M-DeepAssembly: enhanced DeepAssembly based on multi-objective multi-domain protein conformation sampling
Background Association and cooperation among structural domains play an important role in protein function and drug design. Despite remarkable advancements in highly accurate single-domain protein structure prediction through the collaborative efforts of the community using deep learning, challenges...
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| Vydáno v: | BMC bioinformatics Ročník 26; číslo 1; s. 120 - 13 |
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| Médium: | Journal Article |
| Jazyk: | angličtina |
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BioMed Central
05.05.2025
BioMed Central Ltd Springer Nature B.V BMC |
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| Abstract | Background
Association and cooperation among structural domains play an important role in protein function and drug design. Despite remarkable advancements in highly accurate single-domain protein structure prediction through the collaborative efforts of the community using deep learning, challenges still exist in predicting multi-domain protein structures when the evolutionary signal for a given domain pair is weak or the protein structure is large.
Results
To alleviate the above challenges, we proposed M-DeepAssembly, a protocol based on multi-objective protein conformation sampling algorithm for multi-domain protein structure prediction. Firstly, the inter-domain interactions and full-length sequence distance features are extracted through DeepAssembly and AlphaFold2, respectively. Secondly, subject to these features, we constructed a multi-objective energy model and designed a sampling algorithm for exploring and exploiting conformational space to generate ensembles. Finally, the output protein structure was selected from the ensembles using our in-house developed model quality assessment algorithm. On the test set of 164 multi-domain proteins, the results show that the average TM-score of M-DeepAssembly is 15.4% and 2.0% higher than AlphaFold2 and DeepAssembly, respectively. It is worth noting that there are models with higher accuracy in ensembles, achieving an improvement of 20.3% and 6.4% relative to the two baseline methods, although these models were not selected. Furthermore, when compared to the prediction results of AlphaFold2 for CASP15 multi-domain targets, M-DeepAssembly demonstrates certain performance advantages.
Conclusions
M-DeepAssembly provides a distinctive multi-domain protein assembly algorithm, which can alleviate the current challenges of weak evolutionary signals and large structures to some extent by forming diverse ensembles using multi-objective protein conformation sampling algorithm. The proposed method contributes to exploring the functions of multi-domain proteins, especially providing new insights into targets with multiple conformational states. |
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| AbstractList | Background
Association and cooperation among structural domains play an important role in protein function and drug design. Despite remarkable advancements in highly accurate single-domain protein structure prediction through the collaborative efforts of the community using deep learning, challenges still exist in predicting multi-domain protein structures when the evolutionary signal for a given domain pair is weak or the protein structure is large.
Results
To alleviate the above challenges, we proposed M-DeepAssembly, a protocol based on multi-objective protein conformation sampling algorithm for multi-domain protein structure prediction. Firstly, the inter-domain interactions and full-length sequence distance features are extracted through DeepAssembly and AlphaFold2, respectively. Secondly, subject to these features, we constructed a multi-objective energy model and designed a sampling algorithm for exploring and exploiting conformational space to generate ensembles. Finally, the output protein structure was selected from the ensembles using our in-house developed model quality assessment algorithm. On the test set of 164 multi-domain proteins, the results show that the average TM-score of M-DeepAssembly is 15.4% and 2.0% higher than AlphaFold2 and DeepAssembly, respectively. It is worth noting that there are models with higher accuracy in ensembles, achieving an improvement of 20.3% and 6.4% relative to the two baseline methods, although these models were not selected. Furthermore, when compared to the prediction results of AlphaFold2 for CASP15 multi-domain targets, M-DeepAssembly demonstrates certain performance advantages.
