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Li H, Nithin C, Kmiecik S, Huang SY. Computational methods for modeling protein-protein interactions in the AI era: Current status and future directions. Drug Discov Today 2025:104382. [PMID: 40398752 DOI: 10.1016/j.drudis.2025.104382] [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: 01/31/2025] [Revised: 04/30/2025] [Accepted: 05/14/2025] [Indexed: 05/23/2025]
Abstract
The modeling of protein-protein interactions (PPIs) has been revolutionized by artificial intelligence, with deep learning and end-to-end frameworks such as AlphaFold and its derivatives now dominating the field. This review surveys the current computational landscape for predicting protein complex structures, outlining the role of traditional docking approaches as well as focusing on recent advances in AI-driven methods. We discuss key challenges, including protein flexibility, reliance on co-evolutionary signals, modeling of large assemblies, and interactions involving intrinsically disordered regions (IDRs). Recent innovations aimed at improving sampling diversity, integrating experimental data, and enhancing robustness are also highlighted. Although classical methods remain relevant in specific contexts, the continued evolution of AI-based tools offers transformative potential for structural biology. These advances are poised to deepen our understanding of biomolecular interactions and accelerate the design of therapeutic interventions.
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Affiliation(s)
- Hao Li
- School of Physics, Huazhong University of Science and Technology, Wuhan, Hubei 430074, China
| | - Chandran Nithin
- University of Warsaw, Biological and Chemical Research Centre, Faculty of Chemistry, Warsaw, Poland
| | - Sebastian Kmiecik
- University of Warsaw, Biological and Chemical Research Centre, Faculty of Chemistry, Warsaw, Poland
| | - Sheng-You Huang
- School of Physics, Huazhong University of Science and Technology, Wuhan, Hubei 430074, China
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2
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Grassmann G, Di Rienzo L, Ruocco G, Miotto M, Milanetti E. Compact Assessment of Molecular Surface Complementarities Enhances Neural Network-Aided Prediction of Key Binding Residues. J Chem Inf Model 2025; 65:2695-2709. [PMID: 39982412 PMCID: PMC11898074 DOI: 10.1021/acs.jcim.4c02286] [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: 12/06/2024] [Revised: 02/09/2025] [Accepted: 02/13/2025] [Indexed: 02/22/2025]
Abstract
Predicting interactions between proteins is fundamental for understanding the mechanisms underlying cellular processes, since protein-protein complexes are crucial in physiological conditions but also in many diseases, for example by seeding aggregates formation. Despite the many advancements made so far, the performance of docking protocols is deeply dependent on their capability to identify binding regions. From this, the importance of developing low-cost and computationally efficient methods in this field. We present an integrated novel protocol mainly based on compact modeling of protein surface patches via sets of orthogonal polynomials to identify regions of high shape/electrostatic complementarity. By incorporating both hydrophilic and hydrophobic contributions, we define new binding matrices, which serve as effective inputs for training a neural network. In this work, we propose a new Neural Network (NN)-based architecture, Core Interacting Residues Network (CIRNet), which achieves a performance in terms of Area Under the Receiver Operating Characteristic Curve (ROC AUC) of approximately 0.87 in identifying pairs of core interacting residues on a balanced data set. In a blind search for core interacting residues, CIRNet distinguishes them from random decoys with an ROC AUC of 0.72. We test this protocol to enhance docking algorithms by filtering the proposed poses, addressing one of the still open problems in computational biology. Notably, when applied to the top ten models from three widely used docking servers, CIRNet improves docking outcomes, significantly reducing the average RMSD between the selected poses and the native state. Compared to another state-of-the-art tool for rescaling docking poses, CIRNet more efficiently identified the worst poses generated by the three docking servers under consideration and achieved superior rescaling performance in two cases.
