AptaNet as a deep learning approach for aptamer–protein interaction prediction

Emami, Neda; Ferdousi, Reza

doi:10.1038/s41598-021-85629-0

Cited by 31 publications

(37 citation statements)

References 113 publications

(102 reference statements)

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“…On the bright side, these algorithms are constantly being refined through the release of new and improved software [ 107 , 108 , 109 ]. Finally, special emphasis should be placed on deep learning methods, whose efficiency has outperformed other methodologies in 3D RNA structure ranking [ 110 , 111 ] and have begun to appear in the computational aptamer field as well [ 112 , 113 ].…”

Section: Nucleic Acid Aptamersmentioning

confidence: 99%

Electrochemical Aptasensors for Antibiotics Detection: Recent Achievements and Applications for Monitoring Food Safety

Evtugyn

Porfireva

Tsekenis

et al. 2022

Sensors

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Antibiotics are often used in human and veterinary medicine for the treatment of bacterial diseases. However, extensive use of antibiotics in agriculture can result in the contamination of common food staples such as milk. Consumption of contaminated products can cause serious illness and a rise in antibiotic resistance. Conventional methods of antibiotics detection such are microbiological assays chromatographic and mass spectroscopy methods are sensitive; however, they require qualified personnel, expensive instruments, and sample pretreatment. Biosensor technology can overcome these drawbacks. This review is focused on the recent achievements in the electrochemical biosensors based on nucleic acid aptamers for antibiotic detection. A brief explanation of conventional methods of antibiotic detection is also provided. The methods of the aptamer selection are explained, together with the approach used for the improvement of aptamer affinity by post-SELEX modification and computer modeling. The substantial focus of this review is on the explanation of the principles of the electrochemical detection of antibiotics by aptasensors and on recent achievements in the development of electrochemical aptasensors. The current trends and problems in practical applications of aptasensors are also discussed.

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Section: Nucleic Acid Aptamersmentioning

confidence: 99%

Electrochemical Aptasensors for Antibiotics Detection: Recent Achievements and Applications for Monitoring Food Safety

Evtugyn

Porfireva

Tsekenis

et al. 2022

Sensors

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“…In his study, [23] revealed an innovative deep learning methodology for predicting API that he dubbed AptaNet. AptaNet is one of a kind since it is capable of predicting API by using the sequencebased attributes of aptamers in addition to the physicochemical and conformational properties of targets.…”

Section: Literature Reviewmentioning

confidence: 99%

“…In addition to that, we use a deep neural network as well as a system for balancing things out. To determine how effectively AptaNet functions, [23] has conducted a great deal of research and testing. Experiments show that AptaNet has higher accuracy than other methods that were investigated for this study on their 32 benchmark datasets, where Aptamers were encoded utilizing two distinct strategies (k-mer frequency and reverse complement k-mer frequency).…”

Section: Literature Reviewmentioning

confidence: 99%

Neural Network-Based Prediction of Potential Ribonucleic Acid Aptamers to Target Protein

Mahmood¹,

Malik²,

Khan³

et al. 2022

JCBI

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Aptamers are short strands of nucleic acid with a single strand that may unite to target a certain molecule in a selective and specific manner. SELEX experiments are the typical method used for identifying aptamers in vitro (systematic evolution of ligands by exponential enrichment). Several different computational methods have been developed to locate aptamers. The purpose of this research is to identify and make predictions on the possible RNA aptamers that may be used to target the protein. To do this, we propose the use of a multi-layer perceptron neural network with sixteen layers that are trained to locate possible aptamers of a protein target. This network is trained by extracting the main properties of RNA sequences. The outcome of our proposed model is compared to the output of two well-known machine learning classifiers, namely random forest (RF) and support vector machine (SVM). Additionally, we undertake the independent testing of our model on the benchmark dataset, which allows us to reach the highest accuracy possible. As a consequence of this, our model obtains an accuracy of 98.44% and an MCC of 0.9123 during the 15-fold cross-validation, and it achieves an accuracy of 98.10% and an MCC of 0.9354 when the leave-one-out cross-validation is performed. We are certain that our approach will contribute to a reduction in the amount of money and time spent on in vitro testing. Therefore, restricting the length of the initial pool of potential nucleic acid pattern combinations.

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“…Unfortunately, these methods present the drawbacks of increasing the process costs and complexity [ 37 , 45 , 46 ]. Machine learning models have been recently proposed as an additional tool for aptamer design, but up to now, only a few examples of aptamer-protein analyses have been reported [ 47 , 48 , 49 ]. To the best of our knowledge, no machine learning models for small aptamer molecules have been reported and they can have a significant impact on several applications such as DNA, toxin, heavy metal, antibiotic, ion, molecular marker, and virus detection.…”

Section: Introductionmentioning

confidence: 99%

Smart Approach for the Design of Highly Selective Aptamer-Based Biosensors

et al. 2022

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Aptamers are chemically synthesized single-stranded DNA or RNA oligonucleotides widely used nowadays in sensors and nanoscale devices as highly sensitive biorecognition elements. With proper design, aptamers are able to bind to a specific target molecule with high selectivity. To date, the systematic evolution of ligands by exponential enrichment (SELEX) process is employed to isolate aptamers. Nevertheless, this method requires complex and time-consuming procedures. In silico methods comprising machine learning models have been recently proposed to reduce the time and cost of aptamer design. In this work, we present a new in silico approach allowing the generation of highly sensitive and selective RNA aptamers towards a specific target, here represented by ammonium dissolved in water. By using machine learning and bioinformatics tools, a rational design of aptamers is demonstrated. This “smart” SELEX method is experimentally proved by choosing the best five aptamer candidates obtained from the design process and applying them as functional elements in an electrochemical sensor to detect, as the target molecule, ammonium at different concentrations. We observed that the use of five different aptamers leads to a significant difference in the sensor’s response. This can be explained by considering the aptamers’ conformational change due to their interaction with the target molecule. We studied these conformational changes using a molecular dynamics simulation and suggested a possible explanation of the experimental observations. Finally, electrochemical measurements exposing the same sensors to different molecules were used to confirm the high selectivity of the designed aptamers. The proposed in silico SELEX approach can potentially reduce the cost and the time needed to identify the aptamers and potentially be applied to any target molecule.

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AptaNet as a deep learning approach for aptamer–protein interaction prediction

Cited by 31 publications

References 113 publications

Electrochemical Aptasensors for Antibiotics Detection: Recent Achievements and Applications for Monitoring Food Safety

Electrochemical Aptasensors for Antibiotics Detection: Recent Achievements and Applications for Monitoring Food Safety

Neural Network-Based Prediction of Potential Ribonucleic Acid Aptamers to Target Protein

Smart Approach for the Design of Highly Selective Aptamer-Based Biosensors

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