The future of protein secondary structure prediction accuracy

In order to investigate the path of medical education in Iran, indicators of medical education were searched from 1970 to 1994. There have been rises in the number of educational institutions from 10 to 46; student admissions in programmes of medical sciences from 1387 to 18,141; medical student admissions from 632 to 3630; teaching staff from 1573 to 7979; and teaching-bed to student ratio from 1.05 to 2.08. The numbers of students in clinical specialty and MS degrees have increased, and various programmes in clinical sub-specialty and PhD degrees have been initiated. The quality of medical education has improved with increasing field and ambulatory care training, with more emphasis on teaching preventive medicine and a significant rise in the research activities. Most qualitative and quantitative progress has been achieved following the establishment of a joint Ministry of Health and Medical Education in 1985. The results of this review demonstrate the success of Iran in upgrading medical education by the unification of health services and medical education in one ministry.     

Gene structure prediction using information on homologous protein sequence

In this paper a new approach for the prediction of protein coding gene structures is described. The principal scheme of prediction is as follows: first, the exons with the best potential are predicted in a sequence with unknown functions and a list of potential amino acid fragments coded by these exons is formed. Second, testing the homology between each amino acid fragment from the list and proteins from the SWISS-PROT database of amino acid sequences. One protein with the best homology is chosen out of all the homologous sequences. Third, reconstruction of the exon-intron structure, basing it on its homology with the chosen protein sequences. The method was tested on an independent control set (20 genes). The results were as follows: 21% of real exons were lost and 3% of non-real exons were found. This system can be used to refine the results of gene prediction systems, especially if highly homologous proteins are found in the amino acid sequence database.     

Using evolutionary trees in protein secondary structure prediction and other comparative sequence analyses

Previously proposed methods for protein secondary structure prediction from multiple sequence alignments do not efficiently extract the evolutionary information that these alignments contain. The predictions of these methods are less accurate than they could be, because of their failure to consider explicitly the phylogenetic tree that relates aligned protein sequences. As an alternative, we present a hidden Markov model approach to secondary structure prediction that more fully uses the evolutionary information contained in protein sequence alignments. A representative example is presented, and three experiments are performed that illustrate how the appropriate representation of evolutionary relatedness can improve inferences. We explain why similar improvement can be expected in other secondary structure prediction methods and indeed any comparative sequence analysis method.     

Update on protein structure prediction: results of the 1995 IRBM workshop

Computational tools for protein structure prediction are of great interest to molecular, structural and theoretical biologists due to a rapidly increasing number of protein sequences with no known structure. In October 1995, a workshop was held at IRBM to predict as much as possible about a number of proteins of biological interest using ab initio prediction of fold recognition methods. 112 protein sequences were collected via an open invitation for target submissions. 17 were selected for prediction during the workshop and for 11 of these a prediction of some reliability could be made. We believe that this was a worthwhile experiment showing that the use of a range of independent prediction methods and thorough use of existing databases can lead to credible and useful ab initio structure predictions.     

Effects of relative band intensity on prediction of protein secondary structure from CD

Increasing the magnitude of a protein CD spectrum obviously increases the magnitude of each predicted secondary structure by the same amount. However, increasing the magnitude of the negative, long-wave-length portion of a protein CD spectrum usually has the opposite effect from increasing the positive, short-wave-length portion. Thus small distortions in the CD spectra of proteins at short wavelength can have a significant effect on the analysis for secondary structure. This measurement error and its effect on the analysis are systematically investigated for 16 proteins of known structure. The results demonstrate that a two-point calibration of a CD instrument is mandatory to avoid serious errors when estimating secondary structure from protein CD spectra.     

Evaluation of protein 3-D structure prediction: comparison of modelled and X-ray structure of an alkaline serine protease

We describe the modelling of the structure of the highly alkaline subtilisin protease OPTICLEAN from Bacillus alcalophilus. The model was developed through modelling by homology. We used the structure of subtilisin Carlsberg from the Brookhaven protein databank (entry 1CSE) as start structure. Amino acid changes and deletions were performed with the graphic protein design program BRAGI. Force field calculations and molecular dynamic simulations were made with AMBER 3.0 on a Multiflow TRACE 14/300. The comparison of the model and the later solved X-ray structure of OPTICLEAN shows a high similarity between the two structures, but there were also remarkable deviations between the two structures in some loop regions. The comparison shows that the deviations are due to difficulties in the prediction of correct main chain torsion angles of the prolines and the selection of correct loops in deletion or insertion regions. Strategies to avoid these mistakes are discussed.     

