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Yang Q, Jin X, Zhou H, Ying J, Zou J, Liao Y, Lu X, Ge S, Yu H, Min X. SurfPro-NN: A 3D point cloud neural network for the scoring of protein-protein docking models based on surfaces features and protein language models. Comput Biol Chem 2024; 110:108067. [PMID: 38714420 DOI: 10.1016/j.compbiolchem.2024.108067] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/18/2024] [Revised: 03/18/2024] [Accepted: 04/01/2024] [Indexed: 05/09/2024]
Abstract
Protein-protein interactions (PPI) play a crucial role in numerous key biological processes, and the structure of protein complexes provides valuable clues for in-depth exploration of molecular-level biological processes. Protein-protein docking technology is widely used to simulate the spatial structure of proteins. However, there are still challenges in selecting candidate decoys that closely resemble the native structure from protein-protein docking simulations. In this study, we introduce a docking evaluation method based on three-dimensional point cloud neural networks named SurfPro-NN, which represents protein structures as point clouds and learns interaction information from protein interfaces by applying a point cloud neural network. With the continuous advancement of deep learning in the field of biology, a series of knowledge-rich pre-trained models have emerged. We incorporate protein surface representation models and language models into our approach, greatly enhancing feature representation capabilities and achieving superior performance in protein docking model scoring tasks. Through comprehensive testing on public datasets, we find that our method outperforms state-of-the-art deep learning approaches in protein-protein docking model scoring. Not only does it significantly improve performance, but it also greatly accelerates training speed. This study demonstrates the potential of our approach in addressing protein interaction assessment problems, providing strong support for future research and applications in the field of biology.
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Affiliation(s)
- Qianli Yang
- Institute of Artifical Intelligence, XiaMen University, No. 422, Siming South Road, XiaMen, 361005, Fujian, China.
| | - Xiaocheng Jin
- National Institute of Diagnostics and Vaccine Development in Infectious Diseases, XiaMen University, No. 422, Siming South Road, XiaMen, 361005, Fujian, China; State Key Laboratory of Molecular Vaccinology and Molecular Diagnostics, XiaMen University, No. 422, Siming South Road, XiaMen, 361005, Fujian, China; School of Public Health, XiaMen University, No. 422, Siming South Road, XiaMen, 361005, Fujian, China
| | - Haixia Zhou
- School of Public Health, XiaMen University, No. 422, Siming South Road, XiaMen, 361005, Fujian, China
| | - Junjie Ying
- Institute of Artifical Intelligence, XiaMen University, No. 422, Siming South Road, XiaMen, 361005, Fujian, China
| | - JiaJun Zou
- School of Informatics, XiaMen University, No. 422, Siming South Road, XiaMen, 361005, Fujian, China
| | - Yiyang Liao
- School of Informatics, XiaMen University, No. 422, Siming South Road, XiaMen, 361005, Fujian, China
| | - Xiaoli Lu
- Information and Networking Center, XiaMen University, No. 422, Siming South Road, XiaMen, 361005, Fujian, China
| | - Shengxiang Ge
- National Institute of Diagnostics and Vaccine Development in Infectious Diseases, XiaMen University, No. 422, Siming South Road, XiaMen, 361005, Fujian, China; State Key Laboratory of Molecular Vaccinology and Molecular Diagnostics, XiaMen University, No. 422, Siming South Road, XiaMen, 361005, Fujian, China; School of Public Health, XiaMen University, No. 422, Siming South Road, XiaMen, 361005, Fujian, China
| | - Hai Yu
- National Institute of Diagnostics and Vaccine Development in Infectious Diseases, XiaMen University, No. 422, Siming South Road, XiaMen, 361005, Fujian, China; State Key Laboratory of Molecular Vaccinology and Molecular Diagnostics, XiaMen University, No. 422, Siming South Road, XiaMen, 361005, Fujian, China; School of Public Health, XiaMen University, No. 422, Siming South Road, XiaMen, 361005, Fujian, China.
| | - Xiaoping Min
- School of Informatics, XiaMen University, No. 422, Siming South Road, XiaMen, 361005, Fujian, China; National Institute of Diagnostics and Vaccine Development in Infectious Diseases, XiaMen University, No. 422, Siming South Road, XiaMen, 361005, Fujian, China; State Key Laboratory of Molecular Vaccinology and Molecular Diagnostics, XiaMen University, No. 422, Siming South Road, XiaMen, 361005, Fujian, China.
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2
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Zhao H, Petrey D, Murray D, Honig B. ZEPPI: Proteome-scale sequence-based evaluation of protein-protein interaction models. Proc Natl Acad Sci U S A 2024; 121:e2400260121. [PMID: 38743624 PMCID: PMC11127014 DOI: 10.1073/pnas.2400260121] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/08/2024] [Accepted: 04/18/2024] [Indexed: 05/16/2024] Open
Abstract
We introduce ZEPPI (Z-score Evaluation of Protein-Protein Interfaces), a framework to evaluate structural models of a complex based on sequence coevolution and conservation involving residues in protein-protein interfaces. The ZEPPI score is calculated by comparing metrics for an interface to those obtained from randomly chosen residues. Since contacting residues are defined by the structural model, this obviates the need to account for indirect interactions. Further, although ZEPPI relies on species-paired multiple sequence alignments, its focus on interfacial residues allows it to leverage quite shallow alignments. ZEPPI can be implemented on a proteome-wide scale and is applied here to millions of structural models of dimeric complexes in the Escherichia coli and human interactomes found in the PrePPI database. PrePPI's scoring function is based primarily on the evaluation of protein-protein interfaces, and ZEPPI adds a new feature to this analysis through the incorporation of evolutionary information. ZEPPI performance is evaluated through applications to experimentally determined complexes and to decoys from the CASP-CAPRI experiment. As we discuss, the standard CAPRI scores used to evaluate docking models are based on model quality and not on the ability to give yes/no answers as to whether two proteins interact. ZEPPI is able to detect weak signals from PPI models that the CAPRI scores define as incorrect and, similarly, to identify potential PPIs defined as low confidence by the current PrePPI scoring function. A number of examples that illustrate how the combination of PrePPI and ZEPPI can yield functional hypotheses are provided.
