Comment on: ``Solving the riddle of the bright mismatches: Labeling and effective binding in oligonucleotide arrays"

E. Carlon; T. Heim; J. Klein Wolterink; G. T. Barkema

doi:10.1103/physreve.73.063901

Home/Archive/arXiv

arXiv · q-bio/0607023

Comment on: ``Solving the riddle of the bright mismatches: Labeling and effective binding in oligonucleotide arrays"

E. Carlon·T. Heim·J. Klein Wolterink·G. T. Barkema

View original source ↗Read source document ↗Export metadata ↓

Abstract

In a recent paper [Phys. Rev. E 68, 011906 (2003)], Naef and Magnasco suggested that the "bright" mismatches observed in Affymetrix microarray experiments are caused by the fluorescent molecules used to label RNA target sequences, which would impede target-probe hybridization. Their conclusion is based on the observation of "unexpected" asymmetries in the affinities obtained by fitting microarray data from publicly available experiments. We point out here that the observed asymmetry is due to the inequivalence of RNA and DNA, and that the reported affinities are consistent with stacking free energies obtained from melting experiments of unlabeled nucleic acids in solution. The conclusion of Naef and Magnasco is therefore based on an unjustified assumption.

Explore related subjects

q-bio.BM cond-mat.soft physics.chem-ph

Keep this discovery

Explore connections, maps & timelines

BibTeX RIS

E. Carlon, T. Heim, J. Klein Wolterink, G. T. Barkema. 2006-07-18. Comment on: ``Solving the riddle of the bright mismatches: Labeling and effective binding in oligonucleotide arrays". https://doi.org/10.1103/physreve.73.063901

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

KEEP EXPLORING

Related papers

arXiv2026

Predicting protein structures at any folding stage from sequence

Protein structure predictors reliably identify native conformations, but do not describe the continuum connecting unfolded ensembles to folded states. Here we present FoldScape, a sequence-conditioned deep learning framework that generates C$α$ conformational ensembles at a specified folding fraction $Q$. FoldScape reconstructs three-dimensional chains through hierarchical, sliding-window regressions of one-dimensional inter-residue distances, followed by chain assembly and sampling with a rectified-flow decoder. It is trained on multi-scale coarse-grained folding trajectories and all-atom mechanical-unfolding trajectories. Across 20 human proteins (36-857 residues), at matched sample size, FoldScape covers about an order of magnitude more of the folding coordinate than seed-varied AlphaFold 3 ensembles, robustly across proteins, order parameters and binning choices. AlphaFold 3 better anchors the immediate native vicinity, which FoldScape does not reach, making the approaches complementary. FoldScape reconstruction accuracy remains approximately constant along $Q$, from collapsed coil to near-native globule, rather than deteriorating away from the native state. A stride-one crop-and-superpose scheme extends inference to chains several times longer than a single chain-level forward pass. We also show that training trajectories resolve mechanical unfolding at residue resolution, that elongation-conditioned ensembles exhibit reproducible continuous observables across pulling velocities, and that partially unfolded chains form geometrically coherent segments. FoldScape thus combines broad coverage of folding stages with stable reconstruction accuracy, without per-protein simulation.

q-bio.BM↗

arXiv2026

Ablating Design Choices for Protein Ensemble Generators Across Structural Regimes

Generative models of protein conformational ensembles inherit much of their design from structure prediction for folded proteins. Ordered and disordered proteins, however, occupy distinct structural regimes, and we investigate how design choices hold up across them. Using a minimal C$α$ flow-matching model, we ablate choices of architecture, sequence conditioning, rigid-body alignment and training source, and evaluate each variant against experimental SAXS and NMR chemical-shift data partitioned into ordered, disordered, multi-domain and transitional proteins. Mixture-of-experts routing, a static pair representation and a disorder probe have no detectable effect, a smaller protein language model performs worse on ordered proteins but not on disordered ones, and alignment in the loss degrades agreement with SAXS. Replacing coarse-grained simulations of disordered proteins with fragment-assembled ensembles improves agreement with chemical shifts at a cost to SAXS, while an equal mixture of the two keeps most of the gain at a smaller cost. This points to the training data, rather than the architecture, as the main constraint.

q-bio.BM↗

arXiv2026

Automatic Generation of Novel Highly-Functional Molecular Sequences

While multiple methods now exist to generate functional sequence variants from existing annotated examples, it is unclear how well such methods can be used to generate sequences that exceed the functionality of those contained in the example set. Using two complete fitness landscapes, we quantify how effectively generative tools can search sequence space and find rare, novel, highly functional sequences, focusing on variational auto-encoders and information-theoretic methods. We find that an information-theoretic method that does not require training and runs significantly faster than standard Machine-Learning/Artificial Intelligence (ML/AI) methods also is significantly more effective in finding rare highly-functional sequences. We discuss the application of this novel method to discover rare polypeptides with anti-bacterial properties that are specific to bacterial, but not eukaryotic, cell membranes.

q-bio.BM↗