1
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Banico EC, Sira EMJS, Fajardo LE, Dulay ANG, Odchimar NMO, Simbulan AM, Orosco FL. Advancing one health vaccination: In silico design and evaluation of a multi-epitope subunit vaccine against Nipah virus for cross-species immunization using immunoinformatics and molecular modeling. PLoS One 2024; 19:e0310703. [PMID: 39325755 PMCID: PMC11426463 DOI: 10.1371/journal.pone.0310703] [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: 04/01/2024] [Accepted: 09/05/2024] [Indexed: 09/28/2024] Open
Abstract
The resurgence of the Nipah virus (NiV) in 2023 has raised concerns for another potentially severe pandemic, given its history of high mortality from previous outbreaks. Unfortunately, no therapeutics and vaccines have been available for the virus. This study used immunoinformatics and molecular modeling to design and evaluate a multi-epitope subunit vaccine targeting NiV. The designed vaccine construct aims to stimulate immune responses in humans and two other intermediate animal hosts of the virus-swine and equine. Using several epitope prediction tools, ten peptides that induced B-lymphocyte responses, 17 peptides that induced cytotoxic T-lymphocyte (CTL) responses, and 12 peptides that induced helper T-lymphocyte (HTL) responses were mapped from nine NiV protein sequences. However, the CTL and HTL-inducing peptides were reduced to ten and eight, respectively, following molecular docking and dynamics. These screened peptides exhibited stability with 30 common major histocompatibility complex (MHC) receptors found in humans, swine, and equine. All peptides were linked using peptide linkers to form the multi-epitope construct and various adjuvants were tested to enhance its immunogenicity. The vaccine construct with resuscitation-promoting factor E (RpfE) adjuvant was selected as the final design based on its favorable physicochemical properties and superior immune response profile. Molecular docking was used to visualize the interaction of the vaccine to toll-like receptor 4 (TLR4), while molecular dynamics confirmed the structural stability of this interaction. Physicochemical property evaluation and computational simulations showed that the designed vaccine construct exhibited favorable properties and elicited higher antibody titers than the six multi-epitope NiV vaccine designs available in the literature. Further in vivo and in vitro experiments are necessary to validate the immunogenicity conferred by the designed vaccine construct and its epitope components. This study demonstrates the capability of computational methodologies in rational vaccine design and highlights the potential of cross-species vaccination strategies for mitigating potential NiV threats.
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Affiliation(s)
- Edward Coralde Banico
- Department of Science and Technology, Virology and Vaccine Research Program, Industrial Development Technology Institute, Taguig City, Metro Manila, Philippines
| | - Ella Mae Joy Sinco Sira
- Department of Science and Technology, Virology and Vaccine Research Program, Industrial Development Technology Institute, Taguig City, Metro Manila, Philippines
| | - Lauren Emily Fajardo
- Department of Science and Technology, Virology and Vaccine Research Program, Industrial Development Technology Institute, Taguig City, Metro Manila, Philippines
| | - Albert Neil Gura Dulay
- Department of Science and Technology, Virology and Vaccine Research Program, Industrial Development Technology Institute, Taguig City, Metro Manila, Philippines
| | - Nyzar Mabeth Obenio Odchimar
- Department of Science and Technology, Virology and Vaccine Research Program, Industrial Development Technology Institute, Taguig City, Metro Manila, Philippines
| | - Alea Maurice Simbulan
- Department of Science and Technology, Virology and Vaccine Research Program, Industrial Development Technology Institute, Taguig City, Metro Manila, Philippines
| | - Fredmoore Legaspi Orosco
- Department of Science and Technology, Virology and Vaccine Research Program, Industrial Development Technology Institute, Taguig City, Metro Manila, Philippines
- Department of Science and Technology, S&T Fellows Program, Taguig City, Metro Manila, Philippines
- Department of Biology, College of Arts and Sciences, University of the Philippines Manila, Manila City, Metro Manila, Philippines
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2
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Ma Y, Li B, Zhao X, Lu Y, Li X, Zhang J, Wang Y, Zhang J, Wang L, Meng S, Hao J. Computational modeling of mast cell tryptase family informs selective inhibitor development. iScience 2024; 27:110739. [PMID: 39280611 PMCID: PMC11396024 DOI: 10.1016/j.isci.2024.110739] [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: 05/03/2024] [Revised: 07/13/2024] [Accepted: 08/12/2024] [Indexed: 09/18/2024] Open
Abstract
Mast cell tryptases, a family of serine proteases involved in inflammatory responses and cancer development, present challenges in structural characterization and inhibitor development. We employed state-of-the-art protein structure prediction algorithms to model the three-dimensional structures of tryptases α, β, δ, γ, and ε with high accuracy. Computational docking identified potential substrates and inhibitors, suggesting overlapping yet distinct activities. Tryptases β, δ, and ε were predicted to act on phenolic compounds, with β and ε additionally hydrolyzing cyanides. Tryptase δ may possess unique formyl-CoA dehydrogenase activity. Virtual screening revealed 63 compounds exhibiting strong binding to tryptase β (TPSB2), 12 exceeding the affinity of the known inhibitor. Notably, the top hit (3-chloro-4-methylbenzimidamide) displayed over 10-fold selectivity for tryptase β over other isoforms. Our integrative approach combining protein modeling, functional annotation, and molecular docking provides a framework for characterizing tryptase isoforms and developing selective inhibitors of therapeutic potential in inflammatory and cancer conditions.
