Searcharxiv⌕ Search

arXiv · q-bio/0401036

Homochiral growth through enantiomeric cross-inhibition

Abstract

The stability and conservation properties of a recently proposed polymerization model are studied. The achiral (racemic) solution is linearly unstable once the relevant control parameter (here the fidelity of the catalyst) exceeds a critical value. The growth rate is calculated for different fidelity parameters and cross-inhibition rates. A chirality parameter is defined and shown to be conserved by the nonlinear terms of the model. Finally, a truncated version of the model is used to derive a set of two ordinary differential equations and it is argued that these equations are more realistic than those used in earlier models of that form.

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

A. Brandenburg, A. C. Andersen, S. Höfner, M. Nilsson. 2004-04-26. Homochiral growth through enantiomeric cross-inhibition. https://doi.org/10.1007/s11084-005-0656-9

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

AI-driven structural informatics reveals diverse glycosylation machinery across double-stranded DNA viruses

Glycosylation is widespread in biology and shapes molecular recognition, yet the diversity and biosynthetic organization of viral glycosyltransferases (GTs) remain poorly understood. Here we integrate structural-alphabet-guided searches and structure similarity networks with genomic-neighbourhood and biosynthetic gene cluster embedding analyses to examine viral GT-A proteins. This approach reveals extensive structural diversity overlooked by sequence-based annotation, including previously uncharacterized viral GT lineages. Most strikingly, we identified a jumbo-phage locus encoding five tandem glycosyltransferases alongside hydroxymethyl-pyrimidine biosynthetic and sulfation-associated enzymes, suggesting a previously unrecognized form of viral DNA modification involving a sulfated pentasaccharide. Beyond these examples, predicted saccharide-associated loci span divergent double-stranded DNA viruses, from giant viruses to tailed phages, and occupy multiple regions of the reference biosynthetic landscape. Together, our findings uncover an underappreciated repertoire of virus-encoded glycosylation machinery and suggest that viral glycan biosynthesis encompasses diverse molecular contexts and potential biological functions.

q-bio.BM↗

Contribution of chromosome-naïve transposon-seeded network of DNA templates to intracellular pools of protein dimers of liquid-liquid phase-separated condensate-forming proteins

Molecular mechanisms governing protein-protein interactions (PPI) contributing to the biogenesis of membraneless condensates remain poorly understood. Observations reported in this contribution demonstrate that liquid-liquid phase-separated (LLPS) condensate-forming proteins and their variants harboring condensate-disrupting human pathogenic mutations are significantly enriched among PPI cascade constituents of the Hybrid Assembly Pathway (HAP) of endogenous multiprotein complexes (EMC). In contrast, no structured or disordered proteins defined as condensate-negative molecules were detected among PPI cascade constituents. Cumulative evidence derived from different lines of analytical inquiries support the concept of the significant contributions of PPI cascade constituents of HAP to the biogenesis of intracellular pools of cell type-specific protein heterodimers assembled with the engagement of at least one protein monomer selected from condensate-forming protein molecules. Analytical evidence documented in this study support the concept that genome-wide chromosome-naïve networks of transposon-seeded DNA-templates nucleate the initiation of PPI cascade chain reactions with the capacity to facilitate the concurrent iterative assembly in human cells of both the membraneless biomolecular condensates and the structured multiprotein complexes. AlphaFold 3D modelling of mutation patterns and analysis of partner-bound experimental structures identified variants located at a measured protein dimerization interface. This contribution nominates numerous protein heterodimers of potential relevance to human pathophysiology for follow-up mechanistic studies of potential fundamental and translational impacts.

q-bio.BM↗

D2ML: Models for Direct-to-Biology Small Molecule Screening

Direct-to-biology (D2B) screening enables rapid testing of newly synthesized molecules without purification. However, uncertain concentrations in crude mixtures and limited purified-compound measurements complicate the use of these data in machine learning. Here we show that models tailored to the structure of D2B data can improve prediction and guide the discovery of more potent and selective compounds. Our assay-aware models predict activity from single-point screens, relative potency across targets from crude dose-response assays, and absolute potency from purified-compound measurements. For crude assays, potency ratios between targets tested from the same well can retain selectivity information despite uncertain concentrations. A joint model that shares information across stages and balances their contributions to training improved predictions over separate neural networks. The largest gain was in crude dose-response selectivity prediction, where rank correlation increased from 0.27 to 0.40. In a separate experiment, independently trained models guided the selection of 84 new compounds to improve ROS1 potency while limiting activity against the anti-target TRKB. Synthesis and testing identified compounds that extended the measured Pareto front, achieving better combinations of potency and selectivity than those in the training data. The most selective new compound achieved a ROS1 IC50 of 14.7 nM and 233-fold selectivity over TRKB. By comparison, the most selective training compound had a ROS1 IC50 of 20.3 nM and 79-fold selectivity. ATLAS provides the experimental setting for this approach, supporting synthesis and screening of 1,000-10,000 model-selected compounds in two- to three-week cycles. Together, these results show how assay-aware learning can use the full D2B assay sequence to guide multi-objective molecular optimization.

q-bio.BM↗