A new randomization assay reveals Unexpected elements of sequence bias in model 'randomized' gene libraries:Implications for biopanning

Although techniques such as biopanning rely heavily upon the screening of randomized gene libraries, there is surprisingly little information available on the construction of those libraries. In general, it is based on the cloning of 'randomized' synthetic oligonucleotides, in which given position(s) contain an equal mixture of all four bases. Yet, many supposedly 'randomized' libraries contain significant elements of bias and/or omission. Here, we report the development and validation of a new, PCR-based assay that enables rapid examination of library composition both prior to and after cloning. By using our assay to analyse model libraries, we demonstrate that the cloning of a given distribution of sequences does not necessarily result in a similarly composed library of clones. Thus, while bias in randomized synthetic oligonucleotide mixtures can be virtually eliminated by using unequal ratios of the four phosphoramidites, the use of such mixtures does not ensure retrieval of a truly randomized library. We propose that in the absence of a technique to control cloning frequencies, the ability to analyse the composition of libraries after cloning will enhance significantly the quality of information derived from those libraries. (C) 2000 Published by Elsevier Science B.V. All rights reserved.

Novel peptide antagonists of adrenomedullin and calcitonin gene-related peptide receptors:identification, pharmacological characterization, and interactions with position 74 in receptor activity-modifying protein 1/3

Human adrenomedullin (AM) is a 52-amino acid peptide belonging to the calcitonin peptide family, which also includes calcitonin gene-related peptide (CGRP) and AM2. The two AM receptors, AM(1) and AM(2), are calcitonin receptor-like receptor (CL)/receptor activity-modifying protein (RAMP) (RAMP2 and RAMP3, respectively) heterodimers. CGRP receptors comprise CL/RAMP1. The only human AM receptor antagonist (AM(22-52)) is a truncated form of AM; it has low affinity and is only weakly selective for AM(1) over AM(2) receptors. To develop novel AM receptor antagonists, we explored the importance of different regions of AM in interactions with AM(1), AM(2), and CGRP receptors. AM(22-52) was the framework for generating further AM fragments (AM(26-52) and AM(30-52)), novel AM/alphaCGRP chimeras (C1-C5 and C9), and AM/AM(2) chimeras (C6-C8). cAMP assays were used to screen the antagonists at all receptors to determine their affinity and selectivity. Circular dichroism spectroscopy was used to investigate the secondary structures of AM and its related peptides. The data indicate that the structures of AM, AM2, and alphaCGRP differ from one another. Our chimeric approach enabled the identification of two nonselective high-affinity antagonists of AM(1), AM(2), and CGRP receptors (C2 and C6), one high-affinity antagonist of AM(2) receptors (C7), and a weak antagonist selective for the CGRP receptor (C5). By use of receptor mutagenesis, we also determined that the C-terminal nine amino acids of AM seem to be responsible for its interaction with Glu74 of RAMP3. We provide new information on the structure-activity relationship of AM, alphaCGRP, and AM2 and how AM interacts with CGRP and AM(2) receptors.

Structure-activity relationship of calcitonin gene-related peptide and its receptor

DUE TO COPYRIGHT RESTRICTIONS ONLY AVAILABLE FOR CONSULTATION AT ASTON UNIVERSITY LIBRARY AND INFORMATION SERVICES WITH PRIOR ARRANGEMENT

Hybrid sampling on mutual information entropy-based clustering ensembles for optimizations

In this paper, we focus on the design of bivariate EDAs for discrete optimization problems and propose a new approach named HSMIEC. While the current EDAs require much time in the statistical learning process as the relationships among the variables are too complicated, we employ the Selfish gene theory (SG) in this approach, as well as a Mutual Information and Entropy based Cluster (MIEC) model is also set to optimize the probability distribution of the virtual population. This model uses a hybrid sampling method by considering both the clustering accuracy and clustering diversity and an incremental learning and resample scheme is also set to optimize the parameters of the correlations of the variables. Compared with several benchmark problems, our experimental results demonstrate that HSMIEC often performs better than some other EDAs, such as BMDA, COMIT, MIMIC and ECGA. © 2009 Elsevier B.V. All rights reserved.