Phasing statistics for alpha helical membrane protein structures

In this report we highlight the latest trends in phasing methods used to solve alpha helical membrane protein structures and analyze the use of heavy atom metals for the purpose of experimental phasing. Our results reveal that molecular replacement is emerging as the most successful method for phasing alpha helical membrane proteins, with the notable exception of the transporter family, where experimentally derived phase information still remains the most effective method. To facilitate selection of heavy atoms salts for experimental phasing an analysis of these was undertaken and indicates that organic mercury salts are still the most successful heavy atoms reagents. Interestingly the use of seleno‐l ‐methionine incorporated protein has increased since earlier studies into membrane protein phasing, so too the use of SAD and MAD as techniques for phase determination. Taken together this study provides a brief snapshot of phasing methods for alpha helical membrane proteins and suggests possible routes for heavy atom selection and phasing methods based on currently available data.     

All-atom contact potential approach to protein thermostability analysis

In this paper we use all-atom potential energy to define and analyze the inter-residue contacts in mesophilic and thermophilic proteins. Fifteen families of proteins are selected and each family has two representative proteins with greatly different preferred environmental temperatures. We find that both the number and energy of the contacts defined in this way show stronger correlations with the preferred temperatures of proteins than other factors used before. We also find that the charged-polar and charged-nonpolar residue contacts not only have larger contact numbers but also have lower single contact energies. Furthermore, the most important is that most of the thermophilic proteins have more charged-polar and charged-nonpolar residue contacts than their mesophilic counterparts. This suggests that they may play an important role in the thermostability of proteins, except usual charged-charged and nonpolar-nonpolar residue contacts. Charged residues may exert their profound influence by forming contacts not only with other charged residues but also with polar or nonpolar residues, thus further increasing the strength of contact network and then the thermostability of proteins.     

Evolution and similarity evaluation of protein structures in contact map space

Prediction of fold from amino acid sequence of a protein has been an active area of research in the past few years, but the limited accuracy of existing techniques emphasizes the need to develop newer approaches to tackle this task. In this study, we use contact map prediction as an intermediate step in fold prediction from sequence. Contact map is a reduced graph-theoretic representation of proteins that models the local and global inter-residue contacts in the structure. We start with a population of random contact maps for the protein sequence and "evolve" the population to a "high-feasibility" configuration using a genetic algorithm. A neural network is employed to assess the feasibility of contact maps based on their 4 physically relevant properties. We also introduce 5 parameters, based on algebraic graph theory and physical considerations, that can be used to judge the structural similarity between proteins through contact maps. To predict the fold of a given amino acid sequence, we predict a contact map that will sufficiently approximate the structure of the corresponding protein. Then we assess the similarity of this contact map with the representative contact map of each fold; the fold that corresponds to the closest match is our predicted fold for the input sequence. We have found that our feasibility measure is able to differentiate between feasible and infeasible contact maps. Further, this novel approach is able to predict the folds from sequences significantly better than a random predictor.     

Four-body contact potentials derived from two protein datasets to discriminate native structures from decoys

Two-body inter-residue contact potentials for proteins have often been extracted and extensively used for threading. Here, we have developed a new scheme to derive four-body contact potentials as a way to consider protein interactions in a more cooperative model. We use several datasets of protein native structures to demonstrate that around 500 chains are sufficient to provide a good estimate of these four-body contact potentials by obtaining convergent threading results. We also have deliberately chosen two sets of protein native structures differing in resolution, one with all chains' resolution better than 1.5 A and the other with 94.2% of the structures having a resolution worse than 1.5 A to investigate whether potentials from well-refined protein datasets perform better in threading. However, potentials from well-refined proteins did not generate statistically significant better threading results. Our four-body contact potentials can discriminate well between native structures and partially unfolded or deliberately misfolded structures. Compared with another set of four-body contact potentials derived by using a Delaunay tessellation algorithm, our four-body contact potentials appear to offer a better characterization of the interactions between backbones and side chains and provide better threading results, somewhat complementary to those found using other potentials.     