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Affiliation(s)
- Haiqing Zhao
- Department of Systems Biology, Columbia University Irving Medical Center, New York, NY10032
| | - Donald Petrey
- Department of Systems Biology, Columbia University Irving Medical Center, New York, NY10032
| | - Diana Murray
- Department of Systems Biology, Columbia University Irving Medical Center, New York, NY10032
| | - Barry Honig
- Department of Systems Biology, Columbia University Irving Medical Center, New York, NY10032
- Department of Biochemistry and Molecular Biophysics, Columbia University Irving Medical Center, New York, NY10032
- Department of Medicine, Columbia University, New York, NY10032
- Zuckerman Institute, Columbia University, New York, NY10027
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3
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Chen X, Liu J, Park N, Cheng J. A Survey of Deep Learning Methods for Estimating the Accuracy of Protein Quaternary Structure Models. Biomolecules 2024; 14:574. [PMID: 38785981 PMCID: PMC11117562 DOI: 10.3390/biom14050574] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2024] [Revised: 04/07/2024] [Accepted: 05/09/2024] [Indexed: 05/25/2024] Open
Abstract
The quality prediction of quaternary structure models of a protein complex, in the absence of its true structure, is known as the Estimation of Model Accuracy (EMA). EMA is useful for ranking predicted protein complex structures and using them appropriately in biomedical research, such as protein-protein interaction studies, protein design, and drug discovery. With the advent of more accurate protein complex (multimer) prediction tools, such as AlphaFold2-Multimer and ESMFold, the estimation of the accuracy of protein complex structures has attracted increasing attention. Many deep learning methods have been developed to tackle this problem; however, there is a noticeable absence of a comprehensive overview of these methods to facilitate future development. Addressing this gap, we present a review of deep learning EMA methods for protein complex structures developed in the past several years, analyzing their methodologies, data and feature construction. We also provide a prospective summary of some potential new developments for further improving the accuracy of the EMA methods.
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Affiliation(s)
- Xiao Chen
- Department of Electrical Engineering and Computer Science, University of Missouri, Columbia, MO 65211, USA
| | - Jian Liu
- Department of Electrical Engineering and Computer Science, University of Missouri, Columbia, MO 65211, USA
- NextGen Precision Health Institute, University of Missouri, Columbia, MO 65211, USA
| | - Nolan Park
- Department of Electrical Engineering and Computer Science, University of Missouri, Columbia, MO 65211, USA
| | - Jianlin Cheng
- Department of Electrical Engineering and Computer Science, University of Missouri, Columbia, MO 65211, USA
- NextGen Precision Health Institute, University of Missouri, Columbia, MO 65211, USA
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4
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Frasnetti E, Magni A, Castelli M, Serapian SA, Moroni E, Colombo G. Structures, dynamics, complexes, and functions: From classic computation to artificial intelligence. Curr Opin Struct Biol 2024; 87:102835. [PMID: 38744148 DOI: 10.1016/j.sbi.2024.102835] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/23/2024] [Revised: 04/14/2024] [Accepted: 04/22/2024] [Indexed: 05/16/2024]
Abstract
Computational approaches can provide highly detailed insight into the molecular recognition processes that underlie drug binding, the assembly of protein complexes, and the regulation of biological functional processes. Classical simulation methods can bridge a wide range of length- and time-scales typically involved in such processes. Lately, automated learning and artificial intelligence methods have shown the potential to expand the reach of physics-based approaches, ushering in the possibility to model and even design complex protein architectures. The synergy between atomistic simulations and AI methods is an emerging frontier with a huge potential for advances in structural biology. Herein, we explore various examples and frameworks for these approaches, providing select instances and applications that illustrate their impact on fundamental biomolecular problems.
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Affiliation(s)
- Elena Frasnetti
- Department of Chemistry, University of Pavia, via Taramelli 12, 27100 Pavia, Italy
| | - Andrea Magni
- Department of Chemistry, University of Pavia, via Taramelli 12, 27100 Pavia, Italy
| | - Matteo Castelli
- Department of Chemistry, University of Pavia, via Taramelli 12, 27100 Pavia, Italy
| | - Stefano A Serapian
- Department of Chemistry, University of Pavia, via Taramelli 12, 27100 Pavia, Italy
| | | | - Giorgio Colombo
- Department of Chemistry, University of Pavia, via Taramelli 12, 27100 Pavia, Italy.
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5
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Yao R, Shen Z, Xu X, Ling G, Xiang R, Song T, Zhai F, Zhai Y. Knowledge mapping of graph neural networks for drug discovery: a bibliometric and visualized analysis. Front Pharmacol 2024; 15:1393415. [PMID: 38799167 PMCID: PMC11116974 DOI: 10.3389/fphar.2024.1393415] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/29/2024] [Accepted: 04/12/2024] [Indexed: 05/29/2024] Open
Abstract
Introduction In recent years, graph neural network has been extensively applied to drug discovery research. Although researchers have made significant progress in this field, there is less research on bibliometrics. The purpose of this study is to conduct a comprehensive bibliometric analysis of graph neural network applications in drug discovery in order to identify current research hotspots and trends, as well as serve as a reference for future research. Methods Publications from 2017 to 2023 about the application of graph neural network in drug discovery were collected from the Web of Science Core Collection. Bibliometrix, VOSviewer, and Citespace were mainly used for bibliometric studies. Results and Discussion In this paper, a total of 652 papers from 48 countries/regions were included. Research interest in this field is continuously increasing. China and the United States have a significant advantage in terms of funding, the number of publications, and collaborations with other institutions and countries. Although some cooperation networks have been formed in this field, extensive worldwide cooperation still needs to be strengthened. The results of the keyword analysis clarified that graph neural network has primarily been applied to drug-target interaction, drug repurposing, and drug-drug interaction, while graph convolutional neural network and its related optimization methods are currently the core algorithms in this field. Data availability and ethical supervision, balancing computing resources, and developing novel graph neural network models with better interpretability are the key technical issues currently faced. This paper analyzes the current state, hot spots, and trends of graph neural network applications in drug discovery through bibliometric approaches, as well as the current issues and challenges in this field. These findings provide researchers with valuable insights on the current status and future directions of this field.
