Improving ligand‐ranking of AutoDock Vina by changing the empirical parameters

AutoDock Vina (Vina) achieved a very high docking‐success rate, p^, but give a rather low correlation coefficient, R, for binding affinity with respect to experiments. This low correlation can be an obstacle for ranking of ligand‐binding affinity, which is the main objective of docking simulations....

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Vydané v:	Journal of computational chemistry Ročník 43; číslo 3; s. 160 - 169
Hlavní autori:	Pham, T. Ngoc Han, Nguyen, Trung Hai, Tam, Nguyen Minh, Y. Vu, Thien, Pham, Nhat Truong, Huy, Nguyen Truong, Mai, Binh Khanh, Tung, Nguyen Thanh, Pham, Minh Quan, V. Vu, Van, Ngo, Son Tung
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Hoboken, USA John Wiley & Sons, Inc 30.01.2022 Wiley Subscription Services, Inc
Predmet:	Affinity AutoDock Vina Binding binding affinity Correlation coefficients Docking empirical parameter Hydrogen bonds Ligands Mathematical analysis modified Vina Parameters Ranking screening binding affinity screening empirical parameter AutoDock Vina modified Vina
ISSN:	0192-8651, 1096-987X, 1096-987X
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Abstract	AutoDock Vina (Vina) achieved a very high docking‐success rate, p^, but give a rather low correlation coefficient, R, for binding affinity with respect to experiments. This low correlation can be an obstacle for ranking of ligand‐binding affinity, which is the main objective of docking simulations. In this context, we evaluated the dependence of Vina R coefficient upon its empirical parameters. R is affected more by changing the gauss2 and rotation than other terms. The docking‐success rate p^ is sensitive to the alterations of the gauss1, gauss2, repulsion, and hydrogen bond parameters. Based on our benchmarks, the parameter set1 has been suggested to be the most optimal. The testing study over 800 complexes indicated that the modified Vina provided higher correlation with experiment Rset1=0.556±0.025 compared with RDefault=0.493±0.028 obtained by the original Vina and RVina1.2=0.503±0.029 by Vina version 1.2. Besides, the modified Vina can be also applied more widely, giving R≥0.500 for 32/48 targets, compared with the default package, giving R≥0.500 for 31/48 targets. In addition, validation calculations for 1036 complexes obtained from version 2019 of PDBbind refined structures showed that the set1 of parameters gave higher correlation coefficient (Rset1=0.617±0.017) than the default package (RDefault=0.543±0.020) and Vina version 1.2 (RVina1.2=0.540±0.020). The version of Vina with set1 of parameters can be downloaded at https://github.com/sontungngo/mvina. The outcomes would enhance the ranking of ligand‐binding affinity using Autodock Vina. A new set of empirical parameters of AutoDock Vina was proposed. The accuracy of affinity prediction was significantly increased from RDefault=0.493±0.028 to Rset1=0.556±0.025 over 800 testing complexes. Over 1036 validating complexes, the proposed parameter formed Rset1=0.617±0.017, which is rigidly larger than the default package (RDefault=0.543±0.020) and Vina version 1.2 (RVina1.2=0.540±0.020).
AbstractList	AutoDock Vina (Vina) achieved a very high docking-success rate, p^ , but give a rather low correlation coefficient, R , for binding affinity with respect to experiments. This low correlation can be an obstacle for ranking of ligand-binding affinity, which is the main objective of docking simulations. In this context, we evaluated the dependence of Vina R coefficient upon its empirical parameters. R is affected more by changing the gauss2 and rotation than other terms. The docking-success rate p^ is sensitive to the alterations of the gauss1, gauss2, repulsion, and hydrogen bond parameters. Based on our benchmarks, the parameter set1 has been suggested to be the most optimal. The testing study over 800 complexes indicated that the modified Vina provided higher correlation with experiment Rset1=0.556±0.025 compared with RDefault=0.493±0.028 obtained by the original Vina and RVina1.2=0.503±0.029 by Vina version 1.2. Besides, the modified Vina can be also applied more widely, giving R≥0.500 for 32/48 targets, compared with the default package, giving R≥0.500 for 31/48 targets. In addition, validation calculations for 1036 complexes obtained from version 2019 of PDBbind refined structures showed that the set1 of parameters gave higher correlation coefficient ( Rset1=0.617±0.017 ) than the default package ( RDefault=0.543±0.020 ) and Vina version 1.2 ( RVina1.2=0.540±0.020 ). The version of Vina with set1 of parameters can be downloaded at https://github.com/sontungngo/mvina. The outcomes would enhance the ranking of ligand-binding affinity using Autodock Vina.AutoDock Vina (Vina) achieved a very high docking-success rate, p^ , but give a rather low correlation coefficient, R , for binding affinity with respect to experiments. This low correlation can be an obstacle for ranking of ligand-binding affinity, which is the main objective of docking simulations. In this context, we evaluated the dependence of Vina R coefficient upon its empirical parameters. R is affected more by changing the gauss2 and rotation than other