Conclusions
M-DeepAssembly provides a distinctive multi-domain protein assembly algorithm, which can alleviate the current challenges of weak evolutionary signals and large structures to some extent by forming diverse ensembles using multi-objective protein conformation sampling algorithm. The proposed method contributes to exploring the functions of multi-domain proteins, especially providing new insights into targets with multiple conformational states. Association and cooperation among structural domains play an important role in protein function and drug design. Despite remarkable advancements in highly accurate single-domain protein structure prediction through the collaborative efforts of the community using deep learning, challenges still exist in predicting multi-domain protein structures when the evolutionary signal for a given domain pair is weak or the protein structure is large. To alleviate the above challenges, we proposed M-DeepAssembly, a protocol based on multi-objective protein conformation sampling algorithm for multi-domain protein structure prediction. Firstly, the inter-domain interactions and full-length sequence distance features are extracted through DeepAssembly and AlphaFold2, respectively. Secondly, subject to these features, we constructed a multi-objective energy model and designed a sampling algorithm for exploring and exploiting conformational space to generate ensembles. Finally, the output protein structure was selected from the ensembles using our in-house developed model quality assessment algorithm. On the test set of 164 multi-domain proteins, the results show that the average TM-score of M-DeepAssembly is 15.4% and 2.0% higher than AlphaFold2 and DeepAssembly, respectively. It is worth noting that there are models with higher accuracy in ensembles, achieving an improvement of 20.3% and 6.4% relative to the two baseline methods, although these models were not selected. Furthermore, when compared to the prediction results of AlphaFold2 for CASP15 multi-domain targets, M-DeepAssembly demonstrates certain performance advantages. M-DeepAssembly provides a distinctive multi-domain protein assembly algorithm, which can alleviate the current challenges of weak evolutionary signals and large structures to some extent by forming diverse ensembles using multi-objective protein conformation sampling algorithm. The proposed method contributes to exploring the functions of multi-domain proteins, especially providing new insights into targets with multiple conformational states. Background Association and cooperation among structural domains play an important role in protein function and drug design. Despite remarkable advancements in highly accurate single-domain protein structure prediction through the collaborative efforts of the community using deep learning, challenges still exist in predicting multi-domain protein structures when the evolutionary signal for a given domain pair is weak or the protein structure is large. Results To alleviate the above challenges, we proposed M-DeepAssembly, a protocol based on multi-objective protein conformation sampling algorithm for multi-domain protein structure prediction. Firstly, the inter-domain interactions and full-length sequence distance features are extracted through DeepAssembly and AlphaFold2, respectively. Secondly, subject to these features, we constructed a multi-objective energy model and designed a sampling algorithm for exploring and exploiting conformational space to generate ensembles. Finally, the output protein structure was selected from the ensembles using our in-house developed model quality assessment algorithm. On the test set of 164 multi-domain proteins, the results show that the average TM-score of M-DeepAssembly is 15.4% and 2.0% higher than AlphaFold2 and DeepAssembly, respectively. It is worth noting that there are models with higher accuracy in ensembles, achieving an improvement of 20.3% and 6.4% relative to the two baseline methods, although these models were not selected. Furthermore, when compared to the prediction results of AlphaFold2 for CASP15 multi-domain targets, M-DeepAssembly demonstrates certain performance advantages. Conclusions M-DeepAssembly provides a distinctive multi-domain protein assembly