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Affiliation(s)
- Ying Ma
- Department of Pancreatic Cancer, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Tianjin's Clinical Research Center for Cancer, Tianjin Key Laboratory of Digestive Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin 300060, China
| | - Bole Li
- Department of Pharmacy, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin's Clinical Research Center for Cancer, Tianjin 300060, China
| | - Xiangqin Zhao
- Department of Pancreatic Cancer, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Tianjin's Clinical Research Center for Cancer, Tianjin Key Laboratory of Digestive Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin 300060, China
| | - Yi Lu
- Department of Pancreatic Cancer, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Tianjin's Clinical Research Center for Cancer, Tianjin Key Laboratory of Digestive Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin 300060, China
| | - Xuesong Li
- Department of Pancreatic Cancer, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Tianjin's Clinical Research Center for Cancer, Tianjin Key Laboratory of Digestive Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin 300060, China
| | - Jin Zhang
- Department of Bone and Soft Tissue Tumors, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin's Clinical Research Center for Cancer, Tianjin 300060, China
| | - Yifei Wang
- Department of Pancreatic Cancer, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Tianjin's Clinical Research Center for Cancer, Tianjin Key Laboratory of Digestive Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin 300060, China
| | - Jie Zhang
- Department of Pharmacy, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin's Clinical Research Center for Cancer, Tianjin 300060, China
| | - Lulu Wang
- Tianjin Key Laboratory of Technologies Enabling Development of Clinical Therapeutics and Diagnostics, The Province and Ministry Co-sponsored Collaborative Innovation Center for Medical Epigenetics, School of Pharmacy, Tianjin Medical University, Tianjin 300070, China
| | - Shuai Meng
- Department of Pharmacy, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin's Clinical Research Center for Cancer, Tianjin 300060, China
| | - Jihui Hao
- Department of Pancreatic Cancer, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Tianjin's Clinical Research Center for Cancer, Tianjin Key Laboratory of Digestive Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin 300060, China
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Rakibova Y, Dunham DT, Seed KD, Freddolino L. Nucleoid-associated proteins shape the global protein occupancy and transcriptional landscape of a clinical isolate of Vibrio cholerae. mSphere 2024; 9:e0001124. [PMID: 38920383 PMCID: PMC11288032 DOI: 10.1128/msphere.00011-24] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/08/2024] [Accepted: 05/21/2024] [Indexed: 06/27/2024] Open
Abstract
Vibrio cholerae, the causative agent of the diarrheal disease cholera, poses an ongoing health threat due to its wide repertoire of horizontally acquired elements (HAEs) and virulence factors. New clinical isolates of the bacterium with improved fitness abilities, often associated with HAEs, frequently emerge. The appropriate control and expression of such genetic elements is critical for the bacteria to thrive in the different environmental niches they occupy. H-NS, the histone-like nucleoid structuring protein, is the best-studied xenogeneic silencer of HAEs in gamma-proteobacteria. Although H-NS and other highly abundant nucleoid-associated proteins (NAPs) have been shown to play important roles in regulating HAEs and virulence in model bacteria, we still lack a comprehensive understanding of how different NAPs modulate transcription in V. cholerae. By obtaining genome-wide measurements of protein occupancy and active transcription in a clinical isolate of V. cholerae, harboring recently discovered HAEs encoding for phage defense systems, we show that a lack of H-NS causes a robust increase in the expression of genes found in many HAEs. We further found that TsrA, a protein with partial homology to H-NS, regulates virulence genes primarily through modulation of H-NS activity. We also identified few sites that are affected by TsrA independently of H-NS, suggesting TsrA may act with diverse regulatory mechanisms. Our results demonstrate how the combinatorial activity of NAPs is employed by a clinical isolate of an important pathogen to regulate recently discovered HAEs. IMPORTANCE New strains of the bacterial pathogen Vibrio cholerae, bearing novel horizontally acquired elements (HAEs), frequently emerge. HAEs provide beneficial traits to the bacterium, such as antibiotic resistance and defense against invading bacteriophages. Xenogeneic silencers are proteins that help bacteria harness new HAEs and silence those HAEs until they are needed. H-NS is the best-studied xenogeneic silencer; it is one of the nucleoid-associated proteins (NAPs) in gamma-proteobacteria and is responsible for the proper regulation of HAEs within the bacterial transcriptional network. We studied the effects of H-NS and other NAPs on the HAEs of a clinical isolate of V. cholerae. Importantly, we found that H-NS partners with a small and poorly characterized protein, TsrA, to help domesticate new HAEs involved in bacterial survival and in causing disease. A proper understanding of the regulatory state in emerging isolates of V. cholerae will provide improved therapies against new isolates of the pathogen.
