Computational design of membrane proteins

Membrane Barrels Are Taller, Fatter, Inside-Out Soluble Barrels

Up-and-down β-barrel topology exists in both the membrane and soluble environment. By comparing features of these structurally similar proteins, we can determine what features are particular to the environment rather than the fold. Here we compare structures of membrane β-barrels to soluble β-barrels and evaluate their relative size, shape, amino acid composition, hydrophobicity, and periodicity. We find that membrane β-barrels are generally larger than soluble β-barrels, with more strands per barrel and more amino acids per strand, making them wider and taller. We also find that membrane β-barrels are inside-out soluble β-barrels. The inward region of membrane β-barrels has similar hydrophobicity to the outward region of soluble β-barrels, and the outward region of membrane β-barrels has similar hydrophobicity to the inward region of the soluble β-barrels. Moreover, even though both types of β-barrel have been assumed to have strands with amino acids that alternate in direction and hydrophobicity, we find that the membrane β-barrels have more regular alternation than soluble β-barrels. These features give insight into how membrane barrels maintain their fold and function in the membrane.

A comprehensive computational study of amino acid interactions in membrane proteins

Transmembrane proteins play a fundamental role in a wide series of biological processes but, despite their importance, they are less studied than globular proteins, essentially because their embedding in lipid membranes hampers their experimental characterization. In this paper, we improved our understanding of their structural stability through the development of new knowledge-based energy functions describing amino acid pair interactions that prevail in the transmembrane and extramembrane regions of membrane proteins. The comparison of these potentials and those derived from globular proteins yields an objective view of the relative strength of amino acid interactions in the different protein environments, and their role in protein stabilization. Separate potentials were also derived from α-helical and β-barrel transmembrane regions to investigate possible dissimilarities. We found that, in extramembrane regions, hydrophobic residues are less frequent but interactions between aromatic and aliphatic amino acids as well as aromatic-sulfur interactions contribute more to stability. In transmembrane regions, polar residues are less abundant but interactions between residues of equal or opposite charges or non-charged polar residues as well as anion-π interactions appear stronger. This shows indirectly the preference of the water and lipid molecules to interact with polar and hydrophobic residues, respectively. We applied these new energy functions to predict whether a residue is located in the trans- or extramembrane region, and obtained an AUC score of 83% in cross validation, which demonstrates their accuracy. As their application is, moreover, extremely fast, they are optimal instruments for membrane protein design and large-scale investigations of membrane protein stability.

Computational design of membrane proteins using RosettaMembrane

Computational membrane protein design is challenging due to the small number of high-resolution structures available to elucidate the physical basis of membrane protein structure, multiple functionally important conformational states, and a limited number of high-throughput biophysical assays to monitor function. However, structural determination of membrane proteins has made tremendous progress in the past years. Concurrently the field of soluble computational design has made impressive inroads. These developments allow us to tackle the formidable challenge of designing functional membrane proteins. Herein, Rosetta is benchmarked for membrane protein design. We evaluate strategies to cope with the often reduced quality of experimental membrane protein structures. Further, we test the usage of symmetry in design protocols, which is particularly important as many membrane proteins exist as homo-oligomers. We compare a soluble scoring function with a scoring function optimized for membrane proteins, RosettaMembrane. Both scoring functions recovered around half of the native sequence when completely redesigning membrane proteins. However, RosettaMembrane recovered the most native-like amino acid property composition. While leucine was overrepresented in the inner and outer-hydrophobic regions of RosettaMembrane designs, it resulted in a native-like surface hydrophobicity indicating that it is currently the best option for designing membrane proteins with Rosetta.

