Combinatorial Virtual Library Screening Study of Transforming Growth Factor-β2–Chondroitin Sulfate System

Transforming growth factor-beta (TGF-β), a member of the TGF-β cytokine superfamily, is known to bind to sulfated glycosaminoglycans (GAGs), but the nature of this interaction remains unclear. In a recent study, we found that preterm human milk TGF-β2 is sequestered by chondroitin sulfate (CS) in its proteoglycan form. To understand the molecular basis of the TGF-β2–CS interaction, we utilized the computational combinatorial virtual library screening (CVLS) approach in tandem with molecular dynamics (MD) simulations. All possible CS oligosaccharides were generated in a combinatorial manner to give 24 di- (CS02), 192 tetra- (CS04), and 1536 hexa- (CS06) saccharides. This library of 1752 CS oligosaccharides was first screened against TGF-β2 using the dual filter CVLS algorithm in which the GOLDScore and root-mean-square-difference (RMSD) between the best bound poses were used as surrogate markers for in silico <i<affinity</i< and in silico <i<specificity</i<. CVLS predicted that both the chain length and level of sulfation are critical for the high affinity and high specificity recognition of TGF-β2. Interestingly, CVLS led to identification of two distinct sites of GAG binding on TGF-β2. CVLS also deduced the preferred composition of the high specificity hexasaccharides, which were further assessed in all-atom explicit solvent MD simulations. The MD results confirmed that both sites of binding form stable GAG–protein complexes. More specifically, the highly selective CS chains were found to engage the TGF-β2 monomer with high affinity. Overall, this work present key principles of recognition with regard to the TGF-β2–CS system. In the process, it led to the generation of the in silico library of all possible CS oligosaccharides, which can be used for advanced studies on other protein–CS systems. Finally, the study led to the identification of unique CS sequences that are predicted to selectively recognize TGF-β2 and may out-compete common natural CS biopolymers. Ausführliche Beschreibung

Gespeichert in:

Autor*in:	Nehru Viji Sankaranarayanan [verfasserIn] Balaji Nagarajan [verfasserIn] Umesh R. Desai [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2021

Schlagwörter:	glycosaminoglycans chondroitin sulfate growth factors TGF-β2 molecular dynamics molecular modeling

Übergeordnetes Werk:	In: International Journal of Molecular Sciences - MDPI AG, 2003, 22(2021), 14, p 7542
Übergeordnetes Werk:	volume:22 ; year:2021 ; number:14, p 7542

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc Journal toc

DOI / URN:	10.3390/ijms22147542

Katalog-ID:	DOAJ057493448

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allfields	10.3390/ijms22147542 doi (DE-627)DOAJ057493448 (DE-599)DOAJd0c2486ef0f046d7a0012b014f0f1820 DE-627 ger DE-627 rakwb eng QH301-705.5 QD1-999 Nehru Viji Sankaranarayanan verfasserin aut Combinatorial Virtual Library Screening Study of Transforming Growth Factor-β2–Chondroitin Sulfate System 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Transforming growth factor-beta (TGF-β), a member of the TGF-β cytokine superfamily, is known to bind to sulfated glycosaminoglycans (GAGs), but the nature of this interaction remains unclear. In a recent study, we found that preterm human milk TGF-β2 is sequestered by chondroitin sulfate (CS) in its proteoglycan form. To understand the molecular basis of the TGF-β2–CS interaction, we utilized the computational combinatorial virtual library screening (CVLS) approach in tandem with molecular dynamics (MD) simulations. All possible CS oligosaccharides were generated in a combinatorial manner to give 24 di- (CS02), 192 tetra- (CS04), and 1536 hexa- (CS06) saccharides. This library of 1752 CS oligosaccharides was first screened against TGF-β2 using the dual filter CVLS algorithm in which the GOLDScore and root-mean-square-difference (RMSD) between the best bound poses were used as surrogate markers for in silico <i<affinity</i< and in silico <i<specificity</i<. CVLS predicted that both the chain length and level of sulfation are critical for the high affinity and high specificity recognition of TGF-β2. Interestingly, CVLS led to identification of two distinct sites of GAG binding on TGF-β2. CVLS also deduced the preferred composition of the high specificity hexasaccharides, which were further assessed in all-atom explicit solvent MD simulations. The MD results confirmed that both sites of binding form stable GAG–protein complexes. More specifically, the