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

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.

All-codon scanning identifies p53 cancer rescue mutations.

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.

Discovery of compounds that reactivate p53 mutants in vitro and in vivo.

The tumor suppressor p53 is the most frequently mutated protein in human cancer. The majority of these mutations are missense mutations in the DNA binding domain of p53. Restoring p53 tumor suppressor function could have a major impact on the therapy for a wide range of cancers. Here we report a virtual screening approach that identified several small molecules with p53 reactivation activities. The UCI-LC0023 compound series was studied in detail and was shown to bind p53, induce a conformational change in mutant p53, restore the ability of p53 hotspot mutants to associate with chromatin, reestablish sequence-specific DNA binding of a p53 mutant in a reconstituted in vitro system, induce p53-dependent transcription programs, and prevent progression of tumors carrying mutant p53, but not p53null or p53WT alleles. Our study demonstrates feasibility of a computation-guided approach to identify small molecule corrector drugs for p53 hotspot mutations.

Identification and functional analysis of CT069 as a novel transcriptional regulator in Chlamydia.

Only a small number of transcription factors have been predicted in Chlamydia spp., which are obligate intracellular bacteria that include a number of important human pathogens. We used a bioinformatics strategy to identify novel transcriptional regulators from the Chlamydia trachomatis genome by predicting proteins with the general structure and characteristic functional domains of a bacterial transcription factor. With this approach, we identified CT069 as a candidate transcription factor with sequence similarity at its C terminus to Treponema pallidum TroR. Like TroR, the gene for CT069 belongs to an operon that encodes components of a putative ABC transporter for importing divalent metal cations. However, CT069 has been annotated as YtgC because of sequence similarity at its N terminus to TroC, a transmembrane component of this metal ion transporter. Instead, CT069 appears to be a fusion protein composed of YtgC and a TroR ortholog that we have called YtgR. Although it has not been previously reported, a similar YtgC-YtgR fusion protein is predicted to be encoded by other Chlamydia spp. and several other bacteria, including Bacillus subtilis. We show that recombinant YtgR polypeptide bound specifically to an operator sequence upstream of the ytg operon and that binding was enhanced by Zn(2+). We also demonstrate that YtgR repressed transcription from the ytg promoter in a heterologous in vivo reporter assay. These results provide evidence that CT069 is a negative regulator of the ytg operon, which encodes a putative metal ion transporter in C. trachomatis.

Novel TUTase associates with an editosome-like complex in mitochondria of Trypanosoma brucei.

Expression of mitochondrial genomes in Kinetoplastida protists requires massive uracil insertion/deletion mRNA editing. The cascade of editing reactions is accomplished by a multiprotein complex, the 20S editosome, and is directed by trans-acting guide RNAs. Two distinct RNA terminal uridylyl transferases (TUTases), RNA Editing TUTase 1 (RET1) and RNA Editing TUTase 2 (RET2), catalyze 3' uridylylation of guide RNAs and U-insertions into the mRNAs, respectively. RET1 is also involved in mitochondrial mRNA turnover and participates in numerous heterogeneous complexes; RET2 is an integral part of the 20S editosome, in which it forms a U-insertion subcomplex with zinc finger protein MP81 and RNA editing ligase REL2. Here we report the identification of a third mitochondrial TUTase from Trypanosoma brucei. The mitochondrial editosome-like complex associated TUTase (MEAT1) interacts with a 20S editosome-like particle, effectively substituting the U-insertion subcomplex. MEAT1 and RET2 are mutually exclusive in their respective complexes, which otherwise share several components. Similarly to RET2, MEAT1 is exclusively U-specific in vitro and is active on gapped double-stranded RNA resembling editing substrates. However, MEAT1 does not require a 5' phosphate group on the 3' mRNA cleavage fragment produced by editing endonucleases. The functional RNAi complementation experiments showed that MEAT1 is essential for viability of bloodstream and insect parasite forms. The growth inhibition phenotype in the latter can be rescued by coexpressing an RNAi-resistant gene with double-stranded RNA targeting the endogenous transcript. However, preliminary RNA analysis revealed no gross effects on RNA editing in MEAT1-depleted cells and indicated its possible role in regulating the mitochondrial RNA stability.

Recombinant human collagen and biomimetic variants using a de novo gene optimized for modular assembly.

