MPMABP: A CNN and Bi-LSTM-Based Method for Predicting Multi-Activities of Bioactive Peptides

An in silico scheme for optimizing the enzymatic acquisition of natural biologically active peptides based on machine learning and virtual digestion

BACKGROUND

As a potential natural active substance, natural biologically active peptides (NBAPs) are recently attracting increasing attention. The traditional proteolysis methods of obtaining effective NBAPs are considerably vexing, especially since multiple proteases can be used, which blocks the exploration of available NBAPs. Although the development of virtual digesting brings some degree of convenience, the activity of the obtained peptides remains unclear, which would still not allow efficient access to the NBAPs. It is necessary to develop an efficient and accurate strategy for acquiring NBAPs.

RESULTS

A new in silico scheme named SSA-LSTM-VD, which combines a sparrow search algorithm-long short-term memory (SSA-LSTM) deep learning and virtually digested, was presented to optimize the proteolysis acquisition of NBAPs. Therein, SSA-LSTM reached the highest Efficiency value reached 98.00 % compared to traditional machine learning algorithms, and basic LSTM algorithm. SSA-LSTM was trained to predict the activity of peptides in the proteins virtually digested results, obtain the percentage of target active peptide, and select the appropriate protease for the actual experiment. As an application, SSA-LSTM was employed to predict the percentage of neuroprotective peptides in the virtual digested result of walnut protein, and trypsin was ultimately found to possess the highest value (85.29 %). The walnut protein was digested by trypsin (WPTrH) and the peptide sequence obtained was analyzed closely matches the theoretical neuroprotective peptide. More importantly, the neuroprotective effects of WPTrH had been demonstrated in nerve damage mouse models.

SIGNIFICANCE

The proposed SSA-LSTM-VD in this paper makes the acquisition of NBAPs efficient and accurate. The approach combines deep learning and virtually digested skillfully. Utilizing the SSA-LSTM-VD based strategy holds promise for discovering and developing peptides with neuroprotective properties or other desired biological activities.

Deep2Pep: A deep learning method in multi-label classification of bioactive peptide

Functional peptides are easy to absorb and have low side effects, which has attracted increasing interest from pharmaceutical scientists. However, due to the limitations in the laboratory funding and human resources, it is difficult to screen the functional peptides from a large number of peptides with unknown functions. With the development of machine learning and Deep learning, the combination of computational methods and biological information provides an effective method for identifying peptide functions. To explore the value of multi-functional active peptides, a new deep learning method named Deep2Pep (Deep learning to Peptides) was constructed, which was based on sequence encoding, embedding, and language tokenizer. It can achieve predictions of peptides on antimicrobial, antihypertensive, antioxidant and antihyperglycemic by converting sequence information into digital vectors, combined BiLSTM, attention-residual algorithm, and BERT Encoder. The results showed that Deep2Pep had a Hamming Loss of 0.095, subset Accuracy of 0.737, and Macro F1-Score of 0.734. which outperformed other models. BiLSTM played a primary role in Deep2Pep, which BERT encoder was in an auxiliary position. Deep learning algorithms was used in this study to accurately predict the four active functions of peptides, and it was expected to provide effective references for predicting multi-functional peptides.

Deep learning for advancing peptide drug development: Tools and methods in structure prediction and design

Peptides can bind challenging disease targets with high affinity and specificity, offering enormous opportunities for addressing unmet medical needs. However, peptides' unique features, including smaller size, increased structural flexibility, and limited data availability, pose additional challenges to the design process compared to proteins. This review explores the dynamic field of peptide therapeutics, leveraging deep learning to enhance structure prediction and design. Our exploration encompasses various facets of peptide research, ranging from dataset curation handling to model development. As deep learning technologies become more refined, we channel our efforts into peptide structure prediction and design, aligning with the fundamental principles of structure-activity relationships in drug development. To guide researchers in harnessing the potential of deep learning to advance peptide drug development, our insights comprehensively explore current challenges and future directions of peptide therapeutics.

Toxicity of Glycyl-l-Prolyl-l-Glutamate Pseudotripeptides: Cytotoxic, Oxidative, Genotoxic, and Embryotoxic Perspectives

The tripeptide H-Gly-Pro-Glu-OH (GPE) and its analogs began to take much interest from scientists for developing effective novel molecules in the treatment of several disorders including Alzheimer’s disease, Parkinson’s disease, and stroke. The peptidomimetics of GPEs exerted significant biological properties involving anti-inflammatory, antiapoptotic, and anticancer properties. The assessments of their hematological toxicity potentials are critically required for their possible usage in further preclinical and clinical trials against a wide range of pathological conditions. However, there is so limited information on the safety profiling of GPE and its analogs on human blood tissue from cytotoxic, oxidative, and genotoxic perspectives. And, their embryotoxicity potentials were not investigated yet. Therefore, in this study, measurements of mitochondrial viability (using MTT assay) and lactate dehydrogenase (LDH) release as well as total antioxidant capacity (TAC) assays were performed on cultured human whole blood cells after treatment with GPE and its three novel peptidomimetics for 72 h. Sister chromatid exchange (SCE), micronucleus (MN), and 8-oxo-2-deoxyguanosine (8-OH-dG) assays were performed for determining the genotoxic damage potentials. In addition, the nuclear division index (NDI) was figured out for revealing their cytostatic potentials. Embryotoxicity assessments were performed on cultured human pluripotent NT2 embryonal carcinoma cells by MTT and LDH assays. The present results from cytotoxicity, oxidative, genotoxicity, and embryotoxicity testing clearly propounded that GPEs had good biosafety profiles and were trouble-free from the toxicological point of view. Noncytotoxic, antioxidative, nongenotoxic, noncytostatic, and nonembryotoxic features of GPE analogs are worthwhile exploring further and may exert high potentials for improving the development of novel disease-modifying agents.