highly selective CS chains were found to engage the TGF-β2 monomer with high affinity. Overall, this work present key principles of recognition with regard to the TGF-β2–CS system. In the process, it led to the generation of the in silico library of all possible CS oligosaccharides, which can be used for advanced studies on other protein–CS systems. Finally, the study led to the identification of unique CS sequences that are predicted to selectively recognize TGF-β2 and may out-compete common natural CS biopolymers. glycosaminoglycans chondroitin sulfate growth factors TGF-β2 molecular dynamics molecular modeling Biology (General) Chemistry Balaji Nagarajan verfasserin aut Umesh R. Desai verfasserin aut In International Journal of Molecular Sciences MDPI AG, 2003 22(2021), 14, p 7542 (DE-627)316340715 (DE-600)2019364-6 14220067 nnns volume:22 year:2021 number:14, p 7542 https://doi.org/10.3390/ijms22147542 kostenfrei https://doaj.org/article/d0c2486ef0f046d7a0012b014f0f1820 kostenfrei https://www.mdpi.com/1422-0067/22/14/7542 kostenfrei https://doaj.org/toc/1661-6596 Journal toc kostenfrei https://doaj.org/toc/1422-0067 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2111 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 22 2021 14, p 7542
spelling	10.3390/ijms22147542 doi (DE-627)DOAJ057493448 (DE-599)DOAJd0c2486ef0f046d7a0012b014f0f1820 DE-627 ger DE-627 rakwb eng QH301-705.5 QD1-999 Nehru Viji Sankaranarayanan verfasserin aut Combinatorial Virtual Library Screening Study of Transforming Growth Factor-β2–Chondroitin Sulfate System 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Transforming growth factor-beta (TGF-β), a member of the TGF-β cytokine superfamily, is known to bind to sulfated glycosaminoglycans (GAGs), but the nature of this interaction remains unclear. In a recent study, we found that preterm human milk TGF-β2 is sequestered by chondroitin sulfate (CS) in its proteoglycan form. To understand the molecular basis of the TGF-β2–CS interaction, we utilized the computational combinatorial virtual library screening (CVLS) approach in tandem with molecular dynamics (MD) simulations. All possible CS oligosaccharides were generated in a combinatorial manner to give 24 di- (CS02), 192 tetra- (CS04), and 1536 hexa- (CS06) saccharides. This library of 1752 CS oligosaccharides was first screened against TGF-β2 using the dual filter CVLS algorithm in which the GOLDScore and root-mean-square-difference (RMSD) between the best bound poses were used as surrogate markers for in silico <i<affinity</i< and in silico <i<specificity</i<. CVLS predicted that both the chain length and level of sulfation are critical for the high affinity and high specificity recognition of TGF-β2. Interestingly, CVLS led to identification of two distinct sites of GAG binding on TGF-β2. CVLS also deduced the preferred composition of the high specificity hexasaccharides, which were further assessed in all-atom explicit solvent MD simulations. The MD results confirmed that both sites of binding form stable GAG–protein complexes. More specifically, the highly selective CS chains were found to engage the TGF-β2 monomer with high affinity. Overall, this work present key principles of recognition with regard to the TGF-β2–CS system. In the process, it led to the generation of the in silico library of all possible CS oligosaccharides, which can be used for advanced studies on other protein–CS systems. Finally, the study led to the identification of unique CS sequences that are predicted to selectively recognize TGF-β2 and may out-compete common natural CS biopolymers. glycosaminoglycans chondroitin sulfate growth factors TGF-β2 molecular dynamics molecular modeling Biology (General) Chemistry Balaji Nagarajan verfasserin aut Umesh R. Desai verfasserin aut In International Journal of Molecular Sciences MDPI AG, 2003 22(2021), 14, p 7542 (DE-627)316340715 (DE-600)2019364-6 14220067 nnns volume:22 year:2021 number:14, p 7542 https://doi.org/10.3390/ijms22147542 kostenfrei https://doaj.org/article/d0c2486ef0f046d7a0012b014f0f1820 kostenfrei https://www.mdpi.com/1422-0067/22/14/7542 kostenfrei https://doaj.org/toc/1661-6596 Journal toc kostenfrei https://doaj.org/toc/1422-0067 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2111 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 22 2021 14, p 7542
