Automated Protein Secondary Structure Assignment from Cα Positions Using Neural Networks

Saqib, Mohammad N.; Krys, Justyna; Gront, Dominik

doi:10.3390/biom12060841

Cited by 4 publications

(2 citation statements)

References 48 publications

(55 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This method facilitates the assignment of protein helices based on their geometric characteristics. The SACF method has been extensively discussed and validated in the literature, with relevant references including [ 55 , 56 , 57 ]. Additionally, various algorithms have been developed for the assignment of protein helices, leveraging helix geometry as a primary criterion.…”

Section: The Background Theorymentioning

confidence: 99%

Knotify_V2.0: Deciphering RNA Secondary Structures with H-Type Pseudoknots and Hairpin Loops

Kolaitis,

Makris,

Karagiannis

et al. 2024

Genes

View full text Add to dashboard Cite

Accurately predicting the pairing order of bases in RNA molecules is essential for anticipating RNA secondary structures. Consequently, this task holds significant importance in unveiling previously unknown biological processes. The urgent need to comprehend RNA structures has been accentuated by the unprecedented impact of the widespread COVID-19 pandemic. This paper presents a framework, Knotify_V2.0, which makes use of syntactic pattern recognition techniques in order to predict RNA structures, with a specific emphasis on tackling the demanding task of predicting H-type pseudoknots that encompass bulges and hairpins. By leveraging the expressive capabilities of a Context-Free Grammar (CFG), the suggested framework integrates the inherent benefits of CFG and makes use of minimum free energy and maximum base pairing criteria. This integration enables the effective management of this inherently ambiguous task. The main contribution of Knotify_V2.0 compared to earlier versions lies in its capacity to identify additional motifs like bulges and hairpins within the internal loops of the pseudoknot. Notably, the proposed methodology, Knotify_V2.0, demonstrates superior accuracy in predicting core stems compared to state-of-the-art frameworks. Knotify_V2.0 exhibited exceptional performance by accurately identifying both core base pairing that form the ground truth pseudoknot in 70% of the examined sequences. Furthermore, Knotify_V2.0 narrowed the performance gap with Knotty, which had demonstrated better performance than Knotify and even surpassed it in Recall and F1-score metrics. Knotify_V2.0 achieved a higher count of true positives (tp) and a significantly lower count of false negatives (fn) compared to Knotify, highlighting improvements in Prediction and Recall metrics, respectively. Consequently, Knotify_V2.0 achieved a higher F1-score than any other platform. The source code and comprehensive implementation details of Knotify_V2.0 are publicly available on GitHub.

show abstract

Section: The Background Theorymentioning

confidence: 99%

Knotify_V2.0: Deciphering RNA Secondary Structures with H-Type Pseudoknots and Hairpin Loops

Kolaitis,

Makris,

Karagiannis

et al. 2024

Genes

View full text Add to dashboard Cite

show abstract

“…Recently, many machine learning approaches were developed. One example is the implementation of a neural network-based classifier called HECA [ 37 ]. HECA has two hidden layers, each with 128 neurons.…”

Section: Introductionmentioning

confidence: 99%

Mathematical and Machine Learning Approaches for Classification of Protein Secondary Structure Elements from Cα Coordinates

et al. 2023

View full text Add to dashboard Cite

Determining Secondary Structure Elements (SSEs) for any protein is crucial as an intermediate step for experimental tertiary structure determination. SSEs are identified using popular tools such as DSSP and STRIDE. These tools use atomic information to locate hydrogen bonds to identify SSEs. When some spatial atomic details are missing, locating SSEs becomes a hinder. To address the problem, when some atomic information is missing, three approaches for classifying SSE types using Cα atoms in protein chains were developed: (1) a mathematical approach, (2) a deep learning approach, and (3) an ensemble of five machine learning models. The proposed methods were compared against each other and with a state-of-the-art approach, PCASSO.

show abstract

Recent Advances in Computational Prediction of Secondary and Supersecondary Structures from Protein Sequences

Zhang,

Qian,

Zou

et al. 2024

Methods in Molecular Biology

View full text Add to dashboard Cite

Automated Protein Secondary Structure Assignment from Cα Positions Using Neural Networks

Cited by 4 publications

References 48 publications

Knotify_V2.0: Deciphering RNA Secondary Structures with H-Type Pseudoknots and Hairpin Loops

Knotify_V2.0: Deciphering RNA Secondary Structures with H-Type Pseudoknots and Hairpin Loops

Mathematical and Machine Learning Approaches for Classification of Protein Secondary Structure Elements from Cα Coordinates

Recent Advances in Computational Prediction of Secondary and Supersecondary Structures from Protein Sequences

Contact Info

Product

Resources

About