An automatic method for predicting transmembrane protein structures using cryo-EM and evolutionary data

The transmembrane (TM) domains of many integral membrane proteins are composed of alpha-helix bundles. Structure determination at high resolution (<4 A) of TM domains is still exceedingly difficult experimentally. Hence, some TM-protein structures have only been solved at intermediate (5-10 A) or low (>10 A) resolutions using, for example, cryo-electron microscopy (cryo-EM). These structures reveal the packing arrangement of the TM domain, but cannot be used to determine the positions of individual amino acids. The observation that typically, the lipid-exposed faces of TM proteins are evolutionarily more variable and less charged than their core provides a simple rule for orienting their constituent helices. Based on this rule, we developed score functions and automated methods for orienting TM helices, for which locations and tilt angles have been determined using, e.g., cryo-EM data. The method was parameterized with the aim of retrieving the native structure of bacteriorhodopsin among near- and far-from-native templates. It was then tested on proteins that differ from bacteriorhodopsin in their sequences, architectures, and functions, such as the acetylcholine receptor and rhodopsin. The predicted structures were within 1.5-3.5 A from the native state in all cases. We conclude that the computational method can be used in conjunction with cryo-EM data to obtain approximate model structures of TM domains of proteins for which a sufficiently heterogeneous set of homologs is available. We also show that in those proteins in which relatively short loops connect neighboring helices, the scoring functions can discriminate between near- and far-from-native conformations even without the constraints imposed on helix locations and tilt angles that are derived from cryo-EM.     

Exploiting evolutionary relationships for predicting protein structures

In the last few years there have been many developments in computational biology, particularly with regard to novel, imaginative exploitation of genomic data. Disappointingly, there has been a lack of progress in the methodology for prediction of protein structures. In the last several years, however, promising new methods have finally begun to emerge. These methods are increasing the power and scope of the methodology, but, most importantly, they are generating new areas of investigation that we believe will accelerate progress in the field. In this review we describe recent developments and highlight the implications of their success as well as areas where efforts should be focused.     

An evolutionary method for learning HMM structure: prediction of protein secondary structure

Background  

The prediction of the secondary structure of proteins is one of the most studied problems in bioinformatics. Despite their success in many problems of biological sequence analysis, Hidden Markov Models (HMMs) have not been used much for this problem, as the complexity of the task makes manual design of HMMs difficult. Therefore, we have developed a method for evolving the structure of HMMs automatically, using Genetic Algorithms (GAs).     

Estimation of evolutionary distances from protein spatial structures

New equations are derived to estimate the number of amino acid substitutions per site between two homologous proteins from the root mean square (RMS) deviation between two spatial structures and from the fraction of identical residues between two sequences. The equations are based on evolutionary models, analyzing predominantly structural changes and not sequence changes. Evolution of spatial structure is treated as a diffusion in an elastic force field. Diffusion accounts for structural changes caused by amino acid substitutions, and elastic force reflects selection, which preserves protein fold. Obtained equations are supported by analysis of protein spatial structures. Received: 21 September 1995 / Accepted: 19 May 1997     

An efficient protein complex purification method for functional proteomics in higher eukaryotes

The ensemble of expressed proteins in a given cell is organized in multiprotein complexes. The identification of the individual components of these complexes is essential for their functional characterization. The introduction of the 'tandem affinity purification' (TAP) methodology substantially improved the purification and systematic genome-wide characterization of protein complexes in yeast. The use of this approach in higher eukaryotic cells has lagged behind its use in yeast because the tagged proteins are normally expressed in the presence of the untagged endogenous version, which may compete for incorporation into multiprotein complexes. Here we describe a strategy in which the TAP approach is combined with double-stranded RNA interference (RNAi) to avoid competition from corresponding endogenous proteins while isolating and characterizing protein complexes from higher eukaryotic cells. This strategy allows the determination of the functionality of the tagged protein and increases the specificity and the efficiency of the purification.     

An mRNA-protein fusion at N-terminus for evolutionary protein engineering

A novel method to link a nascent protein (phenotype) to its mRNA (genotype) covalently through the N-terminus was developed. The mRNA harboring amber stop codon at just downstream of initiation site was hybridized with hydrazide-modified ssDNA at upstream of coding region and was ligated to the DNA. This construct was then modified with 4-acetyl-phenylalanyl amber suppressor tRNA. This modified construct was fused with the nascent protein via the phenylalanine derivative when the mRNA uses the amber suppressor tRNA to decode the amber stop codon. The obtained fusion molecule was used successfully in selective enrichment experiments. It will be applicable for high-through-put screening in evolutionary protein engineering. In contrast to fusion molecules generated by other methods in which the protein is linked to genotype molecule through the C- terminus, our fusion molecule will serve to select a protein for which the C-terminus is essential to be active.     

