Identification of Anticancer Peptides from the Genome of : in Silico Screening, in Vitro and in Vivo Validations.

Anticancer peptides (ACPs) are promising future therapeutics, but their experimental discovery remains time-consuming and costly. To accelerate the discovery process, we propose a computational screening workflow to identify, filter, and prioritize peptide sequences based on predicted class probability, antitumor activity, and toxicity. The workflow was applied to identify novel ACPs with potent activity against colorectal cancer from the genome sequences of .

A deep learning method for predicting the minimum inhibitory concentration of antimicrobial peptides against using Multi-Branch-CNN and Attention.

Antimicrobial peptides (AMPs) are a promising alternative to antibiotics to combat drug resistance in pathogenic bacteria. However, the development of AMPs with high potency and specificity remains a challenge, and new tools to evaluate antimicrobial activity are needed to accelerate the discovery process. Therefore, we proposed MBC-Attention, a combination of a multi-branch convolution neural network architecture and attention mechanisms to predict the experimental minimum inhibitory concentration of peptides against .

Discovery and analysis of a novel antimicrobial peptide B1AW from the skin secretion of and improving the membrane-binding affinity through the construction of the lysine-introduced analogue.

In the development and study of antimicrobial peptides (AMPs), researchers have kept a watchful eye on peptides from the brevinin family because of their extensive antimicrobial activities and anticancer potency. In this study, a novel brevinin peptide was isolated from the skin secretions of the Wuyi torrent frog, () named B1AW (FLPLLAGLAANFLPQIICKIARKC). B1AW displayed anti-bacterial activity against Gram-positive bacteria (), methicillin-resistant (), and ().

Recent Progress in the Discovery and Design of Antimicrobial Peptides Using Traditional Machine Learning and Deep Learning.

Antimicrobial resistance has become a critical global health problem due to the abuse of conventional antibiotics and the rise of multi-drug-resistant microbes. Antimicrobial peptides (AMPs) are a group of natural peptides that show promise as next-generation antibiotics due to their low toxicity to the host, broad spectrum of biological activity, including antibacterial, antifungal, antiviral, and anti-parasitic activities, and great therapeutic potential, such as anticancer, anti-inflammatory, etc. Most importantly, AMPs kill bacteria by damaging cell membranes using multiple mechanisms of action rather than targeting a single molecule or pathway, making it difficult for bacterial drug resistance to develop.

Multi-Branch-CNN: Classification of ion channel interacting peptides using multi-branch convolutional neural network.

Ligand peptides that have high affinity for ion channels are critical for regulating ion flux across the plasma membrane. These peptides are now being considered as potential drug candidates for many diseases, such as cardiovascular disease and cancers. In this work, we developed Multi-Branch-CNN, a CNN method with multiple input branches for identifying three types of ion channel peptide binders (sodium, potassium, and calcium) from intra- and inter-feature types.

A Neuropeptide Y/F-like Polypeptide Derived from the Transcriptome of Suppresses LPS-Induced Astrocytic Inflammation.

Neuropeptides are a group of neuronal signaling molecules that regulate physiological and behavioral processes in animals. Here, we used mining to predict the polypeptide composition of available transcriptomic data of . In total, 118 transcripts encoding putative peptide precursors were discovered.

Anti-epileptic Kunitz-like peptides discovered in the branching coral Acropora digitifera through transcriptomic analysis.

Approximately 50 million people are suffering from epilepsy worldwide. Corals have been used for treating epilepsy in traditional Chinese medicine, but the mechanism of this treatment is unknown. In this study, we analyzed the transcriptome of the branching coral Acropora digitifera and obtained its Kyoto Encyclopedia of Genes and Genomes (KEGG), EuKaryotic Orthologous Groups (KOG) and Gene Ontology (GO) annotation.

Toxic Peptide From Acting on the TRPV1 Channel Prevents Pentylenetetrazol-Induced Epilepsy in Zebrafish Larvae.

PcActx peptide, identified from the transcriptome of zoantharian was clustered into the phylogeny of analgesic polypeptides from sea anemone (known as APHC peptides). APHC peptides were considered as inhibitors of transient receptor potential cation channel subfamily V member 1 (TRPV1). TRPV1 is a calcium-permeable channel expressed in epileptic brain areas, serving as a potential target for preventing epileptic seizures.

Chemical toxicity prediction based on semi-supervised learning and graph convolutional neural network.