The Framework of Computational Protein Design

Computational protein design (CPD) has established itself as a leading field in basic and applied science with a strong coupling between the two. Proteins are computationally designed from the level of amino acids to the level of a functional protein complex. Design targets range from increased thermo- (or other) stability to specific requested reactions such as protein-protein binding, enzymatic reactions, or nanotechnology applications. The design scheme may encompass small regions of the proteins or the entire protein. In either case, the design may aim at the side-chains or at the full backbone conformation. Herein, the main framework for the process is outlined highlighting key elements in the CPD iterative cycle. These include the very definition of CPD, the diverse goals of CPD, components of the CPD protocol, methods for searching sequence and structure space, scoring functions, and augmenting the CPD with other optimization tools. Taken together, this chapter aims to introduce the framework of CPD.

Achievements and Challenges in Computational Protein Design

Computational protein design (CPD), a yet evolving field, includes computer-aided engineering for partial or full de novo designs of proteins of interest. Designs are defined by a requested structure, function, or working environment. This chapter describes the birth and maturation of the field by presenting 101 CPD examples in a chronological order emphasizing achievements and pending challenges. Integrating these aspects presents the plethora of CPD approaches with the hope of providing a "CPD 101". These reflect on the broader structural bioinformatics and computational biophysics field and include: (1) integration of knowledge-based and energy-based methods, (2) hierarchical designated approach towards local, regional, and global motifs and the integration of high- and low-resolution design schemes that fit each such region, (3) systematic differential approaches towards different protein regions, (4) identification of key hot-spot residues and the relative effect of remote regions, (5) assessment of shape-complementarity, electrostatics and solvation effects, (6) integration of thermal plasticity and functional dynamics, (7) negative design, (8) systematic integration of experimental approaches, (9) objective cross-assessment of methods, and (10) successful ranking of potential designs. Future challenges also include dissemination of CPD software to the general use of life-sciences researchers and the emphasis of success within an in vivo milieu. CPD increases our understanding of protein structure and function and the relationships between the two along with the application of such know-how for the benefit of mankind. Applied aspects range from biological drugs, via healthier and tastier food products to nanotechnology and environmentally friendly enzymes replacing toxic chemicals utilized in the industry.

Outer membrane protein design

Membrane proteins are the gateway to the cell. These proteins are also a control center of the cell, as information from the outside is passed through membrane proteins as signals to the cellular machinery. The design of membrane proteins seeks to harness the power of these gateways and signal carriers. This review will focus on the design of the membrane proteins that are in the outer membrane, a membrane which only exists for gram negative bacteria, mitochondria, and chloroplasts. Unlike other membrane proteins, outer membrane proteins are uniquely shaped as β-barrels. Herein, I describe most known examples of membrane β-barrel design to date, focusing particularly on categorizing designs as: Firstly, structural deconstruction; secondly, structural changes; thirdly, chemical function design; and finally, the creation of new folds.

Determining the N-terminal orientations of recombinant transmembrane proteins in the Escherichia coli plasma membrane

In silico algorithms have been the common approach for transmembrane (TM) protein topology prediction. However, computational tools may produce questionable results and experimental validation has proven difficult. Although biochemical strategies are available to determine the C-terminal orientation of TM proteins, experimental strategies to determine the N-terminal orientation are still limited but needed because the N-terminal end is essential for membrane targeting. Here, we describe a new and easy method to effectively determine the N-terminal orientation of the target TM proteins in Escherichia coli plasma membrane environment. D94N, the mutant of bacteriorhodopsin from Haloarcula marismortui, can be a fusion partner to increase the production of the target TM proteins if their N-termini are in cytoplasm (Nin orientation). To create a suitable linker for orientating the target TM proteins with the periplasmic N-termini (Nout orientation) correctly, we designed a three-TM-helix linker fused at the C-terminus of D94N fusion partner (termed D94N-3TM) and found that D94N-3TM can specifically improve the production of the Nout target TM proteins. In conclusion, D94N and D94N-3TM fusion partners can be applied to determine the N-terminal end of the target TM proteins oriented either Nin or Nout by evaluating the net expression of the fusion proteins.

An Integrated Framework Advancing Membrane Protein Modeling and Design

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.

