Topology prediction for helical transmembrane proteins at 86% accuracy.

Previously, we introduced a neural network system predicting locations of transmembrane helices (HTMs) based on evolutionary profiles (PHDhtm, Rost B, Casadio R, Fariselli P, Sander C, 1995, Protein Sci 4:521-533). Here, we describe an improvement and an extension of that system. The improvement is achieved by a dynamic programming-like algorithm that optimizes helices compatible with the neural network output. The extension is the prediction of topology (orientation of first loop region with respect to membrane) by applying to the refined prediction the observation that positively charged residues are more abundant in extra-cytoplasmic regions. Furthermore, we introduce a method to reduce the number of false positives, i.e., proteins falsely predicted with membrane helices. The evaluation of prediction accuracy is based on a cross-validation and a double-blind test set (in total 131 proteins). The final method appears to be more accurate than other methods published: (1) For almost 89% (+/-3%) of the test proteins, all HTMs are predicted correctly. (2) For more than 86% (+/-3%) of the proteins, topology is predicted correctly. (3) We define reliability indices that correlate with prediction accuracy: for one half of the proteins, segment accuracy raises to 98%; and for two-thirds, accuracy of topology prediction is 95%. (4) The rate of proteins for which HTMs are predicted falsely is below 2% (+/-1%). Finally, the method is applied to 1,616 sequences of Haemophilus influenzae. We predict 19% of the genome sequences to contain one or more HTMs. This appears to be lower than what we predicted previously for the yeast VIII chromosome (about 25%).     

On the derivation of propensity scales for predicting exposed transmembrane residues of helical membrane proteins

Helical membrane proteins (HMPs) play a crucial role in diverse physiological processes. Given the difficulty in determining their structures by experimental techniques, it is desired to develop computational methods for predicting the burial status of transmembrane residues. Deriving a propensity scale for the 20 amino acids to be exposed to the lipid bilayer from known structures is central to developing such methods. A fundamental problem in this regard is what would be the optimal way of deriving propensity scales. Here, we show that this problem can be reformulated such that an optimal scale is straightforwardly obtained in an analytical fashion. The derived scale favorably compares with others in terms of both algorithmic optimality and practical prediction accuracy. It also allows interesting insights into the structural organization of HMPs. Furthermore, the presented approach can be applied to other bioinformatics problems of HMPs, too. All the data sets and programs used in the study and detailed primary results are available upon request.     

An unfolding story of helical transmembrane proteins

Reversible unfolding of helical transmembrane proteins could provide valuable information about the free energy of interaction between transmembrane helices. Thermal unfolding experiments suggest that this process for integral membrane proteins is irreversible. Chemical unfolding has been accomplished with organic acids, but the unfolding or refolding pathways involve irreversible steps. Sodium dodecyl sulfate (SDS) has been used as a perturbant to study reversible unfolding and refolding kinetics. However, the interpretation of these experiments is not straightforward. It is shown that the results could be explained by SDS binding without substantial unfolding. Furthermore, the SDS-perturbed state is unlikely to include all of the entropy terms involved in an unfolding process. Alternative directions for future research are suggested: fluorinated alcohols in homogeneous solvent systems, inverse micelles, and fragment association studies.     

Buried water molecules in helical transmembrane proteins

Buried water molecules (having no contact with bulk solvent) in 30 helical transmembrane (TM) protein structures were identified. The average amount of buried water in helical TM proteins is about the same as for all water-soluble (WS) proteins, but it is greater than the average for helical WS proteins. Buried waters in TM proteins make more polar contacts, and are more frequently found contacting helices than in WS proteins. The distribution of the buried water binding sites across the membrane profile shows that the sites to some extent reflect protein function. There is also evidence for asymmetry of the sites, with more in the extracellular half of the membrane. Many of the buried water contact sites are conserved across families of proteins, including family members having different functions. This suggests that at least some buried waters play a role in structural stabilization. Disease-causing mutations, which are known to result in misfolded TM proteins, occur at buried water contact sites at a higher than random frequency, which also supports a stabilizing role for buried water molecules.     