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Affiliation(s)
| | | | | | | | | | | | - Fei Zhai
- Faculty of Medical Device, Shenyang Pharmaceutical University, Shenyang, China
| | - Yuxuan Zhai
- Faculty of Medical Device, Shenyang Pharmaceutical University, Shenyang, China
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6
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Yin S, Mi X, Shukla D. Leveraging machine learning models for peptide-protein interaction prediction. RSC Chem Biol 2024; 5:401-417. [PMID: 38725911 PMCID: PMC11078210 DOI: 10.1039/d3cb00208j] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/27/2023] [Accepted: 02/07/2024] [Indexed: 05/12/2024] Open
Abstract
Peptides play a pivotal role in a wide range of biological activities through participating in up to 40% protein-protein interactions in cellular processes. They also demonstrate remarkable specificity and efficacy, making them promising candidates for drug development. However, predicting peptide-protein complexes by traditional computational approaches, such as docking and molecular dynamics simulations, still remains a challenge due to high computational cost, flexible nature of peptides, and limited structural information of peptide-protein complexes. In recent years, the surge of available biological data has given rise to the development of an increasing number of machine learning models for predicting peptide-protein interactions. These models offer efficient solutions to address the challenges associated with traditional computational approaches. Furthermore, they offer enhanced accuracy, robustness, and interpretability in their predictive outcomes. This review presents a comprehensive overview of machine learning and deep learning models that have emerged in recent years for the prediction of peptide-protein interactions.
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Affiliation(s)
- Song Yin
- Department of Chemical and Biomolecular Engineering, University of Illinois Urbana-Champaign Urbana 61801 Illinois USA
| | - Xuenan Mi
- Center for Biophysics and Quantitative Biology, University of Illinois Urbana-Champaign Urbana IL 61801 USA
| | - Diwakar Shukla
- Department of Chemical and Biomolecular Engineering, University of Illinois Urbana-Champaign Urbana 61801 Illinois USA
- Center for Biophysics and Quantitative Biology, University of Illinois Urbana-Champaign Urbana IL 61801 USA
- Department of Bioengineering, University of Illinois Urbana-Champaign Urbana IL 61801 USA
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7
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Ovek D, Keskin O, Gursoy A. ProInterVal: Validation of Protein-Protein Interfaces through Learned Interface Representations. J Chem Inf Model 2024; 64:2979-2987. [PMID: 38526504 DOI: 10.1021/acs.jcim.3c01788] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/26/2024]
Abstract
Proteins are vital components of the biological world and serve a multitude of functions. They interact with other molecules through their interfaces and participate in crucial cellular processes. Disruption of these interactions can have negative effects on organisms, highlighting the importance of studying protein-protein interfaces for developing targeted therapies for diseases. Therefore, the development of a reliable method for investigating protein-protein interactions is of paramount importance. In this work, we present an approach for validating protein-protein interfaces using learned interface representations. The approach involves using a graph-based contrastive autoencoder architecture and a transformer to learn representations of protein-protein interaction interfaces from unlabeled data and then validating them through learned representations with a graph neural network. Our method achieves an accuracy of 0.91 for the test set, outperforming existing GNN-based methods. We demonstrate the effectiveness of our approach on a benchmark data set and show that it provides a promising solution for validating protein-protein interfaces.
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Affiliation(s)
- Damla Ovek
- KUIS AI Center, Koç University, Istanbul 34450, Turkey
- Computer Engineering, Koç University, Istanbul 34450, Turkey
| | - Ozlem Keskin
- Chemical and Biological Engineering, Koç University, Istanbul 34450, Turkey
| | - Attila Gursoy
- Computer Engineering, Koç University, Istanbul 34450, Turkey
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8
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Grassmann G, Miotto M, Desantis F, Di Rienzo L, Tartaglia GG, Pastore A, Ruocco G, Monti M, Milanetti E. Computational Approaches to Predict Protein-Protein Interactions in Crowded Cellular Environments. Chem Rev 2024; 124:3932-3977. [PMID: 38535831 PMCID: PMC11009965 DOI: 10.1021/acs.chemrev.3c00550] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2023] [Revised: 02/20/2024] [Accepted: 02/21/2024] [Indexed: 04/11/2024]
Abstract
Investigating protein-protein interactions is crucial for understanding cellular biological processes because proteins often function within molecular complexes rather than in isolation. While experimental and computational methods have provided valuable insights into these interactions, they often overlook a critical factor: the crowded cellular environment. This environment significantly impacts protein behavior, including structural stability, diffusion, and ultimately the nature of binding. In this review, we discuss theoretical and computational approaches that allow the modeling of biological systems to guide and complement experiments and can thus significantly advance the investigation, and possibly the predictions, of protein-protein interactions in the crowded environment of cell cytoplasm. We explore topics such as statistical mechanics for lattice simulations, hydrodynamic interactions, diffusion processes in high-viscosity environments, and several methods based on molecular dynamics simulations. By synergistically leveraging methods from biophysics and computational biology, we review the state of the art of computational methods to study the impact of molecular crowding on protein-protein interactions and discuss its potential revolutionizing effects on the characterization of the human interactome.
