Search arXivSearch

arXiv · 0708.2527

Model for processive nucleotide and repeat additions by the telomerase

Abstract

A model is presented to describe the nucleotide and repeat addition processivity by the telomerase. In the model, the processive nucleotide addition is implemented on the basis of two requirements: One is that stem IV loop stimulates the chemical reaction of nucleotide incorporation, and the other one is the existence of an ssRNA-binding site adjacent to the polymerase site that has a high affinity for the unpaired base of the template. The unpairing of DNA:RNA hybrid after the incorporation of the nucleotide paired with the last base on the template, which is the prerequisite for repeat addition processivity, is caused by a force acting on the primer. The force is resulted from the unfolding of stem III pseudoknot that is induced by the swinging of stem IV loop towards the nucleotide-bound polymerase site. Based on the model, the dynamics of processive nucleotide and repeat additions by Tetrahymena telomerase are quantitatively studied, which give good explanations to the previous experimental results. Moreover, some predictions are presented. In particular, it is predicted that the repeat addition processivity is mainly determined by the difference between the free energy required to disrupt the DNA:RNA hybrid and that required to unfold the stem III pseudoknot, with the large difference corresponding to a low repeat addition processivity while the small one corresponding to a high repeat addition processivity.

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Ping Xie. 2007-08-19. Model for processive nucleotide and repeat additions by the telomerase. https://arxiv.org/abs/0708.2527

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

KEEP EXPLORING

Related papers

SaltyMeta: a curated benchmark and protein language model-informed web tool for salty peptide prediction

Excess sodium intake remains a major public health challenge, while salty and saltiness-enhancing peptides offer a potential route to preserve sensory saltiness in reduced-sodium foods. Machine-learning studies of salty peptides, however, are constrained by small datasets, heterogeneous evidence standards, uncertain negative labels, and sequence similarity leakage. Here we present SaltyMeta, a curated benchmark and web-accessible screening framework for salty or saltiness-enhancing short peptides. The benchmark contains 580 peptides, including 280 positive peptides and 300 negative peptides, all standardized to 2-15 residue one-letter amino-acid sequences. Quality control found no non-standard residues, exact duplicates, or positive-negative overlaps. A similarity-grouped split retained 456 peptides for training and 124 for held-out testing. We evaluated 548 interpretable peptide descriptors, frozen ESM2 embeddings at 8M, 35M, and 150M parameter scales, and descriptor-embedding fusion models under grouped cross-validation. The traditional ExtraTrees baseline selected by training-set grouped cross-validation achieved ROC-AUC=0.693 in cross-validation and ROC-AUC=0.704, PR-AUC=0.702, F1=0.626, and MCC=0.304 on the held-out test set. The best initial protein-language-model fusion was traditional descriptors plus ESM2-8M embeddings, with grouped CV ROC-AUC=0.696 and test ROC-AUC=0.700. Advanced optimization using PCA95 dimensionality reduction and ExtraTrees feature-importance filtering yielded a practical ESM2-8M PCA95 top-300 model with test ROC-AUC=0.715 and PR-AUC=0.703, although repeated-CV gains remained modest. SaltyMeta is a transparent prioritization tool, not a sensory validation substitute. We provide benchmark, models, scripts, GitHub, and Streamlit for reproducible screening of food-derived peptides.

q-bio.BM

Synthesizing State-of-the-Art Structure Predictions from Soup of Co-folding Models

Co-folding models have advanced rapidly, yet no single model consistently performs best across all biomolecular complexes. This raises the question of whether independently trained co-folding models encode complementary information that can be transferred across co-folding models. We introduce SoupFold, which improves co-folding predictions by learning simple mappings between the representation spaces of co-folding models. At inference time, SoupFold transfers and incorporates representations from other co-folding models to update the representation used for structure prediction. Importantly, this does not re-train the co-folding models. We evaluate SoupFold on protein-protein and protein-ligand prediction tasks of FoldBench using AlphaFold3, Protenix, ESMFold2, and OpenDDE. By combining their representations, SoupFold achieves state-of-the-art performance on both protein-protein and protein-ligand structure prediction, showing that independently trained co-folding models encode complementary information that can be effectively transferred across models.

q-bio.BM

Exploring Optimal Parameters for Ligand-Based Virtual Screening in Early Drug Discovery

Ligand-based virtual screening depends on choices that are often treated as implementation details, including the similarity threshold, fingerprint setting and atom-invariant scheme. We examined how these choices altered the composition of ranked searches against the Enamine library for four aminergic reference ligands: atomoxetine, bupropion, mirtazapine and venlafaxine. Candidate sets were evaluated by compound-weighted scaffold novelty, normalized Shannon scaffold diversity and three computational estimates of synthetic accessibility. We first identified ligand-specific operating points along cumulative Tanimoto-ranked searches. We then compared five extended-connectivity fingerprint settings at matched retrieval depths and compared ECFP4 with the feature-class analogue FCSFP4. Finally, 240 records, corresponding to 229 unique structures, were scored independently by three chemists who were blinded to the computational scores. No single Tanimoto cutoff described all four searches. ECFP8 provided the most stable pooled setting, although venlafaxine favored ECFP2. ECFP4 was the stronger primary fingerprint in pooled comparisons, whereas FCSFP4 contributed nonredundant chemical space. Agreement among individual chemists was moderate, and the mean rating was more reliable than a single rating. SCScore showed the highest association with the blinded consensus, but performance varied by ligand. These results support a staged screening design in which a pooled default is followed by ligand-specific calibration and expert review.

q-bio.BM