Solvent-accessible surface area: How well can be applied to hot-spot detection?
ABSTRACT A detailed comprehension of protein‐based interfaces is essential for the rational drug development. One of the key features of these interfaces is their solvent accessible surface area profile. With that in mind, we tested a group of 12 SASA‐based features for their ability to correlate an...
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| Published in | Proteins, structure, function, and bioinformatics Vol. 82; no. 3; pp. 479 - 490 |
|---|---|
| Main Authors | , , , |
| Format | Journal Article |
| Language | English |
| Published |
United States
Blackwell Publishing Ltd
01.03.2014
Wiley Subscription Services, Inc |
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| Online Access | Get full text |
| ISSN | 0887-3585 1097-0134 1097-0134 |
| DOI | 10.1002/prot.24413 |
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| Abstract | ABSTRACT
A detailed comprehension of protein‐based interfaces is essential for the rational drug development. One of the key features of these interfaces is their solvent accessible surface area profile. With that in mind, we tested a group of 12 SASA‐based features for their ability to correlate and differentiate hot‐ and null‐spots. These were tested in three different data sets, explicit water MD, implicit water MD, and static PDB structure. We found no discernible improvement with the use of more comprehensive data sets obtained from molecular dynamics. The features tested were shown to be capable of discerning between hot‐ and null‐spots, while presenting low correlations. Residue standardization such as relSASAi or rel/resSASAi, improved the features as a tool to predict ΔΔGbinding values. A new method using support machine learning algorithms was developed: SBHD (Sasa‐Based Hot‐spot Detection). This method presents a precision, recall, and F1 score of 0.72, 0.81, and 0.76 for the training set and 0.91, 0.73, and 0.81 for an independent test set. Proteins 2014; 82:479–490. © 2013 Wiley Periodicals, Inc. |
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| AbstractList | ABSTRACT
A detailed comprehension of protein‐based interfaces is essential for the rational drug development. One of the key features of these interfaces is their solvent accessible surface area profile. With that in mind, we tested a group of 12 SASA‐based features for their ability to correlate and differentiate hot‐ and null‐spots. These were tested in three different data sets, explicit water MD, implicit water MD, and static PDB structure. We found no discernible improvement with the use of more comprehensive data sets obtained from molecular dynamics. The features tested were shown to be capable of discerning between hot‐ and null‐spots, while presenting low correlations. Residue standardization such as relSASAi or rel/resSASAi, improved the features as a tool to predict ΔΔGbinding values. A new method using support machine learning algorithms was developed: SBHD (Sasa‐Based Hot‐spot Detection). This method presents a precision, recall, and F1 score of 0.72, 0.81, and 0.76 for the training set and 0.91, 0.73, and 0.81 for an independent test set. Proteins 2014; 82:479–490. © 2013 Wiley Periodicals, Inc. A detailed comprehension of protein-based interfaces is essential for the rational drug development. One of the key features of these interfaces is their solvent accessible surface area profile. With that in mind, we tested a group of 12 SASA-based features for their ability to correlate and differentiate hot- and null-spots. These were tested in three different data sets, explicit water MD, implicit water MD, and static PDB structure. We found no discernible improvement with the use of more comprehensive data sets obtained from molecular dynamics. The features tested were shown to be capable of discerning between hot- and null-spots, while presenting low correlations. Residue standardization such as rel SASAi or rel/res SASAi , improved the features as a tool to predict ΔΔGbinding values. A new method using support machine learning algorithms was developed: SBHD (Sasa-Based Hot-spot Detection). This method presents a precision, recall, and F1 score of 0.72, 0.81, and 0.76 for the training set and 0.91, 0.73, and 0.81 for an independent