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An Integrated Framework Advancing Membrane Protein Modeling and Design.

Alford RF, Koehler Leman J, Weitzner BD, Duran AM, Tilley DC, Elazar A, Gray JJ - PLoS Comput. Biol. (2015)

Bottom Line: Preliminary data show that these algorithms can produce meaningful scores and structures.The data also suggest needed improvements to both sampling routines and score functions.Importantly, the applications collectively demonstrate the potential of combining the flexible nature of RosettaMP with the power of Rosetta algorithms to facilitate membrane protein modeling and design.

View Article: PubMed Central - PubMed

Affiliation: Department of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, Maryland, United States of America; Department of Chemistry, Carnegie Mellon University, Pittsburgh, Pennsylvania, United States of America.

ABSTRACT
Membrane proteins are critical functional molecules in the human body, constituting more than 30% of open reading frames in the human genome. Unfortunately, a myriad of difficulties in overexpression and reconstitution into membrane mimetics severely limit our ability to determine their structures. Computational tools are therefore instrumental to membrane protein structure prediction, consequently increasing our understanding of membrane protein function and their role in disease. Here, we describe a general framework facilitating membrane protein modeling and design that combines the scientific principles for membrane protein modeling with the flexible software architecture of Rosetta3. This new framework, called RosettaMP, provides a general membrane representation that interfaces with scoring, conformational sampling, and mutation routines that can be easily combined to create new protocols. To demonstrate the capabilities of this implementation, we developed four proof-of-concept applications for (1) prediction of free energy changes upon mutation; (2) high-resolution structural refinement; (3) protein-protein docking; and (4) assembly of symmetric protein complexes, all in the membrane environment. Preliminary data show that these algorithms can produce meaningful scores and structures. The data also suggest needed improvements to both sampling routines and score functions. Importantly, the applications collectively demonstrate the potential of combining the flexible nature of RosettaMP with the power of Rosetta algorithms to facilitate membrane protein modeling and design.

No MeSH data available.


Structures of mutant residues at position 210 of OmpLA.(A) The charged residues arginine and lysine (superimposed) cannot reach the interface region. The z-coordinate shows the difference in membrane depth of the two charged side chains. Membrane environment scores are unfavorable for both, with lysine being slightly more unfavorable. (B) Insertion of threonine at position 210 is penalized by a mild clash from the neighboring leucine 225; serine at this position is accommodated more easily (Fig D in S1 File). (C) The tryptophan side chain is close to the neighboring leucine 197, resulting in large repulsive scores. All aromatic mutations have a comparably large repulsive van der Waals and rotamer scores, resulting in over-prediction of their ΔΔG values.
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pcbi.1004398.g005: Structures of mutant residues at position 210 of OmpLA.(A) The charged residues arginine and lysine (superimposed) cannot reach the interface region. The z-coordinate shows the difference in membrane depth of the two charged side chains. Membrane environment scores are unfavorable for both, with lysine being slightly more unfavorable. (B) Insertion of threonine at position 210 is penalized by a mild clash from the neighboring leucine 225; serine at this position is accommodated more easily (Fig D in S1 File). (C) The tryptophan side chain is close to the neighboring leucine 197, resulting in large repulsive scores. All aromatic mutations have a comparably large repulsive van der Waals and rotamer scores, resulting in over-prediction of their ΔΔG values.

Mentions: For OmpLA, an alanine at the center of the membrane is mutated into all 19 other amino acids. Insertion of proline is significantly overestimated because unfavorable dihedral angles cause a kink in the β-strand that cannot be resolved due to the fixed-backbone assumption. The negatively charged residues aspartic acid and glutamic acid are also over-predicted, similar to comparisons of these experimental values with published hydrophobicity scales [50]. When aspartic acid, glutamic acid, and proline are excluded, the correlation between the experimental and calculated values is R = 0.86, and the calculated Rosetta Energy Units (REU) correspond roughly to the measurements in kcal/mol. RosettaMP predicts the insertion of an arginine at the center of the membrane to be less disruptive than insertion of a lysine, and the side chain stretches toward the membrane interface (Fig 5A). This result matches previous experimental values [50,52,53] and occurs because the longer, positively charged side chain of arginine can snorkel further towards the interface region (Fig C in S1 File) and interact with charged lipid head groups or interfacial water molecules [54]. The preference for arginine over lysine arises from the ~0.7 REU difference in the environment score (fa_mpenv_smooth) while small variations in the other score terms balance out. ΔΔG values for the polar residues asparagine and glutamic acid are consistent with the published values [50].


An Integrated Framework Advancing Membrane Protein Modeling and Design.