allfields_unstemmed	10.3390/ijms22147542 doi (DE-627)DOAJ057493448 (DE-599)DOAJd0c2486ef0f046d7a0012b014f0f1820 DE-627 ger DE-627 rakwb eng QH301-705.5 QD1-999 Nehru Viji Sankaranarayanan verfasserin aut Combinatorial Virtual Library Screening Study of Transforming Growth Factor-β2–Chondroitin Sulfate System 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Transforming growth factor-beta (TGF-β), a member of the TGF-β cytokine superfamily, is known to bind to sulfated glycosaminoglycans (GAGs), but the nature of this interaction remains unclear. In a recent study, we found that preterm human milk TGF-β2 is sequestered by chondroitin sulfate (CS) in its proteoglycan form. To understand the molecular basis of the TGF-β2–CS interaction, we utilized the computational combinatorial virtual library screening (CVLS) approach in tandem with molecular dynamics (MD) simulations. All possible CS oligosaccharides were generated in a combinatorial manner to give 24 di- (CS02), 192 tetra- (CS04), and 1536 hexa- (CS06) saccharides. This library of 1752 CS oligosaccharides was first screened against TGF-β2 using the dual filter CVLS algorithm in which the GOLDScore and root-mean-square-difference (RMSD) between the best bound poses were used as surrogate markers for in silico <i<affinity</i< and in silico <i<specificity</i<. CVLS predicted that both the chain length and level of sulfation are critical for the high affinity and high specificity recognition of TGF-β2. Interestingly, CVLS led to identification of two distinct sites of GAG binding on TGF-β2. CVLS also deduced the preferred composition of the high specificity hexasaccharides, which were further assessed in all-atom explicit solvent MD simulations. The MD results confirmed that both sites of binding form stable GAG–protein complexes. More specifically, the highly selective CS chains were found to engage the TGF-β2 monomer with high affinity. Overall, this work present key principles of recognition with regard to the TGF-β2–CS system. In the process, it led to the generation of the in silico library of all possible CS oligosaccharides, which can be used for advanced studies on other protein–CS systems. Finally, the study led to the identification of unique CS sequences that are predicted to selectively recognize TGF-β2 and may out-compete common natural CS biopolymers. glycosaminoglycans chondroitin sulfate growth factors TGF-β2 molecular dynamics molecular modeling Biology (General) Chemistry Balaji Nagarajan verfasserin aut Umesh R. Desai verfasserin aut In International Journal of Molecular Sciences MDPI AG, 2003 22(2021), 14, p 7542 (DE-627)316340715 (DE-600)2019364-6 14220067 nnns volume:22 year:2021 number:14, p 7542 https://doi.org/10.3390/ijms22147542 kostenfrei https://doaj.org/article/d0c2486ef0f046d7a0012b014f0f1820 kostenfrei https://www.mdpi.com/1422-0067/22/14/7542 kostenfrei https://doaj.org/toc/1661-6596 Journal toc kostenfrei https://doaj.org/toc/1422-0067 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2111 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 22 2021 14, p 7542
allfieldsGer	10.3390/ijms22147542 doi (DE-627)DOAJ057493448 (DE-599)DOAJd0c2486ef0f046d7a0012b014f0f1820 DE-627 ger DE-627 rakwb eng QH301-705.5 QD1-999 Nehru Viji Sankaranarayanan verfasserin aut Combinatorial Virtual Library Screening Study of Transforming Growth Factor-β2–Chondroitin Sulfate System 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Transforming growth factor-beta (TGF-β), a member of the TGF-β cytokine superfamily, is known to bind to sulfated glycosaminoglycans (GAGs), but the nature of this interaction remains unclear. In a recent study, we found that preterm human milk TGF-β2 is sequestered by chondroitin sulfate (CS) in its proteoglycan form. To understand the molecular basis of the TGF-β2–CS interaction, we utilized the computational combinatorial virtual library screening (CVLS) approach in tandem with molecular dynamics (MD) simulations. All possible CS oligosaccharides were generated in a combinatorial manner to give 24 di- (CS02), 192 tetra- (CS04), and 1536 hexa- (CS06) saccharides. This library of 1752 CS oligosaccharides was first screened against TGF-β2 using the dual filter CVLS algorithm in which the GOLDScore and root-mean-square-difference (RMSD) between the best bound poses were used as surrogate markers for in silico <i<affinity</i< and in silico <i<specificity</i<. CVLS predicted that both the chain length and level of sulfation are critical for the high affinity and high specificity recognition of TGF-β2. Interestingly, CVLS led to identification of two distinct sites of GAG binding on TGF-β2. CVLS also deduced the preferred composition of the high specificity hexasaccharides, which were further assessed in all-atom explicit solvent MD