1. Funnel Hunting in a Rough Terrain: Learning and Discriminating Native Energy Funnels 
Nir London, Ora Schueler-Furman 
Structure 
Volume 16, Issue 2, Pages (February 2008)
DOI: /j.str
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2. Figure 1
Funnel Tip Ensembles for a Near-Native, TRUE, and Wrong, FALSE, Orientation
Models were generated in a local docking run both for the near-native (TRUE; [B]) and another (FALSE; [A]) orientation (by using the free monomer conformations; Unbound U-U). The models of the cyclophilin-HIV capsid interaction (PDB ID: 1ak4) (Gamble et al., 1996) are plotted as interface energy versus interface rmsd (lower panels—black points; see Experimental Procedures for more details). The upper panels show the conformations of the 25 models at the funnel tip (red in the plot) that represent each funnel in this study. Illustrations of protein structures were created by Chimera (Pettersen et al., 2004).
Structure  , DOI: ( /j.str )
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3. Figure 2 Robustness and Accuracy of the FunHunt Classifier
FunHunt selects a top-ranking model from the TRUE funnel in 50/52 protein complexes (TopRank), and the classification accuracy is 78% in both leave-one-out (l1o) and leave-eight-out (l8o) tests. The error bars indicate the standard deviation. See text and Experimental Procedures for more details.
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4. Figure 3 FunHunt Performance Is General
FunHunt TopRank performance on two subsets of complexes: EI and OTHER (see Table 1A and text for definition). We trained a classifier on three different datasets: a general dataset (All), a system-specific dataset (Specific), and a dataset without system-specific complexes (Excluded). Both for the EI and OTHER subset, the performance is similar (or only slightly better) when trained on a specific dataset. This indicates that FunHunt assesses general features that are not restricted to a subset.
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5. Figure 4
Orientation of Five Different Funnels in the Cyclophilin-HIV Capsid Interaction
The native conformation (TRUE funnel) is indicated in red (PDB ID: 1ak4). The other colors describe FALSE funnels originating from low energy models. FunHunt can select the TRUE conformation out of the five (see Table 3). Note that the free monomer conformations are used (Unbound run; U-U).
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6. Figure 5
Distinct Distribution of Feature Values in FALSE and TRUE Models
(A) Histogram of docking interface environment (Denv): TRUE models (white bars) show better Denv scores than FALSE models (black bars).
(B) Histogram of energy decrease during full atom optimization (Δenergy): TRUE models (white bars) show a larger decrease in energy than FALSE models (black bars).
(C) Scatter plot of number of contacts at interface (Ncont; x axis) versus solvent accessible surface area (SASA; y axis): many FALSE models (black diamonds) can be discarded based on their extended porous interface (small Ncont and small SASA; a small SASA corresponds to a large interface). All plots contain normalized values. Histograms of single features in the classifier are given in Figure S1.
Structure  , DOI: ( /j.str )
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