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Affiliation(s)
- Yulduz Rakibova
- Department of Biological Chemistry, University of Michigan, Ann Arbor, Michigan, USA
| | - Drew T. Dunham
- Department of Plant and Microbial Biology, University of California, Berkeley, California, USA
| | - Kimberley D. Seed
- Department of Plant and Microbial Biology, University of California, Berkeley, California, USA
| | - Lydia Freddolino
- Department of Biological Chemistry, University of Michigan, Ann Arbor, Michigan, USA
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, Michigan, USA
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Mathiowetz AJ, Meymand ES, Deol KK, Parlakgül G, Lange M, Pang SP, Roberts MA, Torres EF, Jorgens DM, Zalpuri R, Kang M, Boone C, Zhang Y, Morgens DW, Tso E, Zhou Y, Talukdar S, Levine TP, Ku G, Arruda AP, Olzmann JA. CLCC1 promotes hepatic neutral lipid flux and nuclear pore complex assembly. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.06.07.597858. [PMID: 38895340 PMCID: PMC11185754 DOI: 10.1101/2024.06.07.597858] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/21/2024]
Abstract
Imbalances in lipid storage and secretion lead to the accumulation of hepatocyte lipid droplets (LDs) (i.e., hepatic steatosis). Our understanding of the mechanisms that govern the channeling of hepatocyte neutral lipids towards cytosolic LDs or secreted lipoproteins remains incomplete. Here, we performed a series of CRISPR-Cas9 screens under different metabolic states to uncover mechanisms of hepatic neutral lipid flux. Clustering of chemical-genetic interactions identified CLIC-like chloride channel 1 (CLCC1) as a critical regulator of neutral lipid storage and secretion. Loss of CLCC1 resulted in the buildup of large LDs in hepatoma cells and knockout in mice caused liver steatosis. Remarkably, the LDs are in the lumen of the ER and exhibit properties of lipoproteins, indicating a profound shift in neutral lipid flux. Finally, remote homology searches identified a domain in CLCC1 that is homologous to yeast Brl1p and Brr6p, factors that promote the fusion of the inner and outer nuclear envelopes during nuclear pore complex assembly. Loss of CLCC1 lead to extensive nuclear membrane herniations, consistent with impaired nuclear pore complex assembly. Thus, we identify CLCC1 as the human Brl1p/Brr6p homolog and propose that CLCC1-mediated membrane remodeling promotes hepatic neutral lipid flux and nuclear pore complex assembly.
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Affiliation(s)
- Alyssa J. Mathiowetz
- Department of Molecular and Cell Biology, University of California, Berkeley, Berkeley, CA 94720, USA
- Department of NutriYonal Sciences and Toxicology, University of California, Berkeley, Berkeley, CA 94720, USA
| | - Emily S. Meymand
- Department of Molecular and Cell Biology, University of California, Berkeley, Berkeley, CA 94720, USA
- Department of NutriYonal Sciences and Toxicology, University of California, Berkeley, Berkeley, CA 94720, USA
| | - Kirandeep K. Deol
- Department of Molecular and Cell Biology, University of California, Berkeley, Berkeley, CA 94720, USA
- Department of NutriYonal Sciences and Toxicology, University of California, Berkeley, Berkeley, CA 94720, USA
| | - Güneş Parlakgül
- Department of NutriYonal Sciences and Toxicology, University of California, Berkeley, Berkeley, CA 94720, USA
| | - Mike Lange
- Department of Molecular and Cell Biology, University of California, Berkeley, Berkeley, CA 94720, USA
- Department of NutriYonal Sciences and Toxicology, University of California, Berkeley, Berkeley, CA 94720, USA
| | - Stephany P. Pang
- Department of Molecular and Cell Biology, University of California, Berkeley, Berkeley, CA 94720, USA
- Department of NutriYonal Sciences and Toxicology, University of California, Berkeley, Berkeley, CA 94720, USA
| | - Melissa A. Roberts
- Department of Molecular and Cell Biology, University of California, Berkeley, Berkeley, CA 94720, USA
- Department of NutriYonal Sciences and Toxicology, University of California, Berkeley, Berkeley, CA 94720, USA
| | - Emily F. Torres
- Department of Molecular and Cell Biology, University of California, Berkeley, Berkeley, CA 94720, USA
- Department of NutriYonal Sciences and Toxicology, University of California, Berkeley, Berkeley, CA 94720, USA
| | - Danielle M. Jorgens
- Electron Microscope Laboratory, University of California, Berkeley, Berkeley, CA 94720, USA
| | - Reena Zalpuri
- Electron Microscope Laboratory, University of California, Berkeley, Berkeley, CA 94720, USA
| | - Misun Kang
- Electron Microscope Laboratory, University of California, Berkeley, Berkeley, CA 94720, USA
| | - Casadora Boone
- Department of Molecular and Cell Biology, University of California, Berkeley, Berkeley, CA 94720, USA
- Department of NutriYonal Sciences and Toxicology, University of California, Berkeley, Berkeley, CA 94720, USA
| | - Yaohuan Zhang
- Department of NutriYonal Sciences and Toxicology, University of California, Berkeley, Berkeley, CA 94720, USA
- Diabetes Center, University of California, San Francisco, San Francisco, CA 94143, USA
| | - David W. Morgens
- Department of Molecular and Cell Biology, University of California, Berkeley, Berkeley, CA 94720, USA
| | - Emily Tso
- Merck & Co., Inc., South San Francisco, CA 94080, USA
| | | | | | - Tim P. Levine
- University College London InsYtute of Ophthalmology, Bath Street London, EC1V 9EL, UK
| | - Gregory Ku
- Diabetes Center, University of California, San Francisco, San Francisco, CA 94143, USA
- Department of Medicine, Division of Endocrinology, University of California, San Francisco, San Francisco, CA 94143, USA
| | - Ana Paula Arruda
- Department of NutriYonal Sciences and Toxicology, University of California, Berkeley, Berkeley, CA 94720, USA
| | - James A. Olzmann
- Department of Molecular and Cell Biology, University of California, Berkeley, Berkeley, CA 94720, USA
- Department of NutriYonal Sciences and Toxicology, University of California, Berkeley, Berkeley, CA 94720, USA
- Chan Zuckerberg Biohub, San Francisco, CA 94158, USA
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5
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Zheng W, Wuyun Q, Zhang Y. One step forward towards deep-learning protein complex structure prediction by precise multiple sequence alignment construction. Clin Transl Med 2024; 14:e1689. [PMID: 38880984 PMCID: PMC11180690 DOI: 10.1002/ctm2.1689] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/20/2024] [Accepted: 04/26/2024] [Indexed: 06/18/2024] Open
