2003
DOI: 10.1002/prot.10550
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Using multiple structure alignments, fast model building, and energetic analysis in fold recognition and homology modeling

Abstract: We participated in the fold recognition and homology sections of CASP5 using primarily in-house software. The central feature of our structure prediction strategy involved the ability to generate good sequence-to-structure alignments and to quickly transform them into models that could be evaluated both with energy-based methods and manually. The in-house tools we used include: a) HMAP (Hybrid Multidimensional Alignment Profile)-a profile-to-profile alignment method that is derived from sequence-enhanced multi… Show more

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Cited by 286 publications
(264 citation statements)
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“…In detail, the amino acid sequence alignment between PSD-1 and GB1 (ref. 22) was used to build the model of the hypothetical 4b1a form of PSD-1 using the homology modeling program NEST (23). Then, flexible docking simulations were used to dock a Myr molecule onto both the a-helical form of the GA module PSD-1 (PDB code 2FS1) (24) and the modeled 4b1a form.…”
Section: Discussionmentioning
confidence: 99%
“…In detail, the amino acid sequence alignment between PSD-1 and GB1 (ref. 22) was used to build the model of the hypothetical 4b1a form of PSD-1 using the homology modeling program NEST (23). Then, flexible docking simulations were used to dock a Myr molecule onto both the a-helical form of the GA module PSD-1 (PDB code 2FS1) (24) and the modeled 4b1a form.…”
Section: Discussionmentioning
confidence: 99%
“…The NL1 and NL3 sequences were first aligned to NL2 with T-COFFEE (35), and the resulting alignments were given as input to the NEST homology modeling program with default parameters (36). Electrostatic calculations were carried out with the program GRASP (26).…”
Section: Methodsmentioning
confidence: 99%
“…Secondary structure prediction programs predict, with low confidence, that this region is either unstructured or partially ␣-helical (40 -42). We used both Modeler6 (43) and Nest (44) to construct models for the FENS-1 FYVE domain based on the alignment extracted from the multiple sequence alignment. We implemented the loop prediction modules that are offered by both programs (45,46).…”
Section: Methodsmentioning
confidence: 99%