Structure prediction of membrane proteins.
Ontology highlight
ABSTRACT:
SUBMITTER: Zhou C
PROVIDER: S-EPMC5172438 | biostudies-literature | 2004 Feb
REPOSITORIES: biostudies-literature
Publications
Structure prediction of membrane proteins.
Zhou Chunlong C Zheng Yao Y Zhou Yan Y
Genomics, proteomics & bioinformatics 20040201 1
There is a large gap between the number of membrane protein (MP) sequences and that of their decoded 3D structures, especially high-resolution structures, due to difficulties in crystal preparation of MPs. However, detailed knowledge of the 3D structure is required for the fundamental understanding of the function of an MP and the interactions between the protein and its inhibitors or activators. In this paper, some computational approaches that have been used to predict MP structures are discus ...[more]
Similar Datasets
Project description:[Formula: see text]-Barrel membrane proteins ([Formula: see text]MPs) play important roles, but knowledge of their structures is limited. We have developed a method to predict their 3D structures. We predict strand registers and construct transmembrane (TM) domains of [Formula: see text]MPs accurately, including proteins for which no prediction has been attempted before. Our method also accurately predicts structures from protein families with a limited number of sequences and proteins with novel folds. An average main-chain rmsd of 3.48 Å is achieved between predicted and experimentally resolved structures of TM domains, which is a significant improvement ([Formula: see text]3 Å) over a recent study. For [Formula: see text]MPs with NMR structures, the deviation between predictions and experimentally solved structures is similar to the difference among the NMR structures, indicating excellent prediction accuracy. Moreover, we can now accurately model the extended [Formula: see text]-barrels and loops in non-TM domains, increasing the overall coverage of structure prediction by [Formula: see text]%. Our method is general and can be applied to genome-wide structural prediction of [Formula: see text]MPs.
| S-EPMC5816179 | biostudies-literature
Project description:The Profiles-3D application, an inverse-folding methodology appropriate for water-soluble proteins, has been modified to allow the determination of structural properties of integral-membrane proteins (IMPs) and for testing the validity of solved and model structures of IMPs. The modification, known as reverse-environment prediction of integral membrane protein structure (REPIMPS), takes into account the fact that exposed areas of side chains for many residues in IMPs are in contact with lipid and not the aqueous phase. This (1) allows lipid-exposed residues to be classified into the correct physicochemical environment class, (2) significantly improves compatibility scores for IMPs whose structures have been solved, and (3) reduces the possibility of rejecting a three-dimensional structure for an IMP because the presence of lipid was not included. Validation tests of REPIMPS showed that it (1) can locate the transmembrane domain of IMPs with single transmembrane helices more frequently than a range of other methodologies, (2) can rotationally orient transmembrane helices with respect to the lipid environment and surrounding helices in IMPs with multiple transmembrane helices, and (3) has the potential to accurately locate transmembrane domains in IMPs with multiple transmembrane helices. We conclude that correcting for the presence of the lipid environment surrounding the transmembrane segments of IMPs is an essential step for reasonable modeling and verification of the three-dimensional structures of these proteins.
| S-EPMC2374085 | biostudies-literature
Project description:Solvent molecules interact intimately with proteins and can profoundly regulate their structure and function. However, accurately and efficiently modeling protein solvation effects at the molecular level has been challenging. Here, we present a method that improves the atomic-level modeling of soluble and membrane protein structures and binding by efficiently predicting de novo protein-solvent molecule interactions. The method predicted with unprecedented accuracy buried water molecule positions, solvated protein conformations, and challenging mutational effects on protein binding. When applied to homology modeling, solvent-bound membrane protein structures, pockets, and cavities were recapitulated with near-atomic precision even from distant homologs. Blindly refined atomic-level structures of evolutionary distant G protein-coupled receptors imply strikingly different functional roles of buried solvent between receptor classes. The method should prove useful for refining low-resolution protein structures, accurately modeling drug-binding sites in structurally uncharacterized receptors, and designing solvent-mediated protein catalysis, recognition, ligand binding, and membrane protein signaling.
