SkipCPP-Pred: an improved and promising sequence-based predictor for predicting cell-penetrating peptides

GraphCPP: The new state-of-the-art method for cell-penetrating peptide prediction via graph neural networks

BACKGROUND AND PURPOSE

Cell-penetrating peptides (CPPs) are short amino acid sequences that can penetrate cell membranes and deliver molecules into cells. Several models have been developed for their discovery, yet these models often face challenges in accurately predicting membrane penetration due to the complex nature of peptide-cell interactions. Hence, there is a need for innovative approaches that can enhance predictive performance.

EXPERIMENTAL APPROACH

In this study, we present the application GraphCPP, a novel graph neural network (GNN) for the prediction of membrane penetration capability of peptides.

KEY RESULTS

A new comprehensive dataset-dubbed CPP1708-was constructed resulting in the largest reliable database of CPPs to date. Comparative analyses with previous methods, such as MLCPP2, C2Pred, CellPPD and CellPPD-Mod, demonstrated the superior predictive performance of our model. Upon testing against other published methods, GraphCPP performs exceptionally, achieving 0.5787 Matthews correlation coefficient and 0.8459 area under the curve (AUC) values on one dataset. This means a 92.8% and 23.3% improvement in Matthews correlation coefficient and AUC measures respectively compared with the next best model. The capability of the model to effectively learn peptide representations was demonstrated through t-distributed stochastic neighbour embedding plots. Additionally, the uncertainty analysis revealed that GraphCPP maintains high confidence in predictions for peptides shorter than 40 amino acids. The source code is available at https://github.com/attilaimre99/GraphCPP.

CONCLUSION AND IMPLICATIONS

These findings indicate the potential of GNN-based models to improve CPP penetration prediction and it may contribute towards the development of more efficient drug delivery systems.

pLM4CPPs: Protein Language Model-Based Predictor for Cell Penetrating Peptides

Cell-penetrating peptides (CPPs) are short peptides capable of penetrating cell membranes, making them valuable for drug delivery and intracellular targeting. Accurate prediction of CPPs can streamline experimental validation in the lab. This study aims to assess pretrained protein language models (pLMs) for their effectiveness in representing CPPs and develop a reliable model for CPP classification. We evaluated peptide embeddings generated from BEPLER, CPCProt, SeqVec, various ESM variants (ESM, ESM-2 with expanded feature set, ESM-1b, and ESM-1v), ProtT5-XL UniRef50, ProtT5-XL BFD, and ProtBERT. We developed pLM4CCPs, a novel deep learning architecture using convolutional neural networks (CNNs) as the classifier for binary classification of CPPs. pLM4CCPs demonstrated superior performance over existing state-of-the-art CPP prediction models, achieving improvements in accuracy (ACC) by 4.9-5.5%, Matthews correlation coefficient (MCC) by 9.3-10.2%, and sensitivity (Sn) by 14.1-19.6%. Among all the tested models, ESM-1280 and ProtT5-XL BFD demonstrated the highest overall performance on the kelm data set. ESM-1280 achieved an ACC of 0.896, an MCC of 0.796, a Sn of 0.844, and a specificity (Sp) of 0.978. ProtT5-XL BFD exhibited superior performance with an ACC of 0.901, an MCC of 0.802, an Sn of 0.885, and an Sp of 0.917. pLM4CCPs combine predictions from multiple models to provide a consensus on whether a given peptide sequence is classified as a CPP or non-CPP. This approach will enhance prediction reliability by leveraging the strengths of each individual model. A user-friendly web server for bioactivity predictions, along with data sets, is available at https://ry2acnp6ep.us-east-1.awsapprunner.com. The source code and protocol for adapting pLM4CPPs can be accessed on GitHub at https://github.com/drkumarnandan/pLM4CPPs. This platform aims to advance CPP prediction and peptide functionality modeling, aiding researchers in exploring peptide functionality effectively.

A bird's-eye view of the biological mechanism and machine learning prediction approaches for cell-penetrating peptides

Cell-penetrating peptides (CPPs) are highly effective at passing through eukaryotic membranes with various cargo molecules, like drugs, proteins, nucleic acids, and nanoparticles, without causing significant harm. Creating drug delivery systems with CPP is associated with cancer, genetic disorders, and diabetes due to their unique chemical properties. Wet lab experiments in drug discovery methodologies are time-consuming and expensive. Machine learning (ML) techniques can enhance and accelerate the drug discovery process with accurate and intricate data quality. ML classifiers, such as support vector machine (SVM), random forest (RF), gradient-boosted decision trees (GBDT), and different types of artificial neural networks (ANN), are commonly used for CPP prediction with cross-validation performance evaluation. Functional CPP prediction is improved by using these ML strategies by using CPP datasets produced by high-throughput sequencing and computational methods. This review focuses on several ML-based CPP prediction tools. We discussed the CPP mechanism to understand the basic functioning of CPPs through cells. A comparative analysis of diverse CPP prediction methods was conducted based on their algorithms, dataset size, feature encoding, software utilities, assessment metrics, and prediction scores. The performance of the CPP prediction was evaluated based on accuracy, sensitivity, specificity, and Matthews correlation coefficient (MCC) on independent datasets. In conclusion, this review will encourage the use of ML algorithms for finding effective CPPs, which will have a positive impact on future research on drug delivery and therapeutics.

Exploring the Chemical Features and Biomedical Relevance of Cell-Penetrating Peptides

Cell-penetrating peptides (CPPs) are a diverse group of peptides, typically composed of 4 to 40 amino acids, known for their unique ability to transport a wide range of substances-such as small molecules, plasmid DNA, small interfering RNA, proteins, viruses, and nanoparticles-across cellular membranes while preserving the integrity of the cargo. CPPs exhibit passive and non-selective behavior, often requiring functionalization or chemical modification to enhance their specificity and efficacy. The precise mechanisms governing the cellular uptake of CPPs remain ambiguous; however, electrostatic interactions between positively charged amino acids and negatively charged glycosaminoglycans on the membrane, particularly heparan sulfate proteoglycans, are considered the initial crucial step for CPP uptake. Clinical trials have highlighted the potential of CPPs in diagnosing and treating various diseases, including cancer, central nervous system disorders, eye disorders, and diabetes. This review provides a comprehensive overview of CPP classifications, potential applications, transduction mechanisms, and the most relevant algorithms to improve the accuracy and reliability of predictions in CPP development.

EnDM-CPP: A Multi-view Explainable Framework Based on Deep Learning and Machine Learning for Identifying Cell-Penetrating Peptides with Transformers and Analyzing Sequence Information

Cell-Penetrating Peptides (CPPs) are a crucial carrier for drug delivery. Since the process of synthesizing new CPPs in the laboratory is both time- and resource-consuming, computational methods to predict potential CPPs can be used to find CPPs to enhance the development of CPPs in therapy. In this study, EnDM-CPP is proposed, which combines machine learning algorithms (SVM and CatBoost) with convolutional neural networks (CNN and TextCNN). For dataset construction, three previous CPP benchmark datasets, including CPPsite 2.0, MLCPP 2.0, and CPP924, are merged to improve the diversity and reduce homology. For feature generation, two language model-based features obtained from the Transformer architecture, including ProtT5 and ESM-2, are employed in CNN and TextCNN. Additionally, sequence features, such as CPRS, Hybrid PseAAC, KSC, etc., are input to SVM and CatBoost. Based on the result of each predictor, Logistic Regression (LR) is built to predict the final decision. The experiment results indicate that ProtT5 and ESM-2 fusion features significantly contribute to predicting CPP and that combining employed features and models demonstrates better association. On an independent test dataset comparison, EnDM-CPP achieved an accuracy of 0.9495 and a Matthews correlation coefficient of 0.9008 with an improvement of 2.23%-9.48% and 4.32%-19.02%, respectively, compared with other state-of-the-art methods. Code and data are available at https://github.com/tudou1231/EnDM-CPP.git .