algorithm, which can alleviate the current challenges of weak evolutionary signals and large structures to some extent by forming diverse ensembles using multi-objective protein conformation sampling algorithm. The proposed method contributes to exploring the functions of multi-domain proteins, especially providing new insights into targets with multiple conformational states. Keywords: Protein structure prediction, Multi-domain protein assembly, Multi-objective energy model, Conformation sampling Association and cooperation among structural domains play an important role in protein function and drug design. Despite remarkable advancements in highly accurate single-domain protein structure prediction through the collaborative efforts of the community using deep learning, challenges still exist in predicting multi-domain protein structures when the evolutionary signal for a given domain pair is weak or the protein structure is large. To alleviate the above challenges, we proposed M-DeepAssembly, a protocol based on multi-objective protein conformation sampling algorithm for multi-domain protein structure prediction. Firstly, the inter-domain interactions and full-length sequence distance features are extracted through DeepAssembly and AlphaFold2, respectively. Secondly, subject to these features, we constructed a multi-objective energy model and designed a sampling algorithm for exploring and exploiting conformational space to generate ensembles. Finally, the output protein structure was selected from the ensembles using our in-house developed model quality assessment algorithm. On the test set of 164 multi-domain proteins, the results show that the average TM-score of M-DeepAssembly is 15.4% and 2.0% higher than AlphaFold2 and DeepAssembly, respectively. It is worth noting that there are models with higher accuracy in ensembles, achieving an improvement of 20.3% and 6.4% relative to the two baseline methods, although these models were not selected. Furthermore, when compared to the prediction results of AlphaFold2 for CASP15 multi-domain targets, M-DeepAssembly demonstrates certain performance advantages. M-DeepAssembly provides a distinctive multi-domain protein assembly algorithm, which can alleviate the current challenges of weak evolutionary signals and large structures to some extent by forming diverse ensembles using multi-objective protein conformation sampling algorithm. The proposed method contributes to exploring the functions of multi-domain proteins, especially providing new insights into targets with multiple conformational states. BackgroundAssociation and cooperation among structural domains play an important role in protein function and drug design. Despite remarkable advancements in highly accurate single-domain protein structure prediction through the collaborative efforts of the community using deep learning, challenges still exist in predicting multi-domain protein structures when the evolutionary signal for a given domain pair is weak or the protein structure is large.ResultsTo alleviate the above challenges, we proposed M-DeepAssembly, a protocol based on multi-objective protein conformation sampling algorithm for multi-domain protein structure prediction. Firstly, the inter-domain interactions and full-length sequence distance features are extracted through DeepAssembly and AlphaFold2, respectively. Secondly, subject to these features, we constructed a multi-objective energy model and designed a sampling algorithm for exploring and exploiting conformational space to generate ensembles. Finally, the output protein structure was selected from the ensembles using our in-house developed model quality assessment algorithm. On the test set of 164 multi-domain proteins, the results show that the average TM-score of M-DeepAssembly is 15.4% and 2.0% higher than AlphaFold2 and DeepAssembly, respectively. It is worth noting that there are models with higher accuracy in ensembles, achieving an improvement of 20.3% and 6.4% relative to the two baseline methods, although these models were not selected. Furthermore, when compared to the prediction results of AlphaFold2 for CASP15 multi-domain targets, M-DeepAssembly demonstrates certain performance advantages.ConclusionsM-DeepAssembly provides