Exploring the conformational space of membrane protein folds matching distance constraints

Abstract

Herein we present a computational technique for generating helix-membrane protein folds matching a predefined set of distance constraints, such as those obtained from NMR NOE, chemical cross-linking, dipolar EPR, and FRET experiments. The purpose of the technique is to provide initial structures for local conformational searches based on either energetic considerations or ad-hoc scoring criteria. In order to properly screen the conformational space, the technique generates an exhaustive list of conformations within a specified root-mean-square deviation (RMSD) where the helices are positioned in order to match the provided distances. Our results indicate that the number of structures decreases exponentially as the number of distances increases, and increases exponentially as the errors associated with the distances increases. We also found the number of solutions to be smaller when all the distances share one helix in common, compared to the case where the distances connect helices in a daisy-chain manner. We found that for 7 helices, at least 15 distances with errors up to 8 Å are needed to produce a number of solutions that is not too large to be processed by local search refinement procedures. Finally, without energetic considerations, our enumeration technique retrieved the transmembrane domains of Bacteriorhodopsin (PDB entry1c3w), Halorhodopsin (1e12), Rhodopsin (1f88), Aquaporin-1 (1fqy), Glycerol uptake facilitator protein (1fx8), Sensory Rhodopsin (1jgj), and a subunit of Fumarate reductase flavoprotein (1qlaC) with C␣ level RMSDs of 3.0 Å, 2.3 Å, 3.2 Å, 4.6 Å, 6.0 Å, 3.7 Å, and 4.4 Å, respectively.

Optimizing an emperical scoring function for transmembrane protein structure determination.

“…Since the two tests were run on the same machine, we can conclude that APPS was more efficient than SA as it required fewer function evaluations. Moreover, since APPS used 84 worker nodes, each processor This graph shows the distribution of the Bundler scores for the 87 initial structures generated using the procedure outlined in [17]. The average initial score is 26, 555 with a maximum of 76, 080 and a minimum of 8, 608.…”

“…For our second set of tests, 87 structures were generated using the procedure outlined in [17] and a set of 27 distance constraints, D 1 , obtained from [57]. This procedure resulted in structures that have no experimental distance penalty, i.e., P E = 0, where P E is as defined in (3.3), for each of the 87 structures with respect to D 1 .…”

“…Instead, it is to our benefit to use an algorithm which sacrafices some accuracy to improve the overall efficiency. The procedure we used in our numerical experiments to generate the 87 initial structures is part of the transmembrane protein structure determination process proposed in [17,45]. Future projects will require optimizing thousands of structures to attain one final candidate which can be further studied in the laboratory.…”

“…It has been shown that distance constraints are an important aspect in determining transmembrane protein structure. In fact, the number of possible structures decreases exponentially with the number of distance constraints and increases exponentially with the error on the distance measures [17]. Hence, Bundler incorporates experimental distance constraints in the term…”

“…Obtaining enough distance constraints to uniquely determine a structure is difficult, particularly for transmembrane proteins [16,17]. Furthermore, these distances are never error free.…”