Interpretable molecular encodings and representations for machine learning tasks

Molecular encodings and their usage in machine learning models have demonstrated significant breakthroughs in biomedical applications, particularly in the classification of peptides and proteins. To this end, we propose a new encoding method: Interpretable Carbon-based Array of Neighborhoods (iCAN). Designed to address machine learning models' need for more structured and less flexible input, it captures the neighborhoods of carbon atoms in a counting array and improves the utility of the resulting encodings for machine learning models. The iCAN method provides interpretable molecular encodings and representations, enabling the comparison of molecular neighborhoods, identification of repeating patterns, and visualization of relevance heat maps for a given data set. When reproducing a large biomedical peptide classification study, it outperforms its predecessor encoding. When extended to proteins, it outperforms a lead structure-based encoding on 71% of the data sets. Our method offers interpretable encodings that can be applied to all organic molecules, including exotic amino acids, cyclic peptides, and larger proteins, making it highly versatile across various domains and data sets. This work establishes a promising new direction for machine learning in peptide and protein classification in biomedicine and healthcare, potentially accelerating advances in drug discovery and disease diagnosis.

Advancements in mitochondrial-targeted nanotherapeutics: overcoming biological obstacles and optimizing drug delivery

In recent decades, nanotechnology has significantly advanced drug delivery systems, particularly in targeting subcellular organelles, thus opening new avenues for disease treatment. Mitochondria, critical for cellular energy and health, when dysfunctional, contribute to cancer, neurodegenerative diseases, and metabolic disorders. This has propelled the development of nanomedicines aimed at precise mitochondrial targeting to modulate their function, marking a research hotspot. This review delves into the recent advancements in mitochondrial-targeted nanotherapeutics, with a comprehensive focus on targeting strategies, nanocarrier designs, and their therapeutic applications. It emphasizes nanotechnology's role in enhancing drug delivery by overcoming biological barriers and optimizing drug design for specific mitochondrial targeting. Strategies exploiting mitochondrial membrane potential differences and specific targeting ligands improve the delivery and mitochondrial accumulation of nanomedicines. The use of diverse nanocarriers, including liposomes, polymer nanoparticles, and inorganic nanoparticles, tailored for effective mitochondrial targeting, shows promise in anti-tumor and neurodegenerative treatments. The review addresses the challenges and future directions in mitochondrial targeting nanotherapy, highlighting the need for precision, reduced toxicity, and clinical validation. Mitochondrial targeting nanotherapy stands at the forefront of therapeutic strategies, offering innovative treatment perspectives. Ongoing innovation and research are crucial for developing more precise and effective treatment modalities.

misORFPred: A Novel Method to Mine Translatable sORFs in Plant Pri-miRNAs Using Enhanced Scalable k-mer and Dynamic Ensemble Voting Strategy

The primary microRNAs (pri-miRNAs) have been observed to contain translatable small open reading frames (sORFs) that can encode peptides as an independent element. Relevant studies have proven that those of sORFs are of significance in regulating the expression of biological traits. The existing methods for predicting the coding potential of sORFs frequently overlook this data or categorize them as negative samples, impeding the identification of additional translatable sORFs in pri-miRNAs. In light of this, a novel method named misORFPred has been proposed. Specifically, an enhanced scalable k-mer (ESKmer) that simultaneously integrates the composition information within a sequence and distance information between sequences is designed to extract the nucleotide sequence features. After feature selection, the optimal features and several machine learning classifiers are combined to construct the ensemble model, where a newly devised dynamic ensemble voting strategy (DEVS) is proposed to dynamically adjust the weights of base classifiers and adaptively select the optimal base classifiers for each unlabeled sample. Cross-validation results suggest that ESKmer and DEVS are essential for this classification task and could boost model performance. Independent testing results indicate that misORFPred outperforms the state-of-the-art methods. Furthermore, we execute misORFPerd on the genomes of various plant species and perform a thorough analysis of the predicted outcomes. Taken together, misORFPred is a powerful tool for identifying the translatable sORFs in plant pri-miRNAs and can provide highly trusted candidates for subsequent biological experiments.

A hybrid residue based sequential encoding mechanism with XGBoost improved ensemble model for identifying 5-hydroxymethylcytosine modifications

RNA modifications play an important role in actively controlling recently created formation in cellular regulation mechanisms, which link them to gene expression and protein. The RNA modifications have numerous alterations, presenting broad glimpses of RNA's operations and character. The modification process by the TET enzyme oxidation is the crucial change associated with cytosine hydroxymethylation. The effect of CR is an alteration in specific biochemical ways of the organism, such as gene expression and epigenetic alterations. Traditional laboratory systems that identify 5-hydroxymethylcytosine (5hmC) samples are expensive and time-consuming compared to other methods. To address this challenge, the paper proposed XGB5hmC, a machine learning algorithm based on a robust gradient boosting algorithm (XGBoost), with different residue based formulation methods to identify 5hmC samples. Their results were amalgamated, and six different frequency residue based encoding features were fused to form a hybrid vector in order to enhance model discrimination capabilities. In addition, the proposed model incorporates SHAP (Shapley Additive Explanations) based feature selection to demonstrate model interpretability by highlighting the high contributory features. Among the applied machine learning algorithms, the XGBoost ensemble model using the tenfold cross-validation test achieved improved results than existing state-of-the-art models. Our model reported an accuracy of 89.97%, sensitivity of 87.78%, specificity of 94.45%, F1-score of 0.8934%, and MCC of 0.8764%. This study highlights the potential to provide valuable insights for enhancing medical assessment and treatment protocols, representing a significant advancement in RNA modification analysis.

Structural and Biochemical Characterization of Three Antimicrobial Peptides from Capsicum annuum L. var. annuum Leaves for Anti-Candida Use

The emergence of resistant microorganisms has reduced the effectiveness of currently available antimicrobials, necessitating the development of new strategies. Plant antimicrobial peptides (AMPs) are promising candidates for novel drug development. In this study, we aimed to isolate, characterize, and evaluate the antimicrobial activities of AMPs isolated from Capsicum annuum. The antifungal potential was tested against Candida species. Three AMPs from C. annuum leaves were isolated and characterized: a protease inhibitor, a defensin-like protein, and a lipid transporter protein, respectively named CaCPin-II, CaCDef-like, and CaCLTP2. All three peptides had a molecular mass between 3.5 and 6.5 kDa and caused morphological and physiological changes in four different species of the genus Candida, such as pseudohyphae formation, cell swelling and agglutination, growth inhibition, reduced cell viability, oxidative stress, membrane permeabilization, and metacaspase activation. Except for CaCPin-II, the peptides showed low or no hemolytic activity at the concentrations used in the yeast assays. CaCPin-II inhibited α-amylase activity. Together, these results suggest that these peptides have the potential as antimicrobial agents against species of the genus Candida and can serve as scaffolds for the development of synthetic peptides for this purpose.

deepAMPNet: a novel antimicrobial peptide predictor employing AlphaFold2 predicted structures and a bi-directional long short-term memory protein language model

Background

Global public health is seriously threatened by the escalating issue of antimicrobial resistance (AMR). Antimicrobial peptides (AMPs), pivotal components of the innate immune system, have emerged as a potent solution to AMR due to their therapeutic potential. Employing computational methodologies for the prompt recognition of these antimicrobial peptides indeed unlocks fresh perspectives, thereby potentially revolutionizing antimicrobial drug development.