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Affiliation(s)
- Greta Grassmann
- Department
of Biochemical Sciences “Alessandro Rossi Fanelli”, Sapienza University of Rome, Rome 00185, Italy
- Center
for Life Nano & Neuro Science, Istituto
Italiano di Tecnologia, Rome 00161, Italy
| | - Mattia Miotto
- Center
for Life Nano & Neuro Science, Istituto
Italiano di Tecnologia, Rome 00161, Italy
| | - Fausta Desantis
- Center
for Life Nano & Neuro Science, Istituto
Italiano di Tecnologia, Rome 00161, Italy
- The
Open University Affiliated Research Centre at Istituto Italiano di
Tecnologia, Genoa 16163, Italy
| | - Lorenzo Di Rienzo
- Center
for Life Nano & Neuro Science, Istituto
Italiano di Tecnologia, Rome 00161, Italy
| | - Gian Gaetano Tartaglia
- Center
for Life Nano & Neuro Science, Istituto
Italiano di Tecnologia, Rome 00161, Italy
- Department
of Neuroscience and Brain Technologies, Istituto Italiano di Tecnologia, Genoa 16163, Italy
- Center
for Human Technologies, Genoa 16152, Italy
| | - Annalisa Pastore
- Experiment
Division, European Synchrotron Radiation
Facility, Grenoble 38043, France
| | - Giancarlo Ruocco
- Center
for Life Nano & Neuro Science, Istituto
Italiano di Tecnologia, Rome 00161, Italy
- Department
of Physics, Sapienza University, Rome 00185, Italy
| | - Michele Monti
- RNA
System Biology Lab, Department of Neuroscience and Brain Technologies, Istituto Italiano di Tecnologia, Genoa 16163, Italy
| | - Edoardo Milanetti
- Center
for Life Nano & Neuro Science, Istituto
Italiano di Tecnologia, Rome 00161, Italy
- Department
of Physics, Sapienza University, Rome 00185, Italy
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9
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Wang Z, Brand R, Adolf-Bryfogle J, Grewal J, Qi Y, Combs SA, Golovach N, Alford R, Rangwala H, Clark PM. EGGNet, a Generalizable Geometric Deep Learning Framework for Protein Complex Pose Scoring. ACS OMEGA 2024; 9:7471-7479. [PMID: 38405499 PMCID: PMC10882658 DOI: 10.1021/acsomega.3c04889] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/21/2023] [Revised: 01/19/2024] [Accepted: 01/23/2024] [Indexed: 02/27/2024]
Abstract
Computational prediction of molecule-protein interactions has been key for developing new molecules to interact with a target protein for therapeutics development. Previous work includes two independent streams of approaches: (1) predicting protein-protein interactions (PPIs) between naturally occurring proteins and (2) predicting binding affinities between proteins and small-molecule ligands [also known as drug-target interaction (DTI)]. Studying the two problems in isolation has limited the ability of these computational models to generalize across the PPI and DTI tasks, both of which ultimately involve noncovalent interactions with a protein target. In this work, we developed Equivariant Graph of Graphs neural Network (EGGNet), a geometric deep learning (GDL) framework, for molecule-protein binding predictions that can handle three types of molecules for interacting with a target protein: (1) small molecules, (2) synthetic peptides, and (3) natural proteins. EGGNet leverages a graph of graphs (GoG) representation constructed from the molecular structures at atomic resolution and utilizes a multiresolution equivariant graph neural network to learn from such representations. In addition, EGGNet leverages the underlying biophysics and makes use of both atom- and residue-level interactions, which improve EGGNet's ability to rank candidate poses from blind docking. EGGNet achieves competitive performance on both a public protein-small-molecule binding affinity prediction task (80.2% top 1 success rate on CASF-2016) and a synthetic protein interface prediction task (88.4% area under the precision-recall curve). We envision that the proposed GDL framework can generalize to many other protein interaction prediction problems, such as binding site prediction and molecular docking, helping accelerate protein engineering and structure-based drug development.
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Affiliation(s)
- Zichen Wang
- Amazon
Web Services, Amazon, Seattle, Washington 98109-5210, United
States
| | - Ryan Brand
- Amazon
Web Services, Amazon, Seattle, Washington 98109-5210, United
States
| | - Jared Adolf-Bryfogle
- Janssen
Biotherapeutics, Janssen Pharmaceutical
Companies of Johnson & Johnson, Spring House, Titusville, New Jersey 08560-1504, United States
| | - Jasleen Grewal
- Amazon
Web Services, Amazon, Seattle, Washington 98109-5210, United
States
| | - Yanjun Qi
- Amazon
Web Services, Amazon, Seattle, Washington 98109-5210, United
States
| | - Steven A. Combs
- Janssen
Biotherapeutics, Janssen Pharmaceutical
Companies of Johnson & Johnson, Spring House, Titusville, New Jersey 08560-1504, United States
| | - Nataliya Golovach
- Janssen
Biotherapeutics, Janssen Pharmaceutical
Companies of Johnson & Johnson, Spring House, Titusville, New Jersey 08560-1504, United States
| | - Rebecca Alford
- Janssen
Biotherapeutics, Janssen Pharmaceutical
Companies of Johnson & Johnson, Spring House, Titusville, New Jersey 08560-1504, United States
| | - Huzefa Rangwala
- Amazon
Web Services, Amazon, Seattle, Washington 98109-5210, United
States
| | - Peter M. Clark
- Janssen
Biotherapeutics, Janssen Pharmaceutical
Companies of Johnson & Johnson, Spring House, Titusville, New Jersey 08560-1504, United States
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10
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Pitfield J, Taylor NT, Hepplestone SP. Predicting Phase Stability at Interfaces. PHYSICAL REVIEW LETTERS 2024; 132:066201. [PMID: 38394598 DOI: 10.1103/physrevlett.132.066201] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/01/2023] [Revised: 09/21/2023] [Accepted: 12/22/2023] [Indexed: 02/25/2024]
Abstract
We present the RAFFLE methodology for structural prediction of the interface between two materials and demonstrate its effectiveness by applying it to MgO encapsulated by two layers of graphene. To address the challenge of interface structure prediction, our methodology combines physical insights derived from morphological features observed in related systems with an iterative machine learning technique. This employs physical-based methods, including void-filling and n-body distribution functions to predict interface structures. For the carbon-MgO encapsulated system, we have shown the rocksalt and hexagonal phases of MgO to be the two most energetically stable in the few-layer regime. We demonstrate that monolayer rocksalt is heavily stabilized by interfacing with graphene, becoming more energetically favorable than the graphenelike monolayer hexagonal MgO. The RAFFLE methodology provides valuable insights into interface behavior, and a route to finding new materials at interfaces.