A collagen-mimetic polymer that can be easily engineered with specific cell-responsive and mechanical properties would be of significant interest for fundamental cell-matrix studies and applications in regenerative medicine. However, oligonucleotide-based synthesis of full-length collagen has been encumbered by the characteristic glycine-X-Y sequence repetition, which promotes mismatched oligonucleotide hybridizations during de novo gene assembly. In this work, we report a novel, modular synthesis strategy that yields full-length human collagen III and specifically defined variants. We used a computational algorithm that applies codon degeneracy to design oligonucleotides that favor correct hybridizations while disrupting incorrect ones for gene synthesis. The resulting recombinant polymers were expressed in Saccharomyces cerevisiae engineered with prolyl-4-hydroxylase. Our modular approach enabled mixing-and-matching domains to fabricate different combinations of collagen variants that contained different secretion signals at the N-terminus and cysteine residues imbedded within the triple-helical domain at precisely defined locations. This work shows the flexibility of our strategy for designing and assembling specifically tailored biomimetic collagen polymers with re-engineered properties.

Predicting positive p53 cancer rescue regions using Most Informative Positive (MIP) active learning.

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.

Genome wide screens in yeast to identify potential binding sites and target genes of DNA-binding proteins.

Knowledge of all binding sites for transcriptional activators and repressors is essential for computationally aided identification of transcriptional networks. The techniques developed for defining the binding sites of transcription factors tend to be cumbersome and not adaptable to high throughput. We refined a versatile yeast strategy to rapidly and efficiently identify genomic targets of DNA-binding proteins. Yeast expressing a transcription factor is mated to yeast containing a library of genomic fragments cloned upstream of the reporter gene URA3. DNA fragments with target-binding sites are identified by growth of yeast clones in media lacking uracil. The experimental approach was validated with the tumor suppressor protein p53 and the forkhead protein FoxI1 using genomic libraries for zebrafish and mouse generated by shotgun cloning of short genomic fragments. Computational analysis of the genomic fragments recapitulated the published consensus-binding site for each protein. Identified fragments were mapped to identify the genomic context of each binding site. Our yeast screening strategy, combined with bioinformatics approaches, will allow both detailed and high-throughput characterization of transcription factors, scalable to the analysis of all putative DNA-binding proteins.

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

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.

DNA deformation energy as an indirect recognition mechanism in protein-DNA interactions.

Proteins that bind to specific locations in genomic DNA control many basic cellular functions. Proteins detect their binding sites using both direct and indirect recognition mechanisms. Deformation energy, which models the energy required to bend DNA from its native shape to its shape when bound to a protein, has been shown to be an indirect recognition mechanism for one particular protein, Integration Host Factor (IHF). This work extends the analysis of deformation to two other DNA-binding proteins, CRP and SRF, and two endonucleases, I-CreI and I-PpoI. Known binding sites for all five proteins showed statistically significant differences in mean deformation energy as compared to random sequences. Binding sites for the three DNA-binding proteins and one of the endonucleases had mean deformation energies lower than random sequences. Binding sites for I-PpoI had mean deformation energy higher than random sequences. Classifiers that were trained using the deformation energy at each base pair step showed good cross-validated accuracy when classifying unseen sequences as binders or nonbinders. These results support DNA deformation energy as an indirect recognition mechanism across a wider range of DNA-binding proteins. Deformation energy may also have a predictive capacity for the underlying catalytic mechanism of DNA-binding enzymes.

Functional census of mutation sequence spaces: the example of p53 cancer rescue mutants.

Many biomedical problems relate to mutant functional properties across a sequence space of interest, e.g., flu, cancer, and HIV. Detailed knowledge of mutant properties and function improves medical treatment and prevention. A functional census of p53 cancer rescue mutants would aid the search for cancer treatments from p53 mutant rescue. We devised a general methodology for conducting a functional census of a mutation sequence space by choosing informative mutants early. The methodology was tested in a double-blind predictive test on the functional rescue property of 71 novel putative p53 cancer rescue mutants iteratively predicted in sets of three (24 iterations). The first double-blind 15-point moving accuracy was 47 percent and the last was 86 percent; r = 0.01 before an epiphanic 16th iteration and r = 0.92 afterward. Useful mutants were chosen early (overall r = 0.80). Code and data are freely available (http://www.igb.uci.edu/research/research.html, corresponding authors: R.H.L. for computation and R.K.B. for biology).

Predicting oligonucleotide-directed mutagenesis failures in protein engineering.

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.

CHOPER filters enable rare mutation detection in complex mutagenesis populations by next-generation sequencing.

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.

Tuning cellular response by modular design of bioactive domains in collagen.

