Rejection diagnosis by endomyocardial biopsy (EMB)is invasive, expensive and variable. We investigated gene expression profiling of peripheral blood mononuclear cells (PBMC) to discriminate ISHLT grade 0 rejection (quiescence) from moderate/severe rejection (ISHLT ≥3A). Patients were followed prospectively with blood sampling at post-transplant visits. Biopsies were graded by ISHLT criteria locally and by three independent pathologists blinded to clinical data. Known alloimmune pathways and leukocyte microarrays identified 252 candidate genes for which real-time PCR assays were developed. An 11 gene realtime PCR test was derived from a training set (n = 145 samples, 107 patients) using linear discriminant analysis (LDA), converted into a score (0-40), and validated prospectively in an independent set (n = 63 samples, 63 patients). The test distinguished biopsydefined moderate/severe rejection from quiescence (p = 0.0018) in the validation set, and had agreement of 84% (95% CI 66% C94%) with grade ISHLT ≥3A rejection. Patients >1 year post-transplant with scores below 30 (approximately 68% of the study population) are very unlikely to have grade ≥3A rejection (NPV = 99.6%). Gene expression testing can detect absence of moderate/severe rejection, thus avoiding biopsy in certain clinical settings. Additional clinical experience is needed to establish the role of molecular testing for clinical event prediction and immunosuppression management.

We have previously shown that varying the N-terminal amino acid in alpha-helical peptides can cause large variations in helix content (Chakrabartty et al., 1993a). The Lifson-Roig theory for the helix-coil transition predicts, however, that substitutions at the N-terminus in an unacetylated peptide should have no effect on alpha-helix stability. We have therefore modified the theory to include these N-capping effects by assigning a statistical weight (the "n-value") to the amino acid immediately preceding a stretch of helical residues. The n-value measures the N-capping propensity of an amino acid, and like the helix propensity (w-value), it is independent of neighboring residues or positions in sequence. The new theory was used, with the experimental data for these substitutions, to calculate n-values and, hence, free energies for N-capping for the amino acids Gln, Ala, Val, Met, Pro, Ile, Leu, Thr, Gly, Ser, and Asn as well as for the acetyl group, which is commonly used to cap peptides. The free energies vary by approximately 1 kcal mol-1 from Gln (worst) to Asn (best), and the acetyl group is nearly as effective as Asn. N-Capping free energies were also found for Leu, Thr, Gly, Ser, and Asn when the N-terminus is charged at pH 5. The unfavorable effect of protonation of the N-terminus in an alpha-helix was found to be approximately 0.5 kcal mol-1. Our results agree well with a survey of N-capping preferences from protein crystal structures and are compared to results from site-directed mutagenesis of N-caps in proteins.

We wish to identify genes associated with disease. To do so, we look for novel genes whose expression patterns mimic those of known disease-associated genes, using a method we call Guilt-by-Association (GBA), on the basis of a combinatoric measure of association. Using GBA, we have examined the expression of 40,000 human genes in 522 cDNA libraries, and have discovered several hundred previously unidentified genes associated with cancer, inflammation, steroid-synthesis, insulin-synthesis, neurotransmitter processing, matrix remodeling, and other disease processes. The majority of the genes thus discovered show no sequence similarity to known genes, and thus could not have been identified by homology searches. We present here an example of the discovery of eight genes associated with prostate cancer. Of the 40,000 most-abundant human genes, these 8 are the most closely linked to the known diagnostic genes, and thus are prime targets for pharmaceutical research.

Whether hydrogen bonds between side chains are energetically significant in proteins and peptides has been controversial. A method is given here for measuring these interactions in peptide helices by comparing the helix contents of peptides with 1, 2, or 3 interactions. Results are given for the glutamine--aspartate (i, i + 4) hydrogen-bond interaction. The Gibbs energy of the interaction is -1.0 kcal/mol when aspartate is charged and -0.4(4) kcal/mol when it is protonated. Magnetic resonance experiments show that the aspartate carboxylate group interacts specifically with the trans amide proton (HE) of glutamine. The interaction is observed only when the glutamine residue is N-terminal to the aspartate and when the spacing is (i, i + 4). The same stereochemistry is found in protein structures, where the (i, i + 4) glutamine-aspartate interaction occurs much more frequently than other possible arrangements.

We have developed a new representation for structural and functional motifs in protein sequences based on correlations between pairs of amino acids and applied it to a-helical and P-sheet sequences. Existing probabilistic methods for representing and analyzing protein sequences have traditionally assumed conditional independence of evidence. In other words, amino acids are assumed to have no effect on each other. However, analyses of protein structures have repeatedly demonstrated the importance of interactions between amino acids in conferring both structure and function. Using Bayesian networks, we are able to model the relationships between amino acids at distinct positions in a protein sequence in addition to the amino acid distributions at each position. We have also developed an automated program for discovering sequence correlations using standard statistical tests and validation techniques. In this paper, we test this program on sequences from secondary structure motifs, namely a-helices and @sheets. In each case, the correlations our program discovers correspond well with known physical and chemical interactions between amino acids in structures. Furthermore, we show that, using different chemical alphabets for the amino acids, we discover structural relationships based on the same chemical principle used in constructing the alphabet. This new representation of 3-dimensional features in protein motifs, such as those arising from structural or functional constraints on the sequence, can be used to improve sequence analysis tools including pattern analysis and database search.Keywords: a-helix structure; amino acid correlations; motif modeling; sequence analysis; side-chain interactions; structure analysis Understanding the 3-dimensional structure of a protein is a necessary and critical step toward understanding the protein's function. For example, only after the structure of hemoglobin was solved was it possible to dissect the mechanisms responsible for the cooperative binding of oxygen, for the effects of pH and 2-3-diphosphoglycerate (DPG) on affinity, and for the defects causing various anemias (Stryer, 1988). Despite the increasing wealth of sequence data, the laborious and time-consuming process of empirical structure determination hampers the availability of detailed structural information. Instead, sequence analysis tools offer the best hope for quickly eliciting structural and functional information from new sequences.Traditional methods for analyzing sequences rely on the prior analyses of known sequences and on procedures for matching sequences. These techniques encompass database search (Wilbur & Lipman, 1983), sequence classification (Klein et al., 1984;Klein & DeLisi, 1986), and analysis for motifs (Bairoch & Boeckmann, 1991;Henikoff & Henikoff, 1991) ~-techniques for both analysis and matching emphasize the conservation of amino acids during evolution. Specifically, one usually assumes that if 2 sequences are homologous, then the amino acids that one observes at corresponding loca...