Methods

In this study, we have developed a model named as deepAMPNet. This model, which leverages graph neural networks, excels at the swift identification of AMPs. It employs structures of antimicrobial peptides predicted by AlphaFold2, encodes residue-level features through a bi-directional long short-term memory (Bi-LSTM) protein language model, and constructs adjacency matrices anchored on amino acids' contact maps.

Results

In a comparative study with other state-of-the-art AMP predictors on two external independent test datasets, deepAMPNet outperformed in accuracy. Furthermore, in terms of commonly accepted evaluation matrices such as AUC, Mcc, sensitivity, and specificity, deepAMPNet achieved the highest or highly comparable performances against other predictors.

Conclusion

deepAMPNet interweaves both structural and sequence information of AMPs, stands as a high-performance identification model that propels the evolution and design in antimicrobial peptide pharmaceuticals. The data and code utilized in this study can be accessed at https://github.com/Iseeu233/deepAMPNet.

TriplEP-CPP: Algorithm for Predicting the Properties of Peptide Sequences

Advancements in medicine and pharmacology have led to the development of systems that deliver biologically active molecules inside cells, increasing drug concentrations at target sites. This improves effectiveness and duration of action and reduces side effects on healthy tissues. Cell-penetrating peptides (CPPs) show promise in this area. While traditional medicinal chemistry methods have been used to develop CPPs, machine learning techniques can speed up and reduce costs in the search for new peptides. A predictive algorithm based on machine learning models was created to identify novel CPP sequences using molecular descriptors using a combination of algorithms like k-nearest neighbors, gradient boosting, and random forest. Some potential CPPs were found and tested for cytotoxicity and penetrating ability. A new low-toxicity CPP was discovered from the Rhopilema esculentum venom proteome through this study.

miPEPPred-FRL: A Novel Method for Predicting Plant MiRNA-Encoded Peptides Using Adaptive Feature Representation Learning

MicroRNAs (miRNAs) are an essential type of small molecule RNAs that play significant regulatory roles in organisms. Recent studies have demonstrated that small open reading frames (sORFs) harbored in primary miRNAs (pri-miRNAs) can encode small peptides, known as miPEPs. Plant miPEPs can increase the abundance and activity of cognate miRNAs by promoting the transcription of their corresponding pri-miRNAs, thereby modulating plant traits. Biological experiments are the most effective way to accurately identify miPEPs; however, they are time-consuming and expensive. Hence, an efficient computational method for the identification of miPEPs on a large scale is highly desirable. Up to now, there have been no specialized computational tools for identifying miPEPs. In this work, a novel predictor named miPEPPred-FRL based on an adaptive feature representation learning framework that consists of the feature transformation module and the cascade architecture has been proposed. The feature transformation module integrating a newly designed feature selection method and classifier selection rule is developed to convert sequence-based features into primary class and probabilistic features, which are then fed into the improved cascade architecture to obtain more stable and discriminative augmented features. Finally, the augmented features are utilized to construct the final predictor. Cross-validation experiments illustrate that the novel feature selection method and classifier selection rule contribute to boosting the feature representation ability of the framework. Furthermore, the high accuracy of miPEPPred-FRL on independent testing data suggests that it is a trustworthy and valuable tool for the identification of miPEPs.

NTpred: a robust and precise machine learning framework for in silico identification of Tyrosine nitration sites in protein sequences

Post-translational modifications (PTMs) either enhance a protein's activity in various sub-cellular processes, or degrade their activity which leads toward failure of intracellular processes. Tyrosine nitration (NT) modification degrades protein's activity that initiates and propagates various diseases including neurodegenerative, cardiovascular, autoimmune diseases and carcinogenesis. Identification of NT modification supports development of novel therapies and drug discoveries for associated diseases. Identification of NT modification in biochemical labs is expensive, time consuming and error-prone. To supplement this process, several computational approaches have been proposed. However these approaches fail to precisely identify NT modification, due to the extraction of irrelevant, redundant and less discriminative features from protein sequences. This paper presents the NTpred framework that is competent in extracting comprehensive features from raw protein sequences using four different sequence encoders. To reap the benefits of different encoders, it generates four additional feature spaces by fusing different combinations of individual encodings. Furthermore, it eradicates irrelevant and redundant features from eight different feature spaces through a Recursive Feature Elimination process. Selected features of four individual encodings and four feature fusion vectors are used to train eight different Gradient Boosted Tree classifiers. The probability scores from the trained classifiers are utilized to generate a new probabilistic feature space, which is used to train a Logistic Regression classifier. On the BD1 benchmark dataset, the proposed framework outperforms the existing best-performing predictor in 5-fold cross validation and independent test evaluation with combined improvement of 13.7% in MCC and 20.1% in AUC. Similarly, on the BD2 benchmark dataset, the proposed framework outperforms the existing best-performing predictor with combined improvement of 5.3% in MCC and 1.0% in AUC. NTpred is publicly available for further experimentation and predictive use at: https://sds_genetic_analysis.opendfki.de/PredNTS/.

POSEIDON: Peptidic Objects SEquence-based Interaction with cellular DOmaiNs: a new database and predictor

Cell-penetrating peptides (CPPs) are short chains of amino acids that have shown remarkable potential to cross the cell membrane and deliver coupled therapeutic cargoes into cells. Designing and testing different CPPs to target specific cells or tissues is crucial to ensure high delivery efficiency and reduced toxicity. However, in vivo/in vitro testing of various CPPs can be both time-consuming and costly, which has led to interest in computational methodologies, such as Machine Learning (ML) approaches, as faster and cheaper methods for CPP design and uptake prediction. However, most ML models developed to date focus on classification rather than regression techniques, because of the lack of informative quantitative uptake values. To address these challenges, we developed POSEIDON, an open-access and up-to-date curated database that provides experimental quantitative uptake values for over 2,300 entries and physicochemical properties of 1,315 peptides. POSEIDON also offers physicochemical properties, such as cell line, cargo, and sequence, among others. By leveraging this database along with cell line genomic features, we processed a dataset of over 1,200 entries to develop an ML regression CPP uptake predictor. Our results demonstrated that POSEIDON accurately predicted peptide cell line uptake, achieving a Pearson correlation of 0.87, Spearman correlation of 0.88, and r2 score of 0.76, on an independent test set. With its comprehensive and novel dataset, along with its potent predictive capabilities, the POSEIDON database and its associated ML predictor signify a significant leap forward in CPP research and development. The POSEIDON database and ML Predictor are available for free and with a user-friendly interface at https://moreiralab.com/resources/poseidon/ , making them valuable resources for advancing research on CPP-related topics. Scientific Contribution Statement: Our research addresses the critical need for more efficient and cost-effective methodologies in Cell-Penetrating Peptide (CPP) research. We introduced POSEIDON, a comprehensive and freely accessible database that delivers quantitative uptake values for over 2,300 entries, along with detailed physicochemical profiles for 1,315 peptides. Recognizing the limitations of current Machine Learning (ML) models for CPP design, our work leveraged the rich dataset provided by POSEIDON to develop a highly accurate ML regression model for predicting CPP uptake.

