Abstract
BackgroundThe present knowledge of protein structures at atomic level derives from some 60,000 molecules. Yet the exponential ever growing set of hypothetical protein sequences comprises some 10 million chains and this makes the problem of protein structure prediction one of the challenging goals of bioinformatics. In this context, the protein representation with contact maps is an intermediate step of fold recognition and constitutes the input of contact map predictors. However contact map representations require fast and reliable methods to reconstruct the specific folding of the protein backbone.MethodsIn this paper, by adopting a GRID technology, our algorithm for 3D reconstruction FT-COMAR is benchmarked on a huge set of non redundant proteins (1716) taking random noise into consideration and this makes our computation the largest ever performed for the task at hand.ResultsWe can observe the effects of introducing random noise on 3D reconstruction and derive some considerations useful for future implementations. The dimension of the protein set allows also statistical considerations after grouping per SCOP structural classes.ConclusionsAll together our data indicate that the quality of 3D reconstruction is unaffected by deleting up to an average 75% of the real contacts while only few percentage of randomly generated contacts in place of non-contacts are sufficient to hamper 3D reconstruction.
Highlights
The present knowledge of protein structures at atomic level derives from some 60,000 molecules
When no template with high sequence homology to the target is found in the Protein Data Base (PDB), building by homology cannot be safely applied
In a contact map representation of the protein 3D structure, all the short and long range interactions promoting protein stability emerge to different extent depending on the threshold value adopted to compute the 2D projection
Summary
The present knowledge of protein structures at atomic level derives from some 60,000 molecules. When no template with high sequence homology to the target is found in the Protein Data Base (PDB), building by homology cannot be safely applied In these cases the protein structure can be predicted with ab initio methods whose scoring capability is poor when no conserved structural domain is recognized in the target. In a contact map representation of the protein 3D structure, all the short and long range interactions promoting protein stability emerge to different extent depending on the threshold value adopted to compute the 2D projection. This representation poses first of all the problem of structure reconstruction. Most of the methods were tested on randomly blurred contact maps derived from small sets of proteins (in the range of 20-30 chains) and no general conclusion was derived
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