RESULTS

Modelling loops with LIP

The table below shows prediction accuracies produced by web servers, compared to results published earlier and to those of the LIP-method.
The value RMSD_global in Å is given, which is the Root Mean Square Deviation for the loop main-chain atoms N, C-alpha, C and O after superposition of the stem residues. In colums indicated by an asterisk, RMSD_global^* is given, which is calculated ignoring the O atoms.
In the first five columns the 14 loops are specified. The last column shows the results by the LIP-method. Columns 6, 7, and 9 show the prediction accuracies published in [6], [4] and [2]. Values obtained by tests of loop prediction web servers are listed in columns 8, 10 and 11. The RAPPER server (column 11) combines the prediction methods published in [1] (selection and modelling) and [3] (sampling). The server ModLoop (results in column 8) is described in [5]. 

Fiser et al. [4,5] and de Bakker/DePristo et al. [1,3] use ab initio methods for loop prediction, the LIP method and that of van Vlijmen et al. [6] are knowledge based. Deane et al. [2] combine both approaches.

Please note that in most cases, methodological changes and concessions have to be made in order to guarantee the availability, functionality and reasonable time consumtion of a prediction procedure on a web server. Therefore, loop predictions coming from a web server are likely to be less accurate than those yielded by the underlying published method.

Entries '-' in the table indicate that there were no loop predictions available.

Loop					RMSDglobal   /  RMSDglobal* (Å)
					van Vlijmen	Fiser		Deane		de Bakker/ DePristo	LIP
					1997	2000	2003	2001	2003	2003	2003
No.	Protein	Residues	Sequence	Length	*	*	ModLoop Server	*	CODA Server	RAPPER Server	(*)
1	3dfr	20..23	PWHL	4	2.6	1.2	1.8	0.4	-	1.0	1.3   (1.1)
2	3dfr	89..93	HLDQE	5	1.6	1.0	1.0	0.6	1.3	1.1	3.3   (3.1)
3	3dfr	120..124	GSFEG	5	0.5	0.3	0.4	0.7	0.7	0.6	2.1   (1.9)
4	3blm	131..135	DNTAN	5	0.8	0.2	0.2	0.2	0.4	0.1	0.2   (0.2)
5	8abp	203..208	GMNDST	6	0.3	0.4	0.4	0.8	0.8	0.5	0.8   (0.8)
6	8tln	E32..E38	DNTRGDG	7	3.7	2.0	3.5	1.9	2.2	3.3	0.3   (0.2)
7	3grs	83..89	ADYGFPS	7	4.6	0.4	0.6	1.4	5.3	0.4	2.4   (2.1)
8	5cpa	231..237	KSLYGTS	7	2.1	1.0	5.8	0.2	2.8	0.7	0.3   (0.2)
9	2fb4	H26..H32	GFIFSSY	7	1.6	4.2	4.4	0.4	0.4	0.6	0.2   (0.2)
10	2fbj	H100..H106	HYYGYNA	7	0.5	0.8	3.1	1.4	1.7	1.0	9.2   (9.0)
11	2apr	76..83	SYGDGSSA	8	5.2	1.3	2.7	2.2	5.3	0.6	0.5   (0.5)
12	2act	198..205	RNVGGAGT	8	1.6	2.0	2.8	3.1	6.2	3.5	0.1   (0.1)
13	8tln	E248..E255	GTHYGVSV	8	1.8	0.9	3.3	1.8	3.7	0.8	0.6   (0.6)
14	3sgb	E199..E211	LYSGTRAIG	9	1.8	0.3	0.7	-	-	0.3	0.2   (0.2)

Last update: 19  January 2004

 

References:

[1] P.I.W. de Bakker, M.A. DePristo, D.F. Burke and T.L. Blundell. Ab Initio Construction of Polypeptide Fragments: Accuracy of Loop Decoy Discrimination by an All-Atom Statistical Potential and the AMBER Force Field With the Generalized Born Solvation Model. Proteins 2003, 51, 21-40.

[2] C.M. Deane and T.L. Blundell. CODA: A combined algorithm for predicting the structurally variable regions of protein models. Protein Science 2001, 10, 599-612.

[3] M.A. DePristo, P.I.W. de Bakker, S.C. Lovell and T.L. Blundell. Ab Initio Construction of Polypeptide Fragments: Efficient Generation of Accurate, Representative Ensembles. Proteins 2003, 51, 41-55.

[4] A. Fiser, R. Kinh Gian Do and A. Šali. Modeling of loops in protein structures. Protein Science 2000, 9, 1753-1773.

[5] A. Fiser and A. Šali. ModLoop: automated modeling of loops in protein structures. Bioinformatics 2003, 19, 2500-2501.

[6] H.W.T. van Vlijmen and M. Karplus. PDB-based Protein Loop Prediction: Parameters for Selection and Methods for Optimization. Journal of Molecular Biology 1997, 267, 975-1001.

Loop prediction servers:

ModLoop   http://salilab.org/modloop/   , see [5]

CODA        http://www-cryst.bioc.cam.ac.uk/coda/   , see [2]

RAPPER       http://www-cryst.bioc.cam.ac.uk/rapper/   , see [1, 3]