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.     

Progress in protein structure prediction

If protein structure prediction methods are to make any impact on the impending onerous task of analyzing the large numbers of unknown protein sequences generated by the ongoing genome-sequencing projects, it is vital that they make the difficult transition from computational 'gedankenexperiments' to practical software tools. This has already happened in the field of comparative modelling and is currently happening in the threading field. Unfortunately, there is little evidence of this transition happening in the field of ab initio tertiary-structure prediction.     

HOMSTRAD: a database of protein structure alignments for homologous families

We describe a database of protein structure alignments for homologous families. The database HOMSTRAD presently contains 130 protein families and 590 aligned structures, which have been selected on the basis of quality of the X-ray analysis and accuracy of the structure. For each family, the database provides a structure-based alignment derived using COMPARER and annotated with JOY in a special format that represents the local structural environment of each amino acid residue. HOMSTRAD also provides a set of superposed atomic coordinates obtained using MNYFIT, which can be viewed with a graphical user interface or used for comparative modeling studies. The database is freely available on the World Wide Web at: http://www-cryst.bioc.cam. ac.uk/-homstrad/, with search facilities and links to other databases.     

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.     

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.     

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.     

Splice site prediction in Arabidopsis thaliana pre-mRNA by combining local and global sequence information

Artificial neural networks have been combined with a rule based system to predict intron splice sites in the dicot plant Arabidopsis thaliana. A two step prediction scheme, where a global prediction of the coding potential regulates a cutoff level for a local prediction of splice sites, is refined by rules based on splice site confidence values, prediction scores, coding context and distances between potential splice sites. In this approach, the prediction of splice sites mutually affect each other in a non-local manner. The combined approach drastically reduces the large amount of false positive splice sites normally haunting splice site prediction. An analysis of the errors made by the networks in the first step of the method revealed a previously unknown feature, a frequent T-tract prolongation containing cryptic acceptor sites in the 5' end of exons. The method presented here has been compared with three other approaches, GeneFinder, Gene-Mark and Grail. Overall the method presented here is an order of magnitude better. We show that the new method is able to find a donor site in the coding sequence for the jelly fish Green Fluorescent Protein, exactly at the position that was experimentally observed in A.thaliana transformants. Predictions for alternatively spliced genes are also presented, together with examples of genes from other dicots, monocots and algae. The method has been made available through electronic mail (NetPlantGene@cbs.dtu.dk), or the WWW at http://www.cbs.dtu.dk/NetPlantGene.html     

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.     

Protein structure and energy landscape dependence on sequence using a continuous energy function

We have recently described a new conformational search strategy for protein folding algorithms called the CGU (convex global underestimator) method. Here we use a simplified protein chain representation and a differentiable form of the Sun/Thomas/Dill energy function to test the CGU method. Standard search methods, such as Monte Carlo and molecular dynamics are slowed by kinetic traps. That is, the computer time depends more strongly on the shape of the energy landscape (dictated by the amino acid sequence) than on the number of degrees of freedom (dictated by the chain length). The CGU method is not subject to this limitation, since it explores the underside of the energy landscape, not the top. We find that the CGU computer time is largely independent of the monomer sequence for different chain folds and scales as O(n4) with chain length. By using different starting points, we show that the method appears to find global minima. Since we can currently find stable states of 36-residue chains in 2.4 hours, the method may be practical for small proteins.     

Gene organization and protein sequence of the small subunits of Schizosaccharomyces pombe RNA polymerase II

RNA polymerase II purified from the fission yeast Schizosaccharomyces pombe contains 10 different species of polypeptides. Previously, we cloned and sequenced both cDNA and the genes encoding the four large subunits, Rpb1, Rpb2, Rpb3 and Rpb5. Later, other groups isolated the genes for Rpb6 and Rpb12 and cDNA for Rpb10. Here, we cloned both cDNA and the genes encoding four small subunits, Rpb7, Rpb8, Rpb10 and Rpb11. These genes were found to encode Rpb7, Rpb8, Rpb10 and Rpb11 consisting of 172 (19,103 Da), 125 (14,300 Da), 71 (8276 Da) and 123 (14,127 Da) amino acid residues, respectively. All these four subunits are homologous to the corresponding subunits of Saccharomyces cerevisiae RNA polymerase II. The rpb7 gene contains one intron, whereas the rpb8, rpb10 and rpb11 genes contain two introns. Taken altogether, the gene organization and the predicted protein sequence have been determined for all 10 subunits of the S. pombe RNA polymerase II.     

Gene recognition via spliced sequence alignment

Gene recognition is one of the most important problems in computational molecular biology. Previous attempts to solve this problem were based on statistics, and applications of combinatorial methods for gene recognition were almost unexplored. Recent advances in large-scale cDNA sequencing open a way toward a new approach to gene recognition that uses previously sequenced genes as a clue for recognition of newly sequenced genes. This paper describes a spliced alignment algorithm and software tool that explores all possible exon assemblies in polynomial time and finds the multiexon structure with the best fit to a related protein. Unlike other existing methods, the algorithm successfully recognizes genes even in the case of short exons or exons with unusual codon usage; we also report correct assemblies for genes with more than 10 exons. On a test sample of human genes with known mammalian relatives, the average correlation between the predicted and actual proteins was 99%. The algorithm correctly reconstructed 87% of genes and the rare discrepancies between the predicted and real exon-intron structures were caused either by short (less than 5 amino acids) initial/terminal exons or by alternative splicing. Moreover, the algorithm predicts human genes reasonably well when the homologous protein is nonvertebrate or even prokaryotic. The surprisingly good performance of the method was confirmed by extensive simulations: in particular, with target proteins at 160 accepted point mutations (PAM) (25% similarity), the correlation between the predicted and actual genes was still as high as 95%.     

