-paragraph version.

Jane: The paper introduces a new way to encode protein sequences as images, preserving spatial information that older methods lost. Using a small pre-trained CNN, they achieved around ninety percent accuracy on binary phage virion protein classification, matching the state of the art. And they went further by quantifying prediction uncertainty, showing that the model is more confident on short sequences and non-virion proteins.

Tom: And the implications?

Jane: For researchers annotating phage genomes, this is a fast, cheap screening tool. For anyone building on this work, the uncertainty framework is a template they can apply to other protein classification tasks.

Lu: I'd add that the encoding itself is the real contribution. The DNA-walk idea is elegant, and adapting it to proteins opens up possibilities for other sequence analysis tasks — maybe enzyme classification or even protein function prediction.

Meng: From a practical standpoint, the fact that they used GoogLeNet with five point five million parameters means this can run on modest hardware. You don't need a cluster of GPUs to use this. That lowers the barrier for labs with limited compute.

Tom: And the limitations?

Jane: Multiclass classification needs improvement, and the point-overlap issue in the encoding is the likely culprit. The authors suggest tweaking the radius, dot size, or splitting the sequence into frames to avoid overlaps.

Lu: I'd also like to see uncertainty analysis applied to the multiclass setting. That's where the stakes are highest.

Tom: Well, we've covered the encoding, the results, the uncertainty work, and the future directions. That's a solid episode.

Jane: It is. ProteoKnight is a thoughtful paper that doesn't just chase accuracy — it asks when the model can be trusted. That's the kind of work that moves the field forward.

Tom: Thanks for listening, everyone. We'll be back with another paper next time. Until then, keep asking questions.

Jane: And keep checking those uncertainties. See you soon.

Samiha Afaf Neha, Md. Ishrak Khan, Abir Ahammed Bhuiyan

BRAC University

cs.LG, cs.AI

Submitted: 2026-08-15

Updated: 2026-08-18

License: http://creativecommons.org/licenses/by/4.0/

Importance score: 71/100

Key concepts

Bacteriophages
Viruses that specifically infect bacteria. Their structural proteins form the outer shell, and classifying these proteins is important for developing phage therapy and drugs.
ProteoKnight
A method that classifies phage virion proteins by converting their amino acid sequences into images. It adapts the DNA-walk algorithm to preserve spatial information for convolutional neural networks.
Convolutional Neural Network (CNN)
A type of AI network used here to process the protein 'images.' It learns patterns from the visual data, allowing it to predict if a protein is a virion structural piece or not.
Prediction Uncertainty
A measure of how reliable a model's prediction is. By calculating variance and entropy, the model can tell users not just *what* the classification is, but *how confident* it should be.

Terminology

Summary

Summary

This paper introduces ProteoKnight, a novel image-based encoding method for the classification of Phage Virion Proteins (PVPs) using pre-trained convolutional neural networks (CNNs), along with an analysis of prediction uncertainty using Monte Carlo Dropout (MCD).

Introduction and Motivation: The authors state that accurate prediction of PVPs is essential for genomic studies due to their role as structural elements in bacteriophages. They note that traditional methods like mass spectrometry and protein arrays are time-consuming and expensive, and alignment-based methods are not always effective due to a lack of collinearity in viral genomes. The paper highlights that most existing computational approaches use 1-D sequence encodings, and while satisfactory, the potential for improved prediction through 2-D image representations remains largely unexplored. The only existing image-encoding study for PVP classification uses Frequency Chaos Game Representation (FCGR), which the authors argue suffers from spatial information loss due to its compact transformation. The paper's motivation is to address this gap by devising an alternative effective encoding strategy and to investigate the impact of uncertainty in phage virion classification, a topic they state has not been previously studied.

Contributions: The core contributions are: (1) proposing a novel encoding strategy called Knight Encoding to convert text-based protein sequences into image-based data; (2) employing state-of-the-art deep learning techniques for comprehensive classification analyses; and (3) exploring uncertainty aspects in PVP classification using Monte Carlo Dropout (MCD), identifying sequence types that exhibit high accuracy yet comparatively low confidence.

Methods:

  • Dataset: The study uses a benchmark dataset constructed from the RefSeq viral protein database (up to December 2022). After data reconditioning, CD-hit clustering with a 90% similarity threshold was applied. The final database comprises 35,213 PVP sequences and 46,883 non-PVP sequences. PVP sequences are further categorized into eight groups (Baseplate, Portal, Tail Fiber, Major Capsid, Minor Capsid, Major Tail, Minor Tail, Others) for multi-class classification.

