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Dong B, Liu Z, Xu D, Hou C, Dong G, Zhang T, Wang G. SERT-StructNet: Protein secondary structure prediction method based on multi-factor hybrid deep model. Comput Struct Biotechnol J 2024; 23:1364-1375. [PMID: 38596312 PMCID: PMC11001767 DOI: 10.1016/j.csbj.2024.03.018] [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: 12/05/2023] [Revised: 03/20/2024] [Accepted: 03/21/2024] [Indexed: 04/11/2024] Open
Abstract
Protein secondary structure prediction (PSSP) is a pivotal research endeavour that plays a crucial role in the comprehensive elucidation of protein functions and properties. Current prediction methodologies are focused on deep-learning techniques, particularly focusing on multi-factor features. Diverging from existing approaches, in this study, we placed special emphasis on the effects of amino acid properties and protein secondary structure propensity scores (SSPs) on secondary structure during the meticulous selection of multi-factor features. This differential feature-selection strategy results in a distinctive and effective amalgamation of the sequence and property features. To harness these multi-factor features optimally, we introduced a hybrid deep feature extraction model. The model initially employs mechanisms such as dilated convolution (D-Conv) and a channel attention network (SENet) for local feature extraction and targeted channel enhancement. Subsequently, a combination of recurrent neural network variants (BiGRU and BiLSTM), along with a transformer module, was employed to achieve global bidirectional information consideration and feature enhancement. This approach to multi-factor feature input and multi-level feature processing enabled a comprehensive exploration of intricate associations among amino acid residues in protein sequences, yielding a Q 3 accuracy of 84.9% and an Sov score of 85.1%. The overall performance surpasses that of the comparable methods. This study introduces a novel and efficient method for determining the PSSP domain, which is poised to deepen our understanding of the practical applications of protein molecular structures.
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Affiliation(s)
- Benzhi Dong
- College of Computer and Control Engineering, Northeast Forestry University, Harbin 150040, China
| | - Zheng Liu
- College of Computer and Control Engineering, Northeast Forestry University, Harbin 150040, China
| | - Dali Xu
- College of Computer and Control Engineering, Northeast Forestry University, Harbin 150040, China
| | - Chang Hou
- College of Computer and Control Engineering, Northeast Forestry University, Harbin 150040, China
| | - Guanghui Dong
- College of Computer and Control Engineering, Northeast Forestry University, Harbin 150040, China
| | - Tianjiao Zhang
- College of Computer and Control Engineering, Northeast Forestry University, Harbin 150040, China
| | - Guohua Wang
- College of Computer and Control Engineering, Northeast Forestry University, Harbin 150040, China
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Kuang K, Chen X, Wang M, Han W, Qiu X, Jin T, Xu R, Yuan B, Qian M, Li C, Xiang R, Li F, Zhang S, Yang Z, Du J, Li D, Zhang C, Wang Q, Jia T. Design and Discovery of New Collagen V-Derived FGF2-Blocking Natural Peptides Inhibiting Lung Squamous Cell Carcinoma In Vitro and In Vivo. J Med Chem 2024. [PMID: 39045829 DOI: 10.1021/acs.jmedchem.4c00654] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/25/2024]
Abstract
Aberrant FGF2/FGFR signaling is implicated in lung squamous cell carcinoma (LSCC), posing treatment challenges due to the lack of targeted therapeutic options. Designing drugs that block FGF2 signaling presents a promising strategy different from traditional kinase inhibitors. We previously reported a ColVα1-derived fragment, HEPV (127AA), that inhibits FGF2-induced angiogenesis. However, its large size may limit therapeutic application. This study combines rational peptide design, molecular dynamics simulations, knowledge-based prediction, and GUV and FRET assays to identify smaller peptides with FGF2-blocking properties. We synthesized two novel peptides, HBS-P1 (45AA) and HBS-P2 (66AA), that retained the heparin-binding site. Both peptides demonstrated anti-LSCC and antiangiogenesis properties in cell viability and microvessel network induction assays. In two LSCC subcutaneous models, HBS-P1, with its affinity for FGF2 and enhanced penetration ability, demonstrated substantial therapeutic potential without apparent toxicities. Our study provides the first evidence supporting the development of collagen V-derived natural peptides as FGF2-blocking agents for LSCC treatment.