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Affiliation(s)
- Greta Grassmann
- Department
of Biochemical Sciences “Alessandro Rossi Fanelli”, Sapienza University of Rome, P.Le A. Moro 5, Rome 00185, Italy
- Center
for Life Nano & Neuro Science, Istituto
Italiano di Tecnologia, Viale Regina Elena 291, Rome 00161, Italy
| | - Lorenzo Di Rienzo
- Center
for Life Nano & Neuro Science, Istituto
Italiano di Tecnologia, Viale Regina Elena 291, Rome 00161, Italy
| | - Giancarlo Ruocco
- Center
for Life Nano & Neuro Science, Istituto
Italiano di Tecnologia, Viale Regina Elena 291, Rome 00161, Italy
- Department
of Physics, Sapienza University, Piazzale Aldo Moro 5, Rome 00185, Italy
| | - Mattia Miotto
- Center
for Life Nano & Neuro Science, Istituto
Italiano di Tecnologia, Viale Regina Elena 291, Rome 00161, Italy
| | - Edoardo Milanetti
- Center
for Life Nano & Neuro Science, Istituto
Italiano di Tecnologia, Viale Regina Elena 291, Rome 00161, Italy
- Department
of Physics, Sapienza University, Piazzale Aldo Moro 5, Rome 00185, Italy
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3
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Han B, Zhang Y, Li L, Gong X, Xia K. TopoQA: a topological deep learning-based approach for protein complex structure interface quality assessment. Brief Bioinform 2025; 26:bbaf083. [PMID: 40062613 PMCID: PMC11891663 DOI: 10.1093/bib/bbaf083] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/19/2024] [Revised: 01/11/2025] [Accepted: 02/17/2025] [Indexed: 05/13/2025] Open
Abstract
Even with the significant advances of AlphaFold-Multimer (AF-Multimer) and AlphaFold3 (AF3) in protein complex structure prediction, their accuracy is still not comparable with monomer structure prediction. Efficient and effective quality assessment (QA) or estimation of model accuracy models that can evaluate the quality of the predicted protein-complexes without knowing their native structures are of key importance for protein structure generation and model selection. In this paper, we leverage persistent homology (PH) to capture the atomic-level topological information around residues and design a topological deep learning-based QA method, TopoQA, to assess the accuracy of protein complex interfaces. We integrate PH from topological data analysis into graph neural networks (GNNs) to characterize complex higher-order structures that GNNs might overlook, enhancing the learning of the relationship between the topological structure of complex interfaces and quality scores. Our TopoQA model is extensively validated based on the two most-widely used benchmark datasets, Docking Benchmark5.5 AF2 (DBM55-AF2) and Heterodimer-AF2 (HAF2), along with our newly constructed ABAG-AF3 dataset to facilitate comparisons with AF3. For all three datasets, TopoQA outperforms AF-Multimer-based AF2Rank and shows an advantage over AF3 in nearly half of the targets. In particular, in the DBM55-AF2 dataset, a ranking loss of 73.6% lower than AF-Multimer-based AF2Rank is obtained. Further, other than AF-Multimer and AF3, we have also extensively compared with nearly-all the state-of-the-art models (as far as we know), it has been found that our TopoQA can achieve the highest Top 10 Hit-rate on the DBM55-AF2 dataset and the lowest ranking loss on the HAF2 dataset. Ablation experiments show that our topological features significantly improve the model's performance. At the same time, our method also provides a new paradigm for protein structure representation learning.
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Affiliation(s)
- Bingqing Han
- Institute for Mathematical Sciences, Renmin University of China, Beijing 100872, China
- Division of Mathematical Sciences, School of Physical and Mathematical Sciences, Nanyang Technological University, Singapore 637371, Singapore
| | - Yipeng Zhang
- Division of Mathematical Sciences, School of Physical and Mathematical Sciences, Nanyang Technological University, Singapore 637371, Singapore
| | - Longlong Li
- Division of Mathematical Sciences, School of Physical and Mathematical Sciences, Nanyang Technological University, Singapore 637371, Singapore
- School of Mathematics, Shandong University, Jinan 250100, China
- Data Science Institute, Shandong University, Jinan 250100, China
| | - Xinqi Gong
- Institute for Mathematical Sciences, Renmin University of China, Beijing 100872, China
| | - Kelin Xia
- Division of Mathematical Sciences, School of Physical and Mathematical Sciences, Nanyang Technological University, Singapore 637371, Singapore
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4
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Shirali A, Stebliankin V, Karki U, Shi J, Chapagain P, Narasimhan G. A comprehensive survey of scoring functions for protein docking models. BMC Bioinformatics 2025; 26:25. [PMID: 39844036 PMCID: PMC11755896 DOI: 10.1186/s12859-024-05991-4] [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: 07/04/2024] [Accepted: 11/18/2024] [Indexed: 01/24/2025] Open
Abstract
BACKGROUND While protein-protein docking is fundamental to our understanding of how proteins interact, scoring protein-protein complex conformations is a critical component of successful docking programs. Without accurate and efficient scoring functions to differentiate between native and non-native binding complexes, the accuracy of current docking tools cannot be guaranteed. Although many innovative scoring functions have been proposed, a good scoring function for docking remains elusive. Deep learning models offer alternatives to using explicit empirical or mathematical functions for scoring protein-protein complexes. RESULTS In this study, we perform a comprehensive survey of the state-of-the-art scoring functions by considering the most popular and highly performant approaches, both classical and deep learning-based, for scoring protein-protein complexes. The methods were also compared based on their runtime as it directly impacts their use in large-scale docking applications. CONCLUSIONS We evaluate the strengths and weaknesses of classical and deep learning-based approaches across seven public and popular datasets to aid researchers in understanding the progress made in this field.