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Affiliation(s)
- J Pitfield
- University of Exeter, Stocker Road, Exeter EX4 4QL, United Kingdom
| | - N T Taylor
- University of Exeter, Stocker Road, Exeter EX4 4QL, United Kingdom
| | - S P Hepplestone
- University of Exeter, Stocker Road, Exeter EX4 4QL, United Kingdom
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11
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Xu M, Chen X, Ruan Y, Zhang X. Cross-User Electromyography Pattern Recognition Based on a Novel Spatial-Temporal Graph Convolutional Network. IEEE Trans Neural Syst Rehabil Eng 2024; 32:72-82. [PMID: 38090843 DOI: 10.1109/tnsre.2023.3342050] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/14/2024]
Abstract
With the goal of promoting the development of myoelectric control technology, this paper focuses on exploring graph neural network (GNN) based robust electromyography (EMG) pattern recognition solutions. Given that high-density surface EMG (HD-sEMG) signal contains rich temporal and spatial information, the multi-view spatial-temporal graph convolutional network (MSTGCN)is adopted as the basic classifier, and a feature extraction convolutional neural network (CNN) module is designed and integrated into MSTGCN to generate a new model called CNN-MSTGCN. The EMG pattern recognition experiments are conducted on HD-sEMG data of 17 gestures from 11 subjects. The ablation experiments show that each functional module of the proposed CNN-MSTGCN network has played a more or less positive role in improving the performance of EMG pattern recognition. The user-independent recognition experiments and the transfer learning-based cross-user recognition experiments verify the advantages of the proposed CNN-MSTGCN network in improving recognition rate and reducing user training burden. In the user-independent recognition experiments, CNN-MSTGCN achieves the recognition rate of 68%, which is significantly better than those obtained by residual network-50 (ResNet50, 47.5%, p < 0.001) and long-short-term-memory (LSTM, 57.1%, p=0.045). In the transfer learning-based cross-user recognition experiments, TL-CMSTGCN achieves an impressive recognition rate of 92.3%, which is significantly superior to both TL-ResNet50 (84.6%, p = 0.003) and TL-LSTM (85.3%, p = 0.008). The research results of this paper indicate that GNN has certain advantages in overcoming the impact of individual differences, and can be used to provide possible solutions for achieving robust EMG pattern recognition technology.
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12
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Xu X, Bonvin AMJJ. DeepRank-GNN-esm: a graph neural network for scoring protein-protein models using protein language model. BIOINFORMATICS ADVANCES 2024; 4:vbad191. [PMID: 38213822 PMCID: PMC10782804 DOI: 10.1093/bioadv/vbad191] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/04/2023] [Revised: 12/19/2023] [Indexed: 01/13/2024]
Abstract
Motivation Protein-Protein interactions (PPIs) play critical roles in numerous cellular processes. By modelling the 3D structures of the correspond protein complexes valuable insights can be obtained, providing, e.g. starting points for drug and protein design. One challenge in the modelling process is however the identification of near-native models from the large pool of generated models. To this end we have previously developed DeepRank-GNN, a graph neural network that integrates structural and sequence information to enable effective pattern learning at PPI interfaces. Its main features are related to the Position Specific Scoring Matrices (PSSMs), which are computationally expensive to generate, significantly limits the algorithm's usability. Results We introduce here DeepRank-GNN-esm that includes as additional features protein language model embeddings from the ESM-2 model. We show that the ESM-2 embeddings can actually replace the PSSM features at no cost in-, or even better performance on two PPI-related tasks: scoring docking poses and detecting crystal artifacts. This new DeepRank version bypasses thus the need of generating PSSM, greatly improving the usability of the software and opening new application opportunities for systems for which PSSM profiles cannot be obtained or are irrelevant (e.g. antibody-antigen complexes). Availability and implementation DeepRank-GNN-esm is freely available from https://github.com/DeepRank/DeepRank-GNN-esm.