  • Knight Encoding: This is a novel walk-based encoding technique adapted from the DNA-walk algorithm. It uses polar coordinates to interpret protein sequences. Each of the 20 amino acids is assigned an angle based on its position in a 20-sided polygon (Icosagon), with each point separated by 18 degrees (360/20). The encoding starts from the center of an M×M image. For each amino acid, horizontal and vertical displacements are calculated using the formulas x = r × cos(θ) and y = r × sin(θ), with a fixed radius (r=15) and point size (2). Each amino acid is assigned a unique RGB color. The process continues sequentially, with each new point starting from the previous point's coordinates. If a point hits the image boundary, encoding restarts from the center.

  • Classifiers: The authors utilized several pre-trained CNN models from the PyTorch library, including GoogleNet, EfficientNet, and MobileNet, focusing on models with fewer parameters. They fine-tuned these models on the encoded image dataset for 25 epochs with a batch size of 32, using binary crossentropy as the loss function. The GoogleNet architecture was selected as the most promising due to its optimal trade-off between computational efficiency and classification quality, with 5.5M parameters.

  • Uncertainty Analysis: To assess uncertainty, the model's dropout layer was activated during the test phase with a 0.2% dropout rate. Data was divided into four categories based on class and sequence length: PVP (350), non-PVP (275). For each category, 100 randomly chosen sequences were predicted 100 times. The mean and variance of the softmax probabilities were determined to compare uncertainty across categories. Entropy was also calculated using the formula Entropy = P × log2(1/P) + (1-P) × log2(1/(1-P)).

Results:

  • Model Predictions: The top four CNN architectures showed competitive performance. EfficientNet v2 yielded the best results, but GoogleNet was chosen for its significantly smaller parameter count (roughly one-fourth of EfficientNet v2). The GoogleNet model achieved an accuracy of 88.60% for binary classification and 76.37% for multiclass classification. The paper reports a sensitivity of 88.54% and an F1-score of 89.87% for binary classification. In a comparative analysis, the paper states that ProteoKnight achieved a binary classification accuracy of approximately 90%, with precision, sensitivity, and F1-score surpassing most previous methods. Specifically, for PVP classification, it reports a recall of 0.91, precision of 0.87, and F1-score of 0.87. For non-PVP classification, it reports a recall of 0.89, precision of 0.93, and F1-score of 0.93.

  • Uncertainty Analysis: The analysis showed that the model exhibited higher confidence (lower prediction variance) when predicting non-PVP sequences compared to PVP sequences, and showed greater certainty for shorter sequences compared to longer ones. This pattern was validated using different dropout rates (0.1, 0.2, 0.3). For example, with MCD 0.2, the variance for PVP (short) was 0.08558, for PVP (long) was 0.09119, for non-PVP (short) was 0.07555, and for non-PVP (long) was 0.07663. Entropy values were also lower for shorter sequences and non-PVP predictions, corroborating the observed uncertainty patterns.

Discussion and Conclusion: The paper concludes that ProteoKnight offers an accurate way to annotate important phage proteins, with binary classification performance comparable to state-of-the-art methods. The authors note that the multi-class prediction task showed moderate outcomes (72-78% accuracy), which they attribute to a characteristic of the encoding where dot points may converge at the same location, potentially leading to overlap. The uncertainty analysis revealed that the model was more confident on non-PVP data, possibly due to the larger volume of training data or distinctive underlying sequence compositions. The paper suggests future work could focus on enhancing the Knight encoding approach by extending it to a higher dimension through segregation of frames for each amino acid or optimizing hyperparameters like radius and dot size to address the point overlap issue and improve multi-class classification.

Improvements for AI systems

Based on the scientific paper, here are the specific improvements I can make to AI systems and what the improved system can do:

  • Improvement: Implement the Knight Encoding algorithm as a reusable preprocessing module that converts 1-D protein sequences into 2-D spatial representations using polar coordinates with color-coded amino acid markers.

  • What it does: The system transforms text-based biological sequences into rich image representations that preserve spatial relationships between amino acids, overcoming the spatial information loss inherent in k-mer frequency-based methods like FCGR.

  • Improvement: Integrate a transfer learning framework using parameter-efficient pre-trained CNNs (specifically GoogLeNet) fine-tuned on encoded protein images, achieving 90.8% binary classification accuracy.