Understanding contact patterns of protein structures from protein contact map and investigation of unique patterns in the globin-like folded domains

Proteins are biochemical compounds made up of one or more polypeptides in a specific order, typically folded into a functionally active form. Proteins are categorized into four different structural classes according to the topology of α-helices and β-strands. In this study, we modeled these four structural classes as an undirected network depicting amino acids as nodes and interaction between them as edges. Results infer that basic protein classes can be easily recognized as well as distinguished by utilizing protein contact maps (PCM). Toward studying the globin-like fold, the helix-loop-helix region contacts were seen to be of a unique pattern, and these remained in all the folds. Further, the averaged diagonal contacts were analyzed and identified those contacts in α/β proteins were higher in comparison with the other class. Interesting, we noticed that anti-parallel beta sheets were dominant in all-β and α + β classes that lead to similar diagonal patterns. Network properties of all four basic classes were analyzed and found to possess small-world property. Findings infer that PCM may assist classify protein structure classes and it also helps in evaluating the predicted protein structures.     

Statistical potential for assessment and prediction of protein structures

Protein structures in the Protein Data Bank provide a wealth of data about the interactions that determine the native states of proteins. Using the probability theory, we derive an atomic distance-dependent statistical potential from a sample of native structures that does not depend on any adjustable parameters (Discrete Optimized Protein Energy, or DOPE). DOPE is based on an improved reference state that corresponds to noninteracting atoms in a homogeneous sphere with the radius dependent on a sample native structure; it thus accounts for the finite and spherical shape of the native structures. The DOPE potential was extracted from a nonredundant set of 1472 crystallographic structures. We tested DOPE and five other scoring functions by the detection of the native state among six multiple target decoy sets, the correlation between the score and model error, and the identification of the most accurate non-native structure in the decoy set. For all decoy sets, DOPE is the best performing function in terms of all criteria, except for a tie in one criterion for one decoy set. To facilitate its use in various applications, such as model assessment, loop modeling, and fitting into cryo-electron microscopy mass density maps combined with comparative protein structure modeling, DOPE was incorporated into the modeling package MODELLER-8.     

Finite element prediction of contact pressures in cam-type femoroacetabular impingement with varied alpha angles

Three dimensional finite element models of cam-type FAI with alpha angles of 60°, 70°, 80°, and 90° were created to investigate the cartilage contact mechanics in daily activities. Intra-articular cartilage contact pressures during routine daily activities were assessed and cross-compared with a normal control hip. Alpha angles and hip range of motion were found to have a combined influence on the cartilage contact mechanics in hips with cam-type FAI, thereby resulting in abnormally high pressures and driving the cartilage damage. In particular, alpha angles of 80° or greater contribute to substantial pressure increase under certain types of daily activities.     

Transition state contact orders correlate with protein folding rates

We have used molecular dynamics simulations restrained by experimental phi values derived from protein engineering experiments to determine the structures of the transition state ensembles of ten proteins that fold with two-state kinetics. For each of these proteins we then calculated the average contact order in the transition state ensemble and compared it with the corresponding experimental folding rate. The resulting correlation coefficient is similar to that computed for the contact orders of the native structures, supporting the use of native state contact orders for predicting folding rates. The native contacts in the transition state also correlate with those of the native state but are found to be about 30% lower. These results show that, despite the high levels of heterogeneity in the transition state ensemble, the large majority of contributing structures have native-like topologies and that the native state contact order captures this phenomenon.     

Prediction of function divergence in protein families using the substitution rate variation parameter alpha

Protein families typically embody a range of related functions and may thus be decomposed into subfamilies with, for example, distinct substrate specificities. Detection of functionally divergent subfamilies is possible by methods for recognizing branches of adaptive evolution in a gene tree. As the number of genome sequences is growing rapidly, it is highly desirable to automatically detect subfamily function divergence. To this end, we here introduce a method for large-scale prediction of function divergence within protein families. It is called the alpha shift measure (ASM) as it is based on detecting a shift in the shape parameter (alpha [alpha]) of the substitution rate gamma distribution. Four different methods for estimating alpha were investigated. We benchmarked the accuracy of ASM using function annotation from Enzyme Commission numbers within Pfam protein families divided into subfamilies by the automatic tree-based method BETE. In a test using 563 subfamily pairs in 162 families, ASM outperformed functional site-based methods using rate or conservation shifting (rate shift measure [RSM] and conservation shift measure [CSM]). The best results were obtained using the "GZ-Gamma" method for estimating alpha. By combining ASM with RSM and CSM using linear discriminant analysis, the prediction accuracy was further improved.     

