1
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Li M, Qing R, Tao F, Xu P, Zhang S. Inhibitory effect of truncated isoforms on GPCR dimerization predicted by combinatorial computational strategy. Comput Struct Biotechnol J 2024; 23:278-286. [PMID: 38173876 PMCID: PMC10762321 DOI: 10.1016/j.csbj.2023.12.008] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/20/2023] [Revised: 12/07/2023] [Accepted: 12/07/2023] [Indexed: 01/05/2024] Open
Abstract
G protein-coupled receptors (GPCRs) play a pivotal role in fundamental biological processes and disease development. GPCR isoforms, derived from alternative splicing, can exhibit distinct signaling patterns. Some highly-truncated isoforms can impact functional performance of full-length receptors, suggesting their intriguing regulatory roles. However, how these truncated isoforms interact with full-length counterparts remains largely unexplored. Here, we computationally investigated the interaction patterns of three human GPCRs from three different classes, ADORA1 (Class A), mGlu2 (Class C) and SMO (Class F) with their respective truncated isoforms because their homodimer structures have been experimentally determined, and they have truncated isoforms deposited and identified at protein level in Uniprot database. Combining the neural network-based AlphaFold2 and two physics-based protein-protein docking tools, we generated multiple complex structures and assessed the binding affinity in the context of atomistic molecular dynamics simulations. Our computational results suggested all the four studied truncated isoforms showed potent binding to their counterparts and overlapping interfaces with homodimers, indicating their strong potential to block homodimerization of their counterparts. Our study offers insights into functional significance of GPCR truncated isoforms and supports the ubiquity of their regulatory roles.
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Affiliation(s)
- Mengke Li
- State Key Laboratory of Microbial Metabolism, Joint International Research Laboratory of Metabolic and Developmental Sciences, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai 200240, China
- Laboratory of Molecular Architecture, Media Lab, Massachusetts Institute of Technology, 77 Massachusetts Avenue, Cambridge, MA 02139, USA
| | - Rui Qing
- State Key Laboratory of Microbial Metabolism, Joint International Research Laboratory of Metabolic and Developmental Sciences, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai 200240, China
| | - Fei Tao
- State Key Laboratory of Microbial Metabolism, Joint International Research Laboratory of Metabolic and Developmental Sciences, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai 200240, China
| | - Ping Xu
- State Key Laboratory of Microbial Metabolism, Joint International Research Laboratory of Metabolic and Developmental Sciences, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai 200240, China
| | - Shuguang Zhang
- Laboratory of Molecular Architecture, Media Lab, Massachusetts Institute of Technology, 77 Massachusetts Avenue, Cambridge, MA 02139, USA
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2
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Geller AM, Shalom M, Zlotkin D, Blum N, Levy A. Identification of type VI secretion system effector-immunity pairs using structural bioinformatics. Mol Syst Biol 2024; 20:702-718. [PMID: 38658795 PMCID: PMC11148199 DOI: 10.1038/s44320-024-00035-8] [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: 01/02/2024] [Revised: 03/24/2024] [Accepted: 04/09/2024] [Indexed: 04/26/2024] Open
Abstract
The type VI secretion system (T6SS) is an important mediator of microbe-microbe and microbe-host interactions. Gram-negative bacteria use the T6SS to inject T6SS effectors (T6Es), which are usually proteins with toxic activity, into neighboring cells. Antibacterial effectors have cognate immunity proteins that neutralize self-intoxication. Here, we applied novel structural bioinformatic tools to perform systematic discovery and functional annotation of T6Es and their cognate immunity proteins from a dataset of 17,920 T6SS-encoding bacterial genomes. Using structural clustering, we identified 517 putative T6E families, outperforming sequence-based clustering. We developed a logistic regression model to reliably quantify protein-protein interaction of new T6E-immunity pairs, yielding candidate immunity proteins for 231 out of the 517 T6E families. We used sensitive structure-based annotation which yielded functional annotations for 51% of the T6E families, again outperforming sequence-based annotation. Next, we validated four novel T6E-immunity pairs using basic experiments in E. coli. In particular, we showed that the Pfam domain DUF3289 is a homolog of Colicin M and that DUF943 acts as its cognate immunity protein. Furthermore, we discovered a novel T6E that is a structural homolog of SleB, a lytic transglycosylase, and identified a specific glutamate that acts as its putative catalytic residue. Overall, this study applies novel structural bioinformatic tools to T6E-immunity pair discovery, and provides an extensive database of annotated T6E-immunity pairs.