Multiple protein structure alignment

A method was developed to compare protein structures and to combine them into a multiple structure consensus. Previous methods of multiple structure comparison have only concatenated pairwise alignments or produced a consensus structure by averaging coordinate sets. The current method is a fusion of the fast structure comparison program SSAP and the multiple sequence alignment program MULTAL. As in MULTAL, structures are progressively combined, producing intermediate consensus structures that are compared directly to each other and all remaining single structures. This leads to a hierarchic "condensation," continually evaluated in the light of the emerging conserved core regions. Following the SSAP approach, all interatomic vectors were retained with well-conserved regions distinguished by coherent vector bundles (the structural equivalent of a conserved sequence position). Each bundle of vectors is summarized by a resultant, whereas vector coherence is captured in an error term, which is the only distinction between conserved and variable positions. Resultant vectors are used directly in the comparison, which is weighted by their error values, giving greater importance to the matching of conserved positions. The resultant vectors and their errors can also be used directly in molecular modeling. Applications of the method were assessed by the quality of the resulting sequence alignments, phylogenetic tree construction, and databank scanning with the consensus. Visual assessment of the structural superpositions and consensus structure for various well-characterized families confirmed that the consensus had identified a reasonable core.     

Structural thermodynamics: prediction of protein stability and protein binding affinities

It has been demonstrated that the prognosis of ovarian cancer is influenced by the dose intensity of cytotoxic treatment. The impact of received dose intensity of platinum-based combination chemotherapy on disease outcome was analysed in 226 stage III-IV ovarian cancer patients entered into two prospective randomised trials. All patients received either cisplatin or carboplatin and cyclophosphamide with or without doxorubicin for six courses after primary surgery. The impact of the received dose intensity of each drug (RDI), the average received dose intensity of the treatment regimen (ARDI) and the relative total drug dose (RTD) on progression-free survival (PFS) and survival were analysed. In the 198 patients receiving the full six courses of treatment, RDI of cisplatin or carboplatin, ARDI and RTD were > 0.76 in 74.2, 61.1 and 65.1% of cases, respectively. Although the differences were not significant, pathological complete response was more frequently observed in the group of patients with ARDI < 0.75, whereas the partial response rate was higher in the ARDI > or = 0.76 group. Median survival and PFS were 19 and 13 months; 22 and 10 months; 23 and 13 months for the groups of patients receiving chemotherapy at a ARDI of < 0.75, > or = 0.76-0.99 and > 1.00, respectively (P = not significant). It appears that modest dose modifications and brief treatment delays during first-line platinum-based chemotherapy do not affect response rate, survival and PFS in advanced ovarian cancer patients.     

alpha-Amylases from Thermoactinomyces vulgaris: characteristics, primary structure and structure prediction

Two amylolytic active protein fractions (named alpha-amylase 1 and alpha-amylase 2) were isolated from the bacterium Thermoactinomyces vulgaris strain 94-2A. alpha-Amylase 1 had a molecular mass of 51.6 kDa, whereas alpha-amylase 2 consists of two fragments which have molecular masses of 17.0 and 34.6 kDa, respectively. These two fragments are products from a proteolytic cleavage of alpha-amylase 1 at amino acid position 303 (tryptophan) by a serine protease (thermitase) which is also produced by T. vulgaris. The purified alpha-amylase 1 and 2 follow the Michaelis-Menten kinetics in the presence of starch as substrate with Km values of 1.37 +/- 0.07 and 1.29 +/- 0.18 mg/mL, respectively. In effect they differ in their stability characteristics. The amino acid sequence of alpha-amylase from T. vulgaris derived from DNA sequence (1) was compared with those of other alpha-amylases. It reveals high homologies to alpha-amylases from other microorganisms (e.g. B. polymyxa, A. oryzae, S. occidentalis and S. fibuligera). A three-dimensional structure model for alpha-amylase 1 on the basis of the 3 A X-ray structure of Taka-amylase was constructed.     

JPred: a consensus secondary structure prediction server

An interactive protein secondary structure prediction Internet server is presented. The server allows a single sequence or multiple alignment to be submitted, and returns predictions from six secondary structure prediction algorithms that exploit evolutionary information from multiple sequences. A consensus prediction is also returned which improves the average Q3 accuracy of prediction by 1% to 72.9%. The server simplifies the use of current prediction algorithms and allows conservation patterns important to structure and function to be identified. AVAILABILITY: http://barton.ebi.ac.uk/servers/jpred.h tml CONTACT: geoff@ebi.ac.uk     

Protein secondary structure prediction using two-level case-based reasoning

We have developed a two-level case-based reasoning architecture for predicting protein secondary structure. The central idea is to break the problem into two levels: (i) reasoning at the object (protein) level and using the global information from this level to focus on a more restricted problem space; (ii) decomposing objects into pieces (segments) and reasoning at the level of internal structures. As a last step to the procedure, inferences from the parts of the internal structure are synthesized into predictions about global structure. The architecture has been developed and tested on a commonly used data set with 69.5% predictive accuracy. It was then tested on a new data set with 68.2% accuracy. With additional tuning, over 70% accuracy was achieved. In addition, a series of experiments were conducted to test various aspects of the method and the results are informative.     