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Affiliation(s)
- Azam Shirali
- Bioinformatics Research Group (BioRG), Knight Foundation School of Computing and Information Sciences, Florida International University, 11200 SW 8th 10 St, Miami, 33199, USA
| | - Vitalii Stebliankin
- Bioinformatics Research Group (BioRG), Knight Foundation School of Computing and Information Sciences, Florida International University, 11200 SW 8th 10 St, Miami, 33199, USA
| | - Ukesh Karki
- Department of Physics, Florida International University, 11200 SW 8th 10 St, Miami, 33199, USA
| | - Jimeng Shi
- Bioinformatics Research Group (BioRG), Knight Foundation School of Computing and Information Sciences, Florida International University, 11200 SW 8th 10 St, Miami, 33199, USA
| | - Prem Chapagain
- Department of Physics, Florida International University, 11200 SW 8th 10 St, Miami, 33199, USA
- Biomolecular Sciences Institute, Florida International University, 11200 SW 8th St, Miami, 33199, USA
| | - Giri Narasimhan
- Bioinformatics Research Group (BioRG), Knight Foundation School of Computing and Information Sciences, Florida International University, 11200 SW 8th 10 St, Miami, 33199, USA.
- Biomolecular Sciences Institute, Florida International University, 11200 SW 8th St, Miami, 33199, USA.
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Shuvo MH, Bhattacharya D. EquiRank: Improved protein-protein interface quality estimation using protein language-model-informed equivariant graph neural networks. Comput Struct Biotechnol J 2024; 27:160-170. [PMID: 39850657 PMCID: PMC11755013 DOI: 10.1016/j.csbj.2024.12.015] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2024] [Revised: 12/18/2024] [Accepted: 12/20/2024] [Indexed: 01/25/2025] Open
Abstract
Quality estimation of the predicted interaction interface of protein complex structural models is not only important for complex model evaluation and selection but also useful for protein-protein docking. Despite recent progress fueled by symmetry-aware deep learning architectures and pretrained protein language models (pLMs), existing methods for estimating protein complex quality have yet to fully exploit the collective potentials of these advances for accurate estimation of protein-protein interface. Here we present EquiRank, an improved protein-protein interface quality estimation method by leveraging the strength of a symmetry-aware E(3) equivariant deep graph neural network (EGNN) and integrating pLM embeddings from the pretrained ESM-2 model. Our method estimates the quality of the protein-protein interface through an effective graph-based representation of interacting residue pairs, incorporating a diverse set of features, including ESM-2 embeddings, and then by learning the representation using symmetry-aware EGNNs. Our experimental results demonstrate improved ranking performance on diverse datasets over existing latest protein complex quality estimation methods including the top-performing CASP15 protein complex quality estimation method VoroIF_GNN and the self-assessment module of AlphaFold-Multimer repurposed for protein complex scoring and across different performance evaluation metrics. Additionally, our ablation studies demonstrate the contributions of both pLMs and the equivariant nature of EGNN for improved protein-protein interface quality estimation performance. EquiRank is freely available at https://github.com/mhshuvo1/EquiRank.