Dynamics and recognition of homeodomain containing protein-DNA complex of IRX4

Iroquois Homeobox 4 (IRX4) belongs to a family of homeobox TFs having roles in embryogenesis, cell specification, and organ development. Recently, large scale genome-wide association studies and epigenetic studies have highlighted the role of IRX4 and its associated variants in prostate cancer. No studies have investigated and characterized the structural aspect of the IRX4 homeodomain and its potential to bind to DNA. The current study uses sequence analysis, homology modeling, and molecular dynamics simulations to explore IRX4 homeodomain-DNA recognition mechanisms and the role of somatic mutations affecting these interactions. Using publicly available databases, gene expression of IRX4 was found in different tissues, including prostate, heart, skin, vagina, and the protein expression was found in cancer cell lines (HCT166, HEK293), B cells, ascitic fluid, and brain. Sequence conservation of the homeodomain shed light on the importance of N- and C-terminal residues involved in DNA binding. The specificity of IRX4 homodimer bound to consensus human DNA sequence was confirmed by molecular dynamics simulations, representing the role of conserved amino acids including R145, A194, N195, S190, R198, and R199 in binding to DNA. Additional N-terminal residues like T144 and G143 were also found to have specific interactions highlighting the importance of N-terminus of the homeodomain in DNA recognition. Additionally, the effects of somatic mutations, including the conserved Arginine (R145, R198, and R199) residues on DNA binding elucidated the importance of these residues in stabilizing the protein-DNA complex. Secondary structure and hydrogen bonding analysis showed the roles of specific residues (R145, T191, A194, N195, R198, and R199) in maintaining the homogeneity of the structure and its interaction with DNA. The differences in relative binding free energies of all the mutants shed light on the structural modularity of this protein and the dynamics behind protein-DNA interaction. We also have predicted that the C-terminal sequence of the IRX4 homeodomain could act as a potential cell-penetrating peptide, emphasizing the role these small peptides could play in targeting homeobox TFs.

PractiCPP: a deep learning approach tailored for extremely imbalanced datasets in cell-penetrating peptide prediction

MOTIVATION

Effective drug delivery systems are paramount in enhancing pharmaceutical outcomes, particularly through the use of cell-penetrating peptides (CPPs). These peptides are gaining prominence due to their ability to penetrate eukaryotic cells efficiently without inflicting significant damage to the cellular membrane, thereby ensuring optimal drug delivery. However, the identification and characterization of CPPs remain a challenge due to the laborious and time-consuming nature of conventional methods, despite advances in proteomics. Current computational models, however, are predominantly tailored for balanced datasets, an approach that falls short in real-world applications characterized by a scarcity of known positive CPP instances.

RESULTS

To navigate this shortfall, we introduce PractiCPP, a novel deep-learning framework tailored for CPP prediction in highly imbalanced data scenarios. Uniquely designed with the integration of hard negative sampling and a sophisticated feature extraction and prediction module, PractiCPP facilitates an intricate understanding and learning from imbalanced data. Our extensive computational validations highlight PractiCPP's exceptional ability to outperform existing state-of-the-art methods, demonstrating remarkable accuracy, even in datasets with an extreme positive-to-negative ratio of 1:1000. Furthermore, through methodical embedding visualizations, we have established that models trained on balanced datasets are not conducive to practical, large-scale CPP identification, as they do not accurately reflect real-world complexities. In summary, PractiCPP potentially offers new perspectives in CPP prediction methodologies. Its design and validation, informed by real-world dataset constraints, suggest its utility as a valuable tool in supporting the acceleration of drug delivery advancements.

AVAILABILITY AND IMPLEMENTATION

The source code of PractiCPP is available on Figshare at https://doi.org/10.6084/m9.figshare.25053878.v1.

DeepTPpred: A Deep Learning Approach With Matrix Factorization for Predicting Therapeutic Peptides by Integrating Length Information

The abuse of traditional antibiotics has led to increased resistance of bacteria and viruses. Efficient therapeutic peptide prediction is critical for peptide drug discovery. However, most of the existing methods only make effective predictions for one class of therapeutic peptides. It is worth noting that currently no predictive method considers sequence length information as a distinct feature of therapeutic peptides. In this article, a novel deep learning approach with matrix factorization for predicting therapeutic peptides (DeepTPpred) by integrating length information are proposed. The matrix factorization layer can learn the potential features of the encoded sequence through the mechanism of first compression and then restoration. And the length features of the sequence of therapeutic peptides are embedded with encoded amino acid sequences. To automatically learn therapeutic peptide predictions, these latent features are input into the neural networks with self-attention mechanism. On eight therapeutic peptide datasets, DeepTPpred achieved excellent prediction results. Based on these datasets, we first integrated eight datasets to obtain a full therapeutic peptide integration dataset. Then, we obtained two functional integration datasets based on the functional similarity of the peptides. Finally, we also conduct experiments on the latest versions of the ACP and CPP datasets. Overall, the experimental results show that our work is effective for the identification of therapeutic peptides.

PACVP: Prediction of Anti-Coronavirus Peptides Using a Stacking Learning Strategy With Effective Feature Representation

Due to the global outbreak of COVID-19 and its variants, antiviral peptides with anti-coronavirus activity (ACVPs) represent a promising new drug candidate for the treatment of coronavirus infection. At present, several computational tools have been developed to identify ACVPs, but the overall prediction performance is still not enough to meet the actual therapeutic application. In this study, we constructed an efficient and reliable prediction model PACVP (Prediction of Anti-CoronaVirus Peptides) for identifying ACVPs based on effective feature representation and a two-layer stacking learning framework. In the first layer, we use nine feature encoding methods with different feature representation angles to characterize the rich sequence information and fuse them into a feature matrix. Secondly, data normalization and unbalanced data processing are carried out. Next, 12 baseline models are constructed by combining three feature selection methods and four machine learning classification algorithms. In the second layer, we input the optimal probability features into the logistic regression algorithm (LR) to train the final model PACVP. The experiments show that PACVP achieves favorable prediction performance on independent test dataset, with ACC of 0.9208 and AUC of 0.9465. We hope that PACVP will become a useful method for identifying, annotating and characterizing novel ACVPs.

DP-AOP: A novel SVM-based antioxidant proteins identifier

The identification of antioxidant proteins is a challenging yet meaningful task, as they can protect against the damage caused by some free radicals. In addition to time-consuming, laborious, and expensive experimental identification methods, efficient identification of antioxidant proteins through machine learning algorithms has become increasingly common. In recent years, researchers have proposed models for identifying antioxidant proteins; unfortunately, although the accuracy of models is already high, their sensitivity is too low, indicating the possibility of overfitting in the model. Therefore, we developed a new model called DP-AOP for the recognition of antioxidant proteins. We used the SMOTE algorithm to balance the dataset, selected Wei's proposed feature extraction algorithm to obtain 473 dimensional feature vectors, and based on the sorting function in MRMD, scored and ranked each feature to obtain a feature set with contribution values ranging from high to low. To effectively reduce the feature dimension, we combined the dynamic programming idea to make the local eight features the optimal subset. After obtaining the 36 dimensional feature vectors, we finally selected 17 features through experimental analysis. The SVM classification algorithm was used to implement the model through the libsvm tool. The model achieved satisfactory performance, with an accuracy rate of 91.076 %, SN of 96.4 %, SP of 85.8 %, MCC of 82.6 %, and F1 core of 91.5 %. Furthermore, we built a free web server to facilitate researchers' subsequent unfolding studies of antioxidant protein recognition. The website is http://112.124.26.17:8003/#/.

