Secondary and Tertiary Structure Prediction of Cytokines

Aptamers are short single-stranded DNA, RNA, or synthetic Xeno nucleic acids (XNA) molecules that can interact with corresponding targets with high affinity. Owing to their unique features, including low cost of production, easy chemical modification, high thermal stability, reproducibility, as well as low levels of immunogenicity and toxicity, aptamers can be used as an alternative to antibodies in diagnostics and therapeutics. Systematic evolution of ligands by exponential enrichment (SELEX), an experimental approach for aptamer screening, allows the selection and identification of in vitro aptamers with high affinity and specificity. However, the SELEX process is time consuming and characterization of the representative aptamer candidates from SELEX is rather laborious. Artificial intelligence (AI) could help to rapidly identify the potential aptamer candidates from a vast number of sequences. This review discusses the advancements of AI pipelines/methods, including structure-based and machine/deep learning-based methods, for predicting the binding ability of aptamers to targets. Structure-based methods are the most used in computer-aided drug design. For this part, we review the secondary and tertiary structure prediction methods for aptamers, molecular docking, as well as molecular dynamic simulation methods for aptamer–target binding. We also performed analysis to compare the accuracy of different secondary and tertiary structure prediction methods for aptamers. On the other hand, advanced machine-/deep-learning models have witnessed successes in predicting the binding abilities between targets and ligands in drug discovery and thus potentially offer a robust and accurate approach to predict the binding between aptamers and targets. The research utilizing machine-/deep-learning techniques for prediction of aptamer–target binding is limited currently. Therefore, perspectives for models, algorithms, and implementation strategies of machine/deep learning-based methods are discussed. This review could facilitate the development and application of high-throughput and less laborious in silico methods in aptamer selection and characterization.

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Direct-Coupling Analysis of nucleotide coevolution facilitates RNA secondary and tertiary structure prediction

Nucleic Acids Research ◽

10.1093/nar/gkv932 ◽

2015 ◽

pp. gkv932 ◽

Cited By ~ 30

Author(s):

Eleonora De Leonardis ◽

Benjamin Lutz ◽

Sebastian Ratz ◽

Simona Cocco ◽

Rémi Monasson ◽

...

Keyword(s):

Structure Prediction ◽

Tertiary Structure ◽

Direct Coupling ◽

Coupling Analysis ◽

Tertiary Structure Prediction ◽

Direct Coupling Analysis ◽

Secondary And Tertiary Structure

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RNA Secondary and Tertiary Structure Prediction by Tracing Nucleotide Co-Evolution with Direct Coupling Analysis

Biophysical Journal ◽

10.1016/j.bpj.2015.11.1960 ◽

2016 ◽

Vol 110 (3) ◽

pp. 364a ◽

Cited By ~ 1

Author(s):

Eleonora De Leonardis ◽

Benjamin Lutz ◽

Sebastian Ratz ◽

Cocco Simona ◽

Remi Monasson ◽

...

Keyword(s):

Structure Prediction ◽

Tertiary Structure ◽

Direct Coupling ◽

Coupling Analysis ◽

Tertiary Structure Prediction ◽

Direct Coupling Analysis ◽

Secondary And Tertiary Structure

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Protein tertiary structure prediction and refinement using deep learning and Rosetta in CASP14

Proteins Structure Function and Bioinformatics ◽

10.1002/prot.26194 ◽

2021 ◽

Author(s):

Ivan Anishchenko ◽

Minkyung Baek ◽

Hahnbeom Park ◽

Naozumi Hiranuma ◽

David E. Kim ◽

...

Keyword(s):

Deep Learning ◽

Structure Prediction ◽

Tertiary Structure ◽

Tertiary Structure Prediction ◽

Protein Tertiary Structure ◽

Protein Tertiary Structure Prediction

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Template-based prediction of protein structure with deep learning

BMC Genomics ◽

10.1186/s12864-020-07249-8 ◽

2020 ◽

Vol 21 (S11) ◽

Author(s):

Haicang Zhang ◽

Yufeng Shen

Keyword(s):

Deep Learning ◽

Protein Structure ◽

Structure Prediction ◽

Tertiary Structure ◽

Query Sequence ◽

Dynamic Programming Algorithm ◽

Tertiary Structure Prediction ◽

Protein Tertiary Structure ◽

Protein Threading ◽

Protein Tertiary Structure Prediction

Abstract Background Accurate prediction of protein structure is fundamentally important to understand biological function of proteins. Template-based modeling, including protein threading and homology modeling, is a popular method for protein tertiary structure prediction. However, accurate template-query alignment and template selection are still very challenging, especially for the proteins with only distant homologs available. Results We propose a new template-based modelling method called ThreaderAI to improve protein tertiary structure prediction. ThreaderAI formulates the task of aligning query sequence with template as the classical pixel classification problem in computer vision and naturally applies deep residual neural network in prediction. ThreaderAI first employs deep learning to predict residue-residue aligning probability matrix by integrating sequence profile, predicted sequential structural features, and predicted residue-residue contacts, and then builds template-query alignment by applying a dynamic programming algorithm on the probability matrix. We evaluated our methods both in generating accurate template-query alignment and protein threading. Experimental results show that ThreaderAI outperforms currently popular template-based modelling methods HHpred, CNFpred, and the latest contact-assisted method CEthreader, especially on the proteins that do not have close homologs with known structures. In particular, in terms of alignment accuracy measured with TM-score, ThreaderAI outperforms HHpred, CNFpred, and CEthreader by 56, 13, and 11%, respectively, on template-query pairs at the similarity of fold level from SCOPe data. And on CASP13’s TBM-hard data, ThreaderAI outperforms HHpred, CNFpred, and CEthreader by 16, 9 and 8% in terms of TM-score, respectively. Conclusions These results demonstrate that with the help of deep learning, ThreaderAI can significantly improve the accuracy of template-based structure prediction, especially for distant-homology proteins.

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