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Affiliation(s)
- Keli Kuang
- Key Laboratory of Drug-Targeting and Drug Delivery System of the Education Ministry and Sichuan Province, Sichuan Engineering Laboratory for Plant-Sourced Drug and Sichuan Research Center for Drug Precision Industrial Technology, West China School of Pharmacy, Sichuan University, Chengdu 610041, China
| | - Xiang Chen
- Key Laboratory of Drug-Targeting and Drug Delivery System of the Education Ministry and Sichuan Province, Sichuan Engineering Laboratory for Plant-Sourced Drug and Sichuan Research Center for Drug Precision Industrial Technology, West China School of Pharmacy, Sichuan University, Chengdu 610041, China
| | - Maolin Wang
- Key Laboratory of Drug-Targeting and Drug Delivery System of the Education Ministry and Sichuan Province, Sichuan Engineering Laboratory for Plant-Sourced Drug and Sichuan Research Center for Drug Precision Industrial Technology, West China School of Pharmacy, Sichuan University, Chengdu 610041, China
| | - Weijing Han
- Songshan Lake Materials Laboratory, Dongguan, Guangdong 523808, China
| | - Xue Qiu
- Key Laboratory of Marine Drug, Ministry of Education, School of Medicine and Pharmacy, Ocean University of China, Qingdao 266003, China
- Laboratory forMarine Drugs and Bioproducts, Qingdao National Laboratory for Marine Scienceand Technology, Ocean University of China, Qingdao 266237, China
| | - Taoli Jin
- Songshan Lake Materials Laboratory, Dongguan, Guangdong 523808, China
| | - Rong Xu
- Songshan Lake Materials Laboratory, Dongguan, Guangdong 523808, China
| | - Bing Yuan
- Songshan Lake Materials Laboratory, Dongguan, Guangdong 523808, China
| | - Meiqi Qian
- Key Laboratory of Marine Drug, Ministry of Education, School of Medicine and Pharmacy, Ocean University of China, Qingdao 266003, China
- Laboratory forMarine Drugs and Bioproducts, Qingdao National Laboratory for Marine Scienceand Technology, Ocean University of China, Qingdao 266237, China
| | - Chunyan Li
- Key Laboratory of Drug-Targeting and Drug Delivery System of the Education Ministry and Sichuan Province, Sichuan Engineering Laboratory for Plant-Sourced Drug and Sichuan Research Center for Drug Precision Industrial Technology, West China School of Pharmacy, Sichuan University, Chengdu 610041, China
| | - Run Xiang
- Department of Thoracic Surgery, Sichuan Clinical Research Center for Cancer, Sichuan Cancer Hospital & Institute, Sichuan Cancer Center, Affiliated Cancer Hospital of University of Electronic Science and Technology of China, Chengdu 610041, China
| | - Fei Li
- Key Laboratory of Drug-Targeting and Drug Delivery System of the Education Ministry and Sichuan Province, Sichuan Engineering Laboratory for Plant-Sourced Drug and Sichuan Research Center for Drug Precision Industrial Technology, West China School of Pharmacy, Sichuan University, Chengdu 610041, China
| | - Shuwen Zhang
- Key Laboratory of Drug-Targeting and Drug Delivery System of the Education Ministry and Sichuan Province, Sichuan Engineering Laboratory for Plant-Sourced Drug and Sichuan Research Center for Drug Precision Industrial Technology, West China School of Pharmacy, Sichuan University, Chengdu 610041, China
| | - Zi Yang
- Key Laboratory of Drug-Targeting and Drug Delivery System of the Education Ministry and Sichuan Province, Sichuan Engineering Laboratory for Plant-Sourced Drug and Sichuan Research Center for Drug Precision Industrial Technology, West China School of Pharmacy, Sichuan University, Chengdu 610041, China
| | - Junrong Du
- Key Laboratory of Drug-Targeting and Drug Delivery System of the Education Ministry and Sichuan Province, Sichuan Engineering Laboratory for Plant-Sourced Drug and Sichuan Research Center for Drug Precision Industrial Technology, West China School of Pharmacy, Sichuan University, Chengdu 610041, China
| | - Dapeng Li
- Key Laboratory of Drug-Targeting and Drug Delivery System of the Education Ministry and Sichuan Province, Sichuan Engineering Laboratory for Plant-Sourced Drug and Sichuan Research Center for Drug Precision Industrial Technology, West China School of Pharmacy, Sichuan University, Chengdu 610041, China