Machine learning-driven multifunctional peptide engineering for sustained ocular drug delivery

Sustained drug delivery strategies have many potential benefits for treating a range of diseases, particularly chronic diseases that require treatment for years. For many chronic ocular diseases, patient adherence to eye drop dosing regimens and the need for frequent intraocular injections are significant barriers to effective disease management. Here, we utilize peptide engineering to impart melanin binding properties to peptide-drug conjugates to act as a sustained-release depot in the eye. We develop a super learning-based methodology to engineer multifunctional peptides that efficiently enter cells, bind to melanin, and have low cytotoxicity. When the lead multifunctional peptide (HR97) is conjugated to brimonidine, an intraocular pressure lowering drug that is prescribed for three times per day topical dosing, intraocular pressure reduction is observed for up to 18 days after a single intracameral injection in rabbits. Further, the cumulative intraocular pressure lowering effect increases ~17-fold compared to free brimonidine injection. Engineered multifunctional peptide-drug conjugates are a promising approach for providing sustained therapeutic delivery in the eye and beyond.

TriNet: A tri-fusion neural network for the prediction of anticancer and antimicrobial peptides

The accurate identification of anticancer peptides (ACPs) and antimicrobial peptides (AMPs) remains a computational challenge. We propose a tri-fusion neural network termed TriNet for the accurate prediction of both ACPs and AMPs. The framework first defines three kinds of features to capture the peptide information contained in serial fingerprints, sequence evolutions, and physicochemical properties, which are then fed into three parallel modules: a convolutional neural network module enhanced by channel attention, a bidirectional long short-term memory module, and an encoder module for training and final classification. To achieve a better training effect, TriNet is trained via a training approach using iterative interactions between the samples in the training and validation datasets. TriNet is tested on multiple challenging ACP and AMP datasets and exhibits significant improvements over various state-of-the-art methods. The web server and source code of TriNet are respectively available at http://liulab.top/TriNet/server and https://github.com/wanyunzh/TriNet.

Bacteriophage P22 Capsid as a Pluripotent Nanotechnology Tool

The Salmonella enterica bacteriophage P22 is one of the most promising models for the development of virus-like particle (VLP) nanocages. It possesses an icosahedral T = 7 capsid, assembled by the combination of two structural proteins: the coat protein (gp5) and the scaffold protein (gp8). The P22 capsid has the remarkable capability of undergoing structural transition into three morphologies with differing diameters and wall-pore sizes. These varied morphologies can be explored for the design of nanoplatforms, such as for the development of cargo internalization strategies. The capsid proteic nature allows for the extensive modification of its structure, enabling the addition of non-native structures to alter the VLP properties or confer them to diverse ends. Various molecules were added to the P22 VLP through genetic, chemical, and other means to both the capsid and the scaffold protein, permitting the encapsulation or the presentation of cargo. This allows the particle to be exploited for numerous purposes-for example, as a nanocarrier, nanoreactor, and vaccine model, among other applications. Therefore, the present review intends to give an overview of the literature on this amazing particle.

Could artificial intelligence revolutionize the development of nanovectors for gene therapy and mRNA vaccines?

Gene therapy enables the introduction of nucleic acids like DNA and RNA into host cells, and is expected to revolutionize the treatment of a wide range of diseases. This growth has been further accelerated by the discovery of CRISPR/Cas technology, which allows accurate genomic editing in a broad range of cells and organisms in vitro and in vivo. Despite many advances in gene delivery and the development of various viral and non-viral gene delivery vectors, the lack of highly efficient non-viral systems with low cellular toxicity remains a challenge. The application of cutting-edge technologies such as artificial intelligence (AI) has great potential to find new paradigms to solve this issue. Herein, we review AI and its major subfields including machine learning (ML), neural networks (NNs), expert systems, deep learning (DL), computer vision and robotics. We discuss the potential of AI-based models and algorithms in the design of targeted gene delivery vehicles capable of crossing extracellular and intracellular barriers by viral mimicry strategies. We finally discuss the role of AI in improving the function of CRISPR/Cas systems, developing novel nanobots, and mRNA vaccine carriers.

Antimicrobial peptides with cell-penetrating activity as prophylactic and treatment drugs

Health is fundamental for the development of individuals and evolution of species. In that sense, for human societies is relevant to understand how the human body has developed molecular strategies to maintain health. In the present review, we summarize diverse evidence that support the role of peptides in this endeavor. Of particular interest to the present review are antimicrobial peptides (AMP) and cell-penetrating peptides (CPP). Different experimental evidence indicates that AMP/CPP are able to regulate autophagy, which in turn regulates the immune system response. AMP also assists in the establishment of the microbiota, which in turn is critical for different behavioral and health aspects of humans. Thus, AMP and CPP are multifunctional peptides that regulate two aspects of our bodies that are fundamental to our health: autophagy and microbiota. While it is now clear the multifunctional nature of these peptides, we are still in the early stages of the development of computational strategies aimed to assist experimentalists in identifying selective multifunctional AMP/CPP to control nonhealthy conditions. For instance, both AMP and CPP are computationally characterized as amphipatic and cationic, yet none of these features are relevant to differentiate these peptides from non-AMP or non-CPP. The present review aims to highlight current knowledge that may facilitate the development of AMP’s design tools for preventing or treating illness.

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.

CD8TCEI-EukPath: A Novel Predictor to Rapidly Identify CD8+ T-Cell Epitopes of Eukaryotic Pathogens Using a Hybrid Feature Selection Approach

Computational prediction to screen potential vaccine candidates has been proven to be a reliable way to provide guarantees for vaccine discovery in infectious diseases. As an important class of organisms causing infectious diseases, pathogenic eukaryotes (such as parasitic protozoans) have evolved the ability to colonize a wide range of hosts, including humans and animals; meanwhile, protective vaccines are urgently needed. Inspired by the immunological idea that pathogen-derived epitopes are able to mediate the CD8⁺ T-cell-related host adaptive immune response and with the available positive and negative CD8⁺ T-cell epitopes (TCEs), we proposed a novel predictor called CD8TCEI-EukPath to detect CD8⁺ TCEs of eukaryotic pathogens. Our method integrated multiple amino acid sequence-based hybrid features, employed a well-established feature selection technique, and eventually built an efficient machine learning classifier to differentiate CD8⁺ TCEs from non-CD8⁺ TCEs. Based on the feature selection results, 520 optimal hybrid features were used for modeling by utilizing the LightGBM algorithm. CD8TCEI-EukPath achieved impressive performance, with an accuracy of 79.255% in ten-fold cross-validation and an accuracy of 78.169% in the independent test. Collectively, CD8TCEI-EukPath will contribute to rapidly screening epitope-based vaccine candidates, particularly from large peptide-coding datasets. To conduct the prediction of CD8⁺ TCEs conveniently, an online web server is freely accessible (http://lab.malab.cn/∼hrs/CD8TCEI-EukPath/).

SgRNA-RF: Identification of SgRNA On-Target Activity With Imbalanced Datasets

Single-guide RNA is a guide RNA (gRNA), which guides the insertion or deletion of uridine residues into kinetoplastid during RNA editing. It is a small non-coding RNA that can be combined with pre -mRNA pairing. SgRNA is a critical component of the CRISPR/Cas9 gene knockout system and play an important role in gene editing and gene regulation. It is important to accurately and quickly identify highly on-target activity sgRNAs. Due to its importance, several computational predictors have been proposed to predict sgRNAs on-target activity. All these methods have clearly contributed to the development of this very important field. However, they also have certain limitations. In the paper, we developed a new classifier SgRNA-RF, which extracts the features of nucleic acid composition and structure of on-target activity sgRNA sequence and identified by random forest algorithm. In addition to solving an imbalanced dataset, this paper proposed a new method called CS-Smote. We compared sgRNA-RF with state-of-the-art predictors on the five datasets, and found SgRNA-RF significantly improved the identification accuracy, with accuracies of 0.8636,0.9161,0.894,0.938,0.965,0.77,0.979,0.973, respectively. The user-friendly web server that implements sgRNA-RF is freely available at http://server.malab.cn/sgRNA-RF/.