Sequence and conformational preferences at termini of α-helices in membrane proteins: role of the helix environment

α-Helices are amongst the most common secondary structural elements seen in membrane proteins and are packed in the form of helix bundles. These α-helices encounter varying external environments (hydrophobic, hydrophilic) that may influence the sequence preferences at their N and C-termini. The role of the external environment in stabilization of the helix termini in membrane proteins is still unknown. Here we analyze α-helices in a high-resolution dataset of integral α-helical membrane proteins and establish that their sequence and conformational preferences differ from those in globular proteins. We specifically examine these preferences at the N and C-termini in helices initiating/terminating inside the membrane core as well as in linkers connecting these transmembrane helices. We find that the sequence preferences and structural motifs at capping (Ncap and Ccap) and near-helical (N' and C') positions are influenced by a combination of features including the membrane environment and the innate helix initiation and termination property of residues forming structural motifs. We also find that a large number of helix termini which do not form any particular capping motif are stabilized by formation of hydrogen bonds and hydrophobic interactions contributed from the neighboring helices in the membrane protein. We further validate the sequence preferences obtained from our analysis with data from an ultradeep sequencing study that identifies evolutionarily conserved amino acids in the rat neurotensin receptor. The results from our analysis provide insights for the secondary structure prediction, modeling and design of membrane proteins.

Backbone dependency further improves side chain prediction efficiency in the Energy-based Conformer Library (bEBL)

Side chain optimization is an integral component of many protein modeling applications. In these applications, the conformational freedom of the side chains is often explored using libraries of discrete, frequently occurring conformations. Because side chain optimization can pose a computationally intensive combinatorial problem, the nature of these conformer libraries is important for ensuring efficiency and accuracy in side chain prediction. We have previously developed an innovative method to create a conformer library with enhanced performance. The Energy-based Library (EBL) was obtained by analyzing the energetic interactions between conformers and a large number of natural protein environments from crystal structures. This process guided the selection of conformers with the highest propensity to fit into spaces that should accommodate a side chain. Because the method requires a large crystallographic data-set, the EBL was created in a backbone-independent fashion. However, it is well established that side chain conformation is strongly dependent on the local backbone geometry, and that backbone-dependent libraries are more efficient in side chain optimization. Here we present the backbone-dependent EBL (bEBL), whose conformers are independently sorted for each populated region of Ramachandran space. The resulting library closely mirrors the local backbone-dependent distribution of side chain conformation. Compared to the EBL, we demonstrate that the bEBL uses fewer conformers to produce similar side chain prediction outcomes, thus further improving performance with respect to the already efficient backbone-independent version of the library.

Designed di-heme binding helical transmembrane protein

De novo designing of functional membrane proteins is fundamental in terms of understanding the structure, folding, and stability of membrane proteins. In this work, we report the design and characterization of a transmembrane protein, termed HETPRO (HEme-binding Transmembrane PROtein), that binds two molecules of heme in a membrane and catalyzes oxidation/reduction reactions. The primary structure of HETPRO has been optimized in a guided fashion, from an antimicrobial peptide, for transmembrane orientation, defined 3D structure, and functions. HETPRO assembles into a tetrameric form, from an apo dimeric helical structure, in complex with cofactor in detergent micelles. The NMR structure of the apo HETPRO in micelles reveals an antiparallel helical dimer that inserts into the nonpolar core of detergent micelles. The well-defined structure of HETPRO and its ability to bind to heme moieties could be utilized to develop a functional membrane protein mimic for electron transport and photosystems.

