Constructive Prediction of Potential RNA Aptamers for a Protein Target

Leveraging attention-enhanced variational autoencoders: Novel approach for investigating latent space of aptamer sequences

Aptamers are increasingly recognized as potent alternatives to antibodies for diagnostic and therapeutic applications. The application of deep learning, particularly attention-based models, for aptamer (DNA/RNA) sequences is an innovative field. The ongoing advancements in aptamer sequencing technologies coupled with machine learning algorithms have resulted in novel developments. Further research is required to investigate the full potential of deep learning models and address the challenges associated with the generation of sequences, like the large search space of possible sequences. In this study, we propose a workflow that integrates an attention mechanism within a framework of a generative variational autoencoder, to generate novel sequences by expanding latent memory. They show 100 % novelty compared with the dataset, and approximately 88 % of them show negative values for the minimum free energy, which may indicate the likelihood of an RNA sequence folding into a functional structure. Because the field of aptamer discovery is affected by data scarcity, advanced strategies that facilitate the generation of diverse and superior sequences are necessitated. The utilization of our workflow can result in novel aptamers. Thus, investigations such as the present study can address the abovementioned challenge. Our research is anticipated to facilitate further discoveries and advancements in aptamer fields.

AptaTrans: a deep neural network for predicting aptamer-protein interaction using pretrained encoders

BACKGROUND

Aptamers, which are biomaterials comprised of single-stranded DNA/RNA that form tertiary structures, have significant potential as next-generation materials, particularly for drug discovery. The systematic evolution of ligands by exponential enrichment (SELEX) method is a critical in vitro technique employed to identify aptamers that bind specifically to target proteins. While advanced SELEX-based methods such as Cell- and HT-SELEX are available, they often encounter issues such as extended time consumption and suboptimal accuracy. Several In silico aptamer discovery methods have been proposed to address these challenges. These methods are specifically designed to predict aptamer-protein interaction (API) using benchmark datasets. However, these methods often fail to consider the physicochemical interactions between aptamers and proteins within tertiary structures.

RESULTS

In this study, we propose AptaTrans, a pipeline for predicting API using deep learning techniques. AptaTrans uses transformer-based encoders to handle aptamer and protein sequences at the monomer level. Furthermore, pretrained encoders are utilized for the structural representation. After validation with a benchmark dataset, AptaTrans has been integrated into a comprehensive toolset. This pipeline synergistically combines with Apta-MCTS, a generative algorithm for recommending aptamer candidates.

CONCLUSION

The results show that AptaTrans outperforms existing models for predicting API, and the efficacy of the AptaTrans pipeline has been confirmed through various experimental tools. We expect AptaTrans will enhance the cost-effectiveness and efficiency of SELEX in drug discovery. The source code and benchmark dataset for AptaTrans are available at https://github.com/pnumlb/AptaTrans .

IGPred-HDnet: Prediction of Immunoglobulin Proteins Using Graphical Features and the Hierarchal Deep Learning-Based Approach

Motivation. Immunoglobulin proteins (IGP) (also called antibodies) are glycoproteins that act as B-cell receptors against external or internal antigens like viruses and bacteria. IGPs play a significant role in diverse cellular processes ranging from adhesion to cell recognition. IGP identifications via the in-silico approach are faster and more cost-effective than wet-lab technological methods. Methods. In this study, we developed an intelligent theoretical deep learning framework, "IGPred-HDnet" for the discrimination of IGPs and non-IGPs. Three types of promising descriptors are feature extraction based on graphical and statistical features (FEGS), amphiphilic pseudo-amino acid composition (Amp-PseAAC), and dipeptide composition (DPC) to extract the graphical, physicochemical, and sequential features. Next, the extracted attributes are evaluated through machine learning, i.e., decision tree (DT), support vector machine (SVM), k-nearest neighbour (KNN), and hierarchical deep network (HDnet) classifiers. The proposed predictor IGPred-HDnet was trained and tested using a 10-fold cross-validation and independent test. Results and Conclusion. The success rates in terms of accuracy (ACC) and Matthew's correlation coefficient (MCC) of IGPred-HDnet on training and independent dataset (Dtrain Dtest) are ACC = 98.00%, 99.10%, and MCC = 0.958, and 0.980 points, respectively. The empirical outcomes demonstrate that the IGPred-HDnet model efficacy on both datasets using the novel FEGS feature and HDnet algorithm achieved superior predictions to other existing computational models. We hope this research will provide great insights into the large-scale identification of IGPs and pharmaceutical companies in new drug design.

DeepCPPred: A Deep Learning Framework for the Discrimination of Cell-Penetrating Peptides and Their Uptake Efficiencies

Cell-penetrating peptides (CPPs) are special peptides capable of carrying a variety of bioactive molecules, such as genetic materials, short interfering RNAs and nanoparticles, into cells. Recently, research on CPP has gained substantial interest from researchers, and the biological mechanisms of CPPS have been assessed in the context of safe drug delivery agents and therapeutic applications. Correct identification and synthesis of CPPs using traditional biochemical methods is an extremely slow, expensive and laborious task particularly due to the large volume of unannotated peptide sequences accumulating in the World Bank repository. Hence, a powerful bioinformatics predictor that rapidly identifies CPPs with a high recognition rate is urgently needed. To date, numerous computational methods have been developed for CPP prediction. However, the available machine-learning (ML) tools are unable to distinguish both the CPPs and their uptake efficiencies. This study aimed to develop a two-layer deep learning framework named DeepCPPred to identify both CPPs in the first phase and peptide uptake efficiency in the second phase. The DeepCPPred predictor first uses four types of descriptors that cover evolutionary, energy estimation, reduced sequence and amino-acid contact information. Then, the extracted features are optimized through the elastic net algorithm and fed into a cascade deep forest algorithm to build the final CPP model. The proposed method achieved 99.45 percent overall accuracy with the CPP924 benchmark dataset in the first layer and 95.43 percent accuracy in the second layer with the CPPSite3 dataset using a 5-fold cross-validation test. Thus, our proposed bioinformatics tool surpassed all the existing state-of-the-art sequence-based CPP approaches.

Predicting aptamer sequences that interact with target proteins using an aptamer-protein interaction classifier and a Monte Carlo tree search approach

Oligonucleotide-based aptamers, which have a three-dimensional structure with a single-stranded fragment, feature various characteristics with respect to size, toxicity, and permeability. Accordingly, aptamers are advantageous in terms of diagnosis and treatment and are materials that can be produced through relatively simple experiments. Systematic evolution of ligands by exponential enrichment (SELEX) is one of the most widely used experimental methods for generating aptamers; however, it is highly expensive and time-consuming. To reduce the related costs, recent studies have used in silico approaches, such as aptamer-protein interaction (API) classifiers that use sequence patterns to determine the binding affinity between RNA aptamers and proteins. Some of these methods generate candidate RNA aptamer sequences that bind to a target protein, but they are limited to producing candidates of a specific size. In this study, we present a machine learning approach for selecting candidate sequences of various sizes that have a high binding affinity for a specific sequence of a target protein. We applied the Monte Carlo tree search (MCTS) algorithm for generating the candidate sequences using a score function based on an API classifier. The tree structure that we designed with MCTS enables nucleotide sequence sampling, and the obtained sequences are potential aptamer candidates. We performed a quality assessment using the scores of docking simulations. Our validation datasets revealed that our model showed similar or better docking scores in ZDOCK docking simulations than the known aptamers. We expect that our method, which is size-independent and easy to use, can provide insights into searching for an appropriate aptamer sequence for a target protein during the simulation step of SELEX.