| S-EPMC5909693 | biostudies-literature
Project description:Membrane-binding peripheral proteins play important roles in many biological processes, including cell signaling and membrane trafficking. Unlike integral membrane proteins, these proteins bind the membrane mostly in a reversible manner. Since peripheral proteins do not have canonical transmembrane segments, it is difficult to identify them from their amino acid sequences. As a first step toward genome-scale identification of membrane-binding peripheral proteins, we built a kernel-based machine learning protocol. Key features of known membrane-binding proteins, including electrostatic properties and amino acid composition, were calculated from their amino acid sequences and tertiary structures, which were then incorporated into the support vector machine to perform the classification. A data set of 40 membrane-binding proteins and 230 non-membrane-binding proteins was used to construct and validate the protocol. Cross-validation and holdout evaluation of the protocol showed that the accuracy of the prediction reached up to 93.7% and 91.6%, respectively. The protocol was applied to the prediction of membrane-binding properties of four C2 domains from novel protein kinases C. Although these C2 domains have 50% sequence identity, only one of them was predicted to bind the membrane, which was verified experimentally with surface plasmon resonance analysis. These results suggest that our protocol can be used for predicting membrane-binding properties of a wide variety of modular domains and may be further extended to genome-scale identification of membrane-binding peripheral proteins.
| S-EPMC2707359 | biostudies-literature
Project description:Magnetotactic bacteria (MTB) are Gram-negative bacteria that can navigate along geomagnetic fields. This ability is a result of a unique intracellular organelle, the magnetosome. These organelles are composed of membrane-enclosed magnetite (Fe3O4) or greigite (Fe3S4) crystals ordered into chains along the cell. Magnetosome formation, assembly, and magnetic nano-crystal biomineralization are controlled by magnetosome-associated proteins (MAPs). Most MAP-encoding genes are located in a conserved genomic region - the magnetosome island (MAI). The MAI appears to be conserved in all MTB that were analyzed so far, although the MAI size and organization differs between species. It was shown that MAI deletion leads to a non-magnetic phenotype, further highlighting its important role in magnetosome formation. Today, about 28 proteins are known to be involved in magnetosome formation, but the structures and functions of most MAPs are unknown. To reveal the structure-function relationship of MAPs we used bioinformatics tools in order to build homology models as a way to understand their possible role in magnetosome formation. Here we present a predicted 3D structural models' overview for all known Magnetospirillum gryphiswaldense strain MSR-1 MAPs.
| S-EPMC3905215 | biostudies-literature
Project description:We describe the adaptation of the Rosetta de novo structure prediction method for prediction of helical transmembrane protein structures. The membrane environment is modeled by embedding the protein chain into a model membrane represented by parallel planes defining hydrophobic, interface, and polar membrane layers for each energy evaluation. The optimal embedding is determined by maximizing the exposure of surface hydrophobic residues within the membrane and minimizing hydrophobic exposure outside of the membrane. Protein conformations are built up using the Rosetta fragment assembly method and evaluated using a new membrane-specific version of the Rosetta low-resolution energy function in which residue-residue and residue-environment interactions are functions of the membrane layer in addition to amino acid identity, distance, and density. We find that lower energy and more native-like structures are achieved by sequential addition of helices to a growing chain, which may mimic some aspects of helical protein biogenesis after translocation, rather than folding the whole chain simultaneously as in the Rosetta soluble protein prediction method. In tests on 12 membrane proteins for which the structure is known, between 51 and 145 residues were predicted with root-mean-square deviation <4 A from the native structure.
| S-EPMC1479309 | biostudies-literature
Project description:Outer membrane proteins (OMPs) are the transmembrane proteins found in the outer membranes of Gram-negative bacteria, mitochondria and plastids. Most prediction methods have focused on analogous features, such as alternating hydrophobicity patterns. Here, we start from the observation that almost all beta-barrel OMPs are related by common ancestry. We identify proteins as OMPs by detecting their homologous relationships to known OMPs using sequence similarity. Given an input sequence, HHomp builds a profile hidden Markov model (HMM) and compares it with an OMP database by pairwise HMM comparison, integrating OMP predictions by PROFtmb. A crucial ingredient is the OMP database, which contains profile HMMs for over 20,000 putative OMP sequences. These were collected with the exhaustive, transitive homology detection method HHsenser, starting from 23 representative OMPs in the PDB database. In a benchmark on TransportDB, HHomp detects 63.5% of the true positives before including the first false positive. This is 70% more than PROFtmb, four times more than BOMP and 10 times more than TMB-Hunt. In Escherichia coli, HHomp identifies 57 out of 59 known OMPs and correctly assigns them to their functional subgroups. HHomp can be accessed at http://toolkit.tuebingen.mpg.de/hhomp.