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Affiliation(s)
- Xiaotong Xu
- Department of Chemistry, Faculty of Science, Computational Structural Biology Group, Bijvoet Centre for Biomolecular Research, Utrecht 3584 CS, The Netherlands
| | - Alexandre M J J Bonvin
- Department of Chemistry, Faculty of Science, Computational Structural Biology Group, Bijvoet Centre for Biomolecular Research, Utrecht 3584 CS, The Netherlands
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13
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Hu M, Yang K, Wang J, Qiu RLJ, Roper J, Kahn S, Shu HK, Yang X. MGMT promoter methylation prediction based on multiparametric MRI via vision graph neural network. J Med Imaging (Bellingham) 2024; 11:014503. [PMID: 38370421 PMCID: PMC10869845 DOI: 10.1117/1.jmi.11.1.014503] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/30/2023] [Revised: 12/24/2023] [Accepted: 01/29/2024] [Indexed: 02/20/2024] Open
Abstract
Purpose Glioblastoma (GBM) is aggressive and malignant. The methylation status of the O 6 -methylguanine-DNA methyltransferase (MGMT) promoter in GBM tissue is considered an important biomarker for developing the most effective treatment plan. Although the standard method for assessing the MGMT promoter methylation status is via bisulfite modification and deoxyribonucleic acid (DNA) sequencing of biopsy or surgical specimens, a secondary automated method based on medical imaging may improve the efficiency and accuracy of those tests. Approach We propose a deep vision graph neural network (ViG) using multiparametric magnetic resonance imaging (MRI) to predict the MGMT promoter methylation status noninvasively. Our model was compared to the RSNA radiogenomic classification winners. The dataset includes 583 usable patient cases. Combinations of MRI sequences were compared. Our multi-sequence fusion strategy was compared with those using single MR sequences. Results Our best model [Fluid Attenuated Inversion Recovery (FLAIR), T1-weighted pre-contrast (T1w), T2-weighted (T2)] outperformed the winning models with a test area under the curve (AUC) of 0.628, an accuracy of 0.632, a precision of 0.646, a recall of 0.677, a specificity of 0.581, and an F1 score of 0.661. Compared to the winning models with single MR sequences, our ViG utilizing fused-MRI showed a significant improvement statistically in AUC scores, which are FLAIR (p = 0.042 ), T1w (p = 0.017 ), T1wCE (p = 0.001 ), and T2 (p = 0.018 ). Conclusions Our model is superior to challenge champions. A graph representation of the medical images enabled good handling of complexity and irregularity. Our work provides an automatic secondary check pipeline to ensure the correctness of MGMT methylation status prediction.
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Affiliation(s)
- Mingzhe Hu
- Emory University, Department of Radiation Oncology and Winship Cancer Institute, Atlanta, Georgia, United States
- Emory University, Department of Computer Science and Informatics, Atlanta, Georgia, United States
| | - Kailin Yang
- Cleveland Clinic, Taussig Cancer Center, Department of Radiation Oncology, Cleveland, Ohio, United States
| | - Jing Wang
- Emory University, Department of Radiation Oncology and Winship Cancer Institute, Atlanta, Georgia, United States
| | - Richard L. J. Qiu
- Emory University, Department of Radiation Oncology and Winship Cancer Institute, Atlanta, Georgia, United States
| | - Justin Roper
- Emory University, Department of Radiation Oncology and Winship Cancer Institute, Atlanta, Georgia, United States
| | - Shannon Kahn
- Emory University, Department of Radiation Oncology and Winship Cancer Institute, Atlanta, Georgia, United States
| | - Hui-Kuo Shu
- Emory University, Department of Radiation Oncology and Winship Cancer Institute, Atlanta, Georgia, United States
| | - Xiaofeng Yang
- Emory University, Department of Radiation Oncology and Winship Cancer Institute, Atlanta, Georgia, United States
- Emory University, Department of Computer Science and Informatics, Atlanta, Georgia, United States
- Georgia Institute of Technology and Emory University, Department of Biomedical Engineering, Atlanta, Georgia, United States
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14
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Olechnovič K, Venclovas Č. VoroIF-GNN: Voronoi tessellation-derived protein-protein interface assessment using a graph neural network. Proteins 2023; 91:1879-1888. [PMID: 37482904 DOI: 10.1002/prot.26554] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/21/2023] [Revised: 06/19/2023] [Accepted: 07/01/2023] [Indexed: 07/25/2023]
Abstract
We present VoroIF-GNN (Voronoi InterFace Graph Neural Network), a novel method for assessing inter-subunit interfaces in a structural model of a protein-protein complex, relying solely on the input structure without any additional information. Given a multimeric protein structural model, we derive interface contacts from the Voronoi tessellation of atomic balls, construct a graph of those contacts, and predict the accuracy of every contact using an attention-based GNN. The contact-level predictions are then summarized to produce whole interface-level scores. VoroIF-GNN was blindly tested for its ability to estimate the accuracy of protein complexes during CASP15 and showed strong performance in selecting the best multimeric model out of many. The method implementation is freely available at https://kliment-olechnovic.github.io/voronota/expansion_js/.
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Affiliation(s)
- Kliment Olechnovič
- Institute of Biotechnology, Life Sciences Center, Vilnius University, Vilnius, Lithuania
| | - Česlovas Venclovas
- Institute of Biotechnology, Life Sciences Center, Vilnius University, Vilnius, Lithuania
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15
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Schweke H, Xu Q, Tauriello G, Pantolini L, Schwede T, Cazals F, Lhéritier A, Fernandez-Recio J, Rodríguez-Lumbreras LÁ, Schueler-Furman O, Varga JK, Jiménez-García B, Réau MF, Bonvin A, Savojardo C, Martelli PL, Casadio R, Tubiana J, Wolfson H, Oliva R, Barradas-Bautista D, Ricciardelli T, Cavallo L, Venclovas Č, Olechnovič K, Guerois R, Andreani J, Martin J, Wang X, Kihara D, Marchand A, Correia B, Zou X, Dey S, Dunbrack R, Levy E, Wodak S. Discriminating physiological from non-physiological interfaces in structures of protein complexes: A community-wide study. Proteomics 2023; 23:e2200323. [PMID: 37365936 PMCID: PMC10937251 DOI: 10.1002/pmic.202200323] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/04/2023] [Revised: 05/11/2023] [Accepted: 05/11/2023] [Indexed: 06/28/2023]
Abstract
Reliably scoring and ranking candidate models of protein complexes and assigning their oligomeric state from the structure of the crystal lattice represent outstanding challenges. A community-wide effort was launched to tackle these challenges. The latest resources on protein complexes and interfaces were exploited to derive a benchmark dataset consisting of 1677 homodimer protein crystal structures, including a balanced mix of physiological and non-physiological complexes. The non-physiological complexes in the benchmark were selected to bury a similar or larger interface area than their physiological counterparts, making it more difficult for scoring functions to differentiate between them. Next, 252 functions for scoring protein-protein interfaces previously developed by 13 groups were collected and evaluated for their ability to discriminate between physiological and non-physiological complexes. A simple consensus score generated using the best performing score of each of the 13 groups, and a cross-validated Random Forest (RF) classifier were created. Both approaches showed excellent performance, with an area under the Receiver Operating Characteristic (ROC) curve of 0.93 and 0.94, respectively, outperforming individual scores developed by different groups. Additionally, AlphaFold2 engines recalled the physiological dimers with significantly higher accuracy than the non-physiological set, lending support to the reliability of our benchmark dataset annotations. Optimizing the combined power of interface scoring functions and evaluating it on challenging benchmark datasets appears to be a promising strategy.