Affiliation(s)
- Wei Zheng
- Department of Computational Medicine and BioinformaticsUniversity of MichiganAnn ArborMichiganUSA
| | - Qiqige Wuyun
- Department of Computer Science and EngineeringMichigan State UniversityEast LansingMichiganUSA
| | - Yang Zhang
- Cancer Science Institute of SingaporeNational University of SingaporeSingaporeSingapore
- Department of Computer Science, School of ComputingNational University of SingaporeSingaporeSingapore
- Department of Biochemistry, Yong Loo Lin School of MedicineNational University of SingaporeSingaporeSingapore
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6
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Rakibova Y, Dunham DT, Seed KD, Freddolino PL. Nucleoid-associated proteins shape the global protein occupancy and transcriptional landscape of a clinical isolate of Vibrio cholerae. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2023.12.30.573743. [PMID: 38260642 PMCID: PMC10802314 DOI: 10.1101/2023.12.30.573743] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/24/2024]
Abstract
Vibrio cholerae, the causative agent of the diarrheal disease cholera, poses an ongoing health threat due to its wide repertoire of horizontally acquired elements (HAEs) and virulence factors. New clinical isolates of the bacterium with improved fitness abilities, often associated with HAEs, frequently emerge. The appropriate control and expression of such genetic elements is critical for the bacteria to thrive in the different environmental niches it occupies. H-NS, the histone-like nucleoid structuring protein, is the best studied xenogeneic silencer of HAEs in gamma-proteobacteria. Although H-NS and other highly abundant nucleoid-associated proteins (NAPs) have been shown to play important roles in regulating HAEs and virulence in model bacteria, we still lack a comprehensive understanding of how different NAPs modulate transcription in V. cholerae. By obtaining genome-wide measurements of protein occupancy and active transcription in a clinical isolate of V. cholerae, harboring recently discovered HAEs encoding for phage defense systems, we show that a lack of H-NS causes a robust increase in the expression of genes found in many HAEs. We further found that TsrA, a protein with partial homology to H-NS, regulates virulence genes primarily through modulation of H-NS activity. We also identified a few sites that are affected by TsrA independently of H-NS, suggesting TsrA may act with diverse regulatory mechanisms. Our results demonstrate how the combinatorial activity of NAPs is employed by a clinical isolate of an important pathogen to regulate recently discovered HAEs. Importance New strains of the bacterial pathogen Vibrio cholerae, bearing novel horizontally acquired elements (HAEs), frequently emerge. HAEs provide beneficial traits to the bacterium, such as antibiotic resistance and defense against invading bacteriophages. Xenogeneic silencers are proteins that help bacteria harness new HAEs and silence those HAEs until they are needed. H-NS is the best-studied xenogeneic silencer; it is one of the nucleoid-associated proteins (NAPs) in gamma-proteobacteria and is responsible for the proper regulation of HAEs within the bacterial transcriptional network. We studied the effects of H-NS and other NAPs on the HAEs of a clinical isolate of V. cholerae. Importantly, we found that H-NS partners with a small and poorly characterized protein, TsrA, to help domesticate new HAEs involved in bacterial survival and in causing disease. Proper understanding of the regulatory state in emerging isolates of V. cholerae will provide improved therapies against new isolates of the pathogen.
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Affiliation(s)
- Yulduz Rakibova
- Department of Biological Chemistry, University of Michigan, Ann Arbor, MI, USA
| | - Drew T. Dunham
- Department of Plant and Microbial Biology, University of California, Berkeley, CA, USA
| | - Kimberley D. Seed
- Department of Plant and Microbial Biology, University of California, Berkeley, CA, USA
| | - P. Lydia Freddolino
- Department of Biological Chemistry, University of Michigan, Ann Arbor, MI, USA
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, USA
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Wuyun Q, Chen Y, Shen Y, Cao Y, Hu G, Cui W, Gao J, Zheng W. Recent Progress of Protein Tertiary Structure Prediction. Molecules 2024; 29:832. [PMID: 38398585 PMCID: PMC10893003 DOI: 10.3390/molecules29040832] [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: 12/30/2023] [Revised: 02/06/2024] [Accepted: 02/08/2024] [Indexed: 02/25/2024] Open
Abstract
The prediction of three-dimensional (3D) protein structure from amino acid sequences has stood as a significant challenge in computational and structural bioinformatics for decades. Recently, the widespread integration of artificial intelligence (AI) algorithms has substantially expedited advancements in protein structure prediction, yielding numerous significant milestones. In particular, the end-to-end deep learning method AlphaFold2 has facilitated the rise of structure prediction performance to new heights, regularly competitive with experimental structures in the 14th Critical Assessment of Protein Structure Prediction (CASP14). To provide a comprehensive understanding and guide future research in the field of protein structure prediction for researchers, this review describes various methodologies, assessments, and databases in protein structure prediction, including traditionally used protein structure prediction methods, such as template-based modeling (TBM) and template-free modeling (FM) approaches; recently developed deep learning-based methods, such as contact/distance-guided methods, end-to-end folding methods, and protein language model (PLM)-based methods; multi-domain protein structure prediction methods; the CASP experiments and related assessments; and the recently released AlphaFold Protein Structure Database (AlphaFold DB). We discuss their advantages, disadvantages, and application scopes, aiming to provide researchers with insights through which to understand the limitations, contexts, and effective selections of protein structure prediction methods in protein-related fields.
