Sensitive methods for determining the relatedness of proteins with limited sequence homology

A database for the provisional identification of species using only genotypes: web-based genome profiling

BACKGROUND

For a long time one could not imagine being able to identify species on the basis of genotype only as there were no technological means to do so. But conventional phenotype-based identification requires much effort and a high level of skill, making it almost impossible to analyze a huge number of organisms, as, for example, in microbe-related biological disciplines. Comparative analysis of 16S rRNA has been changing the situation, however. We report here an approach that will allow rapid and accurate phylogenetic comparison of any unknown strain to all known type strains, enabling tentative assignments of strains to species. The approach is based on two main technologies: genome profiling and Internet-based databases.

RESULTS

A complete procedure for provisional identification of species using only their genomes is presented, using random polymerase chain reaction, temperature-gradient gel electrophoresis, image processing to generate 'species-identification dots' (spiddos) and data processing. A database website for this purpose was also constructed and operated successfully. The protocol was standardized to make the system reproducible and reliable. The overall methodology thus established has remarkable aspects in that it enables non-experts to obtain an initial species identification without a lot of effort and is self-developing; that is, species can be determined more definitively as the database is used more and accumulates more genome profiles.

CONCLUSIONS

We have devised a methodology that enables provisional identification of species on the basis of their genotypes only. It is most useful for microbe-related disciplines as they face the most serious difficulties in species identification.

Recombinant glutathione S-transferase/CD36 fusion proteins define an oxidized low density lipoprotein-binding domain

CD36 is a multifunctional cell-surface receptor that binds adhesion molecules such as thrombospondin-1 and collagen and modified lipids and/or lipoproteins. It participates in cellular uptake of photoreceptor outer segments and scavenging of apoptotic cells and oxidized low density lipoprotein (Ox-LDL). Recognition and internalization of Ox-LDL by mononuclear phagocytes may play an important role in the development of atherosclerotic lesions. We have utilized a series of recombinant bacterial glutathione S-transferase/CD36 fusion proteins that span nearly all of the CD36 molecule to characterize the structural domain on CD36 that recognizes Ox-LDL. We found that the Ox-LDL-binding domain is different from the thrombospondin-1-binding domain located at amino acids 93-120. A fusion protein containing the region extending from amino acids 5 to 143 formed specific, saturable, and reversible complexes with Ox-LDL. As with intact CD36, binding was blocked by excess unlabeled Ox-LDL and antibodies to CD36. The stoichiometry and affinity of the fusion protein for Ox-LDL were similar to those of the intact protein. We also demonstrated that this fusion protein competitively inhibited binding of Ox-LDL to purified platelet CD36 and to CD36 expressed on peripheral blood monocytes and CD36 cDNA-transfected melanoma cells. The use of smaller peptides and fusion proteins including those spanning amino acids 28-93 and 5-93 has further narrowed the binding site to a region from amino acids 28 to 93, although participation of a sequence in the noncontiguous region 120-155 cannot be excluded. This study, for the first time, demonstrates unique regions of the scavenger receptor CD36 that bind the Ox-LDL ligand. Our structural analysis of the receptor provides information as to potential control of the trafficking of modified lipoproteins into the blood vessel wall.

Statistics of sequence-structure threading

The past two years have seen the rapid development of new recognition methods for protein structure prediction. These algorithms 'thread' the sequence of one protein through the known structure of another, looking for an alignment that corresponds to an energetically favorable model structure. Because they are based on energy calculation, rather than evolutionary distance, these methods extend the possibility of structure prediction by comparative modeling to a larger class of new sequences, where similarity to known structures is recognizable by no other means. The strength of the evidence they offer should be judged by objective statistical tests, however, so as to rule out the possibility that favorable scores arise from chance factors such as similarity of length, composition, or the consideration of a large number of alternative alignments. Calculation of objective p-values by analytical means is not yet possible, but it would appear that approximate values may be obtained by simulation, as they are in gapped, global sequence alignment. We propose that the results of threading experiments should include Z-scores relative to the composition-corrected score distribution obtained for shuffled and optimally aligned sequences.

Protein structure prediction: recognition of primary, secondary, and tertiary structural features from amino acid sequence

This review attempts a critical stock-taking of the current state of the science aimed at predicting structural features of proteins from their amino acid sequences. At the primary structure level, methods are considered for detection of remotely related sequences and for recognizing amino acid patterns to predict posttranslational modifications and binding sites. The techniques involving secondary structural features include prediction of secondary structure, membrane-spanning regions, and secondary structural class. At the tertiary structural level, methods for threading a sequence into a mainchain fold, homology modeling and assigning sequences to protein families with similar folds are discussed. A literature analysis suggests that, to date, threading techniques are not able to show their superiority over sequence pattern recognition methods. Recent progress in the state of ab initio structure calculation is reviewed in detail. The analysis shows that many structural features can be predicted from the amino acid sequence much better than just a few years ago and with attendant utility in experimental research. Best prediction can be achieved for new protein sequences that can be assigned to well-studied protein families. For single sequences without homologues, the folding problem has not yet been solved.