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Affiliation(s)
- Alexander M Geller
- Department of Plant Pathology and Microbiology, The Institute of Environmental Science, The Robert H. Smith Faculty of Agriculture, Food and Environment, The Hebrew University of Jerusalem, Rehovot, Israel
| | - Maor Shalom
- Department of Plant Pathology and Microbiology, The Institute of Environmental Science, The Robert H. Smith Faculty of Agriculture, Food and Environment, The Hebrew University of Jerusalem, Rehovot, Israel
| | - David Zlotkin
- Department of Plant Pathology and Microbiology, The Institute of Environmental Science, The Robert H. Smith Faculty of Agriculture, Food and Environment, The Hebrew University of Jerusalem, Rehovot, Israel
| | - Noam Blum
- Department of Plant Pathology and Microbiology, The Institute of Environmental Science, The Robert H. Smith Faculty of Agriculture, Food and Environment, The Hebrew University of Jerusalem, Rehovot, Israel
| | - Asaf Levy
- Department of Plant Pathology and Microbiology, The Institute of Environmental Science, The Robert H. Smith Faculty of Agriculture, Food and Environment, The Hebrew University of Jerusalem, Rehovot, Israel.
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3
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Mischley V, Maier J, Chen J, Karanicolas J. PPIscreenML: Structure-based screening for protein-protein interactions using AlphaFold. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.03.16.585347. [PMID: 38559274 PMCID: PMC10979958 DOI: 10.1101/2024.03.16.585347] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Indexed: 04/04/2024]
Abstract
Protein-protein interactions underlie nearly all cellular processes. With the advent of protein structure prediction methods such as AlphaFold2 (AF2), models of specific protein pairs can be built extremely accurately in most cases. However, determining the relevance of a given protein pair remains an open question. It is presently unclear how to use best structure-based tools to infer whether a pair of candidate proteins indeed interact with one another: ideally, one might even use such information to screen amongst candidate pairings to build up protein interaction networks. Whereas methods for evaluating quality of modeled protein complexes have been co-opted for determining which pairings interact (e.g., pDockQ and iPTM), there have been no rigorously benchmarked methods for this task. Here we introduce PPIscreenML, a classification model trained to distinguish AF2 models of interacting protein pairs from AF2 models of compelling decoy pairings. We find that PPIscreenML out-performs methods such as pDockQ and iPTM for this task, and further that PPIscreenML exhibits impressive performance when identifying which ligand/receptor pairings engage one another across the structurally conserved tumor necrosis factor superfamily (TNFSF). Analysis of benchmark results using complexes not seen in PPIscreenML development strongly suggest that the model generalizes beyond training data, making it broadly applicable for identifying new protein complexes based on structural models built with AF2.
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Affiliation(s)
- Victoria Mischley
- Cancer Signaling & Microenvironment Program, Fox Chase Cancer Center, Philadelphia PA 19111
- Molecular Cell Biology and Genetics, Drexel University, Philadelphia PA 19102
| | | | | | - John Karanicolas
- Cancer Signaling & Microenvironment Program, Fox Chase Cancer Center, Philadelphia PA 19111
- Moulder Center for Drug Discovery Research, Temple University School of Pharmacy, Philadelphia PA 19140
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4
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Wang R, Zhou Z, Wu X, Jiang X, Zhuo L, Liu M, Li H, Fu X, Yao X. An Effective Plant Small Secretory Peptide Recognition Model Based on Feature Correction Strategy. J Chem Inf Model 2024; 64:2798-2806. [PMID: 37643082 DOI: 10.1021/acs.jcim.3c00868] [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] [Indexed: 08/31/2023]
Abstract
Plant small secretory peptides (SSPs) play an important role in the regulation of biological processes in plants. Accurately predicting SSPs enables efficient exploration of their functions. Traditional experimental verification methods are very reliable and accurate, but they require expensive equipment and a lot of time. The method of machine learning speeds up the prediction process of SSPs, but the instability of feature extraction will also lead to further limitations of this type of method. Therefore, this paper proposes a new feature-correction-based model for SSP recognition in plants, abbreviated as SE-SSP. The model mainly includes the following three advantages: First, the use of transformer encoders can better reveal implicit features. Second, design a feature correction module suitable for sequences, named 2-D SENET, to adaptively adjust the features to obtain a more robust feature representation. Third, stack multiple linear modules to further dig out the deep information on the sample. At the same time, the training based on a contrastive learning strategy can alleviate the problem of sparse samples. We construct experiments on publicly available data sets, and the results verify that our model shows an excellent performance. The proposed model can be used as a convenient and effective SSP prediction tool in the future. Our data and code are publicly available at https://github.com/wrab12/SE-SSP/.