| S-EPMC2703889 | biostudies-literature
Project description:Using coarse-grained membrane simulations we show here that peripheral membrane proteins can form a multitude of higher-order structures due to membrane-mediated interactions. Peripheral membrane proteins characteristically perturb the lipid bilayer in their vicinity which supports the formation of protein assemblies not only within the same but surprisingly also across opposing leaflets of a bilayer. In addition, we also observed the formation of lipid-protein domains on heteregeneous membranes. The clustering ability of proteins, as quantified via the potential of mean force, is enhanced when radius and hydrophobic penetration depth of the proteins increases. Based on our data, we propose that membrane-mediated cluster formation of peripheral proteins supports protein assembly in vivo and hence may play a pivotal role in the formation of templates for signaling cascades and in the emergence of transport intermediates in the secretory pathway.
| S-EPMC3121687 | biostudies-literature
Project description:BackgroundMembrane proteins compose up to 30% of coding sequences within genomes. However, their structure determination is lagging behind compared with soluble proteins due to the experimental difficulties. Therefore, it is important to develop reliable computational methods to predict structures of membrane proteins.ResultsWe present a method for prediction of the TM helix orientation, which is an essential step in ab initio modeling of membrane proteins. Our method is based on a canonical model of the heptad repeat originally developed for coiled coils. We identify the helical surface patches that interface with lipid molecules at an accuracy of about 88% from the sequence information alone, using an empirical scoring function LIPS (LIPid-facing Surface), which combines lipophilicity and conservation of residues in the helix. We test and discuss results of prediction of helix-lipid interfaces on 162 transmembrane helices from 18 polytopic membrane proteins and present predicted orientations of TM helices in TRPV1 channel. We also apply our method to two structures of homologous cytochrome b6f complexes and find discrepancy in the assignment of TM helices from subunits PetG, PetN and PetL. The results of LIPS calculations and analysis of packing and H-bonding interactions support the helix assignment found in the cytochrome b6f structure from green alga but not the assignment of TM helices in the cyanobacterium b6f structure.ConclusionLIPS calculations can be used for the prediction of helix orientation in ab initio modeling of polytopic membrane proteins. We also show with the example of two cytochrome b6f structures that our method can identify questionable helix assignments in membrane proteins. The LIPS server is available online at http://gila.bioengr.uic.edu/lab/larisa/lips.html.
| S-EPMC1540425 | biostudies-literature
Project description:The highly anisotropic environment of the lipid bilayer membrane imposes significant constraints on the structures and functions of membrane proteins. However, NMR structure calculations typically use a simple repulsive potential that neglects the effects of solvation and electrostatics, because explicit atomic representation of the solvent and lipid molecules is computationally expensive and impractical for routine NMR-restrained calculations that start from completely extended polypeptide templates. Here, we describe the extension of a previously described implicit solvation potential, eefxPot, to include a membrane model for NMR-restrained calculations of membrane protein structures in XPLOR-NIH. The key components of eefxPot are an energy term for solvation free energy that works together with other nonbonded energy functions, a dedicated force field for conformational and nonbonded protein interaction parameters, and a membrane function that modulates the solvation free energy and dielectric screening as a function of the atomic distance from the membrane center, relative to the membrane thickness. Initial results obtained for membrane proteins with structures determined experimentally in lipid bilayer membranes show that eefxPot affords significant improvements in structural quality, accuracy, and precision. Calculations with eefxPot are straightforward to implement and can be used to both fold and refine structures, as well as to run unrestrained molecular-dynamics simulations. The potential is entirely compatible with the full range of experimental restraints measured by various techniques. Overall, it provides a useful and practical way to calculate membrane protein structures in a physically realistic environment.
| S-EPMC4572468 | biostudies-literature