terms. The docking-success rate p^ is sensitive to the alterations of the gauss1, gauss2, repulsion, and hydrogen bond parameters. Based on our benchmarks, the parameter set1 has been suggested to be the most optimal. The testing study over 800 complexes indicated that the modified Vina provided higher correlation with experiment Rset1=0.556±0.025 compared with RDefault=0.493±0.028 obtained by the original Vina and RVina1.2=0.503±0.029 by Vina version 1.2. Besides, the modified Vina can be also applied more widely, giving R≥0.500 for 32/48 targets, compared with the default package, giving R≥0.500 for 31/48 targets. In addition, validation calculations for 1036 complexes obtained from version 2019 of PDBbind refined structures showed that the set1 of parameters gave higher correlation coefficient ( Rset1=0.617±0.017 ) than the default package ( RDefault=0.543±0.020 ) and Vina version 1.2 ( RVina1.2=0.540±0.020 ). The version of Vina with set1 of parameters can be downloaded at https://github.com/sontungngo/mvina. The outcomes would enhance the ranking of ligand-binding affinity using Autodock Vina. AutoDock Vina (Vina) achieved a very high docking‐success rate, , but give a rather low correlation coefficient, , for binding affinity with respect to experiments. This low correlation can be an obstacle for ranking of ligand‐binding affinity, which is the main objective of docking simulations. In this context, we evaluated the dependence of Vina R coefficient upon its empirical parameters. is affected more by changing the gauss2 and rotation than other terms. The docking‐success rate is sensitive to the alterations of the gauss1, gauss2, repulsion, and hydrogen bond parameters. Based on our benchmarks, the parameter set1 has been suggested to be the most optimal. The testing study over 800 complexes indicated that the modified Vina provided higher correlation with experiment compared with obtained by the original Vina and by Vina version 1.2. Besides, the modified Vina can be also applied more widely, giving for 32/48 targets, compared with the default package, giving for 31/48 targets. In addition, validation calculations for 1036 complexes obtained from version 2019 of PDBbind refined structures showed that the set1 of parameters gave higher correlation coefficient ( ) than the default package ( ) and Vina version 1.2 ( ). The version of Vina with set1 of parameters can be downloaded at https://github.com/sontungngo/mvina . The outcomes would enhance the ranking of ligand‐binding affinity using Autodock Vina. AutoDock Vina (Vina) achieved a very high docking‐success rate, p^, but give a rather low correlation coefficient, R, for binding affinity with respect to experiments. This low correlation can be an obstacle for ranking of ligand‐binding affinity, which is the main objective of docking simulations. In this context, we evaluated the dependence of Vina R coefficient upon its empirical parameters. R is affected more by changing the gauss2 and rotation than other terms. The docking‐success rate p^ is sensitive to the alterations of the gauss1, gauss2, repulsion, and hydrogen bond parameters. Based on our benchmarks, the parameter set1 has been suggested to be the most optimal. The testing study over 800 complexes indicated that the modified Vina provided higher correlation with experiment Rset1=0.556±0.025 compared with RDefault=0.493±0.028 obtained by the original Vina and RVina1.2=0.503±0.029 by Vina version 1.2. Besides, the modified Vina can be also applied more widely, giving R≥0.500 for 32/48 targets, compared with the default package, giving R≥0.500 for 31/48 targets. In addition, validation calculations for 1036 complexes obtained from version 2019 of PDBbind refined structures showed that the set1 of parameters gave higher correlation coefficient (Rset1=0.617±0.017) than the default package (RDefault=0.543±0.020) and Vina version 1.2 (RVina1.2=0.540±0.020). The version of Vina with set1 of parameters can be downloaded at https://github.com/sontungngo/mvina. The outcomes would enhance the ranking of ligand‐binding affinity using Autodock Vina. A new set of empirical parameters of AutoDock Vina was proposed. The accuracy of affinity prediction was significantly increased from RDefault=0.493±0.028 to Rset1=0.556±0.025 over 800 testing complexes. Over 1036 validating complexes, the proposed parameter formed Rset1=0.617±0.017, which is rigidly larger than the default package (RDefault=0.543±0.020) and Vina version 1.2 (RVina1.2=0.540±0.020). AutoDock Vina (Vina) achieved a very high docking-success rate, , but give a rather low correlation coefficient, , for binding affinity with respect to experiments. This low correlation can be an obstacle for ranking of ligand-binding affinity, which is the main objective of docking simulations. In this context, we evaluated the dependence of Vina R coefficient upon its empirical parameters. is affected more by changing the gauss2 and rotation than other terms. The docking-success rate is sensitive to the alterations of the gauss1, gauss2, repulsion, and hydrogen bond parameters. Based on our benchmarks, the parameter set1 has been suggested to be the most optimal. The testing study over 800 complexes indicated that