Wavelet change-point prediction of transmembrane proteins

MOTIVATION: A non-parametric method, based on a wavelet data-dependent threshold technique for change-point analysis, is applied to predict location and topology of helices in transmembrane proteins. A new propensity scale generated from a transmembrane helix database is proposed. RESULTS: We show that wavelet change-point performs well for smoothing hydropathy and transmembrane profiles generated using different scales. We investigate which wavelet bases and threshold functions are overall most appropriate to detect transmembrane segments. Prediction accuracy is based on the analysis of two data sets used as standard benchmarks for transmembrane prediction algorithms. The analysis of a test set of 83 proteins results in accuracy per segment equal to 98.2%; the analysis of a 48 proteins blind-test set, i.e. containing proteins not used to generate the propensity scales, results in accuracy per segment equal to 97.4%. We believe that this method can also be applied to the detection of boundaries of other patterns such as G + Cisochores and dot-plots. AVAILABILITY: The transmembrane database, TMALN and source code are available upon request from the authors.     

Towards genome-scale structure prediction for transmembrane proteins

In this paper we briefly review some of the recent progress made by ourselves and others in developing methods for predicting the structures of transmembrane proteins from amino acid sequence. Transmembrane proteins are an important class of proteins involved in many diverse biological functions, many of which have great impact in terms of disease mechanism and drug discovery. Despite their biological importance, it has proven very difficult to solve the structures of these proteins by experimental techniques, and so there is a great deal of pressure to develop effective methods for predicting their structure. The methods we discuss range from methods for transmembrane topology prediction to new methods for low resolution folding simulations in a knowledge-based force field. This potential is designed to reproduce the properties of the lipid bilayer. Our eventual aim is to apply these methods in tandem so that useful three-dimensional models can be built for a large fraction of the transmembrane protein domains in whole proteomes.     

A structural dissection of amino acid substitutions in helical transmembrane proteins

The evolution of protein folds is under strong constraints from their surrounding environment. Although folding in water‐soluble proteins is driven primarily by hydrophobic forces, the nature of the forces that determine the folding and stability of transmembrane proteins are still not fully understood. Furthermore, the chemically heterogeneous lipid bilayer has a non‐uniform effect on protein structure. In this article, we attempt to get an insight into the nature of this effect by examining the impact of various types of local structure environment on amino acid substitution, based on alignments of high‐resolution structures of polytopic helical transmembrane proteins combined with sequences of close homologs. Compared to globular proteins, burying amino acid sidechains, especially hydrophilic ones, led to a lower increase in conservation in both the lipid‐water interface region and the hydrocarbon core region. This observation is due to surface residues in HTM proteins especially in the HC region being relatively highly conserved, suggesting higher evolutionary constraints from their specific interactions with the surrounding lipid molecules. Polar and small residues, particularly Pro and Gly, show a noticeable increase in conservation as they are positioned more towards the centre of the membrane, which is consistent with their recognized key roles in structural stability. In addition, the examination of hydrogen bonds in the membrane environment identified some exposed hydrophilic residues being better conserved when not hydrogen‐bonded to other residues, supporting the importance of lipid‐protein sidechain interactions. The conclusions presented in this study highlight the distinct features of substitution matrices that take into account the membrane environment, and their potential role in improving sequence‐structure alignments of transmembrane proteins. Proteins 2010; © 2010 Wiley‐Liss, Inc.     

Prediction of the burial status of transmembrane residues of helical membrane proteins

Background  

Helical membrane proteins (HMPs) play a crucial role in diverse cellular processes, yet it still remains extremely difficult to determine their structures by experimental techniques. Given this situation, it is highly desirable to develop sequence-based computational methods for predicting structural characteristics of HMPs.     

Imaging mass spectrometry at cellular length scales

Imaging mass spectrometry (IMS) allows the direct investigation of both the identity and the spatial distribution of the molecular content directly in tissue sections, single cells and many other biological surfaces. In this protocol, we present the steps required to retrieve the molecular information from tissue sections using matrix-enhanced (ME) and metal-assisted (MetA) secondary ion mass spectrometry (SIMS) as well as matrix-assisted laser desorption/ionization (MALDI) IMS. These techniques require specific sample preparation steps directed at optimal signal intensity with minimal redistribution or modification of the sample analytes. After careful sample preparation, different IMS methods offer a unique discovery tool in, for example, the investigation of (i) drug transport and uptake, (ii) biological processing steps and (iii) biomarker distributions. To extract the relevant information from the huge datasets produced by IMS, new bioinformatics approaches have been developed. The duration of the protocol is highly dependent on sample size and technique used, but on average takes approximately 5 h.     

Structure prediction of membrane proteins

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 discussed and compared.     