| | - Chun Zhang
- Key Laboratory of Drug-Targeting and Drug Delivery System of the Education Ministry and Sichuan Province, Sichuan Engineering Laboratory for Plant-Sourced Drug and Sichuan Research Center for Drug Precision Industrial Technology, West China School of Pharmacy, Sichuan University, Chengdu 610041, China
| | - Qiantao Wang
- Key Laboratory of Drug-Targeting and Drug Delivery System of the Education Ministry and Sichuan Province, Sichuan Engineering Laboratory for Plant-Sourced Drug and Sichuan Research Center for Drug Precision Industrial Technology, West China School of Pharmacy, Sichuan University, Chengdu 610041, China
| | - Tao Jia
- Key Laboratory of Drug-Targeting and Drug Delivery System of the Education Ministry and Sichuan Province, Sichuan Engineering Laboratory for Plant-Sourced Drug and Sichuan Research Center for Drug Precision Industrial Technology, West China School of Pharmacy, Sichuan University, Chengdu 610041, China
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3
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Li ZL, Sun CQ, Qing ZL, Li ZM, Liu HL. Engineering the thermal stability of a polyphosphate kinase by ancestral sequence reconstruction to expand the temperature boundary for an industrially applicable ATP regeneration system. Appl Environ Microbiol 2024; 90:e0157423. [PMID: 38236018 PMCID: PMC10880597 DOI: 10.1128/aem.01574-23] [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: 09/09/2023] [Accepted: 12/06/2023] [Indexed: 01/19/2024] Open
Abstract
ATP-dependent energy-consuming enzymatic reactions are widely used in cell-free biocatalysis. However, the direct addition of large amounts of expensive ATP can greatly increase cost, and enzymatic production is often difficult to achieve as a result. Although a polyphosphate kinase (PPK)-polyphosphate-based ATP regeneration system has the potential to solve this challenge, the generally poor thermal stability of PPKs limits the widespread use of this method. In this paper, we evaluated the thermal stability of a PPK from Sulfurovum lithotrophicum (SlPPK2). After directed evolution and computation-supported design, we found that SlPPK2 is very recalcitrant and cannot acquire beneficial mutations. Inspired by the usually outstanding stability of ancestral enzymes, we reconstructed the ancestral sequence of the PPK family and used it as a guide to construct three heat-stable variants of SlPPK2, of which the L35F/T144S variant has a half-life of more than 14 h at 60°C. Molecular dynamics simulations were performed on all enzymes to analyze the reasons for the increased thermal stability. The results showed that mutations at these two positions act synergistically from the interior and surface of the protein, leading to a more compact structure. Finally, the robustness of the L35F/T144S variant was verified in the synthesis of nucleotides at high temperature. In practice, the use of this high-temperature ATP regeneration system can effectively avoid byproduct accumulation. Our work extends the temperature boundary of ATP regeneration and has great potential for industrial applications.IMPORTANCEATP regeneration is an important basic applied study in the field of cell-free biocatalysis. Polyphosphate kinase (PPK) is an enzyme tool widely used for energy regeneration during enzymatic reactions. However, the thermal stability of the PPKs reported to date that can efficiently regenerate ATP is usually poor, which greatly limits their application. In this study, the thermal stability of a difficult-to-engineer PPK from Sulfurovum lithotrophicum was improved, guided by an ancestral sequence reconstruction strategy. The optimal variant has a 4.5-fold longer half-life at 60°C than the wild-type enzyme, thus enabling the extension of the temperature boundary for ATP regeneration. The ability of this variant to regenerate ATP was well demonstrated during high-temperature enzymatic production of nucleotides.
