Assigning secondary structure in proteins using AI

Antony, Jisna Vellara; Madhu, Prayagh; Jayaraj, P. B.; Yadav, Hemant

doi:10.1007/s00894-021-04825-x

Cited by 5 publications

(4 citation statements)

References 52 publications

(41 reference statements)

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“…The number of Secondary Structure Assignment Methods (SSAM) developed is high. At least 40 different ones can be found in the literature [ 29 , 34 ], e.g., DEFINE [ 35 ], SEGNO [ 36 ], XTLSSTR [ 37 ], P-SEA [ 38 ], KAKSI [ 39 ], DLFSA [ 40 ] or P-CURVE [ 41 ]. They are based on very different metrics and rules, leading to a consensus of only 80% [ 26 , 28 ].…”

Section: The Alpha and The Omega Of The βmentioning

confidence: 99%

“…It is therefore particularly unfortunate that they are so often overlooked in analyses when even the proteins involved in SARS-CoV-2 are full of β-turns [ 87 ]. Moreover, as new SSAMs are still proposed [ 40 , 88 , 89 , 90 , 91 , 92 , 93 ], studies on this local conformation continue, both within proteins and peptides; the latter have an extremely high β-turn propensity [ 94 , 95 , 96 ]. A fine and recent example is the famous AlphaFold 2 prediction methodology [ 97 ].…”

Section: Conclusion and At Least Some Perspectivesmentioning

confidence: 99%

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A Perspective on the (Rise and Fall of) Protein β-Turns

Brevern

2022

IJMS

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The β-turn is the third defined secondary structure after the α-helix and the β-sheet. The β-turns were described more than 50 years ago and account for more than 20% of protein residues. Nonetheless, they are often overlooked or even misunderstood. This poor knowledge of these local protein conformations is due to various factors, causes that I discuss here. For example, confusion still exists about the assignment of these local protein structures, their overlaps with other structures, the potential absence of a stabilizing hydrogen bond, the numerous types of β-turns and the software’s difficulty in assigning or visualizing them. I also propose some ideas to potentially/partially remedy this and present why β-turns can still be helpful, even in the AlphaFold 2 era.

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Section: The Alpha and The Omega Of The βmentioning

confidence: 99%

Section: Conclusion and At Least Some Perspectivesmentioning

confidence: 99%

A Perspective on the (Rise and Fall of) Protein β-Turns

Brevern

2022

IJMS

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“…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

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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.

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“…Similarly, it is also 96% accurate with respect to DSSP. More sophisticated methods employ Neural Networks [ 16 ] and Convolutional Neural Networks (CNN), as observed in the DLFSA [ 17 ]. The accuracy reported by the authors in the latter case is somewhat lower: around 83% depending on the PDB files.…”

Section: Introductionmentioning

confidence: 99%

Automated Protein Secondary Structure Assignment from Cα Positions Using Neural Networks

2022

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The assignment of secondary structure elements in protein conformations is necessary to interpret a protein model that has been established by computational methods. The process essentially involves labeling the amino acid residues with H (Helix), E (Strand), or C (Coil, also known as Loop). When particular atoms are absent from an input protein structure, the procedure becomes more complicated, especially when only the alpha carbon locations are known. Various techniques have been tested and applied to this problem during the last forty years. The application of machine learning techniques is the most recent trend. This contribution presents the HECA classifier, which uses neural networks to assign protein secondary structure types. The technique exclusively employs Cα coordinates. The Keras (TensorFlow) library was used to implement and train the neural network model. The BioShell toolkit was used to calculate the neural network input features from raw coordinates. The study’s findings show that neural network-based methods may be successfully used to take on structure assignment challenges when only Cα trace is available. Thanks to the careful selection of input features, our approach’s accuracy (above 97%) exceeded that of the existing methods.

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Assigning secondary structure in proteins using AI

Cited by 5 publications

References 52 publications

A Perspective on the (Rise and Fall of) Protein β-Turns

A Perspective on the (Rise and Fall of) Protein β-Turns

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

Automated Protein Secondary Structure Assignment from Cα Positions Using Neural Networks

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