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Affiliation(s)
| | | | | | | | | | | | | | | | | | | | - Julia K. Varga
- Hebrew University of Jerusalem Institute for Medical Research Israel-Canada
| | | | | | | | | | | | | | - Jérôme Tubiana
- Tel Aviv University Blavatnik School of Computer Science
| | - Haim Wolfson
- Tel Aviv University Blavatnik School of Computer Science
| | | | | | | | | | | | | | | | | | | | | | | | | | | | - Xiaoqin Zou
- Dalton Cardiovascular Research Center, Institute for Data Science and Informatics, University of Missouri
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16
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Hagg A, Kirschner KN. Open-Source Machine Learning in Computational Chemistry. J Chem Inf Model 2023; 63:4505-4532. [PMID: 37466636 PMCID: PMC10430767 DOI: 10.1021/acs.jcim.3c00643] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2023] [Indexed: 07/20/2023]
Abstract
The field of computational chemistry has seen a significant increase in the integration of machine learning concepts and algorithms. In this Perspective, we surveyed 179 open-source software projects, with corresponding peer-reviewed papers published within the last 5 years, to better understand the topics within the field being investigated by machine learning approaches. For each project, we provide a short description, the link to the code, the accompanying license type, and whether the training data and resulting models are made publicly available. Based on those deposited in GitHub repositories, the most popular employed Python libraries are identified. We hope that this survey will serve as a resource to learn about machine learning or specific architectures thereof by identifying accessible codes with accompanying papers on a topic basis. To this end, we also include computational chemistry open-source software for generating training data and fundamental Python libraries for machine learning. Based on our observations and considering the three pillars of collaborative machine learning work, open data, open source (code), and open models, we provide some suggestions to the community.
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Affiliation(s)
- Alexander Hagg
- Institute
of Technology, Resource and Energy-Efficient Engineering (TREE), University of Applied Sciences Bonn-Rhein-Sieg, 53757 Sankt Augustin, Germany
- Department
of Electrical Engineering, Mechanical Engineering and Technical Journalism, University of Applied Sciences Bonn-Rhein-Sieg, 53757 Sankt Augustin, Germany
| | - Karl N. Kirschner
- Institute
of Technology, Resource and Energy-Efficient Engineering (TREE), University of Applied Sciences Bonn-Rhein-Sieg, 53757 Sankt Augustin, Germany
- Department
of Computer Science, University of Applied
Sciences Bonn-Rhein-Sieg, 53757 Sankt Augustin, Germany
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17
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Lee M. Recent Advances in Deep Learning for Protein-Protein Interaction Analysis: A Comprehensive Review. Molecules 2023; 28:5169. [PMID: 37446831 DOI: 10.3390/molecules28135169] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/30/2023] [Revised: 06/30/2023] [Accepted: 06/30/2023] [Indexed: 07/15/2023] Open
Abstract
Deep learning, a potent branch of artificial intelligence, is steadily leaving its transformative imprint across multiple disciplines. Within computational biology, it is expediting progress in the understanding of Protein-Protein Interactions (PPIs), key components governing a wide array of biological functionalities. Hence, an in-depth exploration of PPIs is crucial for decoding the intricate biological system dynamics and unveiling potential avenues for therapeutic interventions. As the deployment of deep learning techniques in PPI analysis proliferates at an accelerated pace, there exists an immediate demand for an exhaustive review that encapsulates and critically assesses these novel developments. Addressing this requirement, this review offers a detailed analysis of the literature from 2021 to 2023, highlighting the cutting-edge deep learning methodologies harnessed for PPI analysis. Thus, this review stands as a crucial reference for researchers in the discipline, presenting an overview of the recent studies in the field. This consolidation helps elucidate the dynamic paradigm of PPI analysis, the evolution of deep learning techniques, and their interdependent dynamics. This scrutiny is expected to serve as a vital aid for researchers, both well-established and newcomers, assisting them in maneuvering the rapidly shifting terrain of deep learning applications in PPI analysis.
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Affiliation(s)
- Minhyeok Lee
- School of Electrical and Electronics Engineering, Chung-Ang University, Seoul 06974, Republic of Korea
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18
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Chen X, Morehead A, Liu J, Cheng J. A gated graph transformer for protein complex structure quality assessment and its performance in CASP15. Bioinformatics 2023; 39:i308-i317. [PMID: 37387159 PMCID: PMC10311325 DOI: 10.1093/bioinformatics/btad203] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/01/2023] Open
Abstract
MOTIVATION Proteins interact to form complexes to carry out essential biological functions. Computational methods such as AlphaFold-multimer have been developed to predict the quaternary structures of protein complexes. An important yet largely unsolved challenge in protein complex structure prediction is to accurately estimate the quality of predicted protein complex structures without any knowledge of the corresponding native structures. Such estimations can then be used to select high-quality predicted complex structures to facilitate biomedical research such as protein function analysis and drug discovery. RESULTS In this work, we introduce a new gated neighborhood-modulating graph transformer to predict the quality of 3D protein complex structures. It incorporates node and edge gates within a graph transformer framework to control information flow during graph message passing. We trained, evaluated and tested the method (called DProQA) on newly-curated protein complex datasets before the 15th Critical Assessment of Techniques for Protein Structure Prediction (CASP15) and then blindly tested it in the 2022 CASP15 experiment. The method was ranked 3rd among the single-model quality assessment methods in CASP15 in terms of the ranking loss of TM-score on 36 complex targets. The rigorous internal and external experiments demonstrate that DProQA is effective in ranking protein complex structures. AVAILABILITY AND IMPLEMENTATION The source code, data, and pre-trained models are available at https://github.com/jianlin-cheng/DProQA.