a distinctive multi-domain protein assembly algorithm, which can alleviate the current challenges of weak evolutionary signals and large structures to some extent by forming diverse ensembles using multi-objective protein conformation sampling algorithm. The proposed method contributes to exploring the functions of multi-domain proteins, especially providing new insights into targets with multiple conformational states. Abstract Background Association and cooperation among structural domains play an important role in protein function and drug design. Despite remarkable advancements in highly accurate single-domain protein structure prediction through the collaborative efforts of the community using deep learning, challenges still exist in predicting multi-domain protein structures when the evolutionary signal for a given domain pair is weak or the protein structure is large. Results To alleviate the above challenges, we proposed M-DeepAssembly, a protocol based on multi-objective protein conformation sampling algorithm for multi-domain protein structure prediction. Firstly, the inter-domain interactions and full-length sequence distance features are extracted through DeepAssembly and AlphaFold2, respectively. Secondly, subject to these features, we constructed a multi-objective energy model and designed a sampling algorithm for exploring and exploiting conformational space to generate ensembles. Finally, the output protein structure was selected from the ensembles using our in-house developed model quality assessment algorithm. On the test set of 164 multi-domain proteins, the results show that the average TM-score of M-DeepAssembly is 15.4% and 2.0% higher than AlphaFold2 and DeepAssembly, respectively. It is worth noting that there are models with higher accuracy in ensembles, achieving an improvement of 20.3% and 6.4% relative to the two baseline methods, although these models were not selected. Furthermore, when compared to the prediction results of AlphaFold2 for CASP15 multi-domain targets, M-DeepAssembly demonstrates certain performance advantages. Conclusions M-DeepAssembly provides a distinctive multi-domain protein assembly algorithm, which can alleviate the current challenges of weak evolutionary signals and large structures to some extent by forming diverse ensembles using multi-objective protein conformation sampling algorithm. The proposed method contributes to exploring the functions of multi-domain proteins, especially providing new insights into targets with multiple conformational states. Association and cooperation among structural domains play an important role in protein function and drug design. Despite remarkable advancements in highly accurate single-domain protein structure prediction through the collaborative efforts of the community using deep learning, challenges still exist in predicting multi-domain protein structures when the evolutionary signal for a given domain pair is weak or the protein structure is large.BACKGROUNDAssociation and cooperation among structural domains play an important role in protein function and drug design. Despite remarkable advancements in highly accurate single-domain protein structure prediction through the collaborative efforts of the community using deep learning, challenges still exist in predicting multi-domain protein structures when the evolutionary signal for a given domain pair is weak or the protein structure is large.To alleviate the above challenges, we proposed M-DeepAssembly, a protocol based on multi-objective protein conformation sampling algorithm for multi-domain protein structure prediction. Firstly, the inter-domain interactions and full-length sequence distance features are extracted through DeepAssembly and AlphaFold2, respectively. Secondly, subject to these features, we constructed a multi-objective energy model and designed a sampling algorithm for exploring and exploiting conformational space to generate ensembles. Finally, the output protein structure was selected from the ensembles using our in-house developed model quality assessment algorithm. On the test set of 164 multi-domain proteins, the results show that the average TM-score of M-DeepAssembly is 15.4% and 2.0% higher than AlphaFold2 and DeepAssembly, respectively. It