Prediction of Cell-Penetrating Peptides Using a Novel HSIC-Based Multiview TSK Fuzzy System

Cell-penetrating peptides (CPPs) are short peptides that can carry cargo into cells. CPPs are widely utilized due to their powerful loading capacity and transduction efficiency. Identifying CPPs is the basis for studying their functions and mechanisms; however, experimental methods to identify CPPs are expensive and time-consuming. Recently, CPP predictors based on machine learning methods have become a research hotspot. Although considerable progress has been made, some challenges remain unresolved. First, most predictors employ a variety of feature descriptors to transform an original sequence into multiview data; however, extant methods ignore the relationships between different views, limiting further performance improvement. Second, most machine learning models are actually black boxes and cannot offer insightful advice. In this paper, a novel Hilbert–Schmidt independence criterion (HSIC)-based multiview TSK fuzzy system is proposed. Compared with other machine learning methods, TSK fuzzy systems have better interpretability, and the introduction of multiview mechanisms provides comprehensive insight into the intrinsic laws of the data. HSIC is utilized here to measure the independence and enhance the complementarity between different views. Notably, the proposed method attained prediction accuracy results of 92.2% and 96.2% for the training and independent test sets, respectively. The empirical results show that our promising approach features greater recognition performance than the state-of-the-art method.

BBPpredict: A Web Service for Identifying Blood-Brain Barrier Penetrating Peptides

Blood-brain barrier (BBB) is a major barrier to drug delivery into the brain in the treatment of central nervous system (CNS) diseases. Blood-brain barrier penetrating peptides (BBPs), a class of peptides that can cross BBB through various mechanisms without damaging BBB, are effective drug candidates for CNS diseases. However, identification of BBPs by experimental methods is time-consuming and laborious. To discover more BBPs as drugs for CNS disease, it is urgent to develop computational methods that can quickly and accurately identify BBPs and non-BBPs. In the present study, we created a training dataset that consists of 326 BBPs derived from previous databases and published manuscripts and 326 non-BBPs collected from UniProt, to construct a BBP predictor based on sequence information. We also constructed an independent testing dataset with 99 BBPs and 99 non-BBPs. Multiple machine learning methods were compared based on the training dataset via a nested cross-validation. The final BBP predictor was constructed based on the training dataset and the results showed that random forest (RF) method outperformed other classification algorithms on the training and independent testing dataset. Compared with previous BBP prediction tools, the RF-based predictor, named BBPpredict, performs considerably better than state-of-the-art BBP predictors. BBPpredict is expected to contribute to the discovery of novel BBPs, or at least can be a useful complement to the existing methods in this area. BBPpredict is freely available at http://i.uestc.edu.cn/BBPpredict/cgi-bin/BBPpredict.pl.

An in silico scientific basis for LL-37 as a therapeutic for Covid-19

A multi-pronged approach with help in all forms possible is essential to completely overcome the Covid-19 pandemic. There is a requirement to research as many new and different types of approaches as possible to cater to the entire world population, complementing the vaccines with promising results. The need is also because SARS-CoV-2 has several unknown or variable facets which get revealed from time to time. In this work, in silico scientific findings are presented, which are indicative of the potential for the use of the LL-37 human anti-microbial peptide as a therapeutic against SARS-CoV-2. This indication is based on the high structural similarity of LL-37 to the N-terminal helix, with which the virus interacts, of the receptor for SARS-CoV-2, Angiotensin Converting Enzyme 2. Moreover, there is positive prediction of binding of LL-37 to the receptor-binding domain of SARS-CoV-2; this is the first study to have described this interaction. In silico data on the safety of LL-37 are also reported. As Vitamin D is known to upregulate the expression of LL-37, the vitamin is a candidate preventive molecule. This work provides the possible basis for an inverse correlation between Vitamin D levels in the body and the severity of or susceptibility to Covid-19, as widely reported in literature. With the scientific link put forth herein, Vitamin D could be used at an effective, medically prescribed, safe dose as a preventive. The information in this report would be valuable in bolstering the worldwide efforts to eliminate the pandemic as early as possible.

Design of Protein Segments and Peptides for Binding to Protein Targets

Recent years have witnessed a rise in methods for accurate prediction of structure and design of novel functional proteins. Design of functional protein fragments and peptides occupy a small, albeit unique, space within the general field of protein design. While the smaller size of these peptides allows for more exhaustive computational methods, flexibility in their structure and sparsity of data compared to proteins, as well as presence of noncanonical building blocks, add additional challenges to their design. This review summarizes the current advances in the design of protein fragments and peptides for binding to targets and discusses the challenges in the field, with an eye toward future directions.

Multivariate Information Fusion for Identifying Antifungal Peptides with Hilbert-Schmidt Independence Criterion

Background:

Antifungal Peptides (AFP) have been found to be effective against many fungal infections.

Objective:

However, it is difficult to identify AFP. Therefore, it is great practical significance to identify AFP via machine learning methods (with sequence information).

Method:

In this study, a Multi-Kernel Support Vector Machine (MKSVM) with Hilbert-Schmidt Independence Criterion (HSIC) is proposed. Proteins are encoded with five types of features (188-bit, AAC, ASDC, CKSAAP, DPC), and then construct kernels using Gaussian kernel function. HSIC are used to combine kernels and multi-kernel SVM model is built.

Results:

Our model performed well on three AFPs datasets and the performance is better than or comparable to other state-of-art predictive models.

Conclusion:

Our method will be a useful tool for identifying antifungal peptides.

Extracellular Secretagogin is internalized into the cells through endocytosis

Secretagogin (SCGN) is a calcium-sensor protein with a regulatory role in glucose metabolism and the secretion of several peptide hormones. Many, but not all, functions of SCGN can be explained by its intracellular manifestation. Despite early data on SCGN secretion, the secretory mechanism, biological fate, physiological implications, and trans-cellular signaling of extracellular SCGN remain unknown. We here report that extracellular SCGN is readily internalized into the C2C12 cells in an energy-dependent manner. Using endocytosis inhibitors, we demonstrate that SCGN internalizes via clathrin-mediated endocytosis, following which, SCGN localizes largely in the cytosol. Exogenous SCGN treatment induces a global proteomic reprogramming in C2C12 cells. Gene ontology search suggests that SCGN-induced proteomic reprogramming favors protein synthesis and cellular growth. We thus validated the cell proliferative action of SCGN using C2C12, HepG2, and NIH-3T3 cell lines. Based on the data, we propose that circulatory SCGN is internalized into the target cells and modulates the expression of cell growth-related proteins. The work suggests that extracellular SCGN is a functional protein, which communicates with specific cell types and directly modulates cell proliferation.

