Secondary structure prediction with support vector machines

Motivation: A new method that uses support vector machines (SVMs) to predict protein secondary structure is described and evaluated. The study is designed to develop a reliable prediction method using an alternative technique and to investigate the applicability of SVMs to this type of bioinformatics problem. Methods: Binary SVMs are trained to discriminate between two structural classes. The binary classifiers are combined in several ways to predict multi-class secondary structure. Results: The average three-state prediction accuracy per protein (Q3) is estimated by cross-validation to be 77.07 ± 0.26% with a segment overlap (Sov) score of 73.32 ± 0.39%. The SVM performs similarly to the 'state-of-the-art' PSIPRED prediction method on a non-homologous test set of 121 proteins despite being trained on substantially fewer examples. A simple consensus of the SVM, PSIPRED and PROFsec achieves significantly higher prediction accuracy than the individual methods. Availability: The SVM classifier is available from the authors. Work is in progress to make the method available on-line and to integrate the SVM predictions into the PSIPRED server.

Rapid protein domain assignment from amino acid sequence using predicted secondary structure

The elucidation of the domain content of a given protein sequence in the absence of determined structure or significant sequence homology to known domains is an important problem in structural biology. Here we address how successfully the delineation of continuous domains can be accomplished in the absence of sequence homology using simple baseline methods, an existing prediction algorithm (Domain Guess by Size), and a newly developed method (DomSSEA). The study was undertaken with a view to measuring the usefulness of these prediction methods in terms of their application to fully automatic domain assignment. Thus, the sensitivity of each domain assignment method was measured by calculating the number of correctly assigned top scoring predictions. We have implemented a new continuous domain identification method using the alignment of predicted secondary structures of target sequences against observed secondary structures of chains with known domain boundaries as assigned by Class Architecture Topology Homology (CATH). Taking top predictions only, the success rate of the method in correctly assigning domain number to the representative chain set is 73.3%. The top prediction for domain number and location of domain boundaries was correct for 24% of the multidomain set (±20 residues). These results have been put into context in relation to the results obtained from the other prediction methods assessed

Benchmarking secondary structure prediction for fold recognition

If secondary structure predictions are to be incorporated into fold recognition methods, an assessment of the effect of specific types of errors in predicted secondary structures on the sensitivity of fold recognition should be carried out. Here, we present a systematic comparison of different secondary structure prediction methods by measuring frequencies of specific types of error. We carry out an evaluation of the effect of specific types of error on secondary structure element alignment (SSEA), a baseline fold recognition method. The results of this evaluation indicate that missing out whole helix or strand elements, or predicting the wrong type of element, is more detrimental than predicting the wrong lengths of elements or overpredicting helix or strand. We also suggest that SSEA scoring is an effective method for assessing accuracy of secondary structure prediction and perhaps may also provide a more appropriate assessment of the “usefulness” and quality of predicted secondary structure, if secondary structure alignments are to be used in fold recognition.

Secondary structure analysis of the dissimilatory sulphite reductase in Desulfovibrio desulfuricans

The complete sequences of the dsrA and dsrB genes coding for the α− and β−subunits, respectively, of the sulphite reductase enzyme in Desulfovibrio desulfuricans were determined. Analyses of the amino acid sequences indicated a number of serohaem/Fe4S4 binding consensus sequences whilst predictive secondary structure analysis revealed a similar pattern of α−helix and β−strand structures between the two subunits which was indicative of gene duplication.

Characterisation of chicken riboflavin carrier protein gene structure and promoter regulation by estrogen

Estrogen is an important steroid hormone that mediates most of its effects on regulation of gene expression by binding to intracellular receptors. The consensus estrogen response element (ERE) is a 13 bp palindromic inverted repeat with a three nucleotide spacer. However, several reports suggest that many estrogen target genes are regulated by diverse elements, such as imperfect EREs and ERE half sites (ERE 1/2),which are either the proximal or the distal half of the palindrome. To gain more insight into ERE half site-mediated gene regulation, we used a region from the estrogen-regulated chicken riboflavin carrier protein (RCP) gene promoter that contains ERE half sites. Using moxestrol, an analogue of estrogen and transient transfection of deletion and mutation containing RCP promoter/reporter constructs in chicken hepatoma (LMH2A) cells, we identified an estrogen response unit (ERU) composed of two consensus ERE 1/2 sites and one non-consensus ERE 1/2 site. Mutation of any of these sites within this ERU abolishes moxestrol response. Further, the ERU is able to confer moxestrol responsiveness to a heterologous promoter. Interestingly, RCP promoter is regulated by moxestrol in estrogen responsive human MCF-7 cells, but not in other cell lines such as NIH3T3 and HepG2 despite estrogen receptor-alpha (ER-�) co transfection. Electrophoretic mobility shift assays (EMSAs) with promoter regions encompassing the half sites and nuclear extracts from LMH2A cells show the presence of a moxestrol-induced complex that is abolished by a polyclonal anti-ER� antibody. Surprisingly, estrogen receptor cannot bind to these promoter elements in isolation. Thus, there appears to be a definite requirement for some other factor(s) in addition to estrogen receptor, for the generation of a suitable response of this promoter to estrogen. Our studies therefore suggest a novel mechanism of gene regulation by estrogen, involving ERE half sites without direct binding of ER to the cognate elements.

