Prediction of Pharmacological and Xenobiotic Responses to Drugs Based on Time Course Gene Expression Profiles

More and more people are concerned by the risk of unexpected side effects observed in the later steps of the development of new drugs, either in late clinical development or after marketing approval. In order to reduce the risk of the side effects, it is important to look out for the possible xenobiotic responses at an early stage. We attempt such an effort through a prediction by assuming that similarities in microarray profiles indicate shared mechanisms of action and/or toxicological responses among the chemicals being compared. A large time course microarray database derived from livers of compound-treated rats with thirty-four distinct pharmacological and toxicological responses were studied. The mRMR (Minimum-Redundancy-Maximum-Relevance) method and IFS (Incremental Feature Selection) were used to select a compact feature set (141 features) for the reduction of feature dimension and improvement of prediction performance. With these 141 features, the Leave-one-out cross-validation prediction accuracy of first order response using NNA (Nearest Neighbor Algorithm) was 63.9%. Our method can be used for pharmacological and xenobiotic responses prediction of new compounds and accelerate drug development

Cooperativity among Short Amyloid Stretches in Long Amyloidogenic Sequences

Amyloid fibrillar aggregates of polypeptides are associated with many neurodegenerative diseases. Short peptide segments in protein sequences may trigger aggregation. Identifying these stretches and examining their behavior in longer protein segments is critical for understanding these diseases and obtaining potential therapies. In this study, we combined machine learning and structure-based energy evaluation to examine and predict amyloidogenic segments. Our feature selection method discovered that windows consisting of long amino acid segments of ∼30 residues, instead of the commonly used short hexapeptides, provided the highest accuracy. Weighted contributions of an amino acid at each position in a 27 residue window revealed three cooperative regions of short stretch, resemble the β-strand-turn-β-strand motif in A-βpeptide amyloid and β-solenoid structure of HET-s(218–289) prion (C). Using an in-house energy evaluation algorithm, the interaction energy between two short stretches in long segment is computed and incorporated as an additional feature. The algorithm successfully predicted and classified amyloid segments with an overall accuracy of 75%. Our study revealed that genome-wide amyloid segments are not only dependent on short high propensity stretches, but also on nearby residues

The distribution of different features in the optimal feature set with 82 features indicated the protein-protein interaction energy dominate the amyloid formation.

<p>Pssm_C describes the likelihood that the amino acid in the sequence mutates to the cystine (C), Pssm_H describes the likelihood that the amino acid in the sequence mutates to the Histidine (H), and so forth.</p

The predicted results of IFS procedure with random forest (RF) algorithm based on the first 11 features in optimal features.

<p>In the table, the “AA14” represents the 14^th amino acid residue of the peptide. Pssm_C describes the likelihood that the amino acid in the sequence mutates to the cystine (C), Pssm_H describes the likelihood that the amino acid in the sequence mutates to the Histidine (H).</p

Delsovation energy penalty.

<p>Delsovation energy penalty.</p

Amyloid interaction energy can be searched by the summation of residue interactions between two short amyloid stretches.

<p>The βstrand-turn-βstrand motif is defined as two six-residue β-strands connected with a flexible turn with a length up to 15 residues, with total window length of 27 residues. When there is no linker (L = 0) or the linker is very short (for example, L = 1−2), the motif may be classified as triangular shape observed for β-solenoid structure in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0039369#pone-0039369-g003" target="_blank">Figure 3</a>.</p

Feature analysis revealed important factor for amyloid formation.

<p>(A) The ratio of each feature category occurred in the selected 446 features in the optimal set compared to the ratio of 48.6% which is the ratio of selected features out of the total number. the disordered factors contribute most to the fibril formation followed by the secondary structure factors, amino acid volume factors and pssm factors. (B) the pssm features of each amino acid contained in the selected 446 features.</p