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Affiliation(s)
- Qiqige Wuyun
- Department of Computer Science and Engineering, Michigan State University, East Lansing, MI 48824, USA
| | - Yihan Chen
- School of Mathematical Sciences and LPMC, Nankai University, Tianjin 300071, China;
| | - Yifeng Shen
- Faculty of Environment and Information Studies, Keio University, Fujisawa 252-0882, Kanagawa, Japan;
| | - Yang Cao
- College of Life Sciences, Sichuan University, Chengdu 610065, China
| | - Gang Hu
- NITFID, School of Statistics and Data Science, LPMC and KLMDASR, Nankai University, Tianjin 300071, China
| | - Wei Cui
- School of Mathematical Sciences and LPMC, Nankai University, Tianjin 300071, China;
| | - Jianzhao Gao
- School of Mathematical Sciences and LPMC, Nankai University, Tianjin 300071, China;
| | - Wei Zheng
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI 48109, USA
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Ullrich SR, Fuchs H, Ashworth-Güth C. Electrochemical and structural characterization of recombinant respiratory proteins of the acidophilic iron oxidizer Ferrovum sp. PN-J47-F6 suggests adaptations to the acidic pH at protein level. Front Microbiol 2024; 15:1357152. [PMID: 38384274 PMCID: PMC10879576 DOI: 10.3389/fmicb.2024.1357152] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/17/2023] [Accepted: 01/23/2024] [Indexed: 02/23/2024] Open
Abstract
The tendency of the periplasmic redox proteins in acidophiles to have more positive redox potentials (Em) than their homologous counterparts in neutrophiles suggests an adaptation to acidic pH at protein level, since thermodynamics of electron transfer processes are also affected by acidic pH. Since this conclusion is mainly based on the electrochemical characterization of redox proteins from extreme acidophiles of the genus Acidithiobacillus, we aimed to characterize three recombinant redox proteins of the more moderate acidophile Ferrovum sp. PN-J47-F6. We applied protein film voltammetry and linear sweep voltammetry coupled to UV/Vis spectroscopy to characterize the redox behavior of HiPIP-41, CytC-18, and CytC-78, respectively. The Em-values of HiPIP-41 (571 ± 16 mV), CytC-18 (276 ± 8 mV, 416 ± 2 mV), and CytC-78 (308 ± 7 mV, 399 ± 7 mV) were indeed more positive than those of homologous redox proteins in neutrophiles. Moreover, our findings suggest that the adaptation of redox proteins with respect to their Em occurs more gradually in response to the pH, since there are also differences between moderate and more extreme acidophiles. In order to address structure function correlations in these redox proteins with respect to structural features affecting the Em, we conducted a comparative structural analysis of the Ferrovum-derived redox proteins and homologs of Acidithiobacillus spp. and neutrophilic proteobacteria. Hydrophobic contacts in the redox cofactor binding pockets resulting in a low solvent accessibility appear to be the major factor contributing to the more positive Em-values in acidophile-derived redox proteins. While additional cysteines in HiPIPs of acidophiles might increase the effective shielding of the [4Fe-4S]-cofactor, the tight shielding of the heme centers in acidophile-derived cytochromes is achieved by a drastic increase in hydrophobic contacts (A.f. Cyc41), and by a larger fraction of aromatic residues in the binding pockets (CytC-18, CytC-78).
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Affiliation(s)
- Sophie R. Ullrich
- Environmental Microbiology Group, Institute for Biological Sciences, TU Bergakademie Freiberg, Freiberg, Germany
- Biohydrometallurgy Group, Institute for Biological Sciences, TU Bergakademie Freiberg, Freiberg, Germany
| | - Helena Fuchs
- Biohydrometallurgy Group, Institute for Biological Sciences, TU Bergakademie Freiberg, Freiberg, Germany
| | - Charlotte Ashworth-Güth
- Salt and Mineral Chemistry Group, Institute for Inorganic Chemistry, TU Bergakademie Freiberg, Freiberg, Germany
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Birchfield AS, McIntosh CA. Expression and Purification of Cp3GT: Structural Analysis and Modeling of a Key Plant Flavonol-3-O Glucosyltransferase from Citrus paradisi. BIOTECH 2024; 13:4. [PMID: 38390907 PMCID: PMC10885057 DOI: 10.3390/biotech13010004] [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: 11/21/2023] [Revised: 12/15/2023] [Accepted: 01/09/2024] [Indexed: 02/24/2024] Open
Abstract
Glycosyltransferases (GTs) are pivotal enzymes in the biosynthesis of various biological molecules. This study focuses on the scale-up, expression, and purification of a plant flavonol-specific 3-O glucosyltransferase (Cp3GT), a key enzyme from Citrus paradisi, for structural analysis and modeling. The challenges associated with recombinant protein production in Pichia pastoris, such as proteolytic degradation, were addressed through the optimization of culture conditions and purification processes. The purification strategy employed affinity, anion exchange, and size exclusion chromatography, leading to greater than 95% homogeneity for Cp3GT. In silico modeling, using D-I-TASSER and COFACTOR integrated with the AlphaFold2 pipeline, provided insights into the structural dynamics of Cp3GT and its ligand binding sites, offering predictions for enzyme-substrate interactions. These models were compared to experimentally derived structures, enhancing understanding of the enzyme's functional mechanisms. The findings present a comprehensive approach to produce a highly purified Cp3GT which is suitable for crystallographic studies and to shed light on the structural basis of flavonol specificity in plant GTs. The significant implications of these results for synthetic biology and enzyme engineering in pharmaceutical applications are also considered.