Protein structure prediction servers at University College London

A number of state-of-the-art protein structure prediction servers have been developed by researchers working in the Bioinformatics Unit at University College London. The popular PSIPRED server allows users to perform secondary structure prediction, transmembrane topology prediction and protein fold recognition. More recent servers include DISOPRED for the prediction of protein dynamic disorder and DomPred for domain boundary prediction.

Assembling novel protein folds from super-secondary structural fragments

The results of applying a fragment-based protein tertiary structure prediction method to the prediction of 14 CASP5 target domains are described. The method is based on the assembly of supersecondary structural fragments taken from highly resolved protein structures using a simulated annealing algorithm. A number of good predictions for proteins with novel folds were produced, although not always as the first model. For two fold recognition targets, FRAGFOLD produced the most accurate model in both cases, despite the fact that the predictions were not based on a template structure. Although clear progress has been made in improving FRAGFOLD since CASP4, the ranking of final models still seems to be the main problem that needs to be addressed before the next CASP experiment

The PSIPRED protein structure prediction server

The PSIPRED protein structure prediction server allows users to submit a protein sequence, perform a prediction of their choice and receive the results of the prediction both textually via e-mail and graphically via the web. The user may select one of three prediction methods to apply to their sequence: PSIPRED, a highly accurate secondary structure prediction method; MEMSAT 2, a new version of a widely used transmembrane topology prediction method; or GenTHREADER, a sequence profile based fold recognition method.

Layer-by-layer electrostatic entrapment of protein molecules on superparamagnetic nanoparticle: new strategy to enhance adsorption capacity and maintain biological activity

There is a recent interest to use inorganic-based magnetic nanoparticles as a vehicle to carry biomolecules for various biophysical applications, but direct attachment of the molecules is known to alter their conformation leading to attenuation in activity. In addition, surface immobilization has been limited to monolayer coverage. It is shown that alternate depositions of negatively charged protein molecules, typically bovine serum albumin (BSA) with a positively charged aminocarbohydrate template such as glycol chitosan (GC) on magnetic iron oxide nanoparticle surface as a colloid, are carried out under pH 7.4. Circular dichroism (CD) clearly reveals that the secondary structure of the entrapped BSA sequential depositions in this manner remains totally unaltered which is in sharp contrast to previous attempts. Probing the binding properties of the entrapped BSA using small molecules (Site I and Site II drug compounds) confirms for the first time the full retention of its biological activity as compared with native BSA, which also implies the ready accessibility of the entrapped protein molecules through the porous overlayers. This work clearly suggests a new method to immobilize and store protein molecules beyond monolayer adsorption on a magnetic nanoparticle surface without much structural alteration. This may find applications in magnetic recoverable enzymes or protein delivery.

Rapid model quality assessment for protein structure predictions using the comparison of multiple models without structural alignments

MOTIVATION: The accurate prediction of the quality of 3D models is a key component of successful protein tertiary structure prediction methods. Currently, clustering or consensus based Model Quality Assessment Programs (MQAPs) are the most accurate methods for predicting 3D model quality; however they are often CPU intensive as they carry out multiple structural alignments in order to compare numerous models. In this study, we describe ModFOLDclustQ - a novel MQAP that compares 3D models of proteins without the need for CPU intensive structural alignments by utilising the Q measure for model comparisons. The ModFOLDclustQ method is benchmarked against the top established methods in terms of both accuracy and speed. In addition, the ModFOLDclustQ scores are combined with those from our older ModFOLDclust method to form a new method, ModFOLDclust2, that aims to provide increased prediction accuracy with negligible computational overhead. RESULTS: The ModFOLDclustQ method is competitive with leading clustering based MQAPs for the prediction of global model quality, yet it is up to 150 times faster than the previous version of the ModFOLDclust method at comparing models of small proteins (<60 residues) and over 5 times faster at comparing models of large proteins (>800 residues). Furthermore, a significant improvement in accuracy can be gained over the previous clustering based MQAPs by combining the scores from ModFOLDclustQ and ModFOLDclust to form the new ModFOLDclust2 method, with little impact on the overall time taken for each prediction. AVAILABILITY: The ModFOLDclustQ and ModFOLDclust2 methods are available to download from: http://www.reading.ac.uk/bioinf/downloads/ CONTACT: l.j.mcguffin@reading.ac.uk.