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Affiliation(s)
- Zong-Lin Li
- State Key Laboratory of Bioreactor Engineering, East China University of Science and Technology, Shanghai, China
| | - Chuan-Qi Sun
- State Key Laboratory of Bioreactor Engineering, East China University of Science and Technology, Shanghai, China
| | - Zhou-Lei Qing
- State Key Laboratory of Bioreactor Engineering, East China University of Science and Technology, Shanghai, China
| | - Zhi-Min Li
- State Key Laboratory of Bioreactor Engineering, East China University of Science and Technology, Shanghai, China
- Shanghai Collaborative Innovation Center for Biomanufacturing Technology, Shanghai, China
| | - Hong-Lai Liu
- School of Chemistry and Molecular Engineering, East China University of Science and Technology, Shanghai, China
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Du Z, Peng Z, Yang J. RNA threading with secondary structure and sequence profile. Bioinformatics 2024; 40:btae080. [PMID: 38341662 PMCID: PMC10893584 DOI: 10.1093/bioinformatics/btae080] [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: 08/30/2022] [Revised: 01/05/2024] [Accepted: 02/09/2024] [Indexed: 02/12/2024] Open
Abstract
MOTIVATION RNA threading aims to identify remote homologies for template-based modeling of RNA 3D structure. Existing RNA alignment methods primarily rely on secondary structure alignment. They are often time- and memory-consuming, limiting large-scale applications. In addition, the accuracy is far from satisfactory. RESULTS Using RNA secondary structure and sequence profile, we developed a novel RNA threading algorithm, named RNAthreader. To enhance the alignment process and minimize memory usage, a novel approach has been introduced to simplify RNA secondary structures into compact diagrams. RNAthreader employs a two-step methodology. Initially, integer programming and dynamic programming are combined to create an initial alignment for the simplified diagram. Subsequently, the final alignment is obtained using dynamic programming, taking into account the initial alignment derived from the previous step. The benchmark test on 80 RNAs illustrates that RNAthreader generates more accurate alignments than other methods, especially for RNAs with pseudoknots. Another benchmark, involving 30 RNAs from the RNA-Puzzles experiments, exhibits that the models constructed using RNAthreader templates have a lower average RMSD than those created by alternative methods. Remarkably, RNAthreader takes less than two hours to complete alignments with ∼5000 RNAs, which is 3-40 times faster than other methods. These compelling results suggest that RNAthreader is a promising algorithm for RNA template detection. AVAILABILITY AND IMPLEMENTATION https://yanglab.qd.sdu.edu.cn/RNAthreader.
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Affiliation(s)
- Zongyang Du
- Chongqing Key Laboratory of Big Data for Bio Intelligence, Chongqing University of Posts and Telecommunications, Chongqing 400065, China
| | - Zhenling Peng
- MOE Frontiers Science Center for Nonlinear Expectations, Research Center for Mathematics and Interdisciplinary Sciences, Shandong University, Qingdao 266237, China
| | - Jianyi Yang
- MOE Frontiers Science Center for Nonlinear Expectations, Research Center for Mathematics and Interdisciplinary Sciences, Shandong University, Qingdao 266237, China
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Huang WC, Lin WT, Hung MS, Lee JC, Tung CW. Decrypting orphan GPCR drug discovery via multitask learning. J Cheminform 2024; 16:10. [PMID: 38263092 PMCID: PMC10804799 DOI: 10.1186/s13321-024-00806-3] [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: 09/28/2023] [Accepted: 01/18/2024] [Indexed: 01/25/2024] Open
Abstract
The drug discovery of G protein-coupled receptors (GPCRs) superfamily using computational models is often limited by the availability of protein three-dimensional (3D) structures and chemicals with experimentally measured bioactivities. Orphan GPCRs without known ligands further complicate the process. To enable drug discovery for human orphan GPCRs, multitask models were proposed for predicting half maximal effective concentrations (EC50) of the pairs of chemicals and GPCRs. Protein multiple sequence alignment features, and physicochemical properties and fingerprints of chemicals were utilized to encode the protein and chemical information, respectively. The protein features enabled the transfer of data-rich GPCRs to orphan receptors and the transferability based on the similarity of protein features. The final model was trained using both agonist and antagonist data from 200 GPCRs and showed an excellent mean squared error (MSE) of 0.24 in the validation dataset. An independent test using the orphan dataset consisting of 16 receptors associated with less than 8 bioactivities showed a reasonably good MSE of 1.51 that can be further improved to 0.53 by considering the transferability based on protein features. The informative features were identified and mapped to corresponding 3D structures to gain insights into the mechanism of GPCR-ligand interactions across the GPCR family. The proposed method provides a novel perspective on learning ligand bioactivity within the diverse human GPCR superfamily and can potentially accelerate the discovery of therapeutic agents for orphan GPCRs.
