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Peer Reviewed

Heterogeneous Biomedical Database Integration using a Hybrid Strategy: A P53 Cancer Research Database

UC Irvine Previously Published Works (2006)

Complex problems in life science research give rise to multidisciplinary collaboration, and hence, to the need for heterogeneous database integration. The tumor suppressor p53 is mutated in close to 50% of human cancers, and a small drug-like molecule with the ability to restore native function to cancerous p53 mutants is a long-held medical goal of cancer treatment. The Cancer Research DataBase (CRDB) was designed in support of a project to find such small molecules. As a cancer informatics project, the CRDB involved small molecule data, computational docking results, functional assays, and protein structure data. As an example of the hybrid strategy for data integration, it combined the mediation and data warehousing approaches. This paper uses the CRDB to illustrate the hybrid strategy as a viable approach to heterogeneous data integration in biomedicine, and provides a design method for those considering similar systems. More efficient data sharing implies increased productivity, and, hopefully, improved chances of success in cancer research. (Code and database schemas are freely downloadable, http://www.igb.uci.edu/research/research.html.).

Cover page: Heterogeneous Biomedical Database Integration using a Hybrid Strategy: A P53 Cancer Research Database

Article
Peer Reviewed

Predicting oligonucleotide-directed mutagenesis failures in protein engineering

UC Irvine Previously Published Works (2004)

Protein engineering uses oligonucleotide-directed mutagenesis to modify DNA sequences through a two-step process of hybridization and enzymatic synthesis. Inefficient reactions confound attempts to introduce mutations, especially for the construction of vast combinatorial protein libraries. This paper applied computational approaches to the problem of inefficient mutagenesis. Several results implicated oligonucleotide annealing to non-target sites, termed ‘cross-hybridization’, as a significant contributor to mutagenesis reaction failures. Test oligonucleotides demonstrated control over reaction outcomes. A novel cross-hybridization score, quickly computable for any plasmid and oligonucleotide mixture, directly correlated with yields of deleterious mutagenesis side products. Cross-hybridization was confirmed conclusively by partial incorporation of an oligonucleotide at a predicted cross-hybridization site, and by modification of putative template secondary structure to control cross-hybridization. Even in low concentrations, cross-hybridizing species in mixtures poisoned reactions. These results provide a basis for improved mutagenesis efficiencies and increased diversities of cognate protein libraries.

Cover page: Predicting oligonucleotide-directed mutagenesis failures in protein engineering

Creative Commons 'BY' version 4.0 license

Article
Peer Reviewed

Predicting oligonucleotide-directed mutagenesis failures in protein engineering

UC Irvine Previously Published Works (2004)

Protein engineering uses oligonucleotide-directed mutagenesis to modify DNA sequences through a two-step process of hybridization and enzymatic synthesis. Inefficient reactions confound attempts to introduce mutations, especially for the construction of vast combinatorial protein libraries. This paper applied computational approaches to the problem of inefficient mutagenesis. Several results implicated oligonucleotide annealing to non-target sites, termed 'cross-hybridization', as a significant contributor to mutagenesis reaction failures. Test oligonucleotides demonstrated control over reaction outcomes. A novel cross-hybridization score, quickly computable for any plasmid and oligonucleotide mixture, directly correlated with yields of deleterious mutagenesis side products. Cross-hybridization was confirmed conclusively by partial incorporation of an oligonucleotide at a predicted cross-hybridization site, and by modification of putative template secondary structure to control cross-hybridization. Even in low concentrations, cross-hybridizing species in mixtures poisoned reactions. These results provide a basis for improved mutagenesis efficiencies and increased diversities of cognate protein libraries.

Article
Peer Reviewed

Choosing where to look next in a mutation sequence space: Active Learning of informative p53 cancer rescue mutants.