Research on the Computational Prediction of Essential Genes

Genes, the nucleotide sequences that encode a polypeptide chain or functional RNA, are the basic genetic unit controlling biological traits. They are the guarantee of the basic structures and functions in organisms, and they store information related to biological factors and processes such as blood type, gestation, growth, and apoptosis. The environment and genetics jointly affect important physiological processes such as reproduction, cell division, and protein synthesis. Genes are related to a wide range of phenomena including growth, decline, illness, aging, and death. During the evolution of organisms, there is a class of genes that exist in a conserved form in multiple species. These genes are often located on the dominant strand of DNA and tend to have higher expression levels. The protein encoded by it usually either performs very important functions or is responsible for maintaining and repairing these essential functions. Such genes are called persistent genes. Among them, the irreplaceable part of the body’s life activities is the essential gene. For example, when starch is the only source of energy, the genes related to starch digestion are essential genes. Without them, the organism will die because it cannot obtain enough energy to maintain basic functions. The function of the proteins encoded by these genes is thought to be fundamental to life. Nowadays, DNA can be extracted from blood, saliva, or tissue cells for genetic testing, and detailed genetic information can be obtained using the most advanced scientific instruments and technologies. The information gained from genetic testing is useful to assess the potential risks of disease, and to help determine the prognosis and development of diseases. Such information is also useful for developing personalized medication and providing targeted health guidance to improve the quality of life. Therefore, it is of great theoretical and practical significance to identify important and essential genes. In this paper, the research status of essential genes and the essential genome database of bacteria are reviewed, the computational prediction method of essential genes based on communication coding theory is expounded, and the significance and practical application value of essential genes are discussed.

Novel Lysine-Rich Delivery Peptides of Plant Origin ERD and Human S100: The Effect of Carboxyfluorescein Conjugation, Influence of Aromatic and Proline Residues, Cellular Internalization, and Penetration Ability

The need for novel drug delivery peptides is an important issue of the modern pharmaceutical research. Here, we test K-rich peptides from plant dehydrin ERD14 (ERD-A, ERD-B, and ERD-C) and the C-terminal CPP-resembling region of S100A4 (S100) using the 5(6)-carboxyfluorescein (Cf) tag at the N-terminus. Via a combined pH-dependent NMR and fluorescence study, we analyze the effect of the Cf conjugation/modification on the structural behavior, separately investigating the (5)-Cf and (6)-Cf forms. Flow cytometry results show that all peptides internalize; however, there is a slight difference between the cellular internalization of (5)- and (6)-Cf-peptides. We indicate the possible importance of residues with an aromatic sidechain and proline. We prove that ERD-A localizes mostly in the cytosol, ERD-B and S100 have partial colocalization with lysosomal staining, and ERD-C mainly localizes within vesicle-like compartments, while the uptake mechanism mainly occurs through energy-dependent paths.

iTTCA-RF: a random forest predictor for tumor T cell antigens

BACKGROUND

Cancer is one of the most serious diseases threatening human health. Cancer immunotherapy represents the most promising treatment strategy due to its high efficacy and selectivity and lower side effects compared with traditional treatment. The identification of tumor T cell antigens is one of the most important tasks for antitumor vaccines development and molecular function investigation. Although several machine learning predictors have been developed to identify tumor T cell antigen, more accurate tumor T cell antigen identification by existing methodology is still challenging.

METHODS

In this study, we used a non-redundant dataset of 592 tumor T cell antigens (positive samples) and 393 tumor T cell antigens (negative samples). Four types feature encoding methods have been studied to build an efficient predictor, including amino acid composition, global protein sequence descriptors and grouped amino acid and peptide composition. To improve the feature representation ability of the hybrid features, we further employed a two-step feature selection technique to search for the optimal feature subset. The final prediction model was constructed using random forest algorithm.

RESULTS

Finally, the top 263 informative features were selected to train the random forest classifier for detecting tumor T cell antigen peptides. iTTCA-RF provides satisfactory performance, with balanced accuracy, specificity and sensitivity values of 83.71%, 78.73% and 88.69% over tenfold cross-validation as well as 73.14%, 62.67% and 83.61% over independent tests, respectively. The online prediction server was freely accessible at http://lab.malab.cn/~acy/iTTCA .

CONCLUSIONS

We have proven that the proposed predictor iTTCA-RF is superior to the other latest models, and will hopefully become an effective and useful tool for identifying tumor T cell antigens presented in the context of major histocompatibility complex class I.

Deep learning to design nuclear-targeting abiotic miniproteins

There are more amino acid permutations within a 40-residue sequence than atoms on Earth. This vast chemical search space hinders the use of human learning to design functional polymers. Here we show how machine learning enables the de novo design of abiotic nuclear-targeting miniproteins to traffic antisense oligomers to the nucleus of cells. We combined high-throughput experimentation with a directed evolution-inspired deep-learning approach in which the molecular structures of natural and unnatural residues are represented as topological fingerprints. The model is able to predict activities beyond the training dataset, and simultaneously deciphers and visualizes sequence-activity predictions. The predicted miniproteins, termed 'Mach', reach an average mass of 10 kDa, are more effective than any previously known variant in cells and can also deliver proteins into the cytosol. The Mach miniproteins are non-toxic and efficiently deliver antisense cargo in mice. These results demonstrate that deep learning can decipher design principles to generate highly active biomolecules that are unlikely to be discovered by empirical approaches.

Better Performance with Transformer: CPPFormer in precise prediction of cell-Penetrating Peptides

With its superior performance, the Transformer model, which is based on the 'Encoder-Decoder' paradigm, has become the mainstream in natural language processing. On the other hand, bioinformatics has embraced machine learning and made great progress in drug design and protein property prediction. Cell-penetrating peptides (CPPs) are one kind of permeable protein that is convenient as a kind of 'postman' in drug penetration tasks. However, a small number of CPPs have been discovered by research, let alone practical applications in drug permeability. Therefore, correctly identifying the CPPs has opened up a new way to take macromolecules into cells without other potentially harmful materials in the drug. Most of the previous work only uses trivial machine learning techniques and hand-crafted features to construct a simple classifier. In CPPFormer, we learn from the idea of implementing the attention structure of Transformer, rebuilding the network based on the characteristics of CPPs according to its short length, and using an automatic feature extractor with a few manual engineered features to co-direct the predicted results. Compared to all previous methods and other classic text classification models, the empirical result has shown that our proposed deep model-based method has achieved the best performance of 92.16% accuracy in the CPP924 dataset and has passed various index tests.

Cell-Penetrating Peptides Predicted From CASC3, AKIP1, and AHRR Proteins

Peptides can be used as research tools and for diagnostic or therapeutic applications. Peptides, alongside small molecules and antibodies, are used and are gaining further interest as protein-protein interaction (PPI) modulators. Peptides have high target specificity and high affinity, but, unlike small molecule modulators, they are not able to cross the cell membranes to reach their intracellular targets. To overcome this limitation, the special property of the cell-penetrating peptides (CPPs) could benefit their cause. CPPs are a class of peptides that can enter the cells and with them also deliver the attached cargoes. Today, with the advancement of in silico prediction tools and the availability of protein databases, designing new and multifunctional peptides that are able to reach intracellular targets and inhibit certain cellular processes in a very specific manner is reachable. Although there are several efficient CPP sequences already known, the discovery of new CPPs is crucial for the development of efficient delivery methods for both biotechnological and therapeutic applications. In this work, we chose 10 human nuclear proteins from which we predicted new potential CPP sequences by using three different CPP predictors: cell-penetrating peptide prediction tool, CellPPD, and SkipCPP-Pred. From each protein, one predicted CPP sequence was synthesized and its internalization into cells was assessed. Out of the tested sequences, three peptides displayed features characteristic to CPPs. These peptides and also the predicted peptide sequences could be used to design and modify new CPPs. In this work, we show that we can use protein sequences as input for generating new peptides with cell internalization properties. Three new CPPs, AHRR_8-24, CASC3_251-264, and AKIP1_27-37, can be further used for the delivery of other cargoes or designed into multifunctional peptides with capability of internalizing cells.