test set. A detailed comprehension of protein-based interfaces is essential for the rational drug development. One of the key features of these interfaces is their solvent accessible surface area profile. With that in mind, we tested a group of 12 SASA-based features for their ability to correlate and differentiate hot- and null-spots. These were tested in three different data sets, explicit water MD, implicit water MD, and static PDB structure. We found no discernible improvement with the use of more comprehensive data sets obtained from molecular dynamics. The features tested were shown to be capable of discerning between hot- and null-spots, while presenting low correlations. Residue standardization such as sub(rel)SASA sub(i) or sub(rel/res)SASA sub(i), improved the features as a tool to predict Delta Delta G sub(binding) values. A new method using support machine learning algorithms was developed: SBHD (Sasa-Based Hot-spot Detection). This method presents a precision, recall, and F1 score of 0.72, 0.81, and 0.76 for the training set and 0.91, 0.73, and 0.81 for an independent test set. Proteins 2014; 82:479-490. copyright 2013 Wiley Periodicals, Inc. A detailed comprehension of protein-based interfaces is essential for the rational drug development. One of the key features of these interfaces is their solvent accessible surface area profile. With that in mind, we tested a group of 12 SASA-based features for their ability to correlate and differentiate hot- and null-spots. These were tested in three different data sets, explicit water MD, implicit water MD, and static PDB structure. We found no discernible improvement with the use of more comprehensive data sets obtained from molecular dynamics. The features tested were shown to be capable of discerning between hot- and null-spots, while presenting low correlations. Residue standardization such as relSASAi or rel/resSASAi, improved the features as a tool to predict [Delta][Delta]Gbinding values. A new method using support machine learning algorithms was developed: SBHD (Sasa-Based Hot-spot Detection). This method presents a precision, recall, and F1 score of 0.72, 0.81, and 0.76 for the training set and 0.91, 0.73, and 0.81 for an independent test set. Proteins 2014; 82:479-490. © 2013 Wiley Periodicals, Inc. [PUBLICATION ABSTRACT] A detailed comprehension of protein-based interfaces is essential for the rational drug development. One of the key features of these interfaces is their solvent accessible surface area profile. With that in mind, we tested a group of 12 SASA-based features for their ability to correlate and differentiate hot- and null-spots. These were tested in three different data sets, explicit water MD, implicit water MD, and static PDB structure. We found no discernible improvement with the use of more comprehensive data sets obtained from molecular dynamics. The features tested were shown to be capable of discerning between hot- and null-spots, while presenting low correlations. Residue standardization such as rel SASAi or rel/res SASAi , improved the features as a tool to predict ΔΔGbinding values. A new method using support machine learning algorithms was developed: SBHD (Sasa-Based Hot-spot Detection). This method presents a precision, recall, and F1 score of 0.72, 0.81, and 0.76 for the training set and 0.91, 0.73, and 0.81 for an independent test set.A detailed comprehension of protein-based interfaces is essential for the rational drug development. One of the key features of these interfaces is their solvent accessible surface area profile. With that in mind, we tested a group of 12 SASA-based features for their ability to correlate and differentiate hot- and null-spots. These were tested in three different data sets, explicit water MD, implicit water MD, and static PDB structure. We found no discernible improvement with the use of more comprehensive data sets obtained from molecular dynamics. The features tested were shown to be capable of discerning between hot- and null-spots, while presenting low correlations. Residue standardization such as rel SASAi or rel/res SASAi , improved the features as a tool to predict ΔΔGbinding values. A new method using support machine learning algorithms was developed: SBHD (Sasa-Based Hot-spot Detection). This method presents a