Comparative folding G protein-coupled receptors is obstructed by distant homology and limited experimental templates.
To explore the possibility of ab initio modeling, we established an entirely ab initio approach, GPCR-AIM,
which does not use any homology templates or fragments and employs cost-effective 2D and 3D helical topology simulating stages.
The a new ab initio approach GPCR-AIM algorithm to assemble GPCR structure from the primary sequence.
In GPCR-I-TASSER, although it also includes a component for assembling GPCR helix bundle from scratch,
the procedure was guided by an over-simplified generic statistical potential,
which is unable to accommodate the complicated architecture of helix arrangements.
In GPCR-AIM, we use sequence-based contact map and disulfide-bond prediction,
which are combined with a variety of GPCR-specific knowledge-based force fields to guide the helix-bundle assembly procedure.
The entire ab initio modeling consists of four stages of 2D-helix optimization, 3D-helix bundle assemble, and reduced- and atomic structure refinement (see Figure 1 below),
which has been shown to significantly improve the ability of the ab initio GPCR modeling of previous approaches.
The approach was evaluated in 28 solved GPCRs where GPCR-AIM correctly folded 21 (75%) targets (TM-score>0.5) and demonstrated more robust than homology approach and fragment-based ab initio approach on the families without enough experimental structures. Then, GPCR-AIM was applied to model 94 hard and unsolved GPCRs in the human genome, whose C-score<-1.5 and without any experimental data in GPCR-RD. Three types of UniProt annotations were used to investigate the quality of the GPCR-AIM models. 18 (31.0%) out of 58 UniProt annotated disulfide bonds are successfully detected, 56 (77.8%), 62 (86.1%) and 40 (58.3%) targets are correctly predicted for biological process (BP) function, cellular component (CC) function, molecular function (MF) function, and 65 (78.3%) targets are assigned to correct superfamily.
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