Thermodynamic modeling reveals widespread multivalent binding by RNA-binding proteins

Sohrabi-Jahromi, Salma; Söding, Johannes

doi:10.1093/bioinformatics/btab300

Cited by 4 publications

(1 citation statement)

References 49 publications

(78 reference statements)

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“…Thermodynamic models can help to understand the basis of multivalency effects. [12][13][14] Here we develop such a simplistic model with the aid of macroscopic rate constants and equilibrium constants. We only consider the formation of complexes with 1:1 stoichiometry and we assume identical chemical activity of unbound DNJ inhitopes at all steps along the binding pathway.…”

Section: Introductionmentioning

confidence: 99%

Dissecting Key Multivalent Processes in Glycosidase Inhibition: Insights from Thermodynamic Modelling and Atomistic Simulations

Spichty,

Liang,

Schettini

et al. 2023

Preprint

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Multivalency represents a powerful approach to increase the inhibition potency of moderate glycosidase inhibitors. Regarding the key role of catalytic glycoside hydrolysis in biology, understanding the molecular mechanisms and origin of the multivalent inhibitory effect is of great interest and presents a fascinating playground for theoretical studies. Our teams have recently dissected key processes of multivalent glycosidase inhibition through the use of different neoglycoclusters based on deoxynojirimycin (DNJ) inhitopes and a cyclopeptoid scaffold. This companion article details the theoretical aspects of this former study. A thermodynamic model is developed and validated, compared to literature, and extended to account for particularities of the charged DNJ inhitopes.

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Section: Introductionmentioning

confidence: 99%

Dissecting Key Multivalent Processes in Glycosidase Inhibition: Insights from Thermodynamic Modelling and Atomistic Simulations

Spichty,

Liang,

Schettini

et al. 2023

Preprint

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Sequence pre-training-based graph neural network for predicting lncRNA-miRNA associations

Wang,

Liang,

Liu

et al. 2023

Briefings in Bioinformatics

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MicroRNAs (miRNAs) silence genes by binding to messenger RNAs, whereas long non-coding RNAs (lncRNAs) act as competitive endogenous RNAs (ceRNAs) that can relieve miRNA silencing effects and upregulate target gene expression. The ceRNA association between lncRNAs and miRNAs has been a research hotspot due to its medical importance, but it is challenging to verify experimentally. In this paper, we propose a novel deep learning scheme, i.e. sequence pre-training-based graph neural network (SPGNN), that combines pre-training and fine-tuning stages to predict lncRNA–miRNA associations from RNA sequences and the existing interactions represented as a graph. First, we utilize a sequence-to-vector technique to generate pre-trained embeddings based on the sequences of all RNAs during the pre-training stage. In the fine-tuning stage, we use Graph Neural Network to learn node representations from the heterogeneous graph constructed using lncRNA–miRNA association information. We evaluate our proposed scheme SPGNN on our newly collected animal lncRNA–miRNA association dataset and demonstrate that combining the $k$-mer technique and Doc2vec model for pre-training with the Simple Graph Convolution Network for fine-tuning is effective in predicting lncRNA–miRNA associations. Our approach outperforms state-of-the-art baselines across various evaluation metrics. We also conduct an ablation study and hyperparameter analysis to verify the effectiveness of each component and parameter of our scheme. The complete code and dataset are available on GitHub: https://github.com/zixwang/SPGNN.

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Bayesian Markov models improve the prediction of binding motifs beyond first order

Meier

Roth

et al. 2021

NAR Genomics and Bioinformatics

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Transcription factors (TFs) regulate gene expression by binding to specific DNA motifs. Accurate models for predicting binding affinities are crucial for quantitatively understanding of transcriptional regulation. Motifs are commonly described by position weight matrices, which assume that each position contributes independently to the binding energy. Models that can learn dependencies between positions, for instance, induced by DNA structure preferences, have yielded markedly improved predictions for most TFs on in vivo data. However, they are more prone to overfit the data and to learn patterns merely correlated with rather than directly involved in TF binding. We present an improved, faster version of our Bayesian Markov model software, BaMMmotif2. We tested it with state-of-the-art motif discovery tools on a large collection of ChIP-seq and HT-SELEX datasets. BaMMmotif2 models of fifth-order achieved a median false-discovery-rate-averaged recall 13.6% and 12.2% higher than the next best tool on 427 ChIP-seq datasets and 164 HT-SELEX datasets, respectively, while being 8 to 1000 times faster. BaMMmotif2 models showed no signs of overtraining in cross-cell line and cross-platform tests, with similar improvements on the next-best tool. These results demonstrate that dependencies beyond first order clearly improve binding models for most TFs.

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Thermodynamic modeling reveals widespread multivalent binding by RNA-binding proteins

Cited by 4 publications

References 49 publications

Dissecting Key Multivalent Processes in Glycosidase Inhibition: Insights from Thermodynamic Modelling and Atomistic Simulations

Dissecting Key Multivalent Processes in Glycosidase Inhibition: Insights from Thermodynamic Modelling and Atomistic Simulations

Sequence pre-training-based graph neural network for predicting lncRNA-miRNA associations

Bayesian Markov models improve the prediction of binding motifs beyond first order

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