rsRNASP: A residue-separation-based statistical potential for RNA 3D structure evaluation

Tan, Ya-Lan; Wang, Xunxun; Shi, Ya-Zhou; Zhang, Wenbing; Tan, Zhi-Jie

doi:10.1016/j.bpj.2021.11.016

Cited by 29 publications

(99 citation statements)

References 95 publications

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“…In analogy to the all-atom rsRNASP (77), the average reference state was used for short-ranged energy E short , while for long-ranged energy E long , the finite-ideal-gas reference state (59) was used in cgRNASP instead of the random-walk-chain reference state in rsRNASP since the former one also has relatively good performance and has been widely used in protein structure evaluation (48,72). Please see the Supplementary Materials for the details of the use of the reference states for deriving the short- and long-ranged potentials, and see Refs (54,59,72) for the detailed description of the existing reference states.…”

Section: Methodsmentioning

confidence: 99%

“…The short-ranged energy E short in cgRNASP is treated in a more complete and subtle way than the all-atom rsRNASP (77) and is given by and where k ∈ range means that residue separation k should in the k ranges in Eq. (4).…”

Section: Methodsmentioning

confidence: 99%

“…The dataset was produced based on the RNA 3D Hub non-redundant set (81) (Release 3.102), which can be found at http://rna.bgsu.edu/rna3dhub/nrlist. The training native set contains 191 RNA structures with chain length >10nt, sequence identity > 80%, coverage > 80% and X-ray resolution < 3.5 Å, and excludes those RNAs complexed with proteins (77). The PDB IDs of these 191 RNAs were also listed in Table S1 in the Supplementary Material.…”

Section: Methodsmentioning

confidence: 99%

“…Moreover, similar to rsRNASP (77), the long-ranged potential in cgRNASP also captures some tertiary base-stacking interactions including base-stacking between two residues in two adjacent branches of RNA 3D structures (e.g., tetraloop receptor), coaxial-stacking at junction region and base-stacking in triple-helical region; see Fig. S5 in the Supplementary Material and see also Ref (77).…”

Section: How Can Cgrnasp With Coarse-grained Representation Have Good...mentioning

confidence: 98%

“…Despite of “black-box” training/learning process, RNA3DCNN built through 3D convolutional neural networks exhibits remarkably improved performance in identifying native structures for RNA-Puzzles dataset compared with the top traditional statistical potentials (75), and the newly developed scoring function of ARES from deep neural network based on training data from FARFAR2 showed rather good performance for evaluating structures from FARFAR2 (76). Very recently, we have developed an all-atom statistical potential of rsRNASP by distinguishing short- and long-ranged interactions at residue separation level, and for the test datasets from various 3D structure prediction models, rsRNASP has visibly improved performance than existing statistical potentials and scoring functions from neural networks (47,77).…”

Section: Introductionmentioning

confidence: 99%

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cgRNASP: coarse-grained statistical potentials with residue separation for RNA structure evaluation

Tan

Wang

et al. 2022

Preprint

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Knowledge-based statistical potentials are very important for RNA 3-dimensional (3D) structure prediction and evaluation. In recent years, various coarse-grained (CG) and all-atom models have been developed for predicting RNA 3D structures, while there is still lack of reliable CG statistical potentials not only for CG structure evaluation but also for all-atom structure evaluation at high efficiency. In this work, we have developed a series of residue-separation-based CG statistical potentials at different CG levels for RNA 3D structure evaluation, namely cgRNASP, which is composed of long-ranged and short-ranged interactions by residue separation. Compared with the newly developed all-atom rsRNASP, the short-ranged interaction in cgRNASP was involved more subtly and completely through explicitly adding the interactions between nearest neighbor residues and between next-nearest ones. Our examinations show that, the performance of cgRNASP varies with CG levels and compared with rsRNASP, cgRNASP can have similarly good performance for extensive test datasets and slightly better performance for the realistic RNA-Puzzles dataset. Furthermore, cgRNASP is strikingly more efficient than all-atom potentials such as rsRNASP, and can be apparently superior to other all-atom statistical potentials and scoring functions trained from neural networks for the RNA-Puzzles dataset. cgRNASP is available at https://github.com/Tan-group/cgRNASP.

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: How Can Cgrnasp With Coarse-grained Representation Have Good...mentioning

confidence: 98%

Section: Introductionmentioning

confidence: 99%

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cgRNASP: coarse-grained statistical potentials with residue separation for RNA structure evaluation

Tan

Wang

et al. 2022

Preprint

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Deep dive into RNA: a systematic literature review on RNA structure prediction using machine learning methods

Budnik,

Wawrzyniak,

Grala

et al. 2024

Artif Intell Rev

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The discovery of non-coding RNAs (ncRNAs) has expanded our comprehension of RNAs’ inherent nature and capabilities. The intricate three-dimensional structures assumed by RNAs dictate their specific functions and molecular interactions. However, the limited number of mapped structures, partly due to experimental constraints of methods such as nuclear magnetic resonance (NMR), highlights the importance of in silico prediction solutions. This is particularly crucial in potential applications in therapeutic drug discovery. In this context, machine learning (ML) methods have emerged as prominent candidates, having previously demonstrated prowess in solving complex challenges across various domains. This review focuses on analyzing the development of ML-based solutions for RNA structure prediction, specifically oriented toward recent advancements in the deep learning (DL) domain. A systematic analysis of 33 works reveals insights into the representation of RNA structures, secondary structure motifs, and tertiary interactions. The review highlights current trends in ML methods used for RNA structure prediction, demonstrates the growing research involvement in this field, and summarizes the most valuable findings.

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FebRNA: An automated fragment-ensemble-based model for building RNA 3D structures

Zhou¹,

Wang²,

Yu³

et al. 2022

Biophysical Journal

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rsRNASP: A residue-separation-based statistical potential for RNA 3D structure evaluation

Cited by 29 publications

References 95 publications

cgRNASP: coarse-grained statistical potentials with residue separation for RNA structure evaluation

cgRNASP: coarse-grained statistical potentials with residue separation for RNA structure evaluation

Deep dive into RNA: a systematic literature review on RNA structure prediction using machine learning methods

FebRNA: An automated fragment-ensemble-based model for building RNA 3D structures

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