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

• URL: http://arxiv.org/abs/2508.07345v1
• Date: Sun, 10 Aug 2025 13:45:08 GMT
• Title: ProteoKnight: Convolution-based phage virion protein classification and uncertainty analysis
• Authors: Samiha Afaf Neha, Abir Ahammed Bhuiyan, Md. Ishrak Khan,
• Abstract summary: This paper introduces ProteoKnight, a new image-based encoding method that addresses spatial constraints in existing techniques.<n>Our study evaluates prediction uncertainty in binary PVP classification through Monte Carlo Dropout.<n>Our experiments achieved 90.8% accuracy in binary classification, comparable to state-of-the-art methods.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: \textbf{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). \textbf{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. \textbf{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. \textbf{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.

Related papers

• S$^2$Drug: Bridging Protein Sequence and 3D Structure in Contrastive Representation Learning for Virtual Screening [72.89086338778098]
  We propose a two-stage framework for protein-ligand contrastive representation learning.<n>In the first stage, we perform protein sequence pretraining on ChemBL using an ESM2-based backbone.<n>In the second stage, we fine-tune on PDBBind by fusing sequence and structure information through a residue-level gating module.<n>This auxiliary task guides the model to accurately localize binding residues within the protein sequence and capture their 3D spatial arrangement.
  arXiv  Detail & Related papers  (2025-11-10T11:57:47Z)
• Position Specific Scoring Is All You Need? Revisiting Protein Sequence Classification Tasks [41.7345616221048]
  We propose a weighted PSS kernel matrix (or W-PSSKM) that combines a PSS representation of protein sequences with the notion of the string kernel. This results in a novel kernel function that outperforms many other approaches for protein sequence classification.
  arXiv  Detail & Related papers  (2024-10-16T15:16:50Z)
• NaNa and MiGu: Semantic Data Augmentation Techniques to Enhance Protein Classification in Graph Neural Networks [60.48306899271866]
  We propose novel semantic data augmentation methods to incorporate backbone chemical and side-chain biophysical information into protein classification tasks. Specifically, we leverage molecular biophysical, secondary structure, chemical bonds, andionic features of proteins to facilitate classification tasks.
  arXiv  Detail & Related papers  (2024-03-21T13:27:57Z)
• DeepGATGO: A Hierarchical Pretraining-Based Graph-Attention Model for Automatic Protein Function Prediction [4.608328575930055]
  Automatic protein function prediction (AFP) is classified as a large-scale multi-label classification problem. Currently, popular methods primarily combine protein-related information and Gene Ontology (GO) terms to generate final functional predictions. We propose a sequence-based hierarchical prediction method, DeepGATGO, which processes protein sequences and GO term labels hierarchically.
  arXiv  Detail & Related papers  (2023-07-24T07:01:32Z)
• Efficient Prediction of Peptide Self-assembly through Sequential and Graphical Encoding [57.89530563948755]
  This work provides a benchmark analysis of peptide encoding with advanced deep learning models. It serves as a guide for a wide range of peptide-related predictions such as isoelectric points, hydration free energy, etc.
  arXiv  Detail & Related papers  (2023-07-17T00:43:33Z)
• PCD2Vec: A Poisson Correction Distance-Based Approach for Viral Host Classification [0.966840768820136]
  Coronaviruses are membrane-enveloped, non-segmented positive-strand RNA viruses belonging to the Coronaviridae family. In the Coronavirus genome, an essential structural region is the spike region, and it's responsible for attaching the virus to the host cell membrane. We propose a novel method for predicting the host specificity of coronaviruses by analyzing spike protein sequences from different viral subgenera and species.
  arXiv  Detail & Related papers  (2023-04-13T03:02:22Z)
• Reprogramming Pretrained Language Models for Protein Sequence Representation Learning [68.75392232599654]
  We propose Representation Learning via Dictionary Learning (R2DL), an end-to-end representation learning framework. R2DL reprograms a pretrained English language model to learn the embeddings of protein sequences. Our model can attain better accuracy and significantly improve the data efficiency by up to $105$ times over the baselines set by pretrained and standard supervised methods.
  arXiv  Detail & Related papers  (2023-01-05T15:55:18Z)
• Protein Representation Learning by Geometric Structure Pretraining [27.723095456631906]
  Existing approaches usually pretrain protein language models on a large number of unlabeled amino acid sequences. We first present a simple yet effective encoder to learn protein geometry features. Experimental results on both function prediction and fold classification tasks show that our proposed pretraining methods outperform or are on par with the state-of-the-art sequence-based methods using much less data.
  arXiv  Detail & Related papers  (2022-03-11T17:52:13Z)
• EBM-Fold: Fully-Differentiable Protein Folding Powered by Energy-based Models [53.17320541056843]
  We propose a fully-differentiable approach for protein structure optimization, guided by a data-driven generative network. Our EBM-Fold approach can efficiently produce high-quality decoys, compared against traditional Rosetta-based structure optimization routines.
  arXiv  Detail & Related papers  (2021-05-11T03:40:29Z)
• Bayesian neural network with pretrained protein embedding enhances prediction accuracy of drug-protein interaction [3.499870393443268]
  Deep learning approaches can predict drug-protein interactions without trial-and-error by humans. We propose two methods to construct a deep learning framework that exhibits superior performance with a small labeled dataset.
  arXiv  Detail & Related papers  (2020-12-15T10:24:34Z)
• Transfer Learning for Protein Structure Classification at Low Resolution [124.5573289131546]
  We show that it is possible to make accurate ($geq$80%) predictions of protein class and architecture from structures determined at low ($leq$3A) resolution. We provide proof of concept for high-speed, low-cost protein structure classification at low resolution, and a basis for extension to prediction of function.
  arXiv  Detail & Related papers  (2020-08-11T15:01:32Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.