is worth noting that there are models with higher accuracy in ensembles, achieving an improvement of 20.3% and 6.4% relative to the two baseline methods, although these models were not selected. Furthermore, when compared to the prediction results of AlphaFold2 for CASP15 multi-domain targets, M-DeepAssembly demonstrates certain performance advantages.RESULTSTo alleviate the above challenges, we proposed M-DeepAssembly, a protocol based on multi-objective protein conformation sampling algorithm for multi-domain protein structure prediction. Firstly, the inter-domain interactions and full-length sequence distance features are extracted through DeepAssembly and AlphaFold2, respectively. Secondly, subject to these features, we constructed a multi-objective energy model and designed a sampling algorithm for exploring and exploiting conformational space to generate ensembles. Finally, the output protein structure was selected from the ensembles using our in-house developed model quality assessment algorithm. On the test set of 164 multi-domain proteins, the results show that the average TM-score of M-DeepAssembly is 15.4% and 2.0% higher than AlphaFold2 and DeepAssembly, respectively. It is worth noting that there are models with higher accuracy in ensembles, achieving an improvement of 20.3% and 6.4% relative to the two baseline methods, although these models were not selected. Furthermore, when compared to the prediction results of AlphaFold2 for CASP15 multi-domain targets, M-DeepAssembly demonstrates certain performance advantages.M-DeepAssembly provides a distinctive multi-domain protein assembly algorithm, which can alleviate the current challenges of weak evolutionary signals and large structures to some extent by forming diverse ensembles using multi-objective protein conformation sampling algorithm. The proposed method contributes to exploring the functions of multi-domain proteins, especially providing new insights into targets with multiple conformational states.CONCLUSIONSM-DeepAssembly provides a distinctive multi-domain protein assembly algorithm, which can alleviate the current challenges of weak evolutionary signals and large structures to some extent by forming diverse ensembles using multi-objective protein conformation sampling algorithm. The proposed method contributes to exploring the functions of multi-domain proteins, especially providing new insights into targets with multiple conformational states. |
| ArticleNumber | 120 |
| Audience | Academic |
| Author | Wang, Suhui Cui, Xinyue Hou, Minghua Xia, Yuhao Zhao, Xuanfeng Zhang, Guijun |
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| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/40325375$$D View this record in MEDLINE/PubMed |
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| Cites_doi | 10.1016/j.bbrc.2013.03.065 10.1093/bioinformatics/btab484 10.1038/s41586-021-03819-2 10.1002/prot.20264 10.1080/23311916.2018.1502242 10.1093/nsr/nwad259 10.1046/j.1365-2958.1999.01614.x 10.1126/science.abj8754 10.1006/jmbi.2001.5331 10.1007/s12539-023-00597-5 10.1038/s41586-019-1923-7 10.1023/A:1026113408773 10.1016/S0959-440X(02)00338-X 10.1093/bioinformatics/btad591 10.1038/s42003-023-05610-7 10.1038/s41586-024-07487-w 10.1021/acs.jcim.3c01387 10.1073/pnas.1914677117 10.1093/nar/gkw1081 10.1038/s41467-021-21511-x 10.1042/EBC20200108 10.1186/s12859-019-3019-7 10.1016/j.str.2019.03.018 10.1038/s41596-022-00728-0 10.1093/bib/bbad219 10.1002/prot.25792 10.1093/bioinformatics/btac553 10.1093/bib/bbad420 10.1038/s42256-021-00348-5 10.1093/bioinformatics/btv092 10.1107/S1744309109023082 10.1109/TCBB.2022.3175905 10.1073/pnas.1831973100 10.1002/prot.26598 10.1093/nar/gkab1061 10.1126/science.1195821 10.1109/4235.797969 10.1002/prot.22339 10.1110/ps.062270707 10.1073/pnas.1905068116 10.1093/bioinformatics/btab500 10.1093/nar/28.1.235 |
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| Keywords | Protein structure prediction Conformation sampling Multi-objective energy model Multi-domain protein assembly |