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Affiliation(s)
- Rui Wang
- Wenzhou University of Technology, 325000 Wenzhou, China
| | - Zhecheng Zhou
- Wenzhou University of Technology, 325000 Wenzhou, China
| | - Xiaonan Wu
- Wenzhou University of Technology, 325000 Wenzhou, China
| | - Xin Jiang
- Wenzhou University of Technology, 325000 Wenzhou, China
| | - Linlin Zhuo
- Wenzhou University of Technology, 325000 Wenzhou, China
| | - Mingzhe Liu
- Wenzhou University of Technology, 325000 Wenzhou, China
| | - Hao Li
- Central South University, 410083 Changsha, China
| | - Xiangzheng Fu
- Faculty of Applied Sciences, Macao Polytechnic University, 999078, Macao
| | - Xiaojun Yao
- Faculty of Applied Sciences, Macao Polytechnic University, 999078, Macao
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5
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Lee CY, Hubrich D, Varga JK, Schäfer C, Welzel M, Schumbera E, Djokic M, Strom JM, Schönfeld J, Geist JL, Polat F, Gibson TJ, Keller Valsecchi CI, Kumar M, Schueler-Furman O, Luck K. Systematic discovery of protein interaction interfaces using AlphaFold and experimental validation. Mol Syst Biol 2024; 20:75-97. [PMID: 38225382 PMCID: PMC10883280 DOI: 10.1038/s44320-023-00005-6] [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: 08/03/2023] [Revised: 12/04/2023] [Accepted: 12/05/2023] [Indexed: 01/17/2024] Open
Abstract
Structural resolution of protein interactions enables mechanistic and functional studies as well as interpretation of disease variants. However, structural data is still missing for most protein interactions because we lack computational and experimental tools at scale. This is particularly true for interactions mediated by short linear motifs occurring in disordered regions of proteins. We find that AlphaFold-Multimer predicts with high sensitivity but limited specificity structures of domain-motif interactions when using small protein fragments as input. Sensitivity decreased substantially when using long protein fragments or full length proteins. We delineated a protein fragmentation strategy particularly suited for the prediction of domain-motif interfaces and applied it to interactions between human proteins associated with neurodevelopmental disorders. This enabled the prediction of highly confident and likely disease-related novel interfaces, which we further experimentally corroborated for FBXO23-STX1B, STX1B-VAMP2, ESRRG-PSMC5, PEX3-PEX19, PEX3-PEX16, and SNRPB-GIGYF1 providing novel molecular insights for diverse biological processes. Our work highlights exciting perspectives, but also reveals clear limitations and the need for future developments to maximize the power of Alphafold-Multimer for interface predictions.
