Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Modeling loops is a necessary step in protein structure determination even with experimental NMR data. It is ll known to be difficult. Database techniques have the advantage of producing a higher proportion of predictions with sub-angstrom accuracy, when compared with ab initio techniques, but the disadvantage of also producing a higher proportion of clashing or highly inaccurate predictions.  introduce Loopaver, a database method that uses multidimensional scaling to achieve better clash-free placement of loops obtained from a database of protein structures. This allows us to maintain the above-mentioned advantage while avoiding the disadvantage. Test results show that  achieve significantly better results than all other methods, including Modeler, Loopy, SuperLooper, and Rapper before refinement. With refinement, our results (Loopaver and Loopy consensus) are better than ROSETTA, with 0.42A RMSD on average for 206 length 6 loops, 0.64A local RMSD for 168 length 7 loops, 0.81A RMSD for 117 length 8 loops, and 0.98A RMSD for length 9 loops, while ROSETTA has 0.55, 0.79, 1.16, 1.42, respectively, at the same average time limit (3 hours). When  allow ROSETTA run for over a ek, it approaches, but does not surpass, our accuracy.
['Daniel Holtby', 'Shuai Cheng Li', 'Ming Li']
I am writing a research paper on a idea LoopWeaver --- loop modeling by the weighted scaling of verified proteins
[{"from": "human", "value": "I am writing a research paper on a idea LoopWeaver --- loop modeling by the weighted scaling of verified proteins"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Modeling loops is a necessary step in protein structure determination even with experimental NMR data. It is ll known to be difficult. Database techniques have the advantage of producing a higher proportion of predictions with sub-angstrom accuracy, when compared with ab initio techniques, but the disadvantage of also producing a higher proportion of clashing or highly inaccurate predictions.  introduce Loopaver, a database method that uses multidimensional scaling to achieve better clash-free placement of loops obtained from a database of protein structures. This allows us to maintain the above-mentioned advantage while avoiding the disadvantage. Test results show that  achieve significantly better results than all other methods, including Modeler, Loopy, SuperLooper, and Rapper before refinement. With refinement, our results (Loopaver and Loopy consensus) are better than ROSETTA, with 0.42A RMSD on average for 206 length 6 loops, 0.64A local RMSD for 168 length 7 loops, 0.81A RMSD for 117 length 8 loops, and 0.98A RMSD for length 9 loops, while ROSETTA has 0.55, 0.79, 1.16, 1.42, respectively, at the same average time limit (3 hours). When  allow ROSETTA run for over a ek, it approaches, but does not surpass, our accuracy."}]