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Affiliation(s)
- Aaron S Birchfield
- Department of Biological Sciences, East Tennessee State University, P.O. Box 70703, Johnson City, TN 37614, USA
| | - Cecilia A McIntosh
- Department of Biological Sciences, East Tennessee State University, P.O. Box 70703, Johnson City, TN 37614, USA
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Roy BG, Choi J, Fuchs MF. Predictive Modeling of Proteins Encoded by a Plant Virus Sheds a New Light on Their Structure and Inherent Multifunctionality. Biomolecules 2024; 14:62. [PMID: 38254661 PMCID: PMC10813169 DOI: 10.3390/biom14010062] [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: 11/29/2023] [Revised: 12/29/2023] [Accepted: 12/30/2023] [Indexed: 01/24/2024] Open
Abstract
Plant virus genomes encode proteins that are involved in replication, encapsidation, cell-to-cell, and long-distance movement, avoidance of host detection, counter-defense, and transmission from host to host, among other functions. Even though the multifunctionality of plant viral proteins is well documented, contemporary functional repertoires of individual proteins are incomplete. However, these can be enhanced by modeling tools. Here, predictive modeling of proteins encoded by the two genomic RNAs, i.e., RNA1 and RNA2, of grapevine fanleaf virus (GFLV) and their satellite RNAs by a suite of protein prediction software confirmed not only previously validated functions (suppressor of RNA silencing [VSR], viral genome-linked protein [VPg], protease [Pro], symptom determinant [Sd], homing protein [HP], movement protein [MP], coat protein [CP], and transmission determinant [Td]) and previously identified putative functions (helicase [Hel] and RNA-dependent RNA polymerase [Pol]), but also predicted novel functions with varying levels of confidence. These include a T3/T7-like RNA polymerase domain for protein 1AVSR, a short-chain reductase for protein 1BHel/VSR, a parathyroid hormone family domain for protein 1EPol/Sd, overlapping domains of unknown function and an ABC transporter domain for protein 2BMP, and DNA topoisomerase domains, transcription factor FBXO25 domain, or DNA Pol subunit cdc27 domain for the satellite RNA protein. Structural predictions for proteins 2AHP/Sd, 2BMP, and 3A? had low confidence, while predictions for proteins 1AVSR, 1BHel*/VSR, 1CVPg, 1DPro, 1EPol*/Sd, and 2CCP/Td retained higher confidence in at least one prediction. This research provided new insights into the structure and functions of GFLV proteins and their satellite protein. Future work is needed to validate these findings.
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Affiliation(s)
- Brandon G. Roy
- Plant Pathology and Plant-Microbe Biology Section, School of Integrative Plant Science, Cornell University, 15 Castle Creek Drive, Geneva, NY 14456, USA; (J.C.); (M.F.F.)
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11
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Karlin DG. WIV, a protein domain found in a wide number of arthropod viruses, which probably facilitates infection. J Gen Virol 2024; 105. [PMID: 38193819 DOI: 10.1099/jgv.0.001948] [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: 01/10/2024] Open
Abstract
The most powerful approach to detect distant homologues of a protein is based on structure prediction and comparison. Yet this approach is still inapplicable to many viral proteins. Therefore, we applied a powerful sequence-based procedure to identify distant homologues of viral proteins. It relies on three principles: (1) traces of sequence similarity can persist beyond the significance cutoff of homology detection programmes; (2) candidate homologues can be identified among proteins with weak sequence similarity to the query by using 'contextual' information, e.g. taxonomy or type of host infected; (3) these candidate homologues can be validated using highly sensitive profile-profile comparison. As a test case, this approach was applied to a protein without known homologues, encoded by ORF4 of Lake Sinai viruses (which infect bees). We discovered that the ORF4 protein contains a domain that has homologues in proteins from >20 taxa of viruses infecting arthropods. We called this domain 'widespread, intriguing, versatile' (WIV), because it is found in proteins with a wide variety of functions and within varied domain contexts. For example, WIV is found in the NSs protein of tospoviruses, a global threat to food security, which infect plants as well as their arthropod vectors; in the RNA2 ORF1-encoded protein of chronic bee paralysis virus, a widespread virus of bees; and in various proteins of cypoviruses, which infect the silkworm Bombyx mori. Structural modelling with AlphaFold indicated that the WIV domain has a previously unknown fold, and bibliographical evidence suggests that it facilitates infection of arthropods.
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Affiliation(s)
- David G Karlin
- Division Phytomedicine, Thaer-Institute of Agricultural and Horticultural Sciences, Humboldt-Universität zu Berlin, Lentzeallee 55/57, D-14195 Berlin, Germany
- Independent Researcher, Marseille, France
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Affiliation(s)
- Sriram Subramaniam
- Department of Biochemistry and Molecular Biology, University of British Columbia, Vancouver, British Columbia, Canada.
- Gandeeva Therapeutics Inc., Burnaby, British Columbia, Canada.
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13
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Simpkin AJ, Mesdaghi S, Sánchez Rodríguez F, Elliott L, Murphy DL, Kryshtafovych A, Keegan RM, Rigden DJ. Tertiary structure assessment at CASP15. Proteins 2023; 91:1616-1635. [PMID: 37746927 PMCID: PMC10792517 DOI: 10.1002/prot.26593] [Citation(s) in RCA: 14] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/24/2023] [Revised: 08/25/2023] [Accepted: 09/07/2023] [Indexed: 09/26/2023]
Abstract
The results of tertiary structure assessment at CASP15 are reported. For the first time, recognizing the outstanding performance of AlphaFold 2 (AF2) at CASP14, all single-chain predictions were assessed together, irrespective of whether a template was available. At CASP15, there was no single stand-out group, with most of the best-scoring groups-led by PEZYFoldings, UM-TBM, and Yang Server-employing AF2 in one way or another. Many top groups paid special attention to generating deep Multiple Sequence Alignments (MSAs) and testing variant MSAs, thereby allowing them to successfully address some of the hardest targets. Such difficult targets, as well as lacking templates, were typically proteins with few homologues. Local divergence between prediction and target correlated with localization at crystal lattice or chain interfaces, and with regions exhibiting high B-factor factors in crystal structure targets, and should not necessarily be considered as representing error in the prediction. However, analysis of exposed and buried side chain accuracy showed room for improvement even in the latter. Nevertheless, a majority of groups produced high-quality predictions for most targets, which are valuable for experimental structure determination, functional analysis, and many other tasks across biology. These include those applying methods similar to those used to generate major resources such as the AlphaFold Protein Structure Database and the ESM Metagenomic atlas: the confidence estimates of the former were also notably accurate.
