Using structural diversity to measure the complexity of technologies.
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ABSTRACT:
SUBMITTER: Broekel T
PROVIDER: S-EPMC6528977 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:Based on the conservation of nucleotides at splicing sites and the features of base composition and base correlation around these sites we use the method of increment of diversity combined with quadratic discriminant analysis (IDQD) to study the dependence structure of splicing sites and predict the exons/introns and their boundaries for four model genomes: Caenorhabditis elegans, Arabidopsis thaliana, Drosophila melanogaster and human. The comparison of compositional features between two sequences and the comparison of base dependencies at adjacent or non-adjacent positions of two sequences can be integrated automatically in the increment of diversity (ID). Eight feature variables around a potential splice site are defined in terms of ID. They are integrated in a single formal framework given by IDQD. In our calculations 7 (8) base region around the donor (acceptor) sites have been considered in studying the conservation of nucleotides and sequences of 48 bp on either side of splice sites have been used in studying the compositional and base-correlating features. The windows are enlarged to 16 (donor), 29 (acceptor) and 80 bp (either side) to improve the prediction for human splice sites. The prediction capability of the present method is comparable with the leading splice site detector--GeneSplicer.
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Project description:In vitro selection technologies are an important means of affinity maturing antibodies to generate the optimal therapeutic profile for a particular disease target. Here, we describe the isolation of a parent antibody, KENB061 using phage display and solution phase selections with soluble biotinylated human IL-1R1. KENB061 was affinity matured using phage display and targeted mutagenesis of VH and VL CDR3 using NNS randomization. Affinity matured VHCDR3 and VLCDR3 library blocks were recombined and selected using phage and ribosome display protocol. A direct comparison of the phage and ribosome display antibodies generated was made to determine their functional characteristics.In our analyses, we observed distinct differences in the pattern of beneficial mutations in antibodies derived from phage and ribosome display selections, and discovered the lead antibody Jedi067 had a ~3700-fold improvement in KD over the parent KENB061. We constructed a homology model of the Fv region of Jedi067 to map the specific positions where mutations occurred in the CDR3 loops. For VL CDR3, positions 94 to 97 carry greater diversity in the ribosome display variants compared with the phage display. The positions 95a, 95b and 96 of VLCDR3 form part of the interface with VH in this model. The model shows that positions 96, 98, 100e, 100f, 100 g, 100h, 100i and 101 of the VHCDR3 include residues at the VH and VL interface. Importantly, Leu96 and Tyr98 are conserved at the interface positions in both phage and ribosome display indicating their importance in maintaining the VH-VL interface. For antibodies derived from ribosome display, there is significant diversity at residues 100a to 100f of the VH CDR3 compared with phage display. A unique deletion of isoleucine at position 102 of the lead candidate, Jedi067, also occurs in the VHCDR3.As anticipated, recombining the mutations via ribosome display led to a greater structural diversity, particularly in the heavy chain CDR3, which in turn led to antibodies with improved potencies. For this particular analysis, we also found that VH-VL interface positions provided a source of structural diversity for those derived from the ribosome display selections. This greater diversity is a likely consequence of the presence of a larger pool of recombinants in the ribosome display system, or the evolutionary capacity of ribosome display, but may also reflect differential selection of antibodies in the two systems.
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Project description:Causality testing methods are being widely used in various disciplines of science. Model-free methods for causality estimation are very useful, as the underlying model generating the data is often unknown. However, existing model-free/data-driven measures assume separability of cause and effect at the level of individual samples of measurements and unlike model-based methods do not perform any intervention to learn causal relationships. These measures can thus only capture causality which is by the associational occurrence of 'cause' and 'effect' between well separated samples. In real-world processes, often 'cause' and 'effect' are inherently inseparable or become inseparable in the acquired measurements. We propose a novel measure that uses an adaptive interventional scheme to capture causality which is not merely associational. The scheme is based on characterizing complexities associated with the dynamical evolution of processes on short windows of measurements. The formulated measure, Compression-Complexity Causality is rigorously tested on simulated and real datasets and its performance is compared with that of existing measures such as Granger Causality and Transfer Entropy. The proposed measure is robust to the presence of noise, long-term memory, filtering and decimation, low temporal resolution (including aliasing), non-uniform sampling, finite length signals and presence of common driving variables. Our measure outperforms existing state-of-the-art measures, establishing itself as an effective tool for causality testing in real world applications.
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