Position Specific Scoring Is All You Need? Revisiting Protein Sequence Classification Tasks

• URL: http://arxiv.org/abs/2410.12655v1
• Date: Wed, 16 Oct 2024 15:16:50 GMT
• Title: Position Specific Scoring Is All You Need? Revisiting Protein Sequence Classification Tasks
• Authors: Sarwan Ali, Taslim Murad, Prakash Chourasia, Haris Mansoor, Imdad Ullah Khan, Pin-Yu Chen, Murray Patterson,
• Abstract summary: 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.
• Score: 41.7345616221048
• License:
• Abstract: Understanding the structural and functional characteristics of proteins are crucial for developing preventative and curative strategies that impact fields from drug discovery to policy development. An important and popular technique for examining how amino acids make up these characteristics of the protein sequences with position-specific scoring (PSS). While the string kernel is crucial in natural language processing (NLP), it is unclear if string kernels can extract biologically meaningful information from protein sequences, despite the fact that they have been shown to be effective in the general sequence analysis tasks. In this work, we propose a weighted PSS kernel matrix (or W-PSSKM), that combines a PSS representation of protein sequences, which encodes the frequency information of each amino acid in a sequence, with the notion of the string kernel. This results in a novel kernel function that outperforms many other approaches for protein sequence classification. We perform extensive experimentation to evaluate the proposed method. Our findings demonstrate that the W-PSSKM significantly outperforms existing baselines and state-of-the-art methods and achieves up to 45.1\% improvement in classification accuracy.

Related papers

• Reinforcement Learning for Sequence Design Leveraging Protein Language Models [14.477268882311991]
  We propose to use protein language models (PLMs) as a reward function to generate new sequences. We perform extensive experiments on various sequence lengths to benchmark RL-based approaches. We provide comprehensive evaluations along biological plausibility and diversity of the protein.
  arXiv  Detail & Related papers  (2024-07-03T14:31:36Z)
• Protein Representation Learning with Sequence Information Embedding: Does it Always Lead to a Better Performance? [4.7077642423577775]
  We propose ProtLOCA, a local geometry alignment method based solely on amino acid structure representation. Our method outperforms existing sequence- and structure-based representation learning methods by more quickly and accurately matching structurally consistent protein domains.
  arXiv  Detail & Related papers  (2024-06-28T08:54:37Z)
• NovoBench: Benchmarking Deep Learning-based De Novo Peptide Sequencing Methods in Proteomics [58.03989832372747]
  We present the first unified benchmark NovoBench for emphde novo peptide sequencing. It comprises diverse mass spectrum data, integrated models, and comprehensive evaluation metrics. Recent methods, including DeepNovo, PointNovo, Casanovo, InstaNovo, AdaNovo and $pi$-HelixNovo are integrated into our framework.
  arXiv  Detail & Related papers  (2024-06-16T08:23:21Z)
• Clustering for Protein Representation Learning [72.72957540484664]
  We propose a neural clustering framework that can automatically discover the critical components of a protein. Our framework treats a protein as a graph, where each node represents an amino acid and each edge represents a spatial or sequential connection between amino acids. We evaluate on four protein-related tasks: protein fold classification, enzyme reaction classification, gene term prediction, and enzyme commission number prediction.
  arXiv  Detail & Related papers  (2024-03-30T05:51:09Z)
• 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)
• Structure-informed Language Models Are Protein Designers [69.70134899296912]
  We present LM-Design, a generic approach to reprogramming sequence-based protein language models (pLMs) We conduct a structural surgery on pLMs, where a lightweight structural adapter is implanted into pLMs and endows it with structural awareness. Experiments show that our approach outperforms the state-of-the-art methods by a large margin.
  arXiv  Detail & Related papers  (2023-02-03T10:49:52Z)
• Deep Learning Methods for Protein Family Classification on PDB Sequencing Data [0.0]
  We demonstrate and compare the performance of several deep learning frameworks, including novel bi-directional LSTM and convolutional models, on widely available sequencing data. Our results show that our deep learning models deliver superior performance to classical machine learning methods, with the convolutional architecture providing the most impressive inference performance.
  arXiv  Detail & Related papers  (2022-07-14T06:11:32Z)
• Structure-aware Protein Self-supervised Learning [50.04673179816619]
  We propose a novel structure-aware protein self-supervised learning method to capture structural information of proteins. In particular, a well-designed graph neural network (GNN) model is pretrained to preserve the protein structural information. We identify the relation between the sequential information in the protein language model and the structural information in the specially designed GNN model via a novel pseudo bi-level optimization scheme.
  arXiv  Detail & Related papers  (2022-04-06T02:18:41Z)
• 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)
• Leveraging Sequence Embedding and Convolutional Neural Network for Protein Function Prediction [27.212743275697825]
  Main challenges of protein function prediction are the large label space and the lack of labeled training data. Our method leverages unsupervised sequence embedding and the success of deep convolutional neural network to overcome these challenges.
  arXiv  Detail & Related papers  (2021-12-01T08:31:01Z)

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.