---
title: 'ProteoKnight: PVP Classifier with Knight Encoding'
url: https://www.emergentmind.com/topics/proteoknight
type: topic
---

# ProteoKnight: PVP Classifier with Knight Encoding

ProteoKnight is a phage virion protein (PVP) classification framework centered on a protein-to-image encoding called **Knight Encoding**, followed by classification with pre-trained convolutional neural networks and uncertainty analysis using Monte Carlo Dropout. It targets binary discrimination between PVP and non-PVP sequences and multiclass prediction over 8 PVP subclasses. The method is motivated by the claim that an image representation preserving sequential spatial structure better than frequency chaos game representation (FCGR) can yield competitive PVP classification while exposing uncertainty patterns associated with protein class and sequence length [2508.07345].

## 1. Biological scope and problem formulation

ProteoKnight is situated in the problem of annotating **phage virion proteins**, the structural proteins of bacteriophages associated with the head, tail, baseplate, portal, capsid, and related virion components. The paper frames accurate PVP annotation as important for bacteriophage genomics because high-throughput sequencing produces phage sequences faster than they can be experimentally characterized, and because structural protein annotation affects downstream applications including phage therapy, antibacterial discovery, diagnostics, host interaction studies, and broader metagenomic analysis. It also emphasizes that conventional laboratory approaches such as mass spectrometry and protein arrays are expensive and laborious, while alignment-based computational methods are often unreliable for phages because of low sequence conservation, horizontal gene transfer, and lack of collinearity [2508.07345].

Within that setting, ProteoKnight addresses two linked problems. The first is representational: how to encode protein sequences for deep learning-based PVP classification without losing sequence-order information. The second is inferential: how to quantify prediction uncertainty in a domain where low conservation and curated training data may encourage overconfident but unreliable predictions. The paper therefore treats ProteoKnight not only as a classifier but also as a framework for uncertainty-aware annotation.

The classification tasks are explicitly split into **binary classification**, defined as PVP versus non-PVP, and **multiclass classification**, defined over 8 PVP subclasses. The benchmark derives from **PhaVIP**, using a dataset curated from the RefSeq viral protein database with annotations up to December 2022. The original curation process included removing low-confidence labels, extracting structural proteins via diverse keyword search, collecting non-structural proteins by identifying enzymes with names ending in “ase,” and applying CD-HIT clustering at **90% sequence similarity**, keeping the longest sequence per cluster. The final binary dataset contains **35,213 PVP sequences** and **46,883 non-PVP sequences**. The PVP subset is partitioned into 8 classes: **Baseplate 3362**, **Portal 2770**, **Tail Fiber 2305**, **Major Capsid 2443**, **Minor Capsid 398**, **Major Tail 5083**, **Minor Tail 1458**, and **Others 17,385** [2508.07345].

## 2. Knight Encoding

The defining component of ProteoKnight is **Knight Encoding**, an adaptation of the classical **DNA-Walk** concept to protein sequences. Instead of compressing sequence information into k-mer frequencies, the method maps a protein into a colored 2D trajectory intended to preserve positional evolution through sequence space. The amino-acid alphabet is fixed as:

```text
['A','C','D','E','F','G','H','I','K','L','M','N','P','Q','R','S','T','V','W','Y']
```

These 20 residues are placed on the vertices of an **icosagon**, so adjacent residues differ by a constant angular increment of

$$
18^\circ = \frac{360^\circ}{20}.
$$

If a residue has index \(i\) in the amino-acid list, its direction is

$$
\theta = i \times 18^\circ.
$$

The paper gives examples such as C at index 1 receiving \(18^\circ\), and G at index 5 receiving \(90^\circ\) [2508.07345].

The image is drawn on an \(M \times M\) canvas initialized at the center,

$$
(x,y) = (M/2, M/2).
$$

For each amino acid, ProteoKnight uses a fixed radius \(r=15\) and point size 2, with directional displacement

$$
x = r \times \cos(\theta) \tag{1}
$$

$$
y = r \times \sin(\theta) \tag{2}
$$

and updates the current position by

$$
x = x + x' \tag{3}
$$

$$
y = y - y' \tag{4}
$$

where \((x', y')\) is the residue-specific displacement. A circular point of size 2 is then drawn at the updated coordinates. If the walk reaches an image boundary, the algorithm resets to the center \((M/2, M/2)\) and continues encoding from there [2508.07345].

Each residue is also assigned a fixed RGB color. The paper lists a full mapping, including examples such as **A: (255, 0, 0)**, **C: (255, 255, 0)**, **D: (0, 234, 255)**, **E: (170, 0, 255)**, **W: (204, 204, 204)**, and **Y: (0, 64, 255)**. The resulting image is therefore a colored ordered walk through sequence space rather than a frequency heatmap. The paper’s principal representational claim is that this mitigates the “spatial information loss” associated with FCGR. It also identifies a new limitation: if multiple residues map to exactly the same coordinates during the walk, points can overlap, which can obscure information, especially in longer or more complex sequences and in multiclass classification [2508.07345].