simulations. The MD results confirmed that both sites of binding form stable GAG–protein complexes. More specifically, the highly selective CS chains were found to engage the TGF-β2 monomer with high affinity. Overall, this work present key principles of recognition with regard to the TGF-β2–CS system. In the process, it led to the generation of the in silico library of all possible CS oligosaccharides, which can be used for advanced studies on other protein–CS systems. Finally, the study led to the identification of unique CS sequences that are predicted to selectively recognize TGF-β2 and may out-compete common natural CS biopolymers. glycosaminoglycans chondroitin sulfate growth factors TGF-β2 molecular dynamics molecular modeling Biology (General) Chemistry Balaji Nagarajan verfasserin aut Umesh R. Desai verfasserin aut In International Journal of Molecular Sciences MDPI AG, 2003 22(2021), 14, p 7542 (DE-627)316340715 (DE-600)2019364-6 14220067 nnns volume:22 year:2021 number:14, p 7542 https://doi.org/10.3390/ijms22147542 kostenfrei https://doaj.org/article/d0c2486ef0f046d7a0012b014f0f1820 kostenfrei https://www.mdpi.com/1422-0067/22/14/7542 kostenfrei https://doaj.org/toc/1661-6596 Journal toc kostenfrei https://doaj.org/toc/1422-0067 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2111 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 22 2021 14, p 7542
allfieldsSound	10.3390/ijms22147542 doi (DE-627)DOAJ057493448 (DE-599)DOAJd0c2486ef0f046d7a0012b014f0f1820 DE-627 ger DE-627 rakwb eng QH301-705.5 QD1-999 Nehru Viji Sankaranarayanan verfasserin aut Combinatorial Virtual Library Screening Study of Transforming Growth Factor-β2–Chondroitin Sulfate System 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Transforming growth factor-beta (TGF-β), a member of the TGF-β cytokine superfamily, is known to bind to sulfated glycosaminoglycans (GAGs), but the nature of this interaction remains unclear. In a recent study, we found that preterm human milk TGF-β2 is sequestered by chondroitin sulfate (CS) in its proteoglycan form. To understand the molecular basis of the TGF-β2–CS interaction, we utilized the computational combinatorial virtual library screening (CVLS) approach in tandem with molecular dynamics (MD) simulations. All possible CS oligosaccharides were generated in a combinatorial manner to give 24 di- (CS02), 192 tetra- (CS04), and 1536 hexa- (CS06) saccharides. This library of 1752 CS oligosaccharides was first screened against TGF-β2 using the dual filter CVLS algorithm in which the GOLDScore and root-mean-square-difference (RMSD) between the best bound poses were used as surrogate markers for in silico <i<affinity</i< and in silico <i<specificity</i<. CVLS predicted that both the chain length and level of sulfation are critical for the high affinity and high specificity recognition of TGF-β2. Interestingly, CVLS led to identification of two distinct sites of GAG binding on TGF-β2. CVLS also deduced the preferred composition of the high specificity hexasaccharides, which were further assessed in all-atom explicit solvent MD simulations. The MD results confirmed that both sites of binding form stable GAG–protein complexes. More specifically, the highly selective CS chains were found to engage the TGF-β2 monomer with high affinity. Overall, this work present key principles of recognition with regard to the TGF-β2–CS system. In the process, it led to the generation of the in silico library of all possible CS oligosaccharides, which can be used for advanced studies on other protein–CS systems. Finally, the study led to the identification of unique CS sequences that are predicted to selectively recognize TGF-β2 and may out-compete common natural CS biopolymers. glycosaminoglycans chondroitin sulfate growth factors TGF-β2 molecular dynamics molecular modeling Biology (General) Chemistry Balaji Nagarajan verfasserin aut Umesh R. Desai verfasserin aut In International Journal of Molecular Sciences MDPI AG, 2003 22(2021), 14, p 7542 (DE-627)316340715 (DE-600)2019364-6 14220067 nnns volume:22 year:2021 number:14, p 7542 https://doi.org/10.3390/ijms22147542 kostenfrei https://doaj.org/article/d0c2486ef0f046d7a0012b014f0f1820 kostenfrei https://www.mdpi.com/1422-0067/22/14/7542 kostenfrei https://doaj.org/toc/1661-6596 Journal toc kostenfrei https://doaj.org/toc/1422-0067 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2111 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 22 2021 14, p 7542
language	English
source	In International Journal of Molecular Sciences 22(2021), 14, p 7542 volume:22 year:2021 number:14, p 7542