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Affiliation(s)
- Xiao Chen
- Department of Electrical Engineering and Computer Science, University of Missouri, Columbia, MO 65201, United States
| | - Alex Morehead
- Department of Electrical Engineering and Computer Science, University of Missouri, Columbia, MO 65201, United States
| | - Jian Liu
- Department of Electrical Engineering and Computer Science, University of Missouri, Columbia, MO 65201, United States
| | - Jianlin Cheng
- Department of Electrical Engineering and Computer Science, University of Missouri, Columbia, MO 65201, United States
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Liu W, Zhang Y, Yang H, Meng Q. A Survey on Differential Privacy for Medical Data Analysis. ANNALS OF DATA SCIENCE 2023; 11:1-15. [PMID: 38625247 PMCID: PMC10257172 DOI: 10.1007/s40745-023-00475-3] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/28/2023] [Revised: 05/16/2023] [Accepted: 05/22/2023] [Indexed: 12/01/2023]
Abstract
Machine learning methods promote the sustainable development of wise information technology of medicine (WITMED), and a variety of medical data brings high value and convenience to medical analysis. However, the applications of medical data have also been confronted with the risk of privacy leakage that is hard to avoid, especially when conducting correlation analysis or data sharing among multiple institutions. Data security and privacy preservation have recently played an essential role in the field of secure and private medical data analysis, where many differential privacy strategies are applied to medical data publishing and mining. In this paper, we survey research work on the applications of differential privacy for medical data analysis, discussing the necessity of medical privacy-preserving, the advantages of differential privacy, and their applications to typical medical data, such as genomic data and wearable device data. Furthermore, we discuss the challenges and potential future research directions for differential privacy in medical applications.
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Affiliation(s)
- WeiKang Liu
- Cyberspace Institute of Advanced Technology, Guangzhou University, Guangzhou, China
| | - Yanchun Zhang
- Cyberspace Institute of Advanced Technology, Guangzhou University, Guangzhou, China
- Institute of Sustainable Industries and Liveable Cities, Victoria University, Melbourne, Australia
| | - Hong Yang
- Cyberspace Institute of Advanced Technology, Guangzhou University, Guangzhou, China
| | - Qinxue Meng
- College of Information Engineering, Suzhou University, Suzhou, China
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20
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Deep Learning with Graph Convolutional Networks: An Overview and Latest Applications in Computational Intelligence. INT J INTELL SYST 2023. [DOI: 10.1155/2023/8342104] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/06/2023]
Abstract
Convolutional neural networks (CNNs) have received widespread attention due to their powerful modeling capabilities and have been successfully applied in natural language processing, image recognition, and other fields. On the other hand, traditional CNN can only deal with Euclidean spatial data. In contrast, many real-life scenarios, such as transportation networks, social networks, reference networks, and so on, exist in graph data. The creation of graph convolution operators and graph pooling is at the heart of migrating CNN to graph data analysis and processing. With the advancement of the Internet and technology, graph convolution network (GCN), as an innovative technology in artificial intelligence (AI), has received more and more attention. GCN has been widely used in different fields such as image processing, intelligent recommender system, knowledge-based graph, and other areas due to their excellent characteristics in processing non-European spatial data. At the same time, communication networks have also embraced AI technology in recent years, and AI serves as the brain of the future network and realizes the comprehensive intelligence of the future grid. Many complex communication network problems can be abstracted as graph-based optimization problems and solved by GCN, thus overcoming the limitations of traditional methods. This survey briefly describes the definition of graph-based machine learning, introduces different types of graph networks, summarizes the application of GCN in various research fields, analyzes the research status, and gives the future research direction.
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21
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Kim HY, Kim S, Park WY, Kim D. G-RANK: an equivariant graph neural network for the scoring of protein-protein docking models. BIOINFORMATICS ADVANCES 2023; 3:vbad011. [PMID: 36818727 PMCID: PMC9927558 DOI: 10.1093/bioadv/vbad011] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/07/2022] [Revised: 01/25/2023] [Accepted: 02/01/2023] [Indexed: 02/05/2023]
Abstract
Motivation Protein complex structure prediction is important for many applications in bioengineering. A widely used method for predicting the structure of protein complexes is computational docking. Although many tools for scoring protein-protein docking models have been developed, it is still a challenge to accurately identify near-native models for unknown protein complexes. A recently proposed model called the geometric vector perceptron-graph neural network (GVP-GNN), a subtype of equivariant graph neural networks, has demonstrated success in various 3D molecular structure modeling tasks. Results Herein, we present G-RANK, a GVP-GNN-based method for the scoring of protein-protein docking models. When evaluated on two different test datasets, G-RANK achieved a performance competitive with or better than the state-of-the-art scoring functions. We expect G-RANK to be a useful tool for various applications in biological engineering. Availability and implementation Source code is available at https://github.com/ha01994/grank. Contact kds@kaist.ac.kr. Supplementary information Supplementary data are available at Bioinformatics Advances online.
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Affiliation(s)
- Ha Young Kim
- Department of Bio and Brain Engineering, Korea Advanced Institute of Science and Technology, Daejeon 34141, South Korea
| | | | - Woong-Yang Park
- GENINUS Inc., Seoul 05836, South Korea,Samsung Genome Institute, Samsung Medical Center, Seoul 06351, South Korea,Department of Molecular Cell Biology, Sungkyunkwan University School of Medicine, Suwon 16419, South Korea
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