Computational modeling of membrane proteins

By Julia Koehler Leman, Martin B. Ulmschneider, Jeffrey J. Gray. 2014

90 citations recorded by the source corpus.

The determination of membrane protein (MP) structures has always trailed that of soluble proteins due to difficulties in their overexpression, reconstitution into membrane mimetics, and subsequent structure determination. The percentage of MP structures in the protein databank (PDB) has been at a constant 1–2% for the last decade. In contrast, over half of all drugs target MPs, only highlighting how little we understand about drug‐specific effects in the human body. To reduce this gap, researchers have attempted to predict structural features of MPs even before the first structure was experimentally elucidated. In this review, we present current computational methods to predict MP structure, starting with secondary structure prediction, prediction of trans‐membrane spans, and topology. Even though these methods generate reliable predictions, challenges such as predicting kinks or precise beginnings and ends of secondary structure elements are still waiting to be addressed. We describe recent developments in the prediction of 3D structures of both α‐helical MPs as well as β‐barrels using comparative modeling techniques, de novo methods, and molecular dynamics (MD) simulations. The increase of MP structures has (1) facilitated comparative modeling due to availability of more and better templates, and (2) improved the statistics for knowledge‐based scoring functions. Moreover, de novo methods have benefited from the use of correlated mutations as restraints. Finally, we outline current advances that will likely shape the field in the forthcoming decade. Proteins 2015; 83:1–24. © 2014 Wiley Periodicals, Inc.

Subfields: Genetics, Molecular Biology

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Citations in the corpus

An Integrated Framework Advancing Membrane Protein Modeling and Design (2015, background): “Despite their importance, our knowledge of membrane protein structure and function remains severely limited, as shown by a constant 1–2% representation of structures in the Protein Data Bank [5] over the past decade [6].”
Computational protein structure refinement: Almost there, yet still so far to go (2017, background): “194,195 198 It may be that similar strategies as for soluble proteins will be successful but limited assessment, during CASP, e.g., has effectively discouraged efforts by the community to date.”
Computing structure-based lipid accessibility of membrane proteins with mp_lipid_acc in RosettaMP (2016, background): “The dearth of structures has in turn led to a lack of prediction tools, which have typically focused on either α -helical or β - barrel membrane proteins [2], or on specific features like the prediction of membrane pores [3].”