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Affiliation(s)
- Chop Yan Lee
- Institute of Molecular Biology (IMB) gGmbH, 55128, Mainz, Germany
| | - Dalmira Hubrich
- Institute of Molecular Biology (IMB) gGmbH, 55128, Mainz, Germany
| | - Julia K Varga
- Department of Microbiology and Molecular Genetics, Institute for Biomedical Research Israel-Canada, Faculty of Medicine, The Hebrew University of Jerusalem, Jerusalem, 9112001, Israel
| | | | - Mareen Welzel
- Institute of Molecular Biology (IMB) gGmbH, 55128, Mainz, Germany
| | - Eric Schumbera
- Institute of Molecular Biology (IMB) gGmbH, 55128, Mainz, Germany
- Computational Biology and Data Mining Group Biozentrum I, 55128, Mainz, Germany
| | - Milena Djokic
- Institute of Molecular Biology (IMB) gGmbH, 55128, Mainz, Germany
| | - Joelle M Strom
- Institute of Molecular Biology (IMB) gGmbH, 55128, Mainz, Germany
| | - Jonas Schönfeld
- Institute of Molecular Biology (IMB) gGmbH, 55128, Mainz, Germany
| | - Johanna L Geist
- Institute of Molecular Biology (IMB) gGmbH, 55128, Mainz, Germany
| | - Feyza Polat
- Institute of Molecular Biology (IMB) gGmbH, 55128, Mainz, Germany
| | - Toby J Gibson
- Structural and Computational Biology Unit, European Molecular Biology Laboratory, Heidelberg, 69117, Germany
| | | | - Manjeet Kumar
- Structural and Computational Biology Unit, European Molecular Biology Laboratory, Heidelberg, 69117, Germany
| | - Ora Schueler-Furman
- Department of Microbiology and Molecular Genetics, Institute for Biomedical Research Israel-Canada, Faculty of Medicine, The Hebrew University of Jerusalem, Jerusalem, 9112001, Israel.
| | - Katja Luck
- Institute of Molecular Biology (IMB) gGmbH, 55128, Mainz, Germany.
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6
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Maaroufi H. Novel gurmarin-like peptides from Gymnema sylvestre and their interactions with the sweet taste receptor T1R2/T1R3. Chem Senses 2024; 49:bjae018. [PMID: 38695158 PMCID: PMC11103048 DOI: 10.1093/chemse/bjae018] [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] [Indexed: 05/21/2024] Open
Abstract
Gymnema sylvestre (GS) is a traditional medicinal plant known for its hypoglycemic and hypolipidemic effects. Gurmarin (hereafter Gur-1) is the only known active peptide in GS. Gur-1 has a suppressive sweet taste effect in rodents but no or only a very weak effect in humans. Here, 8 gurmarin-like peptides (Gur-2 to Gur-9) and their isoforms are reported in the GS transcriptome. The molecular mechanism of sweet taste suppression by Gur-1 is still largely unknown. Therefore, the complete architecture of human and mouse sweet taste receptors T1R2/T1R3 and their interaction with Gur-1 to Gur-9 were predicted by AlphaFold-Multimer (AF-M) and validated. Only Gur-1 and Gur-2 interact with the T1R2/T1R3 receptor. Indeed, Gur-1 and Gur-2 bind to the region of the cysteine-rich domain (CRD) and the transmembrane domain (TMD) of the mouse T1R2 subunit. In contrast, only Gur-2 binds to the TMD of the human T1R2 subunit. This result suggests that Gur-2 may have a suppressive sweet taste effect in humans. Furthermore, AF-M predicted that Gα-gustducin, a protein involved in sweet taste transduction, interacts with the intracellular domain of the T1R2 subunit. These results highlight an unexpected diversity of gurmarin-like peptides in GS and provide the complete predicted architecture of the human and mouse sweet taste receptor with the putative binding sites of Gur-1, Gur-2, and Gα-gustducin. In addition, gurmarin-like peptides may serve as promising drug scaffolds for the development of antidiabetic molecules.