Retroviral integrase protein and intasome nucleoprotein complex structures

Retroviral replication proceeds through the integration of a DNA copy of the viral RNA genome into the host cellular genome, a process that is mediated by the viral integrase(IN) protein. IN catalyzes two distinct chemical reactions: 3'-processing, whereby the viral DNA is recessed by a di- or trinucleotide at its 3'-ends, and strand transfer, in which the processed viral DNA ends are inserted into host chromosomal DNA. Although IN has been studied as a recombinant protein since the 1980 s, detailed structural understanding of its catalytic functions awaited high resolution structures of functional IN-DNA complexes or intasomes, initially obtained in 2010 for the spumavirus prototype foamy virus(PFV). Since then, two additional retroviral intasome structures, from the α-retrovirus Rous sarcoma virus(RSV) and β-retrovirus mouse mammary tumor virus(MMTV), have emerged. Here, we briefly review the history of IN structural biology prior to the intasome era, and then compare the intasome structures of PFV, MMTV and RSV in detail. Whereas the PFV intasome is characterized by a tetrameric assembly of IN around the viral DNA ends, the newer structures harbor octameric IN assemblies. Although the higher order architectures of MMTV and RSV intasomes differ from that of the PFV intasome, they possess remarkably similar intasomal core structures. Thus, retroviral integration machineries have adapted evolutionarily to utilize disparate IN elements to construct convergent intasome core structures for catalytic function.     

Accuracy improvement in protein complex prediction from protein interaction networks by refining cluster overlaps

Background

Recent computational techniques have facilitated analyzing genome-wide protein-protein interaction data for several model organisms. Various graph-clustering algorithms have been applied to protein interaction networks on the genomic scale for predicting the entire set of potential protein complexes. In particular, the density-based clustering algorithms which are able to generate overlapping clusters, i.e. the clusters sharing a set of nodes, are well-suited to protein complex detection because each protein could be a member of multiple complexes. However, their accuracy is still limited because of complex overlap patterns of their output clusters.

Results

We present a systematic approach of refining the overlapping clusters identified from protein interaction networks. We have designed novel metrics to assess cluster overlaps: overlap coverage and overlapping consistency. We then propose an overlap refinement algorithm. It takes as input the clusters produced by existing density-based graph-clustering methods and generates a set of refined clusters by parameterizing the metrics. To evaluate protein complex prediction accuracy, we used the f-measure by comparing each refined cluster to known protein complexes. The experimental results with the yeast protein-protein interaction data sets from BioGRID and DIP demonstrate that accuracy on protein complex prediction has increased significantly after refining cluster overlaps.

Conclusions

The effectiveness of the proposed cluster overlap refinement approach for protein complex detection has been validated in this study. Analyzing overlaps of the clusters from protein interaction networks is a crucial task for understanding of functional roles of proteins and topological characteristics of the functional systems.

     

SCOPmap: Automated assignment of protein structures to evolutionary superfamilies

Background  

Inference of remote homology between proteins is very challenging and remains a prerogative of an expert. Thus a significant drawback to the use of evolutionary-based protein structure classifications is the difficulty in assigning new proteins to unique positions in the classification scheme with automatic methods. To address this issue, we have developed an algorithm to map protein domains to an existing structural classification scheme and have applied it to the SCOP database.     

Common evolutionary trends for SINE RNA structures

Short interspersed elements (SINEs) and long interspersed elements (LINEs) are transposable elements in eukaryotic genomes that mobilize through an RNA intermediate. Understanding their evolution is important because of their impact on the host genome. Most eukaryotic SINEs are ancestrally related to tRNA genes, although the typical tRNA cloverleaf structure is not apparent for most SINE consensus RNAs. Using a cladistic method where RNA structural components were coded as polarized and ordered multistate characters, we showed that related structural motifs are present in most SINE RNAs from mammals, fishes and plants, suggesting common selective constraints imposed at the SINE RNA structural level. Based on these results, we propose a general multistep model for the evolution of tRNA-related SINEs in eukaryotes.     

An evolutionary bridge to a new protein fold

Arc repressor bearing the N11L substitution (Arc-N11L) is an evolutionary intermediate between the wild type protein, in which the region surrounding position 11 forms a beta-sheet, and a double mutant 'switch Arc', in which this region is helical. Here, Arc-N11L is shown to be able to adopt either the wild type or mutant conformations. Exchange between these structures occurs on the millisecond time scale in a dynamic equilibrium in which the relative populations of each fold depend on temperature, solvent conditions and ligand binding. The N11L mutation serves as an evolutionary bridge from the beta-sheet to the helical fold because in the mutant, Leu is an integral part of the hydrophobic core of the new structure but can also occupy a surface position in the wild type structure. Conversely, the polar Asn 11 side chain serves as a negative design element in wild type Arc because it cannot be incorporated into the core of the mutant fold.     

A generic protein purification method for protein complex characterization and proteome exploration.

