Non-biological synthetic oligomers can serve as ligands for antibodies. We hypothesized that a random combinatorial library of synthetic poly-N-substituted glycine oligomers, or peptoids, could represent a random "shape library" in antigen space, and that some of these peptoids would be recognized by the antigen-binding pocket of disease-specific antibodies. We synthesized and screened a one bead one compound combinatorial library of peptoids, in which each bead displayed an 8-mer peptoid with ten possible different amines at each position (10(8) theoretical variants). By screening one million peptoid/beads we found 112 (approximately 1 in 10,000) that preferentially bound immunoglobulins from human sera known to be positive for anti-HIV antibodies. Reactive peptoids were then re-synthesized and rigorously evaluated in plate-based ELISAs. Four peptoids showed very good, and one showed excellent, properties for establishing a sero-diagnosis of HIV. These results demonstrate the feasibility of constructing sero-diagnostic assays for infectious diseases from libraries of random molecular shapes. In this study we sought a proof-of-principle that we could identify a potential diagnostic antibody ligand biomarker for an infectious disease in a random combinatorial library of 100 million peptoids. We believe that this is the first evidence that it is possible to develop sero-diagnostic assays - for any infectious disease - based on screening random libraries of non-biological molecular shapes.
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http://dx.doi.org/10.1016/j.jim.2016.05.001 | DOI Listing |
Front Pharmacol
January 2025
Department of Pathology, College of Korean Medicine, Kyung Hee University, Seoul, Republic of Korea.
The natural world is a vast reservoir of exceptionally varied and inventive chemical compositions. Natural products are used as initial compounds to create combinatorial libraries by targeted modifications and then by analyzing their structure-activity connections. This stage is regarded as a crucial milestone in drug discovery and development.
View Article and Find Full Text PDFACS Appl Bio Mater
January 2025
Department of Biomedical Engineering, McGill University, 3775 University Street, Montreal, Quebec H3A 2B4, Canada.
Synthetic ssDNA oligonucleotides hold great potential for various applications, including DNA aptamers, DNA digital data storage, DNA origami, and synthetic genomes. In these contexts, precise control over the synthesis of the ssDNA strands is essential for generating combinatorial sequences with user-defined parameters. Desired features for creating synthetic DNA oligonucleotides include easy manipulation of DNA strands, effective detection of unique DNA sequences, and a straightforward mechanism for strand elongation and termination.
View Article and Find Full Text PDFCrit Rev Oncog
January 2025
Department of Biotechnology, Dr. B.R. Ambedkar University, Srikakulam 532410, Andhra Pradesh, India.
The heat shock protein 90 kDa (HSP90) is highly conserved across diverse species, including humans, and upregulated in various cancers. As a result, it has been identified as a promising target for advancing anticancer medicine. The introduction of combinatorial chemistry in drug discovery has emphasized the need to develop new technologies in screening, designing, decoding, synthesizing, and screening combinatorial drug libraries.
View Article and Find Full Text PDFBiochemistry
January 2025
Research and Early Development Oncology, Bayer AG, Müllerstr. 178, Berlin 13342, Germany.
The receptor tyrosine kinase EphB4 is involved in tumor angiogenesis, proliferation, and metastasis. Designed ankyrin repeat proteins (DARPins) binding to the EphB4 extracellular domain were identified from a combinatorial library using phage display. Surface plasmon resonance (SPR) allowed us to distinguish between DARPins that either compete with the EphB4 ligand ephrin-B2 for binding to a common site or target a different epitope.
View Article and Find Full Text PDFDigit Discov
January 2025
School of Natural and Environmental Sciences, Newcastle University Newcastle Upon Tyne NE1 7RU UK
FEgrow is an open-source software package for building congeneric series of compounds in protein binding pockets. For a given ligand core and receptor structure, it employs hybrid machine learning/molecular mechanics potential energy functions to optimise the bioactive conformers of supplied linkers and functional groups. Here, we introduce significant new functionality to automate, parallelise and accelerate the building and scoring of compound suggestions, such that it can be used for automated design.
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