precision, recall, and F1 score of 0.72, 0.81, and 0.76 for the training set and 0.91, 0.73, and 0.81 for an independent test set. A detailed comprehension of protein‐based interfaces is essential for the rational drug development. One of the key features of these interfaces is their solvent accessible surface area profile. With that in mind, we tested a group of 12 SASA‐based features for their ability to correlate and differentiate hot‐ and null‐spots. These were tested in three different data sets, explicit water MD, implicit water MD, and static PDB structure. We found no discernible improvement with the use of more comprehensive data sets obtained from molecular dynamics. The features tested were shown to be capable of discerning between hot‐ and null‐spots, while presenting low correlations. Residue standardization such as rel SASA i or rel/res SASA i , improved the features as a tool to predict ΔΔ G binding values. A new method using support machine learning algorithms was developed: SBHD (Sasa‐Based Hot‐spot Detection). This method presents a precision, recall, and F1 score of 0.72, 0.81, and 0.76 for the training set and 0.91, 0.73, and 0.81 for an independent test set. Proteins 2014; 82:479–490. © 2013 Wiley Periodicals, Inc. |
| Author | Moreira, Irina S. Martins, João M. Ramos, Rui M. Pimenta, António C. |
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| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/24105801$$D View this record in MEDLINE/PubMed |
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| Keywords | feature based algorithms hot-spot support vector machine solvent accessible surface area computational alanine scanning mutagenesis |
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DBAC: a simple prediction method for protein binding hot spots based on burial levels and deeply buried atomic cont 2010; 11 1998; 280 2000; 6 2002; 99 1999; 121 2011; 12 1996; 264 2005; 61 1977; 23 1995; 374 1997; 6 2008; 73 1998; 277 2003; 53 2007; 36 2013; 9 2007; 28 1997; 91 2004; 32 1987; 196 2002; 47 2000; 19 2013; 13 2005; 102 1999; 18 1995; 21 1994; 33 1997; 16 1994; 79 1999; 96 2000; 287 1998; 284 2010; 78 2013; 1834 2009; 25 1987; 328 1995; 117 1996; 93 2006 2011; 79 1992; 32 2011; 5 1995; 3 1983; 79 2011; 7 2004; 10 2004; 55 1998; 391 2007; 117 2009; 75 2000; 33 2007; 111 1993; 98 2002; 320 2003; 24 2013 1994; 91 2001; 30 2001; 114 2009; 37 e_1_2_5_27_1 e_1_2_5_25_1 e_1_2_5_48_1 e_1_2_5_23_1 e_1_2_5_46_1 e_1_2_5_21_1 e_1_2_5_44_1 e_1_2_5_29_1 e_1_2_5_61_1 e_1_2_5_63_1 e_1_2_5_42_1 e_1_2_5_40_1 e_1_2_5_15_1 e_1_2_5_38_1 e_1_2_5_17_1 e_1_2_5_36_1 e_1_2_5_59_1 e_1_2_5_9_1 e_1_2_5_11_1 e_1_2_5_34_1 e_1_2_5_57_1 e_1_2_5_7_1 e_1_2_5_13_1 e_1_2_5_55_1 e_1_2_5_5_1 e_1_2_5_3_1 e_1_2_5_19_1 e_1_2_5_30_1 e_1_2_5_53_1 e_1_2_5_51_1 e_1_2_5_28_1 e_1_2_5_49_1 e_1_2_5_26_1 e_1_2_5_47_1 e_1_2_5_24_1 e_1_2_5_45_1 e_1_2_5_22_1 e_1_2_5_43_1 Moreira IS (e_1_2_5_32_1) 2013; 1834 Braden BC (e_1_2_5_35_1) 1996; 264 e_1_2_5_60_1 e_1_2_5_62_1 e_1_2_5_64_1 e_1_2_5_20_1 e_1_2_5_41_1 e_1_2_5_14_1 e_1_2_5_39_1 e_1_2_5_16_1 e_1_2_5_37_1 e_1_2_5_58_1 e_1_2_5_8_1 e_1_2_5_10_1 e_1_2_5_56_1 e_1_2_5_6_1 e_1_2_5_12_1 e_1_2_5_33_1 e_1_2_5_54_1 e_1_2_5_4_1 e_1_2_5_2_1 e_1_2_5_18_1 e_1_2_5_31_1 e_1_2_5_52_1 e_1_2_5_50_1 |
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A detailed comprehension of protein‐based interfaces is essential for the rational drug development. One of the key features of these interfaces is... A detailed comprehension of protein‐based interfaces is essential for the rational drug development. One of the key features of these interfaces is their... A detailed comprehension of protein-based interfaces is essential for the rational drug development. One of the key features of these interfaces is their... |
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| SubjectTerms | Bioinformatics computational alanine scanning mutagenesis Computational Biology - methods Databases, Protein feature based algorithms hot-spot Molecular Dynamics Simulation Proteins - chemistry solvent accessible surface area Solvents Solvents - chemistry Support Vector Machine Surface area Surface Properties Thermodynamics |
| Title | Solvent-accessible surface area: How well can be applied to hot-spot detection? |
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