In silico identification and experimental validation of cellular uptake and intracellular labeling by a new cell penetrating peptide derived from CDN1

Bioactive therapeutic molecules are generally impermeable to the cell membrane, hindering their utility and efficacy. A group of peptides called cell-penetrating peptides (CPPs) were found to have the capability of transporting different types of cargo molecules across the cell membrane. Here, we identified a short peptide named P2, which has a higher proportion of basic residues than the CDN1 (cyclin-dependent kinase inhibitor 1) protein it is derived from, and we used bioinformatic analysis and experimental validation to confirm the penetration property of peptide P2. We found that peptide P2 can efficiently enter different cell lines in a concentration-dependent manner. The endocytosis pathway, especially receptor-related endocytosis, may be involved in the process of P2 penetration. Our data also showed that peptide P2 is safe in cultured cell lines and red blood cells. Lastly, peptide P2 can efficiently deliver self-labeling protein HaloTag into cells for imaging. Our study illustrates that peptide P2 is a promising imaging agent delivery vehicle for future applications.

Empirical comparison and analysis of web-based cell-penetrating peptide prediction tools

Cell-penetrating peptides (CPPs) facilitate the delivery of therapeutically relevant molecules, including DNA, proteins and oligonucleotides, into cells both in vitro and in vivo. This unique ability explores the possibility of CPPs as therapeutic delivery and its potential applications in clinical therapy. Over the last few decades, a number of machine learning (ML)-based prediction tools have been developed, and some of them are freely available as web portals. However, the predictions produced by various tools are difficult to quantify and compare. In particular, there is no systematic comparison of the web-based prediction tools in performance, especially in practical applications. In this work, we provide a comprehensive review on the biological importance of CPPs, CPP database and existing ML-based methods for CPP prediction. To evaluate current prediction tools, we conducted a comparative study and analyzed a total of 12 models from 6 publicly available CPP prediction tools on 2 benchmark validation sets of CPPs and non-CPPs. Our benchmarking results demonstrated that a model from the KELM-CPPpred, namely KELM-hybrid-AAC, showed a significant improvement in overall performance, when compared to the other 11 prediction models. Moreover, through a length-dependency analysis, we find that existing prediction tools tend to more accurately predict CPPs and non-CPPs with the length of 20-25 residues long than peptides in other length ranges.

B3Pred: A Random-Forest-Based Method for Predicting and Designing Blood-Brain Barrier Penetrating Peptides

The blood–brain barrier is a major obstacle in treating brain-related disorders, as it does not allow the delivery of drugs into the brain. We developed a method for predicting blood–brain barrier penetrating peptides to facilitate drug delivery into the brain. These blood–brain barrier penetrating peptides (B3PPs) can act as therapeutics, as well as drug delivery agents. We trained, tested, and evaluated our models on blood–brain barrier peptides obtained from the B3Pdb database. First, we computed a wide range of peptide features. Then, we selected relevant peptide features. Finally, we developed numerous machine-learning-based models for predicting blood–brain barrier peptides using the selected features. The random-forest-based model performed the best with respect to the top 80 selected features and achieved a maximal 85.08% accuracy with an AUROC of 0.93. We also developed a webserver, B3pred, that implements our best models. It has three major modules that allow users to predict/design B3PPs and scan B3PPs in a protein sequence.

CpACpP: In Silico Cell-Penetrating Anticancer Peptide Prediction Using a Novel Bioinformatics Framework

Cell-penetrating anticancer peptides (Cp-ACPs) are considered promising candidates in solid tumor and hematologic cancer therapies. Current approaches for the design and discovery of Cp-ACPs trust the expensive high-throughput screenings that often give rise to multiple obstacles, including instrumentation adaptation and experimental handling. The application of machine learning (ML) tools developed for peptide activity prediction is importantly of growing interest. In this study, we applied the random forest (RF)-, support vector machine (SVM)-, and eXtreme gradient boosting (XGBoost)-based algorithms to predict the active Cp-ACPs using an experimentally validated data set. The model, CpACpP, was developed on the basis of two independent cell-penetrating peptide (CPP) and anticancer peptide (ACP) subpredictors. Various compositional and physiochemical-based features were combined or selected using the multilayered recursive feature elimination (RFE) method for both data sets. Our results showed that the ACP subclassifiers obtain a mean performance accuracy (ACC) of 0.98 with an area under curve (AUC) ≈ 0.98 vis-à-vis the CPP predictors displaying relevant values of ∼0.94 and ∼0.95 via the hybrid-based features and independent data sets, respectively. Also, the predicting evaluation of Cp-ACPs gave accuracies of ∼0.79 and 0.89 on a series of independent sequences by applying our CPP and ACP classifiers, respectively, which leaves the performance of our predictors better than the earlier reported ACPred, mACPpred, MLCPP, and CPPred-RF. The described consensus-based fusion method additionally reached an AUC of 0.94 for the prediction of Cp-ACP (http://cbb1.ut.ac.ir/CpACpP/Index).

In silico identification and experimental validation of cellular uptake by a new cell penetrating peptide P1 derived from MARCKS

Viral vectors for vaccine delivery are challenged by recently reported safety issues like immunogenicity and risk for cancer development, and thus there is a growing need for the development of non-viral vectors. Cell penetrating peptides (CPPs) are non-viral vectors that can enter plasma membranes efficiently and deliver a broad range of cargoes. Our bioinformatic prediction and wet-lab validation data suggested that peptide P1 derived from MARCKS protein phosphorylation site domain is a new potential CPP candidate. We found that peptide P1 can efficiently internalize into various cell lines in a concentration-dependent manner. Receptor-mediated endocytosis pathway is the major mechanism of P1 penetration, although P1 also directly penetrates the plasma membrane. We also found that peptide P1 has low cytotoxicity in cultured cell lines as well as mouse red blood cells. Furthermore, peptide P1 not only can enter into cultured cells itself, but it also can interact with plasmid DNA and mediate the functional delivery of plasmid DNA into cultured cells, even in hard-to-transfect cells. Combined, these findings indicate that P1 may be a promising vector for efficient intracellular delivery of bioactive cargos.

Source and exploration of the peptides used to construct peptide-drug conjugates

Peptide-drug conjugates (PDCs) are a class of novel molecules widely designed and synthesized for delivering payload drugs. The peptide part plays a vital role in the whole molecule, because they determine the ability of the molecules to penetrate the membrane and target to the specific targets. Here, we introduce the source of different kinds of cell-penetrating peptides (CPPs) and cell-targeting peptides (CTPs) that have been used or could be used in constructing PDCs as well as their latest application in delivering drugs. What's more, the approaches of developing CPPs and CTPs and the techniques to discover novel peptides are focused on and summarized in the review. This review aims to help relevant researchers fast understand the research status of peptides in PDCs and carry forward the process of novel peptides discovery.

Evolutionary Timeline of Genetic Delivery and Gene Therapy

There are more than 3,500 genes that are being linked to hereditary diseases or correlated with an elevated risk of certain illnesses. As an alternative to conventional treatments with small molecule drugs, gene therapy has arisen as an effective treatment with the potential to not just alleviate disease conditions but also cure them completely. In order for these treatment regimens to work, genes or editing tools intended to correct diseased genetic material must be efficiently delivered to target sites. There have been many techniques developed to achieve such a goal. In this article, we systematically review a variety of gene delivery and therapy methods that include physical methods, chemical and biochemical methods, viral methods, and genome editing. We discuss their historical discovery, mechanisms, advantages, limitations, safety, and perspectives.