Benchmarking consensus model quality assessment for protein fold recognition

Background: Selecting the highest quality 3D model of a protein structure from a number of alternatives remains an important challenge in the field of structural bioinformatics. Many Model Quality Assessment Programs (MQAPs) have been developed which adopt various strategies in order to tackle this problem, ranging from the so called "true" MQAPs capable of producing a single energy score based on a single model, to methods which rely on structural comparisons of multiple models or additional information from meta-servers. However, it is clear that no current method can separate the highest accuracy models from the lowest consistently. In this paper, a number of the top performing MQAP methods are benchmarked in the context of the potential value that they add to protein fold recognition. Two novel methods are also described: ModSSEA, which based on the alignment of predicted secondary structure elements and ModFOLD which combines several true MQAP methods using an artificial neural network. Results: The ModSSEA method is found to be an effective model quality assessment program for ranking multiple models from many servers, however further accuracy can be gained by using the consensus approach of ModFOLD. The ModFOLD method is shown to significantly outperform the true MQAPs tested and is competitive with methods which make use of clustering or additional information from multiple servers. Several of the true MQAPs are also shown to add value to most individual fold recognition servers by improving model selection, when applied as a post filter in order to re-rank models. Conclusion: MQAPs should be benchmarked appropriately for the practical context in which they are intended to be used. Clustering based methods are the top performing MQAPs where many models are available from many servers; however, they often do not add value to individual fold recognition servers when limited models are available. Conversely, the true MQAP methods tested can often be used as effective post filters for re-ranking few models from individual fold recognition servers and further improvements can be achieved using a consensus of these methods.

Use of a novel Hill-climbing genetic algorithm in protein folding simulations

We have developed a novel Hill-climbing genetic algorithm (GA) for simulation of protein folding. The program (written in C) builds a set of Cartesian points to represent an unfolded polypeptide's backbone. The dihedral angles determining the chain's configuration are stored in an array of chromosome structures that is copied and then mutated. The fitness of the mutated chain's configuration is determined by its radius of gyration. A four-helix bundle was used to optimise simulation conditions, and the program was compared with other, larger, genetic algorithms on a variety of structures. The program ran 50% faster than other GA programs. Overall, tests on 100 non-redundant structures gave comparable results to other genetic algorithms, with the Hill-climbing program running from between 20 and 50% faster. Examples including crambin, cytochrome c, cytochrome B and hemerythrin gave good secondary structure fits with overall alpha carbon atom rms deviations of between 5 and 5.6 Angstrom with an optimised hydrophobic term in the fitness function. (C) 2003 Elsevier Ltd. All rights reserved.

The DISOPRED server for the prediction of protein disorder

Dynamically disordered regions appear to be relatively abundant in eukaryotic proteomes. The DISOPRED server allows users to submit a protein sequence, and returns a probability estimate of each residue in the sequence being disordered. The results are sent in both plain text and graphical formats, and the server can also supply predictions of secondary structure to provide further structural information.

What are the baselines for protein fold recognition?

What constitutes a baseline level of success for protein fold recognition methods? As fold recognition benchmarks are often presented without any thought to the results that might be expected from a purely random set of predictions, an analysis of fold recognition baselines is long overdue. Given varying amounts of basic information about a protein—ranging from the length of the sequence to a knowledge of its secondary structure—to what extent can the fold be determined by intelligent guesswork? Can simple methods that make use of secondary structure information assign folds more accurately than purely random methods and could these methods be used to construct viable hierarchical classifications?

Ruminal dry matter and nitrogen degradation in relation to condensed tannin and protein molecular structures in sainfoin (Onobrychis viciifolia) and lucerne (Medicago sativa).

Sainfoin is a temperate legume that contains condensed tannins (CT), i.e. polyphenols that are able to bind proteins and thus reduce protein degradation in the rumen. A reduction in protein degradation in the rumen can lead to a subsequent increase in amino acid flow to the small intestine. The effects of CT in the rumen and the intestine differ according to the amount and structure of CT and the nature of the protein molecular structure. The objective of the present study was to investigate the degradability in the rumen of three CT-containing sainfoin varieties and CT-free lucerne in relation to CT content and structure (mean degree of polymerization, proportion of prodelphinidins and cis-flavanol units) and protein structure (amide I and II bands, ratio of amide I-to-amide II, α-helix, β-sheet, ratio of α-helix-to-β-sheet). Protein molecular structures were identified using Fourier transform/infrared-attenuated total reflectance (FT/IR-ATR) spectroscopy. The in situ degradability of three sainfoin varieties (Ambra, Esparcette and Villahoz) was studied in 2008, during the first growth cycle at two harvest dates (P1 and P2, i.e. 5 May and 2 June, respectively) and at one date (P3) during the second growth cycle (2 June) and these were compared with a tannin-free legume, lucerne (Aubigny). Loss of dry matter (DMDeg) and nitrogen (NDeg) in polyester bags suspended in the rumen was measured using rumen-fistulated cows. The NDeg of lucerne compared with sainfoin was 0·80 v. 0·77 at P1, 0·78 v. 0·65 at P2 and 0·79 v. 0·70 at P3, respectively. NDeg was related to the rapidly disappearing fraction (‘a’) fraction (r=0·76), the rate of degradation (‘c’) (r=0·92), to the content (r=−0·81) and structure of CT. However, the relationship between NDeg and the slowly disappearing fraction (‘b’) was weak. There was a significant effect of date and species×date, for NDeg and ‘a’ fraction. The secondary protein structure varied with harvest date (species×date) and was correlated with the fraction ‘b’. Both tannin and protein structures influenced the NDeg degradation. CT content and structure were correlated to the ‘a’ fraction and to the ‘c’. Features of the protein molecular secondary structure were correlated to the ‘b’ fraction.