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Affiliation(s)
- Md Hossain Shuvo
- Department of Computer Science, Prairie View A&M University, Prairie View, 77446, TX, USA
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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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Kim DN, McNaughton AD, Kumar N. Leveraging Artificial Intelligence to Expedite Antibody Design and Enhance Antibody-Antigen Interactions. Bioengineering (Basel) 2024; 11:185. [PMID: 38391671 PMCID: PMC10886287 DOI: 10.3390/bioengineering11020185] [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: 12/30/2023] [Revised: 01/30/2024] [Accepted: 02/06/2024] [Indexed: 02/24/2024] Open
Abstract
This perspective sheds light on the transformative impact of recent computational advancements in the field of protein therapeutics, with a particular focus on the design and development of antibodies. Cutting-edge computational methods have revolutionized our understanding of protein-protein interactions (PPIs), enhancing the efficacy of protein therapeutics in preclinical and clinical settings. Central to these advancements is the application of machine learning and deep learning, which offers unprecedented insights into the intricate mechanisms of PPIs and facilitates precise control over protein functions. Despite these advancements, the complex structural nuances of antibodies pose ongoing challenges in their design and optimization. Our review provides a comprehensive exploration of the latest deep learning approaches, including language models and diffusion techniques, and their role in surmounting these challenges. We also present a critical analysis of these methods, offering insights to drive further progress in this rapidly evolving field. The paper includes practical recommendations for the application of these computational techniques, supplemented with independent benchmark studies. These studies focus on key performance metrics such as accuracy and the ease of program execution, providing a valuable resource for researchers engaged in antibody design and development. Through this detailed perspective, we aim to contribute to the advancement of antibody design, equipping researchers with the tools and knowledge to navigate the complexities of this field.
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Affiliation(s)
| | | | - Neeraj Kumar
- Pacific Northwest National Laboratory, 902 Battelle Blvd., Richland, WA 99352, USA; (D.N.K.); (A.D.M.)
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8
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Zhang L, Wang S, Hou J, Si D, Zhu J, Cao R. ComplexQA: a deep graph learning approach for protein complex structure assessment. Brief Bioinform 2023; 24:bbad287. [PMID: 37930021 DOI: 10.1093/bib/bbad287] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/24/2023] [Revised: 05/09/2023] [Accepted: 07/24/2023] [Indexed: 11/07/2023] Open
Abstract
MOTIVATION In recent years, the end-to-end deep learning method for single-chain protein structure prediction has achieved high accuracy. For example, the state-of-the-art method AlphaFold, developed by Google, has largely increased the accuracy of protein structure predictions to near experimental accuracy in some of the cases. At the same time, there are few methods that can evaluate the quality of protein complexes at the residue level. In particular, evaluating the quality of residues at the interface of protein complexes can lead to a wide range of applications, such as protein function analysis and drug design. In this paper, we introduce a new deep graph neural network-based method ComplexQA, to evaluate the local quality of interfaces for protein complexes by utilizing the residue-level structural information in 3D space and the sequence-level constraints. RESULTS We benchmark our method to other state-of-the-art quality assessment approaches on the HAF2 and DBM55-AF2 datasets (high-quality structural models predicted by AlphaFold-Multimer), and the BM5 docking dataset. The experimental results show that our proposed method achieves better or similar performance compared with other state-of-the-art methods, especially on difficult targets which only contain a few acceptable models. Our method is able to suggest a score for each interfac e residue, which demonstrates a powerful assessment tool for the ever-increasing number of protein complexes. AVAILABILITY https://github.com/Cao-Labs/ComplexQA.git. Contact: caora@plu.edu.