An intriguing controversy over protein structural class prediction

A recent report by Bahar et al. [(1997), Proteins 29, 172-185] indicates that the coupling effects among different amino acid components as originally formulated by K. C. Chou [(1995), Proteins 21, 319-344] are important for improving the prediction of protein structural classes. These authors have further proposed a compact lattice model to illuminate the physical insight contained in the component-coupled algorithm. However, a completely opposite result was concluded by Eisenhaber et al. [(1996), Proteins 25, 169 179], using a different dataset constructed according to their definition. To address such an intriguing controversy, tests were conducted by various approaches for the datasets from an objective database, the SCOP database [Murzin et al. (1995), J. Mol. Biol. 247, 536-540]. The results obtained by both self-consistency and jackknife tests indicate that the overall rates of correct prediction by the algorithm incorporating the coupling effect among different amino acid components are significantly higher than those by the algorithms without counting such an effect. This is fully consistent with the physical reality that the folding of a protein is the result of a collective interaction among its constituent amino acid residues, and hence the coupling effects of different amino acid components must be incorporated in order to improve the prediction quality. It was found by a revisiting the calculation procedures by Eisenhaber et al. that there was a conceptual mistake in constructing the structural class datasets and a systematic mistake in applying the component-coupled algorithm. These findings are informative for understanding and utilizing the component-coupled algorithm to study the structural classes of proteins.     

Computational methods for the prediction of protein folds

Osteoblasts and bone tissue of the mandibular and maxillary alveolar processes substantially differ from osteoblasts and bone in other parts of the skeleton. These differences are apparent during embryonic development, maturation, and aging of these bones. The cellular and molecular basis for these differences is still not clear, but it is unfolding at record speed.     

Knowledge-based protein secondary structure assignment

We have developed an automatic algorithm STRIDE for protein secondary structure assignment from atomic coordinates based on the combined use of hydrogen bond energy and statistically derived backbone torsional angle information. Parameters of the pattern recognition procedure were optimized using designations provided by the crystallographers as a standard-of-truth. Comparison to the currently most widely used technique DSSP by Kabsch and Sander (Biopolymers 22:2577-2637, 1983) shows that STRIDE and DSSP assign secondary structural states in 58 and 31% of 226 protein chains in our data sample, respectively, in greater agreement with the specific residue-by-residue definitions provided by the discoverers of the structures while in 11% of the chains, the assignments are the same. STRIDE delineates every 11th helix and every 32nd strand more in accord with published assignments.     

Secondary structure prediction of the hemagglutinin-neuraminidase from a porcine rubulavirus

The Hemagglutinin-Neuraminidase (HN) from 'La Piedad, Michoacan' porcine rubulavirus (LPMV) interacts specifically with NeuAc alpha 2,3 lactose residues on the target cell. In this work we report the secondary structure of this protein, determined with five different theoretical algorithms. Results indicate that the HN protein is organized in: an intracellular region (from amino acid 1 to 25); in a beta-strand transmembrane region (residue 26 to 47), typically hydrophobic, rigid and solvent inaccessible; and extracellular region (48 to 576), which possesses hemagglutinating and neuraminidase activity. The secondary structure in this region is organized in a beta-loop-beta alternated with few alpha-helices. Regions with structural and functional implications were determined by pattern search and multiple alignment of the HN from LPM with 12 rubulaviruses and paramyxoviruses HN sequences. The low diversity observed among the HN sequences evaluated indicates that in general the structural organization of the protein, and in particular its sugar binding domain, is closely related among both genera, thus suggesting that the sugar binding domain is well preserved through evolution.     

Contemporary approaches to protein structure classification

In a similar manner to sequence database searching, it is also possible to compare three-dimensional protein structure. Such methods can be extremely useful because a structural similarity may represent a distant evolutionary relationship that is undetectable by sequence analysis. In this review, we summarise the most popular structure comparison methods, show how they can be used for database searching, and then describe some of the most advanced attempts to develop comprehensive protein structure classifications. With such data, it is possible to identify distant evolutionary relationships, provide libraries of unique folds for structure prediction, estimate the total number of folds that exist, and investigate the preference for certain types of structures over others.