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Affiliation(s)
- Wei-Cheng Huang
- Institute of Biotechnology and Pharmaceutical Research, National Health Research Institutes, Miaoli County, 35053, Taiwan
| | - Wei-Ting Lin
- Institute of Biotechnology and Pharmaceutical Research, National Health Research Institutes, Miaoli County, 35053, Taiwan
| | - Ming-Shiu Hung
- Institute of Biotechnology and Pharmaceutical Research, National Health Research Institutes, Miaoli County, 35053, Taiwan
| | - Jinq-Chyi Lee
- Institute of Biotechnology and Pharmaceutical Research, National Health Research Institutes, Miaoli County, 35053, Taiwan
| | - Chun-Wei Tung
- Institute of Biotechnology and Pharmaceutical Research, National Health Research Institutes, Miaoli County, 35053, Taiwan.
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6
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Bai XC, Gonen T, Gronenborn AM, Perrakis A, Thorn A, Yang J. Challenges and opportunities in macromolecular structure determination. Nat Rev Mol Cell Biol 2024; 25:7-12. [PMID: 37848590 DOI: 10.1038/s41580-023-00659-y] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 08/22/2023] [Indexed: 10/19/2023]
Affiliation(s)
- Xiao-Chen Bai
- Department of Biophysics, University of Texas Southwestern Medical Center, Dallas, TX, USA.
| | - Tamir Gonen
- Department of Biological Chemistry, University of California Los Angeles, Los Angeles, CA, USA.
- Howard Hughes Medical Institute, University of California Los Angeles, Los Angeles, CA, USA.
- Department of Physiology, University of California Los Angeles, Los Angeles, CA, USA.
| | | | - Anastassis Perrakis
- Oncode Institute, Division of Biochemistry, Netherlands Cancer Institute, Amsterdam, Netherlands.
| | - Andrea Thorn
- Institute for Nanostructure and Solid State Physics, University of Hamburg, Hamburg, Germany.
| | - Jianyi Yang
- MOE Frontiers Science Center for Nonlinear Expectations, Research Center for Mathematics and Interdisciplinary Sciences, Shandong University, Qingdao, China.
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7
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Peng Z, Wang W, Wei H, Li X, Yang J. Improved protein structure prediction with trRosettaX2, AlphaFold2, and optimized MSAs in CASP15. Proteins 2023; 91:1704-1711. [PMID: 37565699 DOI: 10.1002/prot.26570] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/03/2023] [Revised: 07/17/2023] [Accepted: 07/31/2023] [Indexed: 08/12/2023]
Abstract
We present the monomer and multimer structure prediction results of our methods in CASP15. We first designed an elaborate pipeline that leverages complementary sequence databases and advanced database searching algorithms to generate high-quality multiple sequence alignments (MSAs). Top MSAs were then selected for the subsequent step of structure prediction. We utilized trRosettaX2 and AlphaFold2 for monomer structure prediction (group name Yang-Server), and AlphaFold-Multimer for multimer structure prediction (group name Yang-Multimer). Yang-Server and Yang-Multimer are ranked at the top and the fourth, respectively, for monomer and multimer structure prediction. For 94 monomers, the average TM-score of the predicted structure models by Yang-Server is 0.876, compared to 0.798 by the default AlphaFold2 (i.e., the group NBIS-AF2-standard). For 42 multimers, the average DockQ score of the predicted structure models by Yang-Multimer is 0.464, compared to 0.389 by the default AlphaFold-Multimer (i.e., the group NBIS-AF2-multimer). Detailed analysis of the results shows that several factors contribute to the improvement, including improved MSAs, iterated modeling for large targets, interplay between monomer and multimer structure prediction for intertwined structures, etc. However, the structure predictions for orphan proteins and multimers remain challenging, and breakthroughs in this area are anticipated in the future.