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Affiliation(s)
- Adam J. Simpkin
- Department of Biochemistry, Cell and Systems BiologyInstitute of Structural, Molecular and Integrative Biology, University of LiverpoolLiverpoolUK
| | - Shahram Mesdaghi
- Department of Biochemistry, Cell and Systems BiologyInstitute of Structural, Molecular and Integrative Biology, University of LiverpoolLiverpoolUK
- Computational Biology Facility, MerseyBio, University of LiverpoolLiverpoolUK
| | - Filomeno Sánchez Rodríguez
- Department of Biochemistry, Cell and Systems BiologyInstitute of Structural, Molecular and Integrative Biology, University of LiverpoolLiverpoolUK
- Life Science, Diamond Light Source, Harwell Science and Innovation CampusOxfordshireUK
- Department of Chemistry, York Structural Biology LaboratoryUniversity of YorkYorkUK
| | - Luc Elliott
- Department of Biochemistry, Cell and Systems BiologyInstitute of Structural, Molecular and Integrative Biology, University of LiverpoolLiverpoolUK
| | - David L. Murphy
- Department of Biochemistry, Cell and Systems BiologyInstitute of Structural, Molecular and Integrative Biology, University of LiverpoolLiverpoolUK
| | | | - Ronan M. Keegan
- UKRI‐STFC, Rutherford Appleton Laboratory, Research Complex at HarwellDidcotUK
| | - Daniel J. Rigden
- Department of Biochemistry, Cell and Systems BiologyInstitute of Structural, Molecular and Integrative Biology, University of LiverpoolLiverpoolUK
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14
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Lee JW, Won JH, Jeon S, Choo Y, Yeon Y, Oh JS, Kim M, Kim S, Joung I, Jang C, Lee SJ, Kim TH, Jin KH, Song G, Kim ES, Yoo J, Paek E, Noh YK, Joo K. DeepFold: enhancing protein structure prediction through optimized loss functions, improved template features, and re-optimized energy function. Bioinformatics 2023; 39:btad712. [PMID: 37995286 PMCID: PMC10699847 DOI: 10.1093/bioinformatics/btad712] [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: 06/14/2023] [Revised: 11/17/2023] [Accepted: 11/22/2023] [Indexed: 11/25/2023] Open
Abstract
MOTIVATION Predicting protein structures with high accuracy is a critical challenge for the broad community of life sciences and industry. Despite progress made by deep neural networks like AlphaFold2, there is a need for further improvements in the quality of detailed structures, such as side-chains, along with protein backbone structures. RESULTS Building upon the successes of AlphaFold2, the modifications we made include changing the losses of side-chain torsion angles and frame aligned point error, adding loss functions for side chain confidence and secondary structure prediction, and replacing template feature generation with a new alignment method based on conditional random fields. We also performed re-optimization by conformational space annealing using a molecular mechanics energy function which integrates the potential energies obtained from distogram and side-chain prediction. In the CASP15 blind test for single protein and domain modeling (109 domains), DeepFold ranked fourth among 132 groups with improvements in the details of the structure in terms of backbone, side-chain, and Molprobity. In terms of protein backbone accuracy, DeepFold achieved a median GDT-TS score of 88.64 compared with 85.88 of AlphaFold2. For TBM-easy/hard targets, DeepFold ranked at the top based on Z-scores for GDT-TS. This shows its practical value to the structural biology community, which demands highly accurate structures. In addition, a thorough analysis of 55 domains from 39 targets with publicly available structures indicates that DeepFold shows superior side-chain accuracy and Molprobity scores among the top-performing groups. AVAILABILITY AND IMPLEMENTATION DeepFold tools are open-source software available at https://github.com/newtonjoo/deepfold.
