PLA-SGCN: Protein-Ligand Binding Affinity Prediction by Integrating Similar Pairs and Semi-supervised Graph Convolutional Network

• URL: http://arxiv.org/abs/2405.07452v2
• Date: Sat, 18 May 2024 08:55:05 GMT
• Title: PLA-SGCN: Protein-Ligand Binding Affinity Prediction by Integrating Similar Pairs and Semi-supervised Graph Convolutional Network
• Authors: Karim Abbasi, Parvin Razzaghi, Amin Ghareyazi, Hamid R. Rabiee,
• Abstract summary: This paper aims to integrate retrieved hard protein-ligand pairs in PLA prediction (i.e., task prediction step) using a semi-supervised graph convolutional network (GCN) The results show that the proposed method significantly performs better than the comparable approaches.
• Score: 6.024776891570197
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: The protein-ligand binding affinity (PLA) prediction goal is to predict whether or not the ligand could bind to a protein sequence. Recently, in PLA prediction, deep learning has received much attention. Two steps are involved in deep learning-based approaches: feature extraction and task prediction step. Many deep learning-based approaches concentrate on introducing new feature extraction networks or integrating auxiliary knowledge like protein-protein interaction networks or gene ontology knowledge. Then, a task prediction network is designed simply using some fully connected layers. This paper aims to integrate retrieved similar hard protein-ligand pairs in PLA prediction (i.e., task prediction step) using a semi-supervised graph convolutional network (GCN). Hard protein-ligand pairs are retrieved for each input query sample based on the manifold smoothness constraint. Then, a graph is learned automatically in which each node is a protein-ligand pair, and each edge represents the similarity between pairs. In other words, an end-to-end framework is proposed that simultaneously retrieves hard similar samples, learns protein-ligand descriptor, learns the graph topology of the input sample with retrieved similar hard samples (learn adjacency matrix), and learns a semi-supervised GCN to predict the binding affinity (as task predictor). The training step adjusts the parameter values, and in the inference step, the learned model is fine-tuned for each input sample. To evaluate the proposed approach, it is applied to the four well-known PDBbind, Davis, KIBA, and BindingDB datasets. The results show that the proposed method significantly performs better than the comparable approaches.

Related papers

• FABind: Fast and Accurate Protein-Ligand Binding [127.7790493202716]
  $mathbfFABind$ is an end-to-end model that combines pocket prediction and docking to achieve accurate and fast protein-ligand binding. Our proposed model demonstrates strong advantages in terms of effectiveness and efficiency compared to existing methods.
  arXiv  Detail & Related papers  (2023-10-10T16:39:47Z)
• On the Equivalence of Graph Convolution and Mixup [70.0121263465133]
  This paper investigates the relationship between graph convolution and Mixup techniques. Under two mild conditions, graph convolution can be viewed as a specialized form of Mixup. We establish this equivalence mathematically by demonstrating that graph convolution networks (GCN) and simplified graph convolution (SGC) can be expressed as a form of Mixup.
  arXiv  Detail & Related papers  (2023-09-29T23:09:54Z)
• Geometric Graph Learning with Extended Atom-Types Features for Protein-Ligand Binding Affinity Prediction [0.17132914341329847]
  We upgrade the graph-based learners for the study of protein-ligand interactions by integrating extensive atom types such as SYBYL. Our approach results in two different methods, namely $textsybyltextGGL$-Score and $texteciftextGGL$-Score. While both of our models achieve state-of-the-art results, the SYBYL atom-type model $textsybyltextGGL$-Score outperforms other methods by a wide margin in all benchmarks.
  arXiv  Detail & Related papers  (2023-01-15T21:30:21Z)
• HAC-Net: A Hybrid Attention-Based Convolutional Neural Network for Highly Accurate Protein-Ligand Binding Affinity Prediction [0.0]
  We present a novel deep learning architecture consisting of a 3-dimensional convolutional neural network and two graph convolutional networks. HAC-Net obtains state-of-the-art results on the PDBbind v.2016 core set. We envision that this model can be extended to a broad range of supervised learning problems related to structure-based biomolecular property prediction.
  arXiv  Detail & Related papers  (2022-12-23T16:14:53Z)
• Line Graph Contrastive Learning for Link Prediction [4.876567687745239]
  We propose a Line Graph Contrastive Learning (LGCL) method to obtain multiview information. With experiments on six public datasets, LGCL outperforms current benchmarks on link prediction tasks.
  arXiv  Detail & Related papers  (2022-10-25T06:57:00Z)
• Transformers Can Do Bayesian Inference [56.99390658880008]
  We present Prior-Data Fitted Networks (PFNs) PFNs leverage in-context learning in large-scale machine learning techniques to approximate a large set of posteriors. We demonstrate that PFNs can near-perfectly mimic Gaussian processes and also enable efficient Bayesian inference for intractable problems.
  arXiv  Detail & Related papers  (2021-12-20T13:07:39Z)
• Pre-training Co-evolutionary Protein Representation via A Pairwise Masked Language Model [93.9943278892735]
  Key problem in protein sequence representation learning is to capture the co-evolutionary information reflected by the inter-residue co-variation in the sequences. We propose a novel method to capture this information directly by pre-training via a dedicated language model, i.e., Pairwise Masked Language Model (PMLM) Our result shows that the proposed method can effectively capture the interresidue correlations and improves the performance of contact prediction by up to 9% compared to the baseline.
  arXiv  Detail & Related papers  (2021-10-29T04:01:32Z)
• Structure-aware Interactive Graph Neural Networks for the Prediction of Protein-Ligand Binding Affinity [52.67037774136973]
  Drug discovery often relies on the successful prediction of protein-ligand binding affinity. Recent advances have shown great promise in applying graph neural networks (GNNs) for better affinity prediction by learning the representations of protein-ligand complexes. We propose a structure-aware interactive graph neural network (SIGN) which consists of two components: polar-inspired graph attention layers (PGAL) and pairwise interactive pooling (PiPool)
  arXiv  Detail & Related papers  (2021-07-21T03:34:09Z)
• Pre-training Protein Language Models with Label-Agnostic Binding Pairs Enhances Performance in Downstream Tasks [1.452875650827562]
  Less than 1% of protein sequences are structurally and functionally annotated. We present a modification to the RoBERTa model by inputting a mixture of binding and non-binding protein sequences. We suggest that Transformer's attention mechanism contributes to protein binding site discovery.
  arXiv  Detail & Related papers  (2020-12-05T17:37:41Z)
• Deep Learning of High-Order Interactions for Protein Interface Prediction [58.164371994210406]
  We propose to formulate the protein interface prediction as a 2D dense prediction problem. We represent proteins as graphs and employ graph neural networks to learn node features. We incorporate high-order pairwise interactions to generate a 3D tensor containing different pairwise interactions.
  arXiv  Detail & Related papers  (2020-07-18T05:39:35Z)

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.