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Affiliation(s)
- Zhenling Peng
- MOE Frontiers Science Center for Nonlinear Expectations, Research Center for Mathematics and Interdisciplinary Sciences, Shandong University, Qingdao, China
| | - Wenkai Wang
- School of Mathematical Sciences, Nankai University, Tianjin, China
| | - Hong Wei
- School of Mathematical Sciences, Nankai University, Tianjin, China
| | - Xiaoge Li
- MOE Frontiers Science Center for Nonlinear Expectations, Research Center for Mathematics and Interdisciplinary Sciences, Shandong University, Qingdao, China
| | - Jianyi Yang
- MOE Frontiers Science Center for Nonlinear Expectations, Research Center for Mathematics and Interdisciplinary Sciences, Shandong University, Qingdao, China
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Lin P, Yan Y, Tao H, Huang SY. Deep transfer learning for inter-chain contact predictions of transmembrane protein complexes. Nat Commun 2023; 14:4935. [PMID: 37582780 PMCID: PMC10427616 DOI: 10.1038/s41467-023-40426-3] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/27/2023] [Accepted: 07/21/2023] [Indexed: 08/17/2023] Open
Abstract
Membrane proteins are encoded by approximately a quarter of human genes. Inter-chain residue-residue contact information is important for structure prediction of membrane protein complexes and valuable for understanding their molecular mechanism. Although many deep learning methods have been proposed to predict the intra-protein contacts or helix-helix interactions in membrane proteins, it is still challenging to accurately predict their inter-chain contacts due to the limited number of transmembrane proteins. Addressing the challenge, here we develop a deep transfer learning method for predicting inter-chain contacts of transmembrane protein complexes, named DeepTMP, by taking advantage of the knowledge pre-trained from a large data set of non-transmembrane proteins. DeepTMP utilizes a geometric triangle-aware module to capture the correct inter-chain interaction from the coevolution information generated by protein language models. DeepTMP is extensively evaluated on a test set of 52 self-associated transmembrane protein complexes, and compared with state-of-the-art methods including DeepHomo2.0, CDPred, GLINTER, DeepHomo, and DNCON2_Inter. It is shown that DeepTMP considerably improves the precision of inter-chain contact prediction and outperforms the existing approaches in both accuracy and robustness.
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Affiliation(s)
- Peicong Lin
- School of Physics, Huazhong University of Science and Technology, Wuhan, Hubei, 430074, China
| | - Yumeng Yan
- School of Physics, Huazhong University of Science and Technology, Wuhan, Hubei, 430074, China
| | - Huanyu Tao
- School of Physics, Huazhong University of Science and Technology, Wuhan, Hubei, 430074, China
| | - Sheng-You Huang
- School of Physics, Huazhong University of Science and Technology, Wuhan, Hubei, 430074, China.
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9
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Sala D, Engelberger F, Mchaourab HS, Meiler J. Modeling conformational states of proteins with AlphaFold. Curr Opin Struct Biol 2023; 81:102645. [PMID: 37392556 DOI: 10.1016/j.sbi.2023.102645] [Citation(s) in RCA: 36] [Impact Index Per Article: 36.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/21/2023] [Revised: 05/16/2023] [Accepted: 06/01/2023] [Indexed: 07/03/2023]
Abstract
Many proteins exert their function by switching among different structures. Knowing the conformational ensembles affiliated with these states is critical to elucidate key mechanistic aspects that govern protein function. While experimental determination efforts are still bottlenecked by cost, time, and technical challenges, the machine-learning technology AlphaFold showed near experimental accuracy in predicting the three-dimensional structure of monomeric proteins. However, an AlphaFold ensemble of models usually represents a single conformational state with minimal structural heterogeneity. Consequently, several pipelines have been proposed to either expand the structural breadth of an ensemble or bias the prediction toward a desired conformational state. Here, we analyze how those pipelines work, what they can and cannot predict, and future directions.