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Affiliation(s)
- Jae-Won Lee
- Department of Computer Science, Hanyang University, Seoul 04763, Korea
- Center for Advanced Computation, Korea Institute for Advanced Study, Seoul 02455, Korea
| | - Jong-Hyun Won
- Department of Computer Science, Hanyang University, Seoul 04763, Korea
- Center for Advanced Computation, Korea Institute for Advanced Study, Seoul 02455, Korea
| | - Seonggwang Jeon
- Department of Computer Science, Hanyang University, Seoul 04763, Korea
- Center for Advanced Computation, Korea Institute for Advanced Study, Seoul 02455, Korea
| | - Yujin Choo
- Center for Advanced Computation, Korea Institute for Advanced Study, Seoul 02455, Korea
- Department of Artificial intelligence, Hanyang University, Seoul 04763, Korea
| | - Yubin Yeon
- Department of Computer Science, Hanyang University, Seoul 04763, Korea
- Center for Advanced Computation, Korea Institute for Advanced Study, Seoul 02455, Korea
| | - Jin-Seon Oh
- Center for Advanced Computation, Korea Institute for Advanced Study, Seoul 02455, Korea
- Department of Artificial intelligence, Hanyang University, Seoul 04763, Korea
| | - Minsoo Kim
- Department of Physics, Sungkyunkwan University, Suwon 16419, Korea
| | - SeonHwa Kim
- School of Electrical Engineering, Korea University, Seoul 02841, Korea
| | | | - Cheongjae Jang
- Artificial Intelligence Institute, Hanyang University, Seoul 04763, Korea
| | - Sung Jong Lee
- Basic Science Research Institute, Changwon National University, Changwon 51140, Korea
| | - Tae Hyun Kim
- Department of Computer Science, Hanyang University, Seoul 04763, Korea
| | - Kyong Hwan Jin
- School of Electrical Engineering, Korea University, Seoul 02841, Korea
| | - Giltae Song
- School of Computer Science and Engineering, Pusan National University, Busan 46241, Korea
| | - Eun-Sol Kim
- Department of Computer Science, Hanyang University, Seoul 04763, Korea
| | - Jejoong Yoo
- Department of Physics, Sungkyunkwan University, Suwon 16419, Korea
| | - Eunok Paek
- Department of Computer Science, Hanyang University, Seoul 04763, Korea
| | - Yung-Kyun Noh
- Department of Computer Science, Hanyang University, Seoul 04763, Korea
- School of Computational Sciences, Korea Institute for Advanced Study, Seoul 02455, Korea
| | - Keehyoung Joo
- Center for Advanced Computation, Korea Institute for Advanced Study, Seoul 02455, Korea
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15
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Ozden B, Kryshtafovych A, Karaca E. The impact of AI-based modeling on the accuracy of protein assembly prediction: Insights from CASP15. Proteins 2023; 91:1636-1657. [PMID: 37861057 PMCID: PMC10873090 DOI: 10.1002/prot.26598] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/21/2023] [Revised: 09/12/2023] [Accepted: 09/14/2023] [Indexed: 10/21/2023]
Abstract
In CASP15, 87 predictors submitted around 11 000 models on 41 assembly targets. The community demonstrated exceptional performance in overall fold and interface contact predictions, achieving an impressive success rate of 90% (compared to 31% in CASP14). This remarkable accomplishment is largely due to the incorporation of DeepMind's AF2-Multimer approach into custom-built prediction pipelines. To evaluate the added value of participating methods, we compared the community models to the baseline AF2-Multimer predictor. In over 1/3 of cases, the community models were superior to the baseline predictor. The main reasons for this improved performance were the use of custom-built multiple sequence alignments, optimized AF2-Multimer sampling, and the manual assembly of AF2-Multimer-built subcomplexes. The best three groups, in order, are Zheng, Venclovas, and Wallner. Zheng and Venclovas reached a 73.2% success rate over all (41) cases, while Wallner attained 69.4% success rate over 36 cases. Nonetheless, challenges remain in predicting structures with weak evolutionary signals, such as nanobody-antigen, antibody-antigen, and viral complexes. Expectedly, modeling large complexes also remains challenging due to their high memory compute demands. In addition to the assembly category, we assessed the accuracy of modeling interdomain interfaces in the tertiary structure prediction targets. Models on seven targets featuring 17 unique interfaces were analyzed. Best predictors achieved a 76.5% success rate, with the UM-TBM group being the leader. In the interdomain category, we observed that the predictors faced challenges, as in the case of the assembly category, when the evolutionary signal for a given domain pair was weak or the structure was large. Overall, CASP15 witnessed unprecedented improvement in interface modeling, reflecting the AI revolution seen in CASP14.
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Affiliation(s)
- Burcu Ozden
- Izmir Biomedicine and Genome Center, Izmir, Türkiye
- Izmir International Biomedicine and Genome Institute, Dokuz Eylul University, Izmir, Türkiye
| | - Andriy Kryshtafovych
- Protein Structure Prediction Center, Genome and Biomedical Sciences Facilities, University of California, Davis, California, USA
| | - Ezgi Karaca
- Izmir Biomedicine and Genome Center, Izmir, Türkiye
- Izmir International Biomedicine and Genome Institute, Dokuz Eylul University, Izmir, Türkiye
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Wang J, Chen C, Yao G, Ding J, Wang L, Jiang H. Intelligent Protein Design and Molecular Characterization Techniques: A Comprehensive Review. Molecules 2023; 28:7865. [PMID: 38067593 PMCID: PMC10707872 DOI: 10.3390/molecules28237865] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/21/2023] [Revised: 11/13/2023] [Accepted: 11/23/2023] [Indexed: 12/18/2023] Open
Abstract
In recent years, the widespread application of artificial intelligence algorithms in protein structure, function prediction, and de novo protein design has significantly accelerated the process of intelligent protein design and led to many noteworthy achievements. This advancement in protein intelligent design holds great potential to accelerate the development of new drugs, enhance the efficiency of biocatalysts, and even create entirely new biomaterials. Protein characterization is the key to the performance of intelligent protein design. However, there is no consensus on the most suitable characterization method for intelligent protein design tasks. This review describes the methods, characteristics, and representative applications of traditional descriptors, sequence-based and structure-based protein characterization. It discusses their advantages, disadvantages, and scope of application. It is hoped that this could help researchers to better understand the limitations and application scenarios of these methods, and provide valuable references for choosing appropriate protein characterization techniques for related research in the field, so as to better carry out protein research.
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Affiliation(s)
| | | | | | - Junjie Ding
- State Key Laboratory of NBC Protection for Civilian, Beijing 102205, China; (J.W.); (C.C.); (G.Y.)
| | - Liangliang Wang
- State Key Laboratory of NBC Protection for Civilian, Beijing 102205, China; (J.W.); (C.C.); (G.Y.)
| | - Hui Jiang
- State Key Laboratory of NBC Protection for Civilian, Beijing 102205, China; (J.W.); (C.C.); (G.Y.)
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