New information on the structure of mycobacteria

Recent progress about the mycobacterial structures have been realized and two major structures have been concerned: the genome and the cell wall. From these acquired new knowledge several lines of clinical research and diagnosis application emerged. Cloning and sequencing of several mycobacterial genes led to the development of diagnostic tools (DNA probes, PCR, finger printings of indated mycobacterial strains) and the potential detection of multiding resistant strains of M. tuberculosis. Genetic manipulations involving various mycobacterial genes do open the way for more precise molecular approaches concerning virulence factors involved in the pathophysiological understanding of mycobacterial diseases. Comparative physico-chemical and ultra-structural analysis of the mycobacterial cell wall evoked a highly complexed cell wall structure, constituted of a double lipidic layer linked to the peptidoglycan (PG). The first layer is constituted of mycolic acids that are linked to the PG by arabinogalactan, and to the superficial layer by hydrophobic interactions of glycolipids. The superficial layer is constituted of amphiphatic glycolipids, having a lipidic banal pole and a polysaccharidic apical pole. The knowledge of the mycobacterial cell wall structure opened the way of: the development of immunological diagnostic tools, being now days in clinical evaluation phase, a better approach for host-bacteria relationship study at the cellular level (macrophage, lymphocytes), and the understanding of the mode of action of antimycobacterial drugs such as isoniazid and ethambutol.     

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.     

Updated sequence information for TEM beta-lactamase genes

Thiopurine methyltransferase (TPMT) catalyses the S-methylation of thiopurine drugs such as 6-mercapto-purine, 6-thioguanine and azathioprine. TPMT activity is inherited as an autosomal co-dominant trait, and several mutations in the TPMT gene have been identified which correlate with a low activity phenotype. Although ethnic differences in TPMT activity have been described, population frequency analysis of TPMT alleles has not been well defined in different ethnic groups. The frequency of four allelic variants of the TPMT gene, TPMT*2, TPMT*3A, TPMT*3B and TPMT*3C were compared in British Caucasian (n = 199) and Ghanaian (n = 217) populations using PCR-RFLP and allele-specific PCR-based assays. TPMT*3C was found in 14.8% of Ghanaians (31 heterozygotes, one homozygote). The TPMT*2, TPMT*3A and TPMT*3B alleles were not detected in any of the Ghanaian samples analysed. In contrast, 10.1% of British subjects had variant alleles, consisting of TPMT*2 (n = 2), TPMT*3A (n = 17) and TPMT*3C (n = 1) alleles. The frequencies of mutant alleles in this study were 5.3 and 7.6% in British Caucasians and Ghanaians, respectively. Among Ghanaian tribes, Ewe subjects had a lower frequency of mutant alleles (5.9%) than Ga (13.2%) or Fanti (11.6%), although this did not reach statistical significance. This study provides the first analysis of TPMT mutant allele frequency in an African population and indicates that, unlike Caucasians, TPMT*3C is the most common allele in African subjects.     

Genetic evaluation by best linear unbiased prediction using marker and trait information in a multibreed population

Genetic evaluation by best linear unbiased prediction (BLUP) requires modeling genetic means, variances, and covariances. This paper presents theory to model means, variances, and covariances in a multibreed population, given marker and breed information, in the presence of gametic disequilibrium between the marker locus (ML) and linked quantitative trait locus (MQTL). Theory and algorithms are presented to construct the matrix of conditional covariances between relatives (Gv) for the MQTL effects in a multibreed population and to obtain the inverse of Gv efficiently. Theory presented here accounts for heterogeneity of variances among pure breeds and for segregation variances between pure breeds. A numerical example was used to illustrate how the theory and algorithms can be used for genetic evaluation by BLUP using marker and trait information in a multibreed population.     

General method for plasmid construction using homologous recombination

We describe a general method for plasmid assembly that uses yeast and extends beyond yeast-specific research applications. This technology exploits the homologous recombination, double-stranded break repair pathway in Saccharomyces cerevisiae to join DNA fragments. Synthetic, double-stranded "recombination linkers" were used to "subclone" a DNA fragment into a plasmid with > 80% efficiency. Quantitative data on the influence of DNA concentration and overlap length on the efficiency of recombination are presented. Using a simple procedure, plasmids were shuttled from yeast into E. coli for subsequent screening and large-scale plasmid preps. This simple method for plasmid construction has several advantages. (i) It bypasses the need for extensive PCR amplification and for purification, modification and/or ligation techniques routinely used for plasmid constructions. (ii) The method does not rely on available restriction sites, thus fragment and vector DNA can be joined within any DNA sequence. This enables the use of multifunctional cloning vectors for protein expression in mammalian cells, other yeast species, E. coli and other expression systems as discussed. (iii) Finally, the technology exploits yeast strains, plasmids and microbial techniques that are inexpensive and readily available.