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Affiliation(s)
- Halim Maaroufi
- Institut de biologie intégrative et des systèmes (IBIS), Université Laval, Quebec City, Quebec, Canada
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7
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Ochoa R, Fox T. Assessing the fast prediction of peptide conformers and the impact of non-natural modifications. J Mol Graph Model 2023; 125:108608. [PMID: 37659134 DOI: 10.1016/j.jmgm.2023.108608] [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: 06/29/2023] [Revised: 08/17/2023] [Accepted: 08/18/2023] [Indexed: 09/04/2023]
Abstract
We present an assessment of different approaches to predict peptide structures using modeling tools. Several small molecule, protein, and peptide-focused methodologies were used for the fast prediction of conformers for peptides shorter than 30 amino acids. We assessed the effect of including restraints based on annotated or predicted secondary structure motifs. A number of peptides in bound conformations and in solution were collected to compare the tools. In addition, we studied the impact of changing single amino acids to non-natural residues using molecular dynamics simulations. Deep learning methods such as AlphaFold2, or the combination of physics-based approaches with secondary structure information, produce the most accurate results for natural sequences. In the case of peptides with non-natural modifications, modeling the peptide containing natural amino acids first and then modifying and simulating the peptide using benchmarked force fields is a recommended pipeline. The results can guide the modeling of oligopeptides for drug discovery projects.
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Affiliation(s)
- Rodrigo Ochoa
- Medicinal Chemistry, Boehringer Ingelheim Pharma GmbH & Co KG, 88397 Biberach/Riss, Germany.
| | - Thomas Fox
- Medicinal Chemistry, Boehringer Ingelheim Pharma GmbH & Co KG, 88397 Biberach/Riss, Germany
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8
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Li M, Qing R, Tao F, Xu P, Zhang S. Dynamic Dimerization of Chemokine Receptors and Potential Inhibitory Role of Their Truncated Isoforms Revealed through Combinatorial Prediction. Int J Mol Sci 2023; 24:16266. [PMID: 38003455 PMCID: PMC10671024 DOI: 10.3390/ijms242216266] [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: 10/07/2023] [Revised: 11/03/2023] [Accepted: 11/08/2023] [Indexed: 11/26/2023] Open
Abstract
Chemokine receptors play crucial roles in fundamental biological processes. Their malfunction may result in many diseases, including cancer, autoimmune diseases, and HIV. The oligomerization of chemokine receptors holds significant functional implications that directly affect their signaling patterns and pharmacological responses. However, the oligomerization patterns of many chemokine receptors remain poorly understood. Furthermore, several chemokine receptors have highly truncated isoforms whose functional role is not yet clear. Here, we computationally show homo- and heterodimerization patterns of four human chemokine receptors, namely CXCR2, CXCR7, CCR2, and CCR7, along with their interaction patterns with their respective truncated isoforms. By combining the neural network-based AlphaFold2 and physics-based protein-protein docking tool ClusPro, we predicted 15 groups of complex structures and assessed the binding affinities in the context of atomistic molecular dynamics simulations. Our results are in agreement with previous experimental observations and support the dynamic and diverse nature of chemokine receptor dimerization, suggesting possible patterns of higher-order oligomerization. Additionally, we uncover the strong potential of truncated isoforms to block homo- and heterodimerization of chemokine receptors, also in a dynamic manner. Our study provides insights into the dimerization patterns of chemokine receptors and the functional significance of their truncated isoforms.
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Affiliation(s)
- Mengke Li
- Laboratory of Molecular Architecture, Media Lab, Massachusetts Institute of Technology, Cambridge, MA 02139, USA;
- State Key Laboratory of Microbial Metabolism, Joint International Research Laboratory of Metabolic and Developmental Sciences, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai 200240, China; (R.Q.); (F.T.); (P.X.)
| | - Rui Qing
- State Key Laboratory of Microbial Metabolism, Joint International Research Laboratory of Metabolic and Developmental Sciences, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai 200240, China; (R.Q.); (F.T.); (P.X.)
| | - Fei Tao
- State Key Laboratory of Microbial Metabolism, Joint International Research Laboratory of Metabolic and Developmental Sciences, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai 200240, China; (R.Q.); (F.T.); (P.X.)
| | - Ping Xu
- State Key Laboratory of Microbial Metabolism, Joint International Research Laboratory of Metabolic and Developmental Sciences, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai 200240, China; (R.Q.); (F.T.); (P.X.)