Pattern recognition methods for prediction of helical region in proteins

An approach is described to the problem of predicting α-helical regions in proteins, based on some concepts of pattern recognition theory. It is shown that for each protein there are functions (signs) of the amino acid sequences of protein segments which permit the identification of α-helical or non-α-helical segments. Rules to recognize useful signs have been formulated.     

Structure elucidation of dimeric transmembrane domains of bitopic proteins

The interaction between transmembrane helices is of a great interest because it directly determines biological activity of a membrane protein. Either destroying or enhancing such interactions can result in many diseases related to dysfunction of different tissues in human body. One much studied form of membrane proteins known as bitopic protein is a dimer containing two membrane-spanning helices associating laterally. Establishing structure-function relationship as well as rational design of new types of drugs targeting membrane proteins requires precise structural information about this class of objects. At present time, to investigate spatial structure and internal dynamics of such transmembrane helical dimers, several strategies were developed based mainly on a combination of NMR spectroscopy, optical spectroscopy, protein engineering and molecular modeling. These approaches were successfully applied to homo- and heterodimeric transmembrane fragments of several bitopic proteins, which play important roles in normal and in pathological conditions of human organism.     

Structure elucidation of dimeric transmembrane domains of bitopic proteins

The interaction between transmembrane helices is of great interest because it directly determines biological activity of a membrane protein. Either destroying or enhancing such interactions can result in many diseases related to dysfunction of different tissues in human body. One much studied form of membrane proteins known as bitopic protein is a dimer containing two membrane-spanning helices associating laterally. Establishing structure-function relationship as well as rational design of new types of drugs targeting membrane proteins requires precise structural information about this class of objects. At present time, to investigate spatial structure and internal dynamics of such transmembrane helical dimers, several strategies were developed based mainly on a combination of NMR spectroscopy, optical spectroscopy, protein engineering and molecular modeling. These approaches were successfully applied to homo- and heterodimeric transmembrane fragments of several bitopic proteins, which play important roles in normal and in pathological conditions of human organism.Key words: bitopic proteins, transmembrane domain dimer, spatial structure, dynamics, protein-protein interactions, protein-membrane interactions, molecular modeling, NMR     

Hidden markov model for the prediction of transmembrane proteins using MATLAB

Since membranous proteins play a key role in drug targeting therefore transmembrane proteins prediction is active and challenging area of biological sciences. Location based prediction of transmembrane proteins are significant for functional annotation of protein sequences. Hidden markov model based method was widely applied for transmembrane topology prediction. Here we have presented a revised and a better understanding model than an existing one for transmembrane protein prediction. Scripting on MATLAB was built and compiled for parameter estimation of model and applied this model on amino acid sequence to know the transmembrane and its adjacent locations. Estimated model of transmembrane topology was based on TMHMM model architecture. Only 7 super states are defined in the given dataset, which were converted to 96 states on the basis of their length in sequence. Accuracy of the prediction of model was observed about 74 %, is a good enough in the area of transmembrane topology prediction. Therefore we have concluded the hidden markov model plays crucial role in transmembrane helices prediction on MATLAB platform and it could also be useful for drug discovery strategy. AVAILABILITY: The database is available for free at bioinfonavneet@gmail.comvinaysingh@bhu.ac.in.     

An all-atom force field for tertiary structure prediction of helical proteins

We have developed an all-atom free-energy force field (PFF01) for protein tertiary structure prediction. PFF01 is based on physical interactions and was parameterized using experimental structures of a family of proteins believed to span a wide variety of possible folds. It contains empirical, although sequence-independent terms for hydrogen bonding. Its solvent-accessible surface area solvent model was first fit to transfer energies of small peptides. The parameters of the solvent model were then further optimized to stabilize the native structure of a single protein, the autonomously folding villin headpiece, against competing low-energy decoys. Here we validate the force field for five nonhomologous helical proteins with 20-60 amino acids. For each protein, decoys with 2-3 A backbone root mean-square deviation and correct experimental Cbeta-Cbeta distance constraints emerge as those with the lowest energy.     