  • What it does: The system classifies phage virion proteins (PVPs) versus non-PVPs with high precision (91.1% recall, 92.7% specificity), outperforming traditional machine learning methods and matching state-of-the-art deep learning approaches while requiring significantly fewer computational resources.

  • Improvement: Implement Monte Carlo Dropout (MCD) with configurable dropout rates (0.1, 0.2, 0.3) during inference to generate prediction distributions, coupled with variance and entropy metrics for confidence quantification.

  • What it does: The system provides calibrated confidence scores for each prediction, identifying data points with high uncertainty. It reveals that the model is more confident on non-PVP sequences and shorter sequences, enabling researchers to flag low-confidence predictions for additional experimental validation.

  • Improvement: Incorporate a sequence-length stratification mechanism (using equilibrium length δ = 350 for PVP, δ = 275 for non-PVP) that automatically categorizes inputs and adjusts uncertainty thresholds accordingly.

  • What it does: The system handles variable-length protein sequences more robustly, providing length-specific confidence intervals and identifying when longer sequences may produce less reliable predictions due to encoding point overlaps.

  • Improvement: Extend the binary classification model to an 8-class PVP subtype classifier (baseplate, portal, tail fiber, major capsid, minor capsid, major tail, minor tail, others) achieving 76.37% accuracy.

  • What it does: The system can distinguish between different functional categories of phage structural proteins, enabling more granular annotation of newly sequenced phage genomes and supporting downstream applications in phage therapy development and metagenomic analysis.

  • Improvement: Implement tunable encoding hyperparameters (radius = 15, point size = 2, image dimensions = 512×512) with automatic boundary reset logic that re-centers encoding when points hit image edges.

  • What it does: The system can be optimized for different protein families or sequence lengths by adjusting encoding parameters, preventing information loss from point overlaps and improving classification performance on diverse datasets.

  1. Accurately annotate phage virion proteins from high-throughput sequencing data with 90.8% accuracy, replacing time-consuming and expensive laboratory methods.

  2. Provide confidence-calibrated predictions with explicit uncertainty quantification, allowing researchers to identify which predictions require experimental verification versus those that can be trusted for downstream analysis.

  3. Process protein sequences of varying lengths (from hundreds to thousands of residues) with length-specific reliability assessments, flagging potentially unreliable predictions for longer sequences.

  4. Classify proteins into functional subtypes (capsid, tail, baseplate, portal, etc.) enabling detailed structural characterization of bacteriophages for applications in:

  • Phage therapy development

  • Antibacterial drug synthesis

  • Disease diagnosis

  • Food production safety

  • Bacterial genome engineering

  1. Serve as a complementary tool to existing methods like FCGR-based approaches, offering a different perspective on sequence features that can be ensemble with other methods for more robust classification results.

  2. Operate efficiently in resource-constrained environments using GoogLeNet's 5.5M parameters (vs. 20M+ for EfficientNet), making it deployable on standard laboratory computing infrastructure without specialized hardware.

Abstract

Introduction: Accurate prediction of Phage Virion Proteins (PVP) is essential for genomic studies due to their crucial role as structural elements in bacteriophages. Computational tools, particularly machine learning, have emerged for annotating phage protein sequences from high-throughput sequencing. However, effective annotation requires specialized sequence encodings. Our paper introduces ProteoKnight, a new image-based encoding method that addresses spatial constraints in existing techniques, yielding competitive performance in PVP classification using pre-trained convolutional neural networks. Additionally, our study evaluates prediction uncertainty in binary PVP classification through Monte Carlo Dropout (MCD). Methods: ProteoKnight adapts the classical DNA-Walk algorithm for protein sequences, incorporating pixel colors and adjusting walk distances to capture intricate protein features. Encoded sequences were classified using multiple pre-trained CNNs. Variance and entropy measures assessed prediction uncertainty across proteins of various classes and lengths. Results: Our experiments achieved 90.8% accuracy in binary classification, comparable to state-of-the-art methods. Multi-class classification accuracy remains suboptimal. Our uncertainty analysis unveils variability in prediction confidence influenced by protein class and sequence length. Conclusions: Our study surpasses frequency chaos game representation (FCGR) by introducing novel image encoding that mitigates spatial information loss limitations. Our classification technique yields accurate and robust PVP predictions while identifying low-confidence predictions.

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