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Improvement in low-homology template-based modeling by employing a model evaluation method with focus on topology.

Dai W, Song T, Wang X, Jin X, Deng L, Wu A, Jiang T - PLoS ONE (2014)

Bottom Line: Our novel method focuses on evaluating the topology by using two novel groups of features.These novel features included secondary structure element (SSE) contact information and 3-dimensional topology information.We further showed that the MEFTop could be a generalized method to improve threading programs for low-homology protein targets.

View Article: PubMed Central - PubMed

Affiliation: Key Laboratory of Protein & Peptide Pharmaceuticals, National Laboratory of Biomacromolecules, Institute of Biophysics, Chinese Academy of Sciences, Beijing, China ; University of the Chinese Academy of Sciences, Beijing, China.

ABSTRACT
Many template-based modeling (TBM) methods have been developed over the recent years that allow for protein structure prediction and for the study of structure-function relationships for proteins. One major problem all TBM algorithms face, however, is their unsatisfactory performance when proteins under consideration are low-homology. To improve the performance of TBM methods for such targets, a novel model evaluation method was developed here, and named MEFTop. Our novel method focuses on evaluating the topology by using two novel groups of features. These novel features included secondary structure element (SSE) contact information and 3-dimensional topology information. By combining MEFTop algorithm with FR-t5, a threading program developed by our group, we found that this modified TBM program, which was named FR-t5-M, exhibited significant improvements in predictive abilities for low-homology protein targets. We further showed that the MEFTop could be a generalized method to improve threading programs for low-homology protein targets. The softwares (FR-t5-M and MEFTop) are available to non-commercial users at our website: http://jianglab.ibp.ac.cn/lims/FRt5M/FRt5M.html.

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Four representative targets with different Top1 models selected by FR-t5-M (M-score) and FR-t5 (Z-score).The native structure (red) of d1b33n_ (A), d2bl8c1 (B), d2rdeb1 (C) and d1sgka1 (D), the Top1 model selected by FR-t5-M using M-score (green) and FR-t5 using Z-score (cyan) are shown. The TM-scores of Top1 models and native structures are presented. 3D structure models are produced with PyMOL (http://www.pymol.org/).
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pone-0089935-g003: Four representative targets with different Top1 models selected by FR-t5-M (M-score) and FR-t5 (Z-score).The native structure (red) of d1b33n_ (A), d2bl8c1 (B), d2rdeb1 (C) and d1sgka1 (D), the Top1 model selected by FR-t5-M using M-score (green) and FR-t5 using Z-score (cyan) are shown. The TM-scores of Top1 models and native structures are presented. 3D structure models are produced with PyMOL (http://www.pymol.org/).

Mentions: To evaluate FR-t5-M, we compared the performance of M-score and Z-score for the 110 targets in the dawn region of SCOP1.75–500 (Table 2). From the data presented in Table 2, it is evident that the M-score outperformed the Z-score for all criteria listed. For instance, the average rank of Top1 models (see Methods section for details) was 9.14 for the M-score, whereas it was 11.48 for the Z-score. Figure 2 gives a more detailed comparison of the two methods by looking at the quality of Top1 models according to TM-score. Notably, FR-t5-M could find high-quality models for 7 low homology proteins (marked by triangles), whereas FR-t5 could not. Four of these 7 low homology proteins were illustrated in Figure 3. One example is a bacterial immunity domain d2bl8c1 containing 81 amino acids (AA). The Top1 model selected by FR-t5-M (M-score) has a TM-score of 0.728, which is in higher quality than the model selected by the FR-t5 (Z-score) (TM-score = 0.300). The other three examples are d1b33n_ (67 AA), d2rdeb1 (110 AA) and d1sgka1 (155 AA). Their Top1 models selected by the FR-t5-M (M-score) were all native-like, whereas models selected by the FR-t5 (Z-score) were native-unlike. These differences in model selection between M-score and Z-score revealed that structural features clearly contributed in model evaluation and selection. As shown in Figure 3, all Top1 models selected according to their Z-score also had similar SSEs type to native structures, whereas the topology relationship between these SSEs was not correct. However, the MEFTop algorithm corrected for this error through utilizing the SSE contact map and introducing topological constraints.