Alford RF, Koehler Leman J, Weitzner BD, Duran AM, Tilley DC, Elazar A, Gray JJ - PLoS Comput. Biol. (2015)

Structures of mutant residues at position 210 of OmpLA.(A) The charged residues arginine and lysine (superimposed) cannot reach the interface region. The z-coordinate shows the difference in membrane depth of the two charged side chains. Membrane environment scores are unfavorable for both, with lysine being slightly more unfavorable. (B) Insertion of threonine at position 210 is penalized by a mild clash from the neighboring leucine 225; serine at this position is accommodated more easily (Fig D in S1 File). (C) The tryptophan side chain is close to the neighboring leucine 197, resulting in large repulsive scores. All aromatic mutations have a comparably large repulsive van der Waals and rotamer scores, resulting in over-prediction of their ΔΔG values.
© Copyright Policy
Related In: Results  -  Collection

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Show All Figures
getmorefigures.php?uid=PMC4556676&req=5

pcbi.1004398.g005: Structures of mutant residues at position 210 of OmpLA.(A) The charged residues arginine and lysine (superimposed) cannot reach the interface region. The z-coordinate shows the difference in membrane depth of the two charged side chains. Membrane environment scores are unfavorable for both, with lysine being slightly more unfavorable. (B) Insertion of threonine at position 210 is penalized by a mild clash from the neighboring leucine 225; serine at this position is accommodated more easily (Fig D in S1 File). (C) The tryptophan side chain is close to the neighboring leucine 197, resulting in large repulsive scores. All aromatic mutations have a comparably large repulsive van der Waals and rotamer scores, resulting in over-prediction of their ΔΔG values.
Mentions: For OmpLA, an alanine at the center of the membrane is mutated into all 19 other amino acids. Insertion of proline is significantly overestimated because unfavorable dihedral angles cause a kink in the β-strand that cannot be resolved due to the fixed-backbone assumption. The negatively charged residues aspartic acid and glutamic acid are also over-predicted, similar to comparisons of these experimental values with published hydrophobicity scales [50]. When aspartic acid, glutamic acid, and proline are excluded, the correlation between the experimental and calculated values is R = 0.86, and the calculated Rosetta Energy Units (REU) correspond roughly to the measurements in kcal/mol. RosettaMP predicts the insertion of an arginine at the center of the membrane to be less disruptive than insertion of a lysine, and the side chain stretches toward the membrane interface (Fig 5A). This result matches previous experimental values [50,52,53] and occurs because the longer, positively charged side chain of arginine can snorkel further towards the interface region (Fig C in S1 File) and interact with charged lipid head groups or interfacial water molecules [54]. The preference for arginine over lysine arises from the ~0.7 REU difference in the environment score (fa_mpenv_smooth) while small variations in the other score terms balance out. ΔΔG values for the polar residues asparagine and glutamic acid are consistent with the published values [50].

Bottom Line: Preliminary data show that these algorithms can produce meaningful scores and structures.The data also suggest needed improvements to both sampling routines and score functions.Importantly, the applications collectively demonstrate the potential of combining the flexible nature of RosettaMP with the power of Rosetta algorithms to facilitate membrane protein modeling and design.

View Article: PubMed Central - PubMed

Affiliation: Department of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, Maryland, United States of America; Department of Chemistry, Carnegie Mellon University, Pittsburgh, Pennsylvania, United States of America.

ABSTRACT
Membrane proteins are critical functional molecules in the human body, constituting more than 30% of open reading frames in the human genome. Unfortunately, a myriad of difficulties in overexpression and reconstitution into membrane mimetics severely limit our ability to determine their structures. Computational tools are therefore instrumental to membrane protein structure prediction, consequently increasing our understanding of membrane protein function and their role in disease. Here, we describe a general framework facilitating membrane protein modeling and design that combines the scientific principles for membrane protein modeling with the flexible software architecture of Rosetta3. This new framework, called RosettaMP, provides a general membrane representation that interfaces with scoring, conformational sampling, and mutation routines that can be easily combined to create new protocols. To demonstrate the capabilities of this implementation, we developed four proof-of-concept applications for (1) prediction of free energy changes upon mutation; (2) high-resolution structural refinement; (3) protein-protein docking; and (4) assembly of symmetric protein complexes, all in the membrane environment. Preliminary data show that these algorithms can produce meaningful scores and structures. The data also suggest needed improvements to both sampling routines and score functions. Importantly, the applications collectively demonstrate the potential of combining the flexible nature of RosettaMP with the power of Rosetta algorithms to facilitate membrane protein modeling and design.

No MeSH data available.