UC Irvine Previously Published Works (2007)

MOTIVATION: Many biomedical projects would benefit from reducing the time and expense of in vitro experimentation by using computer models for in silico predictions. These models may help determine which expensive biological data are most useful to acquire next. Active Learning techniques for choosing the most informative data enable biologists and computer scientists to optimize experimental data choices for rapid discovery of biological function. To explore design choices that affect this desirable behavior, five novel and five existing Active Learning techniques, together with three control methods, were tested on 57 previously unknown p53 cancer rescue mutants for their ability to build classifiers that predict protein function. The best of these techniques, Maximum Curiosity, improved the baseline accuracy of 56-77%. This article shows that Active Learning is a useful tool for biomedical research, and provides a case study of interest to others facing similar discovery challenges.

Cover page: Choosing where to look next in a mutation sequence space: Active Learning of informative p53 cancer rescue mutants.

Article
Peer Reviewed

Directed DNA Shuffling of Retrovirus and Retrotransposon Integrase Protein Domains

UC Irvine Previously Published Works (2013)

Article
Peer Reviewed

Directed DNA shuffling of retrovirus and retrotransposon integrase protein domains.

UC Irvine Previously Published Works (2013)

Chimeric proteins are used to study protein domain functions and to recombine protein domains for novel or optimal functions. We used a library of chimeric integrase proteins to study DNA integration specificity. The library was constructed using a directed shuffling method that we adapted from fusion PCR. This method easily and accurately shuffles multiple DNA gene sequences simultaneously at specific base-pair positions, such as protein domain boundaries. It produced all 27 properly-ordered combinations of the amino-terminal, catalytic core, and carboxyl-terminal domains of the integrase gene from human immunodeficiency virus, prototype foamy virus, and Saccharomyces cerevisiae retrotransposon Ty3. Retrotransposons can display dramatic position-specific integration specificity compared to retroviruses. The yeast retrotransposon Ty3 integrase interacts with RNA polymerase III transcription factors to target integration at the transcription initiation site. In vitro assays of the native and chimeric proteins showed that human immunodeficiency virus integrase was active with heterologous substrates, whereas prototype foamy virus and Ty3 integrases were not. This observation was consistent with a lower substrate specificity for human immunodeficiency virus integrase than for other retrovirus integrases. All eight chimeras containing the Ty3 integrase carboxyl-terminal domain, a candidate targeting domain, failed to target strand transfer in the presence of the targeting protein, suggesting that multiple domains of the Ty3 integrase cooperate in this function.

Article
Peer Reviewed

CHOPER Filters Enable Rare Mutation Detection in Complex Mutagenesis Populations by Next-Generation Sequencing

UC Irvine Previously Published Works (2015)

Next-generation sequencing (NGS) has revolutionized genetics and enabled the accurate identification of many genetic variants across many genomes. However, detection of biologically important low-frequency variants within genetically heterogeneous populations remains challenging, because they are difficult to distinguish from intrinsic NGS sequencing error rates. Approaches to overcome these limitations are essential to detect rare mutations in large cohorts, virus or microbial populations, mitochondria heteroplasmy, and other heterogeneous mixtures such as tumors. Modifications in library preparation can overcome some of these limitations, but are experimentally challenging and restricted to skilled biologists. This paper describes a novel quality filtering and base pruning pipeline, called Complex Heterogeneous Overlapped Paired-End Reads (CHOPER), designed to detect sequence variants in a complex population with high sequence similarity derived from All-Codon-Scanning (ACS) mutagenesis. A novel fast alignment algorithm, designed for the specified application, has O(n) time complexity. CHOPER was applied to a p53 cancer mutant reactivation study derived from ACS mutagenesis. Relative to error filtering based on Phred quality scores, CHOPER improved accuracy by about 13% while discarding only half as many bases. These results are a step toward extending the power of NGS to the analysis of genetically heterogeneous populations.

