Sample size determination in combinatorial chemistry.

Zhao, Peng-Liang; Zambias, Robert A.; Bolognese, James A.; Boulton, D. A.; Chapman, Kevin T.

doi:10.1073/pnas.92.22.10212

“…A convenient measurement of an aliquot’s size is the library equivalent, ε: 30 where | S | is the number of elements in S and | L | is the number of unique library elements. Assuming that library synthesis scale is sufficiently large such that sampling does not influence library content, 1,2 the general form of the Poisson distribution describes the probability, P , of observing a given member of L :where λ is the mean library sampling and integer k is the number of copies of a given bead library member, or replicate class (Figure 1A). For example, in an ε = 2 library aliquot (λ = 2), the fraction of L observed k = 1 time in S is 27% according to the model.…”

Section: Results and Discussionmentioning

confidence: 99%

Poisson Statistics of Combinatorial Library Sampling Predict False Discovery Rates of Screening

MacConnell

¹

,

Paegel

²

2017

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Microfluidic droplet-based screening of DNA-encoded one-bead-one-compound combinatorial libraries is a miniaturized, potentially widely distributable approach to small molecule discovery. In these screens, a microfluidic circuit distributes library beads into droplets of activity assay reagent, photochemically cleaves the compound from the bead, then incubates and sorts the droplets based on assay result for subsequent DNA sequencing-based hit compound structure elucidation. Pilot experimental studies revealed that Poisson statistics describe nearly all aspects of such screens, prompting the development of simulations to understand system behavior. Monte Carlo screening simulation data showed that increasing mean library sampling (ε), mean droplet occupancy, or library hit rate all increase the false discovery rate (FDR). Compounds identified as hits on k > 1 beads (the replicate k class) were much more likely to be authentic hits than singletons (k = 1), in agreement with previous findings. Here, we explain this observation by deriving an equation for authenticity, which reduces to the product of a library sampling bias term (exponential in k) and a sampling saturation term (exponential in ε) setting a threshold that the k-dependent bias must overcome. The equation thus quantitatively describes why each hit structure’s FDR is based on its k class, and further predicts the feasibility of intentionally populating droplets with multiple library beads, assaying the micromixtures for function, and identifying the active members by statistical deconvolution.

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“…General procedure for the combinatorial assays: Approximately 10 mg of the library [12] were suspended in an aqueous solution of AgNO 3 (0.05 m, 660 mL, ca. 6 equiv), sonicated for 5 min and allowed to incubate for another 10 min.…”

Section: Methodsmentioning

confidence: 99%

“…[12] After 8 h, approximately 5 % of the beads had turned dark red, which is a typical color of AgNPs (Figure 1 b, left). [12] After 8 h, approximately 5 % of the beads had turned dark red, which is a typical color of AgNPs (Figure 1 b, left).…”

mentioning

confidence: 96%

Silver Nanoparticle Formation in Different Sizes Induced by Peptides Identified within Split‐and‐Mix Libraries

Belser

¹

,

Slenters

²

,

Pfumbidzai

³

et al. 2009

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Silver nanoparticles (AgNPs) are becoming increasingly important for manifold applications in, for example, imaging, catalysis, electronics, and the development of antimicrobial coatings. [1][2][3] The formation of AgNPs is typically accomplished by chemical reduction or irradiation of Ag + ions with visible light in the presence of additives, such as polymers or surfactants, which induce the formation of AgNPs and stabilize the NPs. [1,2] Recently, peptides bearing functional groups that coordinate to Ag + ions have become popular as additives. [4][5][6][7][8] Their large structural and functional diversity also renders peptides attractive for the controlled formation of AgNPs of defined sizes, which still presents a challenge to date. Since the rational design of peptides that induce metal NP formation is difficult, combinatorial approaches are attractive for the identification of suitable peptides. [7,8] We envisaged that colorimetric on-bead screening of split-andmix libraries could be a particularly powerful tool that would allow the testing of diverse libraries, which contain both natural and unnatural amino acids. [9] The typical size-and shape-dependent coloration of AgNPs [1] was anticipated to allow for a facile identification of active library members.Herein, we introduce the use of combinatorial split-andmix libraries for the identification of peptides that are capable of inducing the formation of AgNPs. In conjunction with scanning electron microscopy (SEM) studies, we also demonstrate that the method allows for the identification of certain types of peptides that induce the formation of AgNPs of specific sizes.We started our investigations by testing the members of peptide library 1 for their ability to induce AgNP formation in the presence of either light or the chemical reducing agent sodium ascorbate (Figure 1 a). Within the library, the amino acids serine (Ser), aspartic acid (Asp), histidine (His), and tyrosine (Tyr), bearing functional groups that were envisaged for Ag + ion coordination, were employed in positions AA1 and AA2 (Figure 1). Tyr was included in the library as it is a well-known photoactive residue. [6] Linkers of varying flexibility and geometry were used to connect the amino acids in order to allow for diverse spatial arrangements of their sidechain functional groups. trans-2-Aminohexanoic acid (Achc), Pro-Aib (Aib = aminoisobutyric acid) and Pro-Gly were chosen as turn-inducing linkers, and 6-aminohexanoic acid (Ahx) and b-alanine as flexible linkers. The library was prepared by encoded [10] split-and-mix synthesis [11] on TentaGel resin by utilizing seven different linkers and seven different l-and d-amino acids in positions AA1 and AA2, hence the library consisted maximally of 7 3 = 343 different peptides (Figure 1 a). Amino acid couplings were performed by following the standard Fmoc/tBu protocol for peptide synthesis using HBTU/iPr 2 NEt as the coupling reagent and piperidine for Fmoc deprotections (Fmoc = 9-fluorenylmethyloxycarbonyl, HBTU = O-(benzotriazol-l-yl)-N,N,N'...