From an implementation standpoint, the preprocessing is intentionally light. The original encoded image size is **512 × 512**, and images are resized to **224 × 224** before CNN input. No explicit normalization, standardization, or data augmentation procedure is described. The method also does not require sequence truncation or padding, since variable-length sequences are naturally represented as longer or shorter trajectories; the sequence-length issue instead reappears through overlap and boundary resets [2508.07345].

## 3. Classification architecture and optimization

After encoding, ProteoKnight applies transfer learning with pre-trained convolutional neural networks. The paper states that several pre-trained architectures from the PyTorch library were evaluated, highlighting the top four models and specifically naming the **GoogLeNet**, **EfficientNet**, and **MobileNet** families. Exact identities of all top four are not fully specified in the text, but the main comparison emphasizes **GoogLeNet** and **EfficientNet V2** [2508.07345].

The training configuration is concise. The reported settings are: **25 epochs**, **batch size 32**, **learning rate 0.001**, **original image size 512 × 512**, and **CNN input size 224 × 224**. The paper states that `binary_crossentropy` was used as the optimization loss function, although it does not explicitly specify the multiclass loss. Evaluation uses **accuracy** as the primary metric, together with **precision**, **recall/sensitivity**, **specificity**, and **F1-score**, with the standard definitions

$$
Accuracy = \frac{TP + TN}{TP + TN + FP + FN}
$$

$$
Precision = \frac{TP}{TP + FP}
$$

$$
Recall/Sensitivity = \frac{TP}{TP + FN}
$$

$$
Specificity = \frac{TN}{TN + FP}
$$

$$
F_1 = \frac{2 \times Precision \times Recall}{Precision + Recall}.
$$

The paper presents **EfficientNet V2** as the best raw classifier, but selects **GoogLeNet** as the most practical model because it achieved nearly comparable performance with far fewer parameters. GoogLeNet is reported to have **5.5M parameters**, described as roughly one-fourth of EfficientNet V2. This efficiency-performance tradeoff becomes important because the paper couples GoogLeNet with the uncertainty-analysis stage [2508.07345].

## 4. Empirical performance

The empirical results distinguish between framework-level best performance and the specific GoogLeNet configuration chosen for practical deployment. The paper reports binary classification accuracy of **90.8%** in the abstract and says **EfficientNet V2** reached around **91.2%**, while **GoogLeNet** achieved **88.60% accuracy**, **89.87% F1**, and **88.54% recall** on the binary task. For multiclass prediction, GoogLeNet achieved **76.37% accuracy**, **76.37% F1**, and **76.37% recall**. The authors describe multiclass performance as “moderate,” with accuracy in the **72–78%** range across models [2508.07345].

| Setting | Reported result | Note |
|---|---:|---|
| Binary, framework-level | 90.8% accuracy | Abstract headline |
| Binary, EfficientNet V2 | around 91.2% | Best raw classifier |
| Binary, GoogLeNet | 88.60% accuracy | Chosen for efficiency |
| Multiclass, GoogLeNet | 76.37% accuracy | 8 PVP subclasses |

In class-wise binary evaluation, ProteoKnight achieved, for **PVP**, **precision 0.83**, **recall 0.91**, and **F1 0.87**; for **non-PVP**, **precision 0.94**, **recall 0.89**, and **F1 0.93**. Compared with prior methods, the paper reports **PhANN** with PVP F1 0.84 and non-PVP F1 0.83; **DeePVP** with PVP precision 0.99 but recall 0.51 and PVP F1 0.68, non-PVP F1 0.80; **VirionFinder** with PVP F1 0.84 and non-PVP F1 0.81; **Meta-iPVP** with PVP F1 0.80 and non-PVP F1 0.77; **PVPred-SCM** with PVP F1 0.66 and non-PVP F1 0.74; and **PhaVIP** with PVP F1 0.94 and non-PVP F1 0.94. ProteoKnight therefore outperformed several older feature-engineered and 1D approaches, but did **not surpass PhaVIP** on binary classification [2508.07345].

The paper interprets these results cautiously. Its claim is not that ProteoKnight establishes dominant binary performance across all baselines, but that it offers a conceptually different encoding that preserves sequence progression better than FCGR while remaining competitive in binary PVP prediction. The weaker multiclass results are attributed partly to the overlap behavior of the walk representation, which can obscure class-discriminative information when many residues land on the same coordinates [2508.07345].

## 5. Uncertainty analysis with Monte Carlo Dropout

A major secondary contribution of ProteoKnight is uncertainty analysis using **Monte Carlo Dropout (MCD)**, presented in the paper as the first such analysis for PVP/protein sequence classification in this setting. During MCD, dropout remains active at test time, with stochastic retention or removal of weights modeled as

$$
Z_{w,t} \sim Bernoulli(p) \quad \forall w \in \mathbf{W} \tag{5}
$$

and predictive mean and variance across \(T\) stochastic forward passes given by

$$
\mathbb{E}(\hat{\mathbf{Y}\mid\mathbf{X}) = \frac{1}{T}\sum_{t=1}^{T} f(\mathbf{X}\mid \mathbf{W}_t) \tag{7}
$$

$$
Var(\hat{\mathbf{Y}\mid \mathbf{X}) = \frac{1}{T}\sum_{t=1}^{T} f(\mathbf{X})^2 - \mathbb{E}(\hat{\mathbf{Y}\mid\mathbf{X})^2. \tag{8}
$$