sourceStr	In International Journal of Molecular Sciences 22(2021), 14, p 7542 volume:22 year:2021 number:14, p 7542
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	glycosaminoglycans chondroitin sulfate growth factors TGF-β2 molecular dynamics molecular modeling Biology (General) Chemistry
isfreeaccess_bool	true
container_title	International Journal of Molecular Sciences
authorswithroles_txt_mv	Nehru Viji Sankaranarayanan @@aut@@ Balaji Nagarajan @@aut@@ Umesh R. Desai @@aut@@
publishDateDaySort_date	2021-01-01T00:00:00Z
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callnumber-first	Q - Science
author	Nehru Viji Sankaranarayanan
spellingShingle	Nehru Viji Sankaranarayanan misc QH301-705.5 misc QD1-999 misc glycosaminoglycans misc chondroitin sulfate misc growth factors misc TGF-β2 misc molecular dynamics misc molecular modeling misc Biology (General) misc Chemistry Combinatorial Virtual Library Screening Study of Transforming Growth Factor-β2–Chondroitin Sulfate System
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topic_title	QH301-705.5 QD1-999 Combinatorial Virtual Library Screening Study of Transforming Growth Factor-β2–Chondroitin Sulfate System glycosaminoglycans chondroitin sulfate growth factors TGF-β2 molecular dynamics molecular modeling
topic	misc QH301-705.5 misc QD1-999 misc glycosaminoglycans misc chondroitin sulfate misc growth factors misc TGF-β2 misc molecular dynamics misc molecular modeling misc Biology (General) misc Chemistry
topic_unstemmed	misc QH301-705.5 misc QD1-999 misc glycosaminoglycans misc chondroitin sulfate misc growth factors misc TGF-β2 misc molecular dynamics misc molecular modeling misc Biology (General) misc Chemistry
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title	Combinatorial Virtual Library Screening Study of Transforming Growth Factor-β2–Chondroitin Sulfate System
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title_full	Combinatorial Virtual Library Screening Study of Transforming Growth Factor-β2–Chondroitin Sulfate System
author_sort	Nehru Viji Sankaranarayanan
journal	International Journal of Molecular Sciences
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author_browse	Nehru Viji Sankaranarayanan Balaji Nagarajan Umesh R. Desai
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title_sort	combinatorial virtual library screening study of transforming growth factor-β2–chondroitin sulfate system
callnumber	QH301-705.5
title_auth	Combinatorial Virtual Library Screening Study of Transforming Growth Factor-β2–Chondroitin Sulfate System
abstract	Transforming growth factor-beta (TGF-β), a member of the TGF-β cytokine superfamily, is known to bind to sulfated glycosaminoglycans (GAGs), but the nature of this interaction remains unclear. In a recent study, we found that preterm human milk TGF-β2 is sequestered by chondroitin sulfate (CS) in its proteoglycan form. To understand the molecular basis of the TGF-β2–CS interaction, we utilized the computational combinatorial virtual library screening (CVLS) approach in tandem with molecular dynamics (MD) simulations. All possible CS oligosaccharides were generated in a combinatorial manner to give 24 di- (CS02), 192 tetra- (CS04), and 1536 hexa- (CS06) saccharides. This library of 1752 CS oligosaccharides was first screened against TGF-β2 using the dual filter CVLS algorithm in which the GOLDScore and root-mean-square-difference (RMSD) between the best bound poses were used as surrogate markers for in silico <i<affinity</i< and in silico <i<specificity</i<. CVLS predicted that both the chain length and level of sulfation are critical for the high affinity and high specificity recognition of TGF-β2. Interestingly, CVLS led to identification of two distinct sites of GAG binding on TGF-β2. CVLS also deduced the preferred composition of the high specificity hexasaccharides, which were further assessed in all-atom explicit solvent MD simulations. The MD results confirmed that both sites of binding form stable GAG–protein complexes. More specifically, the highly selective CS chains were found to engage the TGF-β2 monomer with high affinity. Overall, this work present key principles of recognition with regard to the TGF-β2–CS system. In the process, it led to the generation of the in silico library of all possible CS oligosaccharides, which can be used for advanced studies on other protein–CS systems. Finally, the study led to the identification of unique CS sequences that are predicted to selectively recognize TGF-β2 and may out-compete common natural CS biopolymers.