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Affiliation(s)
- Lei Zhang
- Department of Computer Science and Technology, AnHui University, Hefei, 230601, Anhui, China
| | - Sheng Wang
- Department of Computer Science and Technology, AnHui University, Hefei, 230601, Anhui, China
| | - Jie Hou
- Department of Computer Science, Saint Louis University, Saint. Louis, 63103, MO, USA
| | - Dong Si
- Division of Computing and Software Systems, University of Washington Bothell, Bothell, 98011, WA, USA
| | - Junyong Zhu
- Department of Computer Science and Technology, AnHui University, Hefei, 230601, Anhui, China
| | - Renzhi Cao
- Department of Humanities, Pacific Lutheran University, Tacoma, 98447, WA, USA
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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: 12] [Impact Index Per Article: 6.0] [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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10
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Shuvo MH, Karim M, Roche R, Bhattacharya D. PIQLE: protein-protein interface quality estimation by deep graph learning of multimeric interaction geometries. BIOINFORMATICS ADVANCES 2023; 3:vbad070. [PMID: 37351310 PMCID: PMC10281963 DOI: 10.1093/bioadv/vbad070] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 02/22/2023] [Revised: 05/17/2023] [Accepted: 06/01/2023] [Indexed: 06/24/2023]
Abstract
Motivation Accurate modeling of protein-protein interaction interface is essential for high-quality protein complex structure prediction. Existing approaches for estimating the quality of a predicted protein complex structural model utilize only the physicochemical properties or energetic contributions of the interacting atoms, ignoring evolutionarily information or inter-atomic multimeric geometries, including interaction distance and orientations. Results Here, we present PIQLE, a deep graph learning method for protein-protein interface quality estimation. PIQLE leverages multimeric interaction geometries and evolutionarily information along with sequence- and structure-derived features to estimate the quality of individual interactions between the interfacial residues using a multi-head graph attention network and then probabilistically combines the estimated quality for scoring the overall interface. Experimental results show that PIQLE consistently outperforms existing state-of-the-art methods including DProQA, TRScore, GNN-DOVE and DOVE on multiple independent test datasets across a wide range of evaluation metrics. Our ablation study and comparison with the self-assessment module of AlphaFold-Multimer repurposed for protein complex scoring reveal that the performance gains are connected to the effectiveness of the multi-head graph attention network in leveraging multimeric interaction geometries and evolutionary information along with other sequence- and structure-derived features adopted in PIQLE. Availability and implementation An open-source software implementation of PIQLE is freely available at https://github.com/Bhattacharya-Lab/PIQLE. Supplementary information Supplementary data are available at Bioinformatics Advances online.
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Affiliation(s)
- Md Hossain Shuvo
- Department of Computer Science, Virginia Tech, Blacksburg, VA 24061, USA
| | - Mohimenul Karim
- Department of Computer Science, Virginia Tech, Blacksburg, VA 24061, USA
| | - Rahmatullah Roche
- Department of Computer Science, Virginia Tech, Blacksburg, VA 24061, USA
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Shuvo MH, Karim M, Roche R, Bhattacharya D. PIQLE: protein-protein interface quality estimation by deep graph learning of multimeric interaction geometries. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.02.14.528528. [PMID: 36824789 PMCID: PMC9949034 DOI: 10.1101/2023.02.14.528528] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/17/2023]
Abstract
Accurate modeling of protein-protein interaction interface is essential for high-quality protein complex structure prediction. Existing approaches for estimating the quality of a predicted protein complex structural model utilize only the physicochemical properties or energetic contributions of the interacting atoms, ignoring evolutionarily information or inter-atomic multimeric geometries, including interaction distance and orientations. Here we present PIQLE, a deep graph learning method for protein-protein interface quality estimation. PIQLE leverages multimeric interaction geometries and evolutionarily information along with sequence- and structure-derived features to estimate the quality of the individual interactions between the interfacial residues using a multihead graph attention network and then probabilistically combines the estimated quality of the interfacial residues for scoring the overall interface. Experimental results show that PIQLE consistently outperforms existing state-of-the-art methods on multiple independent test datasets across a wide range of evaluation metrics. Our ablation study reveals that the performance gains are connected to the effectiveness of the multihead graph attention network in leveraging multimeric interaction geometries and evolutionary information along with other sequence- and structure-derived features adopted in PIQLE. An open-source software implementation of PIQLE, licensed under the GNU General Public License v3, is freely available at https://github.com/Bhattacharya-Lab/PIQLE .
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Affiliation(s)
- Md Hossain Shuvo
- Department of Computer Science, Virginia Tech, Blacksburg, VA 24061, United States of America
| | - Mohimenul Karim
- Department of Computer Science, Virginia Tech, Blacksburg, VA 24061, United States of America
| | - Rahmatullah Roche
- Department of Computer Science, Virginia Tech, Blacksburg, VA 24061, United States of America
| | - Debswapna Bhattacharya
- Department of Computer Science, Virginia Tech, Blacksburg, VA 24061, United States of America
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