For the uncertainty experiments, the authors selected **100 randomly chosen sequences** from each of four categories: **PVP short**, **PVP long**, **non-PVP short**, and **non-PVP long**. Each sequence was predicted **100 times**, so the uncertainty study used \(T=100\) stochastic forward passes per sample. The class-specific length thresholds were defined as **\(\delta = 350\)** for PVP and **\(\delta = 275\)** for non-PVP, yielding the following training subsets: **PVP \(\le 350\): 12,589**, **PVP \(>350\): 12,060**, **non-PVP \(\le 275\): 16,502**, and **non-PVP \(>275\): 16,316** [2508.07345].

Using GoogLeNet, the paper concludes that predictions were **more confident for non-PVP than PVP** and **more confident for short than long sequences**. At dropout 0.2, the reported variances were:

- **PVP short**: **0.08558**
- **non-PVP short**: **0.07555**
- **PVP long**: **0.09119**
- **non-PVP long**: **0.07663**

The same trend generally holds at dropout 0.1 and 0.3. Across those settings, the highest uncertainty appears for **long PVPs**, and the lowest for **short non-PVPs**. The paper offers two explanations: data imbalance, since there are more non-PVP training sequences than PVPs, and intrinsic sequence complexity, since PVPs may be harder because of lower conservation and more variable structure-related composition. It also links uncertainty in long sequences to the encoding itself, because longer walks create more opportunities for point overlap and boundary resets [2508.07345].

The paper additionally computes predictive entropy for selected high-variance and low-variance examples using

$$
Entropy = \left(P \times \log_2(\frac{1}{P})\right) + \left((1-P)\times \log_2 \frac{1}{(1-P)}\right) \tag{9}
$$

where \(P\) is the predicted probability for the class of interest. The reported entropy values are numerically irregular: **PVP short** high-var **0.1122**, low-var **0.1259**; **non-PVP short** high-var **0.1369**, low-var **0.1398**; **PVP long** high-var **0.2111**, low-var **0.3943**; **non-PVP long** high-var **0.06539**, low-var **0.1716**. The paper nevertheless draws the broader qualitative conclusion that long PVPs are more uncertainty-prone and should be treated with more caution before annotation. A plausible implication is that ProteoKnight is intended not merely to label proteins, but also to identify cases where automatic annotation should be deprioritized or manually reviewed [2508.07345].

## 6. Limitations, interpretation, and relation to adjacent protein-AI systems

The paper identifies several limitations. First, **multiclass classification accuracy remains suboptimal** relative to binary classification. Second, the walk representation can suffer from **point overlap**, especially in longer sequences. Third, boundary resets to the image center may distort trajectory continuity. Fourth, there is no detailed ablation isolating the impact of colors, radius, image size, or reset behavior, nor a controlled comparison to FCGR under the same backbone beyond broad qualitative claims. Fifth, reproducibility is partial: hardware and software are reported as **Python 3.11**, **TensorFlow**, **Keras**, **PyTorch**, running on an **AMD Ryzen 7 3700X 8-core CPU**, **16 GB DDR4 RAM**, **NVIDIA GeForce RTX 3060 Ti**, and **Windows 11 Pro**, but the exact train/test split protocol and code availability are not given in the provided text [2508.07345].

Within the broader protein-AI landscape, ProteoKnight is substantially narrower than systems for protein question answering, ontology-scale annotation, or retrieval-centric protein reasoning. **Prot2Chat** is a multimodal generative protein question-answering system that integrates sequence, structure, and question text through early fusion and question-conditioned virtual protein tokens, aiming at free-text answers rather than fixed-label classification [2502.06846]. **ProtGO** is a sequence-only transformer-based fusion model for multi-label Gene Ontology prediction over the top 100 terms in each of the MF, BP, and CC branches, using ProtBert-style transfer rather than image encoding [2412.05776]. **DistNBLP** is a distributed graph-based label propagation method for large-scale protein annotation in similarity graphs, emphasizing scalability and reviewed-label transfer rather than learned sequence representations [1708.07074]. These comparisons suggest that ProteoKnight belongs to a distinct methodological line: it is an alignment-free, sequence-to-image PVP classifier with explicit uncertainty analysis, not a general protein assistant, ontology predictor, or graph propagation engine.

That specialization clarifies both its value and its limits. ProteoKnight’s core contribution is the proposal that PVP classification can be approached through a colored spatial walk that preserves sequence-order information more directly than FCGR and remains compatible with lightweight transfer-learned CNNs. Its uncertainty analysis extends that proposal into a confidence-aware annotation setting. At the same time, the framework does not attempt multimodal protein reasoning, structure-grounded generation, ontology-aware function prediction, or open-ended retrieval and explanation. In that sense, it is best understood as a focused classifier for phage virion protein annotation whose novelty lies primarily in **Knight Encoding** and in the integration of **Monte Carlo Dropout** into the PVP classification workflow [2508.07345].

Source: https://www.emergentmind.com/topics/proteoknight