| | - Shuguang Zhang
- Laboratory of Molecular Architecture, Media Lab, Massachusetts Institute of Technology, Cambridge, MA 02139, USA;
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9
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Guo L, Zhang Y, Fang G, Tie L, Zhuang Y, Xue C, Liu Q, Zhang M, Zhu K, You C, Xu P, Yuan Q, Zhang C, Liu L, Rong N, Peng S, Liu Y, Wang C, Luo X, Lv Z, Kang D, Yu X, Zhang C, Jiang Y, Dong X, Zhou J, Liu Z, Yang F, Eric Xu H, Sun JP. Ligand recognition and G protein coupling of the human itch receptor MRGPRX1. Nat Commun 2023; 14:5004. [PMID: 37591889 PMCID: PMC10435460 DOI: 10.1038/s41467-023-40705-z] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/18/2023] [Accepted: 08/08/2023] [Indexed: 08/19/2023] Open
Abstract
MRGPRX1, a Mas-related GPCR (MRGPR), is a key receptor for itch perception and targeting MRGPRX1 may have potential to treat both chronic itch and pain. Here we report cryo-EM structures of the MRGPRX1-Gi1 and MRGPRX1-Gq trimers in complex with two peptide ligands, BAM8-22 and CNF-Tx2. These structures reveal a shallow orthosteric pocket and its conformational plasticity for sensing multiple different peptidic itch allergens. Distinct from MRGPRX2, MRGPRX1 contains a unique pocket feature at the extracellular ends of TM3 and TM4 to accommodate the peptide C-terminal "RF/RY" motif, which could serve as key mechanisms for peptidic allergen recognition. Below the ligand binding pocket, the G6.48XP6.50F6.51G6.52X(2)F/W6.55 motif is essential for the inward tilting of the upper end of TM6 to induce receptor activation. Moreover, structural features inside the ligand pocket and on the cytoplasmic side of MRGPRX1 are identified as key elements for both Gi and Gq signaling. Collectively, our studies provide structural insights into understanding itch sensation, MRGPRX1 activation, and downstream G protein signaling.
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Affiliation(s)
- Lulu Guo
- Advanced Medical Research Institute, Cheeloo College of Medicine, Shandong University, Jinan, China
- Key Laboratory of Experimental Teratology of the Ministry of Education, Department of Biochemistry and Molecular Biology, Shandong University School of Medicine, Jinan, China
| | - Yumu Zhang
- State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai, China
- School of Life Science and Technology, ShanghaiTech University, Shanghai, 201210, China
| | - Guoxing Fang
- Key Laboratory Experimental Teratology of the Ministry of Education and Department of Physiology, School of Basic Medical Sciences, Shandong University, Jinan, Shandong, China
| | - Lu Tie
- Department of Pharmacology, School of Basic Medical Sciences, Peking University and Beijing Key Laboratory of Tumor Systems Biology, Peking University, Beijing, China
| | - Yuming Zhuang
- Key Laboratory Experimental Teratology of the Ministry of Education and Department of Physiology, School of Basic Medical Sciences, Shandong University, Jinan, Shandong, China
| | - Chenyang Xue
- Department of Immunology and Microbiology, Southern University of Science and Technology, Shenzhen, Guangdong, China
| | - Qi Liu
- Advanced Medical Research Institute, Cheeloo College of Medicine, Shandong University, Jinan, China
| | - Minghui Zhang
- Key Laboratory of Experimental Teratology of the Ministry of Education, Department of Biochemistry and Molecular Biology, Shandong University School of Medicine, Jinan, China
| | - Kongkai Zhu
- Key Laboratory of Experimental Teratology of the Ministry of Education, Department of Biochemistry and Molecular Biology, Shandong University School of Medicine, Jinan, China
| | - Chongzhao You
- State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai, China
| | - Peiyu Xu
- State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai, China
| | - Qingning Yuan
- State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai, China
| | - Chao Zhang
- Key Laboratory of Experimental Teratology of the Ministry of Education, Department of Biochemistry and Molecular Biology, Shandong University School of Medicine, Jinan, China
| | - Lei Liu
- Advanced Medical Research Institute, Cheeloo College of Medicine, Shandong University, Jinan, China
| | - Naikang Rong
- Key Laboratory of Experimental Teratology of the Ministry of Education, Department of Biochemistry and Molecular Biology, Shandong University School of Medicine, Jinan, China
| | - Shengxuan Peng