abstractGer	Transforming growth factor-beta (TGF-β), a member of the TGF-β cytokine superfamily, is known to bind to sulfated glycosaminoglycans (GAGs), but the nature of this interaction remains unclear. In a recent study, we found that preterm human milk TGF-β2 is sequestered by chondroitin sulfate (CS) in its proteoglycan form. To understand the molecular basis of the TGF-β2–CS interaction, we utilized the computational combinatorial virtual library screening (CVLS) approach in tandem with molecular dynamics (MD) simulations. All possible CS oligosaccharides were generated in a combinatorial manner to give 24 di- (CS02), 192 tetra- (CS04), and 1536 hexa- (CS06) saccharides. This library of 1752 CS oligosaccharides was first screened against TGF-β2 using the dual filter CVLS algorithm in which the GOLDScore and root-mean-square-difference (RMSD) between the best bound poses were used as surrogate markers for in silico <i<affinity</i< and in silico <i<specificity</i<. CVLS predicted that both the chain length and level of sulfation are critical for the high affinity and high specificity recognition of TGF-β2. Interestingly, CVLS led to identification of two distinct sites of GAG binding on TGF-β2. CVLS also deduced the preferred composition of the high specificity hexasaccharides, which were further assessed in all-atom explicit solvent MD simulations. The MD results confirmed that both sites of binding form stable GAG–protein complexes. More specifically, the highly selective CS chains were found to engage the TGF-β2 monomer with high affinity. Overall, this work present key principles of recognition with regard to the TGF-β2–CS system. In the process, it led to the generation of the in silico library of all possible CS oligosaccharides, which can be used for advanced studies on other protein–CS systems. Finally, the study led to the identification of unique CS sequences that are predicted to selectively recognize TGF-β2 and may out-compete common natural CS biopolymers.
abstract_unstemmed	Transforming growth factor-beta (TGF-β), a member of the TGF-β cytokine superfamily, is known to bind to sulfated glycosaminoglycans (GAGs), but the nature of this interaction remains unclear. In a recent study, we found that preterm human milk TGF-β2 is sequestered by chondroitin sulfate (CS) in its proteoglycan form. To understand the molecular basis of the TGF-β2–CS interaction, we utilized the computational combinatorial virtual library screening (CVLS) approach in tandem with molecular dynamics (MD) simulations. All possible CS oligosaccharides were generated in a combinatorial manner to give 24 di- (CS02), 192 tetra- (CS04), and 1536 hexa- (CS06) saccharides. This library of 1752 CS oligosaccharides was first screened against TGF-β2 using the dual filter CVLS algorithm in which the GOLDScore and root-mean-square-difference (RMSD) between the best bound poses were used as surrogate markers for in silico <i<affinity</i< and in silico <i<specificity</i<. CVLS predicted that both the chain length and level of sulfation are critical for the high affinity and high specificity recognition of TGF-β2. Interestingly, CVLS led to identification of two distinct sites of GAG binding on TGF-β2. CVLS also deduced the preferred composition of the high specificity hexasaccharides, which were further assessed in all-atom explicit solvent MD simulations. The MD results confirmed that both sites of binding form stable GAG–protein complexes. More specifically, the highly selective CS chains were found to engage the TGF-β2 monomer with high affinity. Overall, this work present key principles of recognition with regard to the TGF-β2–CS system. In the process, it led to the generation of the in silico library of all possible CS oligosaccharides, which can be used for advanced studies on other protein–CS systems. Finally, the study led to the identification of unique CS sequences that are predicted to selectively recognize TGF-β2 and may out-compete common natural CS biopolymers.
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title_short	Combinatorial Virtual Library Screening Study of Transforming Growth Factor-β2–Chondroitin Sulfate System
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