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“…Due to the statistical distribution of the solid support at each splitting step the combinatorial synthesis should be carried out with an approximately threefold amount of resin beads (termed x-fold, here threefold redundancy), in order to ensure that 95 % of all possible compound members of the combinatorial library are included with a probability greater 99 % [28], [29]. For the commonly used resins (about 100 mm diameter bead), 1 g of the support material corresponds to several million resin beads so that from a statistical point of view, libraries of the order of > 10 5 different compounds are possible in practice [28][29][30]. Depending on the loading capacity of the resin bead, quantities of about 200 pmol (0.1 mg compound, M r ¼ 500) can be obtained per resin bead.…”

Section: Techniques Using Resin Beadsmentioning

confidence: 99%

Combinatorial Chemistry

Tiebes

¹

,

Peters

²

2000

Ullmann's Encyclopedia of Industrial Chemistry

0

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The article contains sections titled: 1. Introduction 2. Concept of Combinatorial Chemistry 3. Methods and Techniques of Combinatorial Synthesis 3.1. Synthetic Strategies Towards Combinatorial Libraries 3.1.1. Split ‐ Pool Synthesis Towards Combinatorial Libraries 3.1.1.1. Techniques using Resin Beads 3.1.1.2. Tea‐Bag Method 3.1.2. Parallel Synthesis Towards Combinatorial Libraries 3.1.2.1. Reaction Apparatus using Resin Beads 3.1.2.2. Multipin Technique 3.1.2.3. Spatially Addressable Parallel Synthesis on Silica Wafer 3.1.3. Reagent Mixture Synthesis Towards Combinatorial Libraries 3.2. Synthetic Methodology for Organic Library Construction 3.2.1. Solid‐Phase Organic Synthesis 3.2.1.1. Solid Support, Linker, and Cleavage Strategies 3.2.1.2. Reaction Portfolio 3.2.2. Synthesis in Solution and Liquid‐Phase Synthesis 4. Characterization of Combinatorial Libraries 4.1. Analytical Characterization 4.2. Hit Identification in Combinatorial Libraries by High‐Throughput Screening 4.2.1. Strategies for Libraries of Compound Mixtures 4.2.1.1. On‐Bead Screening 4.2.1.2. Deconvolution 4.2.1.2.1. Iterative Deconvolution 4.2.1.2.2. Deconvolution by Positional Scanning 4.2.1.2.3. Deconvolution by Orthogonal Libraries 4.2.1.3. Encoding 4.2.1.4. Multiple Cleavable Linkers 4.2.2. Strategies for Libraries of Separate Single Compounds 4.2.3. Conclusion 5. Automation and Data Processing 5.1. Synthesis Automation and Data Processing 5.2. Automated Purification 6. Library Design and Diversity Assessment 6.1. Diversity Assessment for Selection of Building Blocks or Compound 6.2. Iterative Optimization Methods 7. Economic Aspects and Industrial Case Studies 8. Further Developments 8.1. Combinatorial Chemistry and Catalysis 8.2. Combinatorial Chemistry and Material Science

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Sample size determination in combinatorial chemistry.

Cited by 26 publications

References 7 publications

Poisson Statistics of Combinatorial Library Sampling Predict False Discovery Rates of Screening

Poisson Statistics of Combinatorial Library Sampling Predict False Discovery Rates of Screening

Silver Nanoparticle Formation in Different Sizes Induced by Peptides Identified within Split‐and‐Mix Libraries

Combinatorial Chemistry

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