Cover page: CHOPER Filters Enable Rare Mutation Detection in Complex Mutagenesis Populations by Next-Generation Sequencing

Article
Peer Reviewed

All-codon scanning identifies p53 cancer rescue mutations

UC Irvine Previously Published Works (2010)

In vitro scanning mutagenesis strategies are valuable tools to identify critical residues in proteins and to generate proteins with modified properties. We describe the fast and simple All-Codon Scanning (ACS) strategy that creates a defined gene library wherein each individual codon within a specific target region is changed into all possible codons with only a single codon change per mutagenesis product. ACS is based on a multiplexed overlapping mutagenesis primer design that saturates only the targeted gene region with single codon changes. We have used ACS to produce single amino-acid changes in small and large regions of the human tumor suppressor protein p53 to identify single amino-acid substitutions that can restore activity to inactive p53 found in human cancers. Single-tube reactions were used to saturate defined 30-nt regions with all possible codon changes. The same technique was used in 20 parallel reactions to scan the 600-bp fragment encoding the entire p53 core domain. Identification of several novel p53 cancer rescue mutations demonstrated the utility of the ACS approach. ACS is a fast, simple and versatile method, which is useful for protein structure–function analyses and protein design or evolution problems.

Cover page: All-codon scanning identifies p53 cancer rescue mutations

Article
Peer Reviewed

Ensemble-based computational approach discriminates functional activity of p53 cancer and rescue mutants.

ICTS Publications (2011)

The tumor suppressor protein p53 can lose its function upon single-point missense mutations in the core DNA-binding domain ("cancer mutants"). Activity can be restored by second-site suppressor mutations ("rescue mutants"). This paper relates the functional activity of p53 cancer and rescue mutants to their overall molecular dynamics (MD), without focusing on local structural details. A novel global measure of protein flexibility for the p53 core DNA-binding domain, the number of clusters at a certain RMSD cutoff, was computed by clustering over 0.7 µs of explicitly solvated all-atom MD simulations. For wild-type p53 and a sample of p53 cancer or rescue mutants, the number of clusters was a good predictor of in vivo p53 functional activity in cell-based assays. This number-of-clusters (NOC) metric was strongly correlated (r(2) = 0.77) with reported values of experimentally measured ΔΔG protein thermodynamic stability. Interpreting the number of clusters as a measure of protein flexibility: (i) p53 cancer mutants were more flexible than wild-type protein, (ii) second-site rescue mutations decreased the flexibility of cancer mutants, and (iii) negative controls of non-rescue second-site mutants did not. This new method reflects the overall stability of the p53 core domain and can discriminate which second-site mutations restore activity to p53 cancer mutants.

Cover page: Ensemble-based computational approach discriminates functional activity of p53 cancer and rescue mutants.

Article
Peer Reviewed

Predicting Positive p53 Cancer Rescue Regions Using Most Informative Positive (MIP) Active Learning

UC Irvine Previously Published Works (2009)

Many protein engineering problems involve finding mutations that produce proteins with a particular function. Computational active learning is an attractive approach to discover desired biological activities. Traditional active learning techniques have been optimized to iteratively improve classifier accuracy, not to quickly discover biologically significant results. We report here a novel active learning technique, Most Informative Positive (MIP), which is tailored to biological problems because it seeks novel and informative positive results. MIP active learning differs from traditional active learning methods in two ways: (1) it preferentially seeks Positive (functionally active) examples; and (2) it may be effectively extended to select gene regions suitable for high throughput combinatorial mutagenesis. We applied MIP to discover mutations in the tumor suppressor protein p53 that reactivate mutated p53 found in human cancers. This is an important biomedical goal because p53 mutants have been implicated in half of all human cancers, and restoring active p53 in tumors leads to tumor regression. MIP found Positive (cancer rescue) p53 mutants in silico using 33% fewer experiments than traditional non-MIP active learning, with only a minor decrease in classifier accuracy. Applying MIP to in vivo experimentation yielded immediate Positive results. Ten different p53 mutations found in human cancers were paired in silico with all possible single amino acid rescue mutations, from which MIP was used to select a Positive Region predicted to be enriched for p53 cancer rescue mutants. In vivo assays showed that the predicted Positive Region: (1) had significantly more (p < 0.01) new strong cancer rescue mutants than control regions (Negative, and non-MIP active learning); (2) had slightly more new strong cancer rescue mutants than an Expert region selected for purely biological considerations; and (3) rescued for the first time the previously unrescuable p53 cancer mutant P152L.

Cover page: Predicting Positive p53 Cancer Rescue Regions Using Most Informative Positive (MIP) Active Learning