A frequent, GxxxG-mediated, transmembrane association motif is optimized for the formation of interhelical Cα-H hydrogen bonds

Carbon hydrogen bonds between Cα-H donors and carbonyl acceptors are frequently observed between transmembrane helices (Cα-H···O=C). Networks of these interactions occur often at helix-helix interfaces mediated by GxxxG and similar patterns. Cα-H hydrogen bonds have been hypothesized to be important in membrane protein folding and association, but evidence that they are major determinants of helix association is still lacking. Here we present a comprehensive geometric analysis of homodimeric helices that demonstrates the existence of a single region in conformational space with high propensity for Cα-H···O=C hydrogen bond formation. This region corresponds to the most frequent motif for parallel dimers, GASright, whose best-known example is glycophorin A. The finding suggests a causal link between the high frequency of occurrence of GASright and its propensity for carbon hydrogen bond formation. Investigation of the sequence dependency of the motif determined that Gly residues are required at specific positions where only Gly can act as a donor with its "side chain" Hα. Gly also reduces the steric barrier for non-Gly amino acids at other positions to act as Cα donors, promoting the formation of cooperative hydrogen bonding networks. These findings offer a structural rationale for the occurrence of GxxxG patterns at the GASright interface. The analysis identified the conformational space and the sequence requirement of Cα-H···O=C mediated motifs; we took advantage of these results to develop a structural prediction method. The resulting program, CATM, predicts ab initio the known high-resolution structures of homodimeric GASright motifs at near-atomic level.

Membrane protein orientation and refinement using a knowledge-based statistical potential

BACKGROUND

Recent increases in the number of deposited membrane protein crystal structures necessitate the use of automated computational tools to position them within the lipid bilayer. Identifying the correct orientation allows us to study the complex relationship between sequence, structure and the lipid environment, which is otherwise challenging to investigate using experimental techniques due to the difficulty in crystallising membrane proteins embedded within intact membranes.

RESULTS

We have developed a knowledge-based membrane potential, calculated by the statistical analysis of transmembrane protein structures, coupled with a combination of genetic and direct search algorithms, and demonstrate its use in positioning proteins in membranes, refinement of membrane protein models and in decoy discrimination.

CONCLUSIONS

Our method is able to quickly and accurately orientate both alpha-helical and beta-barrel membrane proteins within the lipid bilayer, showing closer agreement with experimentally determined values than existing approaches. We also demonstrate both consistent and significant refinement of membrane protein models and the effective discrimination between native and decoy structures. Source code is available under an open source license from http://bioinf.cs.ucl.ac.uk/downloads/memembed/.

Nanoscopic surfactant behavior of the porin MspA in aqueous media

The mycobacterial porin MspA is one of the most stable channel proteins known to date. MspA forms vesicles at low concentrations in aqueous buffers. Evidence from dynamic light scattering, transmission electron microscopy and zeta-potential measurements by electrophoretic light scattering indicate that MspA behaves like a nanoscale surfactant. The extreme thermostability of MspA allows these investigations to be carried out at temperatures as high as 343 K, at which most other proteins would quickly denature. The principles of vesicle formation of MspA as a function of temperature and the underlying thermodynamic factors are discussed here. The results obtained provide crucial evidence in support of the hypothesis that, during vesicle formation, nanoscopic surfactant molecules, such as MspA, deviate from the principles underlined in classical surface chemistry.

Measuring transmembrane helix interaction strengths in lipid bilayers using steric trapping

We have developed a method to measure strong transmembrane (TM) helix interaction affinities in lipid bilayers that are difficult to measure by traditional dilution methods. The method, called steric trapping, couples dissociation of biotinylated TM helices to a competitive binding by monovalent streptavidin (mSA), so that dissociation is driven by the affinity of mSA for biotin and mSA concentration. By adjusting the binding affinity of mSA through mutation, the method can obtain dissociation constants of TM helix dimers (K d,dimer) over a range of six orders of magnitudes. The K d,dimer limit of measurable target interaction is extended 3-4 orders of magnitude lower than possible by dilution methods. Thus, steric trapping opens up new opportunities to study the folding and assembly of α-helical membrane proteins in lipid bilayer environments. Here we provide detailed methods for applying steric trapping to a TM helix dimer.