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Affiliation(s)
- D Sala
- Institute of Drug Discovery, Faculty of Medicine, University of Leipzig, 04103 Leipzig, Germany. https://twitter.com/sala_davide
| | - F Engelberger
- Institute of Drug Discovery, Faculty of Medicine, University of Leipzig, 04103 Leipzig, Germany. https://twitter.com/fengel97
| | - H S Mchaourab
- Department of Molecular Physiology and Biophysics, Vanderbilt University, Nashville, TN, USA. https://twitter.com/Mchaourablab
| | - J Meiler
- Institute of Drug Discovery, Faculty of Medicine, University of Leipzig, 04103 Leipzig, Germany; Center for Structural Biology, Vanderbilt University, Nashville, TN 37240, USA; Center for Scalable Data Analytics and Artificial Intelligence (ScaDS.AI), Dresden/Leipzig, Germany.
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10
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Filgueiras JL, Varela D, Santos J. Protein structure prediction with energy minimization and deep learning approaches. NATURAL COMPUTING 2023:1-12. [PMID: 37363286 PMCID: PMC10165305 DOI: 10.1007/s11047-023-09943-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Accepted: 04/12/2023] [Indexed: 06/28/2023]
Abstract
In this paper we discuss the advantages and problems of two alternatives for ab initio protein structure prediction. On one hand, recent approaches based on deep learning, which have significantly improved prediction results for a wide variety of proteins, are discussed. On the other hand, methods based on protein conformational energy minimization and with different search strategies are analyzed. In this latter case, our methods based on a memetic combination between differential evolution and the fragment replacement technique are included, incorporating also the possibility of niching in the evolutionary search. Different proteins have been used to analyze the pros and cons in both approaches, proposing possibilities of integration of both alternatives.
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Affiliation(s)
- Juan Luis Filgueiras
- Department of Computer Science and Information Technologies, CITIC (Centre for Information and Communications Technology Research), University of A Coruña, A Coruña, Spain
| | - Daniel Varela
- Department of Computer Science and Information Technologies, CITIC (Centre for Information and Communications Technology Research), University of A Coruña, A Coruña, Spain
| | - José Santos
- Department of Computer Science and Information Technologies, CITIC (Centre for Information and Communications Technology Research), University of A Coruña, A Coruña, Spain
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11
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Pinto ÉSM, Krause MJ, Dorn M, Feltes BC. The nucleotide excision repair proteins through the lens of molecular dynamics simulations. DNA Repair (Amst) 2023; 127:103510. [PMID: 37148846 DOI: 10.1016/j.dnarep.2023.103510] [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: 10/26/2022] [Revised: 04/07/2023] [Accepted: 04/23/2023] [Indexed: 05/08/2023]
Abstract
Mutations that affect the proteins responsible for the nucleotide excision repair (NER) pathway can lead to diseases such as xeroderma pigmentosum, trichothiodystrophy, Cockayne syndrome, and Cerebro-oculo-facio-skeletal syndrome. Hence, understanding their molecular behavior is needed to elucidate these diseases' phenotypes and how the NER pathway is organized and coordinated. Molecular dynamics techniques enable the study of different protein conformations, adaptable to any research question, shedding light on the dynamics of biomolecules. However, as important as they are, molecular dynamics studies focused on DNA repair pathways are still becoming more widespread. Currently, there are no review articles compiling the advancements made in molecular dynamics approaches applied to NER and discussing: (i) how this technique is currently employed in the field of DNA repair, focusing on NER proteins; (ii) which technical setups are being employed, their strengths and limitations; (iii) which insights or information are they providing to understand the NER pathway or NER-associated proteins; (iv) which open questions would be suited for this technique to answer; and (v) where can we go from here. These questions become even more crucial considering the numerous 3D structures published regarding the NER pathway's proteins in recent years. In this work, we tackle each one of these questions, revising and critically discussing the results published in the context of the NER pathway.