the modified Vina provided higher correlation with experiment compared with obtained by the original Vina and by Vina version 1.2. Besides, the modified Vina can be also applied more widely, giving for 32/48 targets, compared with the default package, giving for 31/48 targets. In addition, validation calculations for 1036 complexes obtained from version 2019 of PDBbind refined structures showed that the set1 of parameters gave higher correlation coefficient ( ) than the default package ( ) and Vina version 1.2 ( ). The version of Vina with set1 of parameters can be downloaded at https://github.com/sontungngo/mvina. The outcomes would enhance the ranking of ligand-binding affinity using Autodock Vina. AutoDock Vina (Vina) achieved a very high docking‐success rate, p^, but give a rather low correlation coefficient, R, for binding affinity with respect to experiments. This low correlation can be an obstacle for ranking of ligand‐binding affinity, which is the main objective of docking simulations. In this context, we evaluated the dependence of Vina R coefficient upon its empirical parameters. R is affected more by changing the gauss2 and rotation than other terms. The docking‐success rate p^ is sensitive to the alterations of the gauss1, gauss2, repulsion, and hydrogen bond parameters. Based on our benchmarks, the parameter set1 has been suggested to be the most optimal. The testing study over 800 complexes indicated that the modified Vina provided higher correlation with experiment Rset1=0.556±0.025 compared with RDefault=0.493±0.028 obtained by the original Vina and RVina1.2=0.503±0.029 by Vina version 1.2. Besides, the modified Vina can be also applied more widely, giving R≥0.500 for 32/48 targets, compared with the default package, giving R≥0.500 for 31/48 targets. In addition, validation calculations for 1036 complexes obtained from version 2019 of PDBbind refined structures showed that the set1 of parameters gave higher correlation coefficient (Rset1=0.617±0.017) than the default package (RDefault=0.543±0.020) and Vina version 1.2 (RVina1.2=0.540±0.020). The version of Vina with set1 of parameters can be downloaded at https://github.com/sontungngo/mvina. The outcomes would enhance the ranking of ligand‐binding affinity using Autodock Vina.
Author	V. Vu, Van Nguyen, Trung Hai Huy, Nguyen Truong Mai, Binh Khanh Pham, Minh Quan Ngo, Son Tung Tam, Nguyen Minh Pham, Nhat Truong Pham, T. Ngoc Han Tung, Nguyen Thanh Y. Vu, Thien
Author_xml	– sequence: 1 givenname: T. Ngoc Han surname: Pham fullname: Pham, T. Ngoc Han organization: Ton Duc Thang University – sequence: 2 givenname: Trung Hai orcidid: 0000-0003-1848-3963 surname: Nguyen fullname: Nguyen, Trung Hai organization: Ton Duc Thang University – sequence: 3 givenname: Nguyen Minh orcidid: 0000-0003-3153-4606 surname: Tam fullname: Tam, Nguyen Minh organization: Ton Duc Thang University – sequence: 4 givenname: Thien surname: Y. Vu fullname: Y. Vu, Thien organization: Ton Duc Thang University – sequence: 5 givenname: Nhat Truong orcidid: 0000-0002-8086-6722 surname: Pham fullname: Pham, Nhat Truong organization: Ton Duc Thang University – sequence: 6 givenname: Nguyen Truong surname: Huy fullname: Huy, Nguyen Truong organization: Ton Duc Thang University – sequence: 7 givenname: Binh Khanh orcidid: 0000-0001-8487-1417 surname: Mai fullname: Mai, Binh Khanh organization: University of Pittsburgh – sequence: 8 givenname: Nguyen Thanh orcidid: 0000-0003-0232-7261 surname: Tung fullname: Tung, Nguyen Thanh organization: Vietnam Academy of Science and Technology – sequence: 9 givenname: Minh Quan orcidid: 0000-0001-6922-1627 surname: Pham fullname: Pham, Minh Quan organization: Vietnam Academy of Science and Technology – sequence: 10 givenname: Van orcidid: 0000-0003-0009-6703 surname: V. Vu fullname: V. Vu, Van organization: Nguyen Tat Thanh University – sequence: 11 givenname: Son Tung orcidid: 0000-0003-1034-1768 surname: Ngo fullname: Ngo, Son Tung email: ngosontung@tdtu.edu.vn organization: Ton Duc Thang University
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Keywords	binding affinity screening empirical parameter AutoDock Vina modified Vina
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Snippet	AutoDock Vina (Vina) achieved a very high docking‐success rate, p^, but give a rather low correlation coefficient, R, for binding affinity with respect to... AutoDock Vina (Vina) achieved a very high docking‐success rate, , but give a rather low correlation coefficient, , for binding affinity with respect to... AutoDock Vina (Vina) achieved a very high docking-success rate, , but give a rather low correlation coefficient, , for binding affinity with respect to... AutoDock Vina (Vina) achieved a very high docking-success rate, p^ , but give a rather low correlation coefficient, R , for binding affinity with respect to...
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SubjectTerms	Affinity AutoDock Vina Binding binding affinity Correlation coefficients Docking empirical parameter Hydrogen bonds Ligands Mathematical analysis modified Vina Parameters Ranking screening
Title	Improving ligand‐ranking of AutoDock Vina by changing the empirical parameters
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