Knowledge-based potential for positioning membrane-associated structures and assessing residue-specific energetic contributions

The complex hydrophobic and hydrophilic milieus of membrane-associated proteins pose experimental and theoretical challenges to their understanding. Here, we produce a nonredundant database to compute knowledge-based asymmetric cross-membrane potentials from the per-residue distributions of C(β), C(γ) and functional group atoms. We predict transmembrane and peripherally associated regions from genomic sequence and position peptides and protein structures relative to the bilayer (available at http://www.degradolab.org/ez). The pseudo-energy topological landscapes underscore positional stability and functional mechanisms demonstrated here for antimicrobial peptides, transmembrane proteins, and viral fusion proteins. Moreover, experimental effects of point mutations on the relative ratio changes of dual-topology proteins are quantitatively reproduced. The functional group potential and the membrane-exposed residues display the largest energetic changes enabling to detect native-like structures from decoys. Hence, focusing on the uniqueness of membrane-associated proteins and peptides, we quantitatively parameterize their cross-membrane propensity, thus facilitating structural refinement, characterization, prediction, and design.

Naturally evolved G protein-coupled receptors adopt metastable conformations

A wide range of membrane receptors signal through conformational changes, and the resulting protein conformational flexibility often hinders their structural studies. Because the determinants of membrane receptor conformational stability are still poorly understood, identifying a minimal set of perturbations stabilizing a membrane protein in a given conformation remains a major challenge in membrane protein structure determination. We present a novel approach integrating bioinformatics, computational design and experimental techniques that identifies and stabilizes metastable receptor regions. When applied to the beta1-adrenergic receptor, the method generated 13 novel receptor variants stabilized in the intended inactive state among which two exhibit an apparent thermostability higher than WT and M23 (a receptor variant previously stabilized by extensive scanning mutagenesis) by more than 30 °C and 11 °C, respectively. Targeted regions involve nonconserved unsatisfied polar residues or exhibit significant packing defects, features found in all class A G protein-coupled receptor structures. These findings suggest that natural G protein-coupled receptor sequences have evolved to be conformationally metastable through the design of suboptimal polar and van der Waals tertiary interactions. Given sufficiently accurate structural models, our approach should prove useful for designing stabilized variants of many uncharacterized membrane receptors.

Structural informatics, modeling, and design with an open-source Molecular Software Library (MSL)

We present the Molecular Software Library (MSL), a C++ library for molecular modeling. MSL is a set of tools that supports a large variety of algorithms for the design, modeling, and analysis of macromolecules. Among the main features supported by the library are methods for applying geometric transformations and alignments, the implementation of a rich set of energy functions, side chain optimization, backbone manipulation, calculation of solvent accessible surface area, and other tools. MSL has a number of unique features, such as the ability of storing alternative atomic coordinates (for modeling) and multiple amino acid identities at the same backbone position (for design). It has a straightforward mechanism for extending its energy functions and can work with any type of molecules. Although the code base is large, MSL was created with ease of developing in mind. It allows the rapid implementation of simple tasks while fully supporting the creation of complex applications. Some of the potentialities of the software are demonstrated here with examples that show how to program complex and essential modeling tasks with few lines of code. MSL is an ongoing and evolving project, with new features and improvements being introduced regularly, but it is mature and suitable for production and has been used in numerous protein modeling and design projects. MSL is open-source software, freely downloadable at http://msl-libraries.org. We propose it as a common platform for the development of new molecular algorithms and to promote the distribution, sharing, and reutilization of computational methods.

Computational design of membrane proteins

Membrane proteins are involved in a wide variety of cellular processes, and are typically part of the first interaction a cell has with extracellular molecules. As a result, these proteins comprise a majority of known drug targets. Membrane proteins are among the most difficult proteins to obtain and characterize, and a structure-based understanding of their properties can be difficult to elucidate. Notwithstanding, the design of membrane proteins can provide stringent tests of our understanding of these crucial biological systems, as well as introduce novel or targeted functionalities. Computational design methods have been particularly helpful in addressing these issues, and this review discusses recent studies that tailor membrane proteins to display specific structures or functions and examines how redesigned membrane proteins are being used to facilitate structural and functional studies.