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| References | AM Wollacott (6131_CR17) 2007; 16 6131_CR29 RJ Lewis (6131_CR43) 2002; 316 M Baek (6131_CR5) 2021; 373 J Abramson (6131_CR37) 2024; 630 G Apic (6131_CR2) 2003; 4 6131_CR27 M Hou (6131_CR30) 2024; 16 AW Senior (6131_CR6) 2020; 577 H Zhu (6131_CR12) 2023; 63 J Xu (6131_CR7) 2021; 3 M Mirdita (6131_CR26) 2017; 45 X Zhou (6131_CR15) 2022; 17 Y Xia (6131_CR21) 2023; 6 Y Zhang (6131_CR38) 2004; 57 GJ Mulder (6131_CR1) 1838; 1838 B Ozden (6131_CR10) 2023; 91 Z Yu (6131_CR25) 2022; 20 D Liu (6131_CR32) 2024; 25 Y Zhou (6131_CR4) 2023; 10 Y Xia (6131_CR24) 2021; 37 MT Degiacomi (6131_CR45) 2019; 27 HM Berman (6131_CR14) 2000; 28 N Gunantara (6131_CR22) 2018; 5 M Steinegger (6131_CR28) 2019; 20 F Forneris (6131_CR39) 2010; 330 LN Kinch (6131_CR13) 2002; 12 K Zhao (6131_CR23) 2021; 37 G He (6131_CR34) 2023; 24 S Da Re (6131_CR42) 1999; 34 J Liu (6131_CR33) 2023; 39 GP Poornam (6131_CR44) 2009; 76 X Zhou (6131_CR20) 2019; 116 E Zitzler (6131_CR31) 1999; 3 AK Park (6131_CR41) 2009; 65 C Peng (6131_CR19) 2022; 38 R Morris (6131_CR3) 2022; 66 J Meiler (6131_CR11) 2003; 100 W Zheng (6131_CR16) 2019; 87 N Hiranuma (6131_CR35) 2021; 12 J Jumper (6131_CR9) 2021; 596 J Yang (6131_CR8) 2020; 117 D Xu (6131_CR18) 2015; 31 M Varadi (6131_CR36) 2022; 50 AK Park (6131_CR40) 2013; 434 |
| References_xml | – ident: 6131_CR29 – volume: 434 start-page: 65 issue: 1 year: 2013 ident: 6131_CR40 publication-title: Biochem Biophys Res Commun doi: 10.1016/j.bbrc.2013.03.065 – volume: 37 start-page: 4350 issue: 23 year: 2021 ident: 6131_CR23 publication-title: Bioinformatics doi: 10.1093/bioinformatics/btab484 – volume: 596 start-page: 583 issue: 7873 year: 2021 ident: 6131_CR9 publication-title: Nature doi: 10.1038/s41586-021-03819-2 – volume: 57 start-page: 702 issue: 4 year: 2004 ident: 6131_CR38 publication-title: Proteins doi: 10.1002/prot.20264 – volume: 5 start-page: 1502242 issue: 1 year: 2018 ident: 6131_CR22 publication-title: Cogent Eng doi: 10.1080/23311916.2018.1502242 – ident: 6131_CR27 – volume: 10 start-page: nwad259 issue: 12 year: 2023 ident: 6131_CR4 publication-title: Natl Sci Rev doi: 10.1093/nsr/nwad259 – volume: 34 start-page: 504 issue: 3 year: 1999 ident: 6131_CR42 publication-title: Mol Microbiol doi: 10.1046/j.1365-2958.1999.01614.x – volume: 373 start-page: 871 issue: 6557 year: 2021 ident: 6131_CR5 publication-title: Science doi: 10.1126/science.abj8754 – volume: 316 start-page: 235 issue: 2 year: 2002 ident: 6131_CR43 publication-title: J Mol Biol doi: 10.1006/jmbi.2001.5331 – volume: 16 start-page: 519 issue: 3 year: 2024 ident: 6131_CR30 publication-title: Interdiscip Sci doi: 10.1007/s12539-023-00597-5 – volume: 577 start-page: 706 issue: 7792 year: 2020 ident: 6131_CR6 publication-title: Nature doi: 10.1038/s41586-019-1923-7 – volume: 4 start-page: 67 year: 2003 ident: 6131_CR2 publication-title: J Struct Funct Genomics doi: 10.1023/A:1026113408773 – volume: 12 start-page: 400 issue: 3 year: 2002 ident: 6131_CR13 publication-title: Curr Opin Struct Biol doi: 10.1016/S0959-440X(02)00338-X – volume: 39 start-page: btad591 issue: 10 year: 2023 ident: 6131_CR33 publication-title: Bioinformatics doi: 10.1093/bioinformatics/btad591 – volume: 6 start-page: 1221 issue: 1 year: 2023 ident: 6131_CR21 publication-title: Commun Biol doi: 10.1038/s42003-023-05610-7 – volume: 630 start-page: 493 issue: 8016 year: 2024 ident: 6131_CR37 publication-title: Nature doi: 10.1038/s41586-024-07487-w – volume: 63 start-page: 6451 issue: 20 year: 2023 ident: 6131_CR12 publication-title: J Chem Inf Model doi: 10.1021/acs.jcim.3c01387 – volume: 117 start-page: 1496 issue: 3 year: 2020 ident: 6131_CR8 publication-title: Proc Natl Acad Sci doi: 10.1073/pnas.1914677117 – volume: 45 start-page: D170 issue: D1 year: 2017 ident: 6131_CR26 publication-title: Nucleic Acids Res doi: 10.1093/nar/gkw1081 – volume: 12 start-page: 1340 issue: 1 year: 2021 ident: 6131_CR35 publication-title: Nat Commun doi: 10.1038/s41467-021-21511-x – volume: 66 start-page: 255 issue: 3 year: 2022 ident: 6131_CR3 publication-title: Essays Biochem doi: 10.1042/EBC20200108 – volume: 1838 start-page: 9 issue: 104 year: 1838 ident: 6131_CR1 publication-title: Bull Sci Phys Nat Neerl – volume: 20 start-page: 1 year: 2019 ident: 6131_CR28 publication-title: BMC Bioinform doi: 10.1186/s12859-019-3019-7 – volume: 27 start-page: 1034 issue: 6 year: 2019 ident: 6131_CR45 publication-title: Structure doi: 10.1016/j.str.2019.03.018 – volume: 17 start-page: 2326 issue: 10 year: 2022 ident: 6131_CR15 publication-title: Nat Protoc doi: 10.1038/s41596-022-00728-0 – volume: 24 start-page: 219 issue: 4 year: 2023 ident: 6131_CR34 publication-title: Brief Bioinform doi: 10.1093/bib/bbad219 – volume: 87 start-page: 1149 issue: 12 year: 2019 ident: 6131_CR16 publication-title: Proteins doi: 10.1002/prot.25792 – volume: 38 start-page: 4513 issue: 19 year: 2022 ident: 6131_CR19 publication-title: Bioinformatics doi: 10.1093/bioinformatics/btac553 – volume: 25 start-page: bbad420 issue: 1 year: 2024 ident: 6131_CR32 publication-title: Brief Bioinform doi: 10.1093/bib/bbad420 – volume: 3 start-page: 601 issue: 7 year: 2021 ident: 6131_CR7 publication-title: Nat Mach Intell doi: 10.1038/s42256-021-00348-5 – volume: 31 start-page: 2098 issue: 13 year: 2015 ident: 6131_CR18 publication-title: Bioinformatics doi: 10.1093/bioinformatics/btv092 – volume: 65 start-page: 727 issue: 7 year: 2009 ident: 6131_CR41 publication-title: Struct Biol Cryst Commun doi: 10.1107/S1744309109023082 – volume: 20 start-page: 912 issue: 2 year: 2022 ident: 6131_CR25 publication-title: IEEE/ACM Trans Comput Biol Bioinform doi: 10.1109/TCBB.2022.3175905 – volume: 100 start-page: 12105 issue: 21 year: 2003 ident: 6131_CR11 publication-title: Proc Natl Acad Sci doi: 10.1073/pnas.1831973100 – volume: 91 start-page: 1636 issue: 12 year: 2023 ident: 6131_CR10 publication-title: Proteins doi: 10.1002/prot.26598 – volume: 50 start-page: D439 issue: D1 year: 2022 ident: 6131_CR36 publication-title: Nucleic Acids Res doi: 10.1093/nar/gkab1061 – volume: 330 start-page: 1816 issue: 6012 year: 2010 ident: 6131_CR39 publication-title: Science doi: 10.1126/science.1195821 – volume: 3 start-page: 257 issue: 4 year: 1999 ident: 6131_CR31 publication-title: IEEE Trans Evol Comput doi: 10.1109/4235.797969 – volume: 76 start-page: 201 issue: 1 year: 2009 ident: 6131_CR44 publication-title: Proteins doi: 10.1002/prot.22339 – volume: 16 start-page: 165 issue: 2 year: 2007 ident: 6131_CR17 publication-title: Protein Sci doi: 10.1110/ps.062270707 – volume: 116 start-page: 15930 issue: 32 year: 2019 ident: 6131_CR20 publication-title: Proc Natl Acad Sci doi: 10.1073/pnas.1905068116 – volume: 37 start-page: 4357 issue: 23 year: 2021 ident: 6131_CR24 publication-title: Bioinformatics doi: 10.1093/bioinformatics/btab500 – volume: 28 start-page: 235 issue: 1 year: 2000 ident: 6131_CR14 publication-title: Nucleic Acids Res doi: 10.1093/nar/28.1.235 |
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Association and cooperation among structural domains play an important role in protein function and drug design. Despite remarkable advancements in... Association and cooperation among structural domains play an important role in protein function and drug design. Despite remarkable advancements in highly... Background Association and cooperation among structural domains play an important role in protein function and drug design. Despite remarkable advancements in... BackgroundAssociation and cooperation among structural domains play an important role in protein function and drug design. Despite remarkable advancements in... Abstract Background Association and cooperation among structural domains play an important role in protein function and drug design. Despite remarkable... |
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| SubjectTerms | Ab initio modelling of protein structure Algorithms Bioinformatics Biology Biomedical and Life Sciences Computational Biology - methods Computational Biology/Bioinformatics Computer Appl. in Life Sciences Conformation Conformation sampling Databases, Protein Deep Learning Drug development Energy Feature extraction Forecasts and trends Life Sciences Methods Microarrays Models, Molecular Multi-domain protein assembly Multi-objective energy model Optimization Pareto optimum Predictions Protein Conformation Protein Domains Protein structure Protein structure prediction Proteins Proteins - chemistry Quality assessment Quality control Sampling |
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| Title | M-DeepAssembly: enhanced DeepAssembly based on multi-objective multi-domain protein conformation sampling |
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