Alpha helical trans-membrane proteins: Enhanced prediction using a Bayesian approach

Membrane proteins, which constitute approximately 20% of most genomes, are poorly tractable targets for experimental structure determination, thus analysis by prediction and modelling makes an important contribution to their on-going study. Membrane proteins form two main classes: alpha helical and beta barrel trans-membrane proteins. By using a method based on Bayesian Networks, which provides a flexible and powerful framework for statistical inference, we addressed alpha-helical topology prediction. This method has accuracies of 77.4% for prokaryotic proteins and 61.4% for eukaryotic proteins. The method described here represents an important advance in the computational determination of membrane protein topology and offers a useful, and complementary, tool for the analysis of membrane proteins for a range of applications.     

Dung beetle movements at two spatial scales

To understand the dynamics of spatially structured populations, we need to know the level of movements at different spatial scales. This paper reports on Aphodius dung beetle movements at two scales: movements between dung pats within pastures, and movements between pastures. First, I test an assumption common to many recent models of spatially structured populations – that the probability of an individual moving between habitat patches decreases exponentially with distance. For dung beetles, I find sufficient evidence to reject this assumption. The distribution of dispersal distances was clearly leptokurtic, with more individuals moving short and long distances than expected on the basis of an exponential function. In contrast, the data were well described by a power function. I conclude that dung beetle movements include an element of non-randomness not captured by the simplistic exponential model. The power function offers a promising alternative, but the actual mechanisms behind the pattern need to be clarified. Second, I compare several species of Aphodius to each other. Although these species occur in the same network of habitat patches, their movement patterns are different enough to result in a mixture of different spatial population structures. Movements between pastures were more frequent the larger the species, the more specific its occurrence in relation to pat age, and the more specialized it is on cow dung and open pasture habitats. Within pastures, all species form "patchy" populations, with much movement among individual pats.     

Disturbance and diversity at two spatial scales

The spatial scale of disturbance is a factor potentially influencing the relationship between disturbance and diversity. There has been discussion on whether disturbances that affect local communities and create a mosaic of patches in different successional stages have the same effect on diversity as regional disturbances that affect the whole landscape. In a microcosm experiment with metacommunities of aquatic protists, we compared the effect of local and regional disturbances on the disturbance–diversity relationship. Local disturbances destroyed entire local communities of the metacommunity and required reimmigration from neighboring communities, while regional disturbances affected the whole metacommunity but left part of each local community intact. Both disturbance types led to a negative relationship between disturbance intensity and Shannon diversity. With strong local disturbance, this decrease in diversity was due to species loss, while strong regional disturbance had no effect on species richness but reduced the evenness of the community. Growth rate appeared to be the most important trait for survival after strong local disturbance and dominance after strong regional disturbance. The pattern of the disturbance–diversity relationship was similar for both local and regional diversity. Although local disturbances at least temporally increased beta diversity by creating a mosaic of differently disturbed patches, this high dissimilarity did not result in regional diversity being increased relative to local diversity. The disturbance–diversity relationship was negative for both scales of diversity. The flat competitive hierarchy and absence of a trade-off between competition and colonization ability are a likely explanation for this pattern.     

Predicting the topology of transmembrane helical proteins using mean burial propensity and a hidden-Markov-model-based method

Helices in membrane spanning regions are more tightly packed than the helices in soluble proteins. Thus, we introduce a method that uses a simple scale of burial propensity and a new algorithm to predict transmembrane helical (TMH) segments and a positive-inside rule to predict amino-terminal orientation. The method (the topology predictor of transmembrane helical proteins using mean burial propensity [THUMBUP]) correctly predicted the topology of 55 of 73 proteins (or 75%) with known three-dimensional structures (the 3D helix database). This level of accuracy can be reached by MEMSAT 1.8 (a 200-parameter model-recognition method) and a new HMM-based method (a 111-parameter hidden Markov model, UMDHMM(TMHP)) if they were retrained with the 73-protein database. Thus, a method based on a physiochemical property can provide topology prediction as accurate as those methods based on more complicated statistical models and learning algorithms for the proteins with accurately known structures. Commonly used HMM-based methods and MEMSAT 1.8 were trained with a combination of the partial 3D helix database and a 1D helix database of TMH proteins in which topology information were obtained by gene fusion and other experimental techniques. These methods provide a significantly poorer prediction for the topology of TMH proteins in the 3D helix database. This suggests that the 1D helix database, because of its inaccuracy, should be avoided as either a training or testing database. A Web server of THUMBUP and UMDHMM(TMHP) is established for academic users at http://www.smbs.buffalo.edu/phys_bio/service.htm. The 3D helix database is also available from the same Web site.