Collagen's ability to direct cellular behavior suggests that redesigning it at the molecular level could enable manipulation of cells residing in an engineered microenvironment. However, the fabrication of full-length collagen mimics of specified sequence de novo has been elusive, and applications still rely on material from native tissues. Using a bottom-up strategy, we synthesized modular genes and expressed recombinant human collagen variants in Saccharomyces cerevisiae. The resulting biopolymers contained prescribed cell-interaction sites that can direct and tune cellular responses, with retention of the important triple-helical self-assembled structure. Removal of the native integrin-binding sites GROGER, GAOGER, GLOGEN, GLKGEN, and GMOGER in human collagen III yielded collagen that did not support adhesion of mammalian cells. Introduction of GFOGER sequences to this scaffold at specified locations and densities resulted in varying degrees of cellular attachment. The recruitment of focal adhesion complexes on the different collagens ranged from a 96% reduction to a 56% increase over native collagen I. Adhesion to the GFOGER-containing variants was entirely dependent and partially dependent on the ß1 and α2 subunits of integrin, respectively, with cell adhesion on average reduced by 86% with anti-ß1 and 38% with anti-α2 integrin antibody incubation. Results support the importance of local context in collagen-cell interactions. The investigation demonstrates the flexibility of this approach to introduce targeted changes throughout the collagen polymer for producing fully-prescribed variants with tailored properties.

Information-theoretic dissection of pairwise contact potentials.

Pairwise contact potentials have a long, successful history in protein structure prediction. They provide an easily-estimated representation of many attributes of protein structures, such as the hydrophobic effect. In order to improve on existing potentials, one should develop a clear understanding of precisely what information they convey. Here, using mutual information, we quantified the information in amino acid potentials, and the importance of hydropathy, charge, disulfide bonding, and burial. Sampling error in mutual information was controlled for by estimating how much information cannot be attributed to sampling bias. We found the information in amino acid contacts to be modest: 0.04 bits per contact. Of that, only 0.01 bits of information could not be attributed to hydropathy, charge, disulfide bonding, or burial.

The role of DNA deformation energy at individual base steps for the identification of DNA-protein binding sites.

We examine the use of deformation propensity at individual base steps for the identification of DNA-protein binding sites. We have previously demonstrated that estimates of the total energy to bend DNA to its bound conformation can partially explain indirect DNA-protein interactions. We now show that the deformation propensities at each base step are not equally informative for classifying a sequence as a binding site, and that applying non-uniform weights to the contribution of each base step to aggregate deformation propensity can greatly improve classification accuracy. We show that a perceptron can be trained to use the deformation propensity at each step in a sequence to generate such weights.

Computational identification of a transiently open L1/S3 pocket for reactivation of mutant p53.

The tumour suppressor p53 is the most frequently mutated gene in human cancer. Reactivation of mutant p53 by small molecules is an exciting potential cancer therapy. Although several compounds restore wild-type function to mutant p53, their binding sites and mechanisms of action are elusive. Here computational methods identify a transiently open binding pocket between loop L1 and sheet S3 of the p53 core domain. Mutation of residue Cys124, located at the centre of the pocket, abolishes p53 reactivation of mutant R175H by PRIMA-1, a known reactivation compound. Ensemble-based virtual screening against this newly revealed pocket selects stictic acid as a potential p53 reactivation compound. In human osteosarcoma cells, stictic acid exhibits dose-dependent reactivation of p21 expression for mutant R175H more strongly than does PRIMA-1. These results indicate the L1/S3 pocket as a target for pharmaceutical reactivation of p53 mutants.

iCODA: RNAi-based inducible knock-in system in Trypanosoma brucei.

In vivo mutational analysis is often required to characterize enzymes that function as subunits of the U-insertion/deletion RNA editing core complex (RECC) in mitochondria of Trypanosoma brucei. The mutations may skew phenotypic manifestation of a dominant negative overexpression if complex association is disrupted. Conditional knockouts and knock-ins of essential mitochondrial genes are time consuming and restricted to the bloodstream form parasites, thus limiting biochemical analysis. We have combined CODA (computationally optimized DNA assembly) technology with RNA interference to develop an iCODA inducible knock-in system for expeditious phenotype assessment and affinity purification of the RECC bearing a mutant subunit. For functional knock-in, the gene region targeted by RNAi is replaced with a synthetic sequence bearing at least one silent mutation per 12 contiguous base pairs. Upon co-expression of the double-stranded RNA targeting the endogenous transcript and modified mRNA in a stable cell line, the endogenous mRNA is destroyed and the cell survives on the RNAi-resistant transcript encoding the same polypeptide. In this chapter, we describe the generation of procyclic (insect) transgenic cell lines, RNAi rescue, complex purification, and validation methods for RNA editing TUTase 2 (RET2). These methods should be readily applicable for any gene in T. brucei.