- Advanced Medical Research Institute, Cheeloo College of Medicine, Shandong University, Jinan, China
| | - Yuan Liu
- Qilu Hospital, Cheeloo College of Medicine, Shandong University, Jinan, Shandong, China
| | - Chuanzheng Wang
- Advanced Medical Research Institute, Cheeloo College of Medicine, Shandong University, Jinan, China
| | - Xin Luo
- Advanced Medical Research Institute, Cheeloo College of Medicine, Shandong University, Jinan, China
| | - Zongyao Lv
- Advanced Medical Research Institute, Cheeloo College of Medicine, Shandong University, Jinan, China
| | - Dongwei Kang
- Department of Medicinal Chemistry, Key Laboratory of Chemical Biology (Ministry of Education), School of Pharmaceutical Sciences, Shandong University, Jinan, China
| | - Xiao Yu
- Key Laboratory Experimental Teratology of the Ministry of Education and Department of Physiology, School of Basic Medical Sciences, Shandong University, Jinan, Shandong, China
| | - Cheng Zhang
- Qilu Hospital, Cheeloo College of Medicine, Shandong University, Jinan, Shandong, China
| | - Yi Jiang
- Lingang Laboratory, Shanghai, China
| | - Xinzhong Dong
- The Solomon H. Snyder Department of Neuroscience, Johns Hopkins University School of Medicine, Baltimore, MD, USA
- Howard Hughes Medical Institute, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Jiuyao Zhou
- Department of Pharmacology, School of Pharmaceutical Sciences, Guangzhou University of Chinese Medicine, Guangzhou, China.
| | - Zhongmin Liu
- Department of Immunology and Microbiology, Southern University of Science and Technology, Shenzhen, Guangdong, China.
| | - Fan Yang
- Advanced Medical Research Institute, Cheeloo College of Medicine, Shandong University, Jinan, China.
- Key Laboratory Experimental Teratology of the Ministry of Education and Department of Physiology, School of Basic Medical Sciences, Shandong University, Jinan, Shandong, China.
| | - H Eric Xu
- State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai, China.
| | - Jin-Peng Sun
- Advanced Medical Research Institute, Cheeloo College of Medicine, Shandong University, Jinan, China.
- Key Laboratory of Experimental Teratology of the Ministry of Education, Department of Biochemistry and Molecular Biology, Shandong University School of Medicine, Jinan, China.
- Department of Pharmacology, School of Basic Medical Sciences, Peking University and Beijing Key Laboratory of Tumor Systems Biology, Peking University, Beijing, China.
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10
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Rogers JR, Nikolényi G, AlQuraishi M. Growing ecosystem of deep learning methods for modeling protein-protein interactions. Protein Eng Des Sel 2023; 36:gzad023. [PMID: 38102755 DOI: 10.1093/protein/gzad023] [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: 10/10/2023] [Revised: 12/06/2023] [Accepted: 12/07/2023] [Indexed: 12/17/2023] Open
Abstract
Numerous cellular functions rely on protein-protein interactions. Efforts to comprehensively characterize them remain challenged however by the diversity of molecular recognition mechanisms employed within the proteome. Deep learning has emerged as a promising approach for tackling this problem by exploiting both experimental data and basic biophysical knowledge about protein interactions. Here, we review the growing ecosystem of deep learning methods for modeling protein interactions, highlighting the diversity of these biophysically informed models and their respective trade-offs. We discuss recent successes in using representation learning to capture complex features pertinent to predicting protein interactions and interaction sites, geometric deep learning to reason over protein structures and predict complex structures, and generative modeling to design de novo protein assemblies. We also outline some of the outstanding challenges and promising new directions. Opportunities abound to discover novel interactions, elucidate their physical mechanisms, and engineer binders to modulate their functions using deep learning and, ultimately, unravel how protein interactions orchestrate complex cellular behaviors.
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Affiliation(s)
- Julia R Rogers
- Department of Systems Biology, Columbia University, New York, NY 10032, USA
| | - Gergő Nikolényi
- Department of Systems Biology, Columbia University, New York, NY 10032, USA
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