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Affiliation(s)
| | - Mathias J Krause
- Institute for Applied and Numerical Mathematics, Karlsruhe Institute of Technology, Karlsruhe, Germany
| | - Márcio Dorn
- Center for Biotechnology, Federal University of Rio Grande do Sul, RS, Brazil; Institute of Informatics, Federal University of Rio Grande do Sul, Porto Alegre, RS, Brazil; National Institute of Science and Technology - Forensic Science, Porto Alegre, RS, Brazil
| | - Bruno César Feltes
- Institute of Informatics, Federal University of Rio Grande do Sul, Porto Alegre, RS, Brazil
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12
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Ji Y, Liu D, Zhu H, Bao L, Chang R, Gao X, Yin J. Unstructured Polypeptides as a Versatile Drug Delivery Technology. Acta Biomater 2023; 164:74-93. [PMID: 37075961 DOI: 10.1016/j.actbio.2023.04.019] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/11/2023] [Revised: 03/23/2023] [Accepted: 04/13/2023] [Indexed: 04/21/2023]
Abstract
Although polyethylene glycol (PEG), or "PEGylation" has become a widely applied approach for improving the efficiency of drug delivery, the immunogenicity and non-biodegradability of this synthetic polymer have prompted an evident need for alternatives. To overcome these caveats and to mimic PEG -or other natural or synthetic polymers- for the purpose of drug half-life extension, unstructured polypeptides are designed. Due to their tunable length, biodegradability, low immunogenicity and easy production, unstructured polypeptides have the potential to replace PEG as the preferred technology for therapeutic protein/peptide delivery. This review provides an overview of the evolution of unstructured polypeptides, starting from natural polypeptides to engineered polypeptides and discusses their characteristics. Then, it is described that unstructured polypeptides have been successfully applied to numerous drugs, including peptides, proteins, antibody fragments, and nanocarriers, for half-life extension. Innovative applications of unstructured peptides as releasable masks, multimolecular adaptors and intracellular delivery carriers are also discussed. Finally, challenges and future perspectives of this promising field are briefly presented. STATEMENT OF SIGNIFICANCE: : Polypeptide fusion technology simulating PEGylation has become an important topic for the development of long-circulating peptide or protein drugs without reduced activity, complex processes, and kidney injury caused by PEG modification. Here we provide a detailed and in-depth review of the recent advances in unstructured polypeptides. In addition to the application of enhanced pharmacokinetic performance, emphasis is placed on polypeptides as scaffolders for the delivery of multiple drugs, and on the preparation of reasonably designed polypeptides to manipulate the performance of proteins and peptides. This review will provide insight into future application of polypeptides in peptide or protein drug development and the design of novel functional polypeptides.
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Affiliation(s)
- Yue Ji
- Jiangsu Key Laboratory of Druggability of Biopharmaceuticals and State Key Laboratory of Natural Medicines, School of Life Science and Technology, China Pharmaceutical University, Nanjing 210009, China
| | - Dingkang Liu
- Jiangsu Key Laboratory of Druggability of Biopharmaceuticals and State Key Laboratory of Natural Medicines, School of Life Science and Technology, China Pharmaceutical University, Nanjing 210009, China
| | - Haichao Zhu
- Jiangsu Key Laboratory of Druggability of Biopharmaceuticals and State Key Laboratory of Natural Medicines, School of Life Science and Technology, China Pharmaceutical University, Nanjing 210009, China
| | - Lichen Bao
- Jiangsu Key Laboratory of Neurodegeneration, Nanjing Medical University, Nanjing 210009, China
| | - Ruilong Chang
- Jiangsu Key Laboratory of Druggability of Biopharmaceuticals and State Key Laboratory of Natural Medicines, School of Life Science and Technology, China Pharmaceutical University, Nanjing 210009, China
| | - Xiangdong Gao
- Jiangsu Key Laboratory of Druggability of Biopharmaceuticals and State Key Laboratory of Natural Medicines, School of Life Science and Technology, China Pharmaceutical University, Nanjing 210009, China.
| | - Jun Yin
- Jiangsu Key Laboratory of Druggability of Biopharmaceuticals and State Key Laboratory of Natural Medicines, School of Life Science and Technology, China Pharmaceutical University, Nanjing 210009, China.
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