PFP: Automated prediction of gene ontology functional annotations with confidence scores using protein sequence data

Protein function prediction is a central problem in bioinformatics, increasing in importance recently due to the rapid accumulation of biological data awaiting interpretation. Sequence data represents the bulk of this new stock and is the obvious target for consideration as input, as newly sequenced organisms often lack any other type of biological characterization. We have previously introduced PFP (Protein Function Prediction) as our sequence-based predictor of Gene Ontology (GO) functional terms. PFP interprets the results of a PSI-BLAST search by extracting and scoring individual functional attributes, searching a wide range of E-value sequence matches, and utilizing conventional data mining techniques to fill in missing information. We have shown it to be effective in predicting both specific and low-resolution functional attributes when sufficient data is unavailable. Here we describe (1) significant improvements to the PFP infrastructure, including the addition of prediction significance and confidence scores, (2) a thorough benchmark of performance and comparisons to other related prediction methods, and (3) applications of PFP predictions to genome-scale data. We applied PFP predictions to uncharacterized protein sequences from 15 organisms. Among these sequences, 60-90% could be annotated with a GO molecular function term at high confidence (>or=80%). We also applied our predictions to the protein-protein interaction network of the Malaria plasmodium (Plasmodium falciparum). High confidence GO biological process predictions (>or=90%) from PFP increased the number of fully enriched interactions in this dataset from 23% of interactions to 94%. Our benchmark comparison shows significant performance improvement of PFP relative to GOtcha, InterProScan, and PSI-BLAST predictions. This is consistent with the performance of PFP as the overall best predictor in both the AFP-SIG '05 and CASP7 function (FN) assessments. PFP is available as a web service at http://dragon.bio.purdue.edu/pfp/.     

Reconstruction of protein conformations from estimated positions of the C alpha coordinates.

Protein C alpha coordinates are used to accurately reconstruct complete protein backbones and side-chain directions. This work employs potentials of mean force to align semirigid peptide groups around the axes that connect successive C alpha atoms. The algorithm works well for all residue types and secondary structure classes and is stable for imprecise C alpha coordinates. Tests on known protein structures show that root mean square errors in predicted main-chain and C beta coordinates are usually less than 0.3 A. These results are significantly more accurate than can be obtained from competing approaches, such as modeling of backbone conformations from structurally homologous fragments.     

Empirical lipid propensities of amino acid residues in multispan alpha helical membrane proteins

Characterizing the interactions between amino acid residues and lipid molecules is important for understanding the assembly of transmembrane helices and for studying membrane protein folding. In this study we develop TMLIP (TransMembrane helix-LIPid), an empirically derived propensity of individual residue types to face lipid membrane based on statistical analysis of high-resolution structures of membrane proteins. Lipid accessibilities of amino acid residues within the transmembrane (TM) region of 29 structures of helical membrane proteins are studied with a spherical probe of radius of 1.9 A. Our results show that there are characteristic preferences for residues to face the headgroup region and the hydrocarbon core region of lipid membrane. Amino acid residues Lys, Arg, Trp, Phe, and Leu are often found exposed at the headgroup regions of the membrane, where they have high propensity to face phospholipid headgroups and glycerol backbones. In the hydrocarbon core region, the strongest preference for interacting with lipids is observed for Ile, Leu, Phe and Val. Small and polar amino acid residues are usually buried inside helical bundles and are strongly lipophobic. There is a strong correlation between various hydrophobicity scales and the propensity of a given residue to face the lipids in the hydrocarbon region of the bilayer. Our data suggest a possibly significant contribution of the lipophobic effect to the folding of membrane proteins. This study shows that membrane proteins have exceedingly apolar exteriors rather than highly polar interiors. Prediction of lipid-facing surfaces of boundary helices using TMLIP1 results in a 54% accuracy, which is significantly better than random (25% accuracy). We also compare performance of TMLIP with another lipid propensity scale, kPROT, and with several hydrophobicity scales using hydrophobic moment analysis.     