We have developed a generic procedure to purify proteins expressed at their natural level under native conditions using a novel tandem affinity purification (TAP) tag. The TAP tag allows the rapid purification of complexes from a relatively small number of cells without prior knowledge of the complex composition, activity, or function. Combined with mass spectrometry, the TAP strategy allows for the identification of proteins interacting with a given target protein. The TAP method has been tested in yeast but should be applicable to other cells or organisms.     

RING: networking interacting residues, evolutionary information and energetics in protein structures

MOTIVATION: Residue interaction networks (RINs) have been used in the literature to describe the protein 3D structure as a graph where nodes represent residues and edges physico-chemical interactions, e.g. hydrogen bonds or van-der-Waals contacts. Topological network parameters can be calculated over RINs and have been correlated with various aspects of protein structure and function. Here we present a novel web server, RING, to construct physico-chemically valid RINs interactively from PDB files for subsequent visualization in the Cytoscape platform. The additional structure-based parameters secondary structure, solvent accessibility and experimental uncertainty can be combined with information regarding residue conservation, mutual information and residue-based energy scoring functions. Different visualization styles are provided to facilitate visualization and standard plugins can be used to calculate topological parameters in Cytoscape. A sample use case analyzing the active site of glutathione peroxidase is presented. AVAILABILITY: The RING server, supplementary methods, examples and tutorials are available for non-commercial use at URL: http://protein.bio.unipd.it/ring/.     

A method of searching for amphipathic structures in protein sequences

A simple method for searching amphipathic helices based on estimation of correlation between hydrophobicity distribution and periodic function is proposed. The method was examined in a series of proteins with known T-cell epitopes, which are mostly amphipathic helices. The predictive power of the method is discussed.     

An integrative and practical evolutionary optimization for a complex, dynamic model of biological networks

Computer simulation is an important technique to capture the dynamics of biochemical networks. Numerical optimization is the key to estimate the values of kinetic parameters so that the dynamic model reproduces the behaviors of the existing experimental data. It is required to develop general strategies for the optimization of complex biochemical networks with a huge space of search parameters, under the condition that kinetic and quantitative data are hardly available. We propose an integrative and practical strategy for optimizing a complex dynamic model by using qualitative and incomplete experimental data. The key technologies are the divide and conquer method for reducing the search space, handling of multiple objective functions representing different types of biological behaviors, and design of rule-based objective functions that are suitable for qualitative and error-prone experimental data. This strategy is applied to optimizing a dynamic model of the yeast cell cycle to demonstrate the feasibility of it.     

An accurate,sensitive, and scalable method to identify functional sites in protein structures

Functional sites determine the activity and interactions of proteins and as such constitute the targets of most drugs. However, the exponential growth of sequence and structure data far exceeds the ability of experimental techniques to identify their locations and key amino acids. To fill this gap we developed a computational Evolutionary Trace method that ranks the evolutionary importance of amino acids in protein sequences. Studies show that the best-ranked residues form fewer and larger structural clusters than expected by chance and overlap with functional sites, but until now the significance of this overlap has remained qualitative. Here, we use 86 diverse protein structures, including 20 determined by the structural genomics initiative, to show that this overlap is a recurrent and statistically significant feature. An automated ET correctly identifies seven of ten functional sites by the least favorable statistical measure, and nine of ten by the most favorable one. These results quantitatively demonstrate that a large fraction of functional sites in the proteome may be accurately identified from sequence and structure. This should help focus structure-function studies, rational drug design, protein engineering, and functional annotation to the relevant regions of a protein.     

Recombination estimation under complex evolutionary models with the coalescent composite-likelihood method

The composite-likelihood estimator (CLE) of the population recombination rate considers only sites with exactly two alleles under a finite-sites mutation model (McVean, G. A. T., P. Awadalla, and P. Fearnhead. 2002. A coalescent-based method for detecting and estimating recombination from gene sequences. Genetics 160:1231-1241). While in such a model the identity of alleles is not considered, the CLE has been shown to be robust to minor misspecification of the underlying mutational model. However, there are many situations where the putative mutation and demographic history can be quite complex. One good example is rapidly evolving pathogens, like HIV-1. First we evaluated the performance of the CLE and the likelihood permutation test (LPT) under more complex, realistic models, including a general time reversible (GTR) substitution model, rate heterogeneity among sites (Gamma), positive selection, population growth, population structure, and noncontemporaneous sampling. Second, we relaxed some of the assumptions of the CLE allowing for a four-allele, GTR + Gamma model in an attempt to use the data more efficiently. Through simulations and the analysis of real data, we concluded that the CLE is robust to severe misspecifications of the substitution model, but underestimates the recombination rate in the presence of exponential growth, population mixture, selection, or noncontemporaneous sampling. In such cases, the use of more complex models slightly increases performance in some occasions, especially in the case of the LPT. Thus, our results provide for a more robust application of the estimation of recombination rates.