Improvement in low-homology template-based modeling by employing a model evaluation method with focus on topology.

Dai W, Song T, Wang X, Jin X, Deng L, Wu A, Jiang T - PLoS ONE (2014)

Four representative targets with different Top1 models selected by FR-t5-M (M-score) and FR-t5 (Z-score).The native structure (red) of d1b33n_ (A), d2bl8c1 (B), d2rdeb1 (C) and d1sgka1 (D), the Top1 model selected by FR-t5-M using M-score (green) and FR-t5 using Z-score (cyan) are shown. The TM-scores of Top1 models and native structures are presented. 3D structure models are produced with PyMOL (http://www.pymol.org/).
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC3935967&req=5

pone-0089935-g003: Four representative targets with different Top1 models selected by FR-t5-M (M-score) and FR-t5 (Z-score).The native structure (red) of d1b33n_ (A), d2bl8c1 (B), d2rdeb1 (C) and d1sgka1 (D), the Top1 model selected by FR-t5-M using M-score (green) and FR-t5 using Z-score (cyan) are shown. The TM-scores of Top1 models and native structures are presented. 3D structure models are produced with PyMOL (http://www.pymol.org/).
Mentions: To evaluate FR-t5-M, we compared the performance of M-score and Z-score for the 110 targets in the dawn region of SCOP1.75–500 (Table 2). From the data presented in Table 2, it is evident that the M-score outperformed the Z-score for all criteria listed. For instance, the average rank of Top1 models (see Methods section for details) was 9.14 for the M-score, whereas it was 11.48 for the Z-score. Figure 2 gives a more detailed comparison of the two methods by looking at the quality of Top1 models according to TM-score. Notably, FR-t5-M could find high-quality models for 7 low homology proteins (marked by triangles), whereas FR-t5 could not. Four of these 7 low homology proteins were illustrated in Figure 3. One example is a bacterial immunity domain d2bl8c1 containing 81 amino acids (AA). The Top1 model selected by FR-t5-M (M-score) has a TM-score of 0.728, which is in higher quality than the model selected by the FR-t5 (Z-score) (TM-score = 0.300). The other three examples are d1b33n_ (67 AA), d2rdeb1 (110 AA) and d1sgka1 (155 AA). Their Top1 models selected by the FR-t5-M (M-score) were all native-like, whereas models selected by the FR-t5 (Z-score) were native-unlike. These differences in model selection between M-score and Z-score revealed that structural features clearly contributed in model evaluation and selection. As shown in Figure 3, all Top1 models selected according to their Z-score also had similar SSEs type to native structures, whereas the topology relationship between these SSEs was not correct. However, the MEFTop algorithm corrected for this error through utilizing the SSE contact map and introducing topological constraints.

Bottom Line: Our novel method focuses on evaluating the topology by using two novel groups of features.These novel features included secondary structure element (SSE) contact information and 3-dimensional topology information.We further showed that the MEFTop could be a generalized method to improve threading programs for low-homology protein targets.

View Article: PubMed Central - PubMed

Affiliation: Key Laboratory of Protein & Peptide Pharmaceuticals, National Laboratory of Biomacromolecules, Institute of Biophysics, Chinese Academy of Sciences, Beijing, China ; University of the Chinese Academy of Sciences, Beijing, China.

ABSTRACT
Many template-based modeling (TBM) methods have been developed over the recent years that allow for protein structure prediction and for the study of structure-function relationships for proteins. One major problem all TBM algorithms face, however, is their unsatisfactory performance when proteins under consideration are low-homology. To improve the performance of TBM methods for such targets, a novel model evaluation method was developed here, and named MEFTop. Our novel method focuses on evaluating the topology by using two novel groups of features. These novel features included secondary structure element (SSE) contact information and 3-dimensional topology information. By combining MEFTop algorithm with FR-t5, a threading program developed by our group, we found that this modified TBM program, which was named FR-t5-M, exhibited significant improvements in predictive abilities for low-homology protein targets. We further showed that the MEFTop could be a generalized method to improve threading programs for low-homology protein targets. The softwares (FR-t5-M and MEFTop) are available to non-commercial users at our website: http://jianglab.ibp.ac.cn/lims/FRt5M/FRt5M.html.

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