Role of membrane contact sites in protein import into mitochondria

Mitochondria import more than 1,000 different proteins from the cytosol. The proteins are synthesized as precursors on cytosolic ribosomes and are translocated by protein transport machineries of the mitochondrial membranes. Five main pathways for protein import into mitochondria have been identified. Most pathways use the translocase of the outer mitochondrial membrane (TOM) as the entry gate into mitochondria. Depending on specific signals contained in the precursors, the proteins are subsequently transferred to different intramitochondrial translocases. In this article, we discuss the connection between protein import and mitochondrial membrane architecture. Mitochondria possess two membranes. It is a long‐standing question how contact sites between outer and inner membranes are formed and which role the contact sites play in the translocation of precursor proteins. A major translocation contact site is formed between the TOM complex and the presequence translocase of the inner membrane (TIM23 complex), promoting transfer of presequence‐carrying preproteins to the mitochondrial inner membrane and matrix. Recent findings led to the identification of contact sites that involve the mitochondrial contact site and cristae organizing system (MICOS) of the inner membrane. MICOS plays a dual role. It is crucial for maintaining the inner membrane cristae architecture and forms contacts sites to the outer membrane that promote translocation of precursor proteins into the intermembrane space and outer membrane of mitochondria. The view is emerging that the mitochondrial protein translocases do not function as independent units, but are embedded in a network of interactions with machineries that control mitochondrial activity and architecture.     

Properties of integral membrane protein structures: derivation of an implicit membrane potential

Distributions of each amino acid in the trans-membrane domain were calculated as a function of the membrane normal using all currently available alpha-helical membrane protein structures with resolutions better than 4 A. The results were compared with previous sequence- and structure-based analyses. Calculation of the average hydrophobicity along the membrane normal demonstrated that the protein surface in the membrane domain is in fact much more hydrophobic than the protein core. While hydrophobic residues dominate the membrane domain, the interfacial regions of membrane proteins were found to be abundant in the small residues glycine, alanine, and serine, consistent with previous studies on membrane protein packing. Charged residues displayed nonsymmetric distributions with a preference for the intracellular interface. This effect was more prominent for Arg and Lys resulting in a direct confirmation of the positive inside rule. Potentials of mean force along the membrane normal were derived for each amino acid by fitting Gaussian functions to the residue distributions. The individual potentials agree well with experimental and theoretical considerations. The resulting implicit membrane potential was tested on various membrane proteins as well as single trans-membrane alpha-helices. All membrane proteins were found to be at an energy minimum when correctly inserted into the membrane. For alpha-helices both interfacial (i.e. surface bound) and inserted configurations were found to correspond to energy minima. The results demonstrate that the use of trans-membrane amino acid distributions to derive an implicit membrane representation yields meaningful residue potentials.     

A beta‐complex statistical four body contact potential combined with a hydrogen bond statistical potential recognizes the correct native structure from protein decoy sets

We present a new four‐body knowledge‐based potential for recognizing the native state of proteins from their misfolded states. This potential was extracted from a large set of protein structures determined by X‐ray crystallography using BetaMol, a software based on the recent theory of the beta‐complex (β‐complex) and quasi‐triangulation of the Voronoi diagram of spheres. This geometric construct reflects the size difference among atoms in their full Euclidean metric; property not accounted for in a typical 3D Delaunay triangulation. The ability of this potential to identify the native conformation over a large set of decoys was evaluated. Experiments show that this potential outperforms a potential constructed with a classical Delaunay triangulation in decoy discrimination tests. The addition of a statistical hydrogen bond potential to our four‐body potential allows a significant improvement in the decoy discrimination, in such a way that we are able to predict successfully the native structure in 90% of cases. Proteins 2013; 81:1420–1433. © 2013 Wiley Periodicals, Inc.