As safety is one of the most important properties of drugs, chemical toxicology prediction has received increasing attentions in the drug discovery research. Traditionally, researchers rely on in vitro and in vivo experiments to test the toxicity of chemical compounds. However, not only are these experiments time consuming and costly, but experiments that involve animal testing are increasingly subject to ethical concerns.

xDeep-AcPEP: Deep Learning Method for Anticancer Peptide Activity Prediction Based on Convolutional Neural Network and Multitask Learning.

Cancer is one of the leading causes of death worldwide. Conventional cancer treatment relies on radiotherapy and chemotherapy, but both methods bring severe side effects to patients, as these therapies not only attack cancer cells but also damage normal cells. Anticancer peptides (ACPs) are a promising alternative as therapeutic agents that are efficient and selective against tumor cells.

A novel bioengineered fragment peptide of Vasostatin-1 exerts smooth muscle pharmacological activities and anti-angiogenic effects via blocking VEGFR signalling pathway.

Chromogranin A (CgA) is a hydrophilic glycoprotein released by post-ganglionic sympathetic neurons. CgA consists of a single peptide chain containing numerous paired basic residues, which are typical cleavage sites in prohormones to generate bioactive peptides. It is recognized as a diagnostic and prognostic serum marker for neuroendocrine tumours.

Aggregation and Its Influence on the Bioactivities of a Novel Antimicrobial Peptide, Temporin-PF, and Its Analogues.

Temporin is an antimicrobial peptide (AMP) family discovered in the skin secretion of ranid frog that has become a promising alternative for conventional antibiotic therapy. Herein, a novel temporin peptide, Temporin-PF (TPF), was successfully identified from . It exhibited potent activity against Gram-positive bacteria, but no effect on Gram-negative bacteria.

Effect of Concentration, Chain Length, Hydrophobicity, and an External Electric Field on the Growth of Mixed Alkanethiol Self-Assembled Monolayers: A Molecular Dynamics Study.

Growing functionalized self-assembled monolayers (SAMs) with fewer defects and lower cost is the focus of ongoing investigations. In the present study, molecular dynamics simulations were performed to investigate the process of SAM formation on a gold substrate from mixed alkanethiolates in ethanol solution. Using the mixed-SAM system of 11-mercaptoundecanoic acid (MUA) with either 1-decanethiol (C9CH3) or 6-mercaptohexanol (C6OH) in a 3:7 ratio as the standard SAM model, we systematically investigated the effects of the concentration, chain length, functional group, and an external electric field on SAM growth.

White learning methodology: A case study of cancer-related disease factors analysis in real-time PACS environment.

Background And Objective: Bayesian network is a probabilistic model of which the prediction accuracy may not be one of the highest in the machine learning family. Deep learning (DL) on the other hand possess of higher predictive power than many other models. How reliable the result is, how it is deduced, how interpretable the prediction by DL mean to users, remain obscure.

GWOVina: A grey wolf optimization approach to rigid and flexible receptor docking.

Protein-ligand docking programs are indispensable tools for predicting the binding pose of a ligand to the receptor protein. In this paper, we introduce an efficient flexible docking method, GWOVina, which is a variant of the Vina implementation using the grey wolf optimizer (GWO) and random walk for the global search, and the Dunbrack rotamer library for side-chain sampling. The new method was validated for rigid and flexible-receptor docking using four independent datasets.

Novel venom-based peptides (P13 and its derivative-M6) to maintain self-renewal of human embryonic stem cells by activating FGF and TGFβ signaling pathways.

Background: In our previous study, a venom-based peptide named Gonearrestide (also named P13) was identified and demonstrated with an effective inhibition in the proliferation of colon cancer cells. In this study, we explored if P13 and its potent mutant M6 could promote the proliferation of human embryonic stem cells and even maintain their self-renewal.

Methods: The structure-function relationship analysis on P13 and its potent mutant M6 were explored from the molecular mechanism of corresponding receptor activation by a series of inhibitor assay plus molecular and dynamics simulation studies.

Deep-AmPEP30: Improve Short Antimicrobial Peptides Prediction with Deep Learning.

Antimicrobial peptides (AMPs) are a valuable source of antimicrobial agents and a potential solution to the multi-drug resistance problem. In particular, short-length AMPs have been shown to have enhanced antimicrobial activities, higher stability, and lower toxicity to human cells. We present a short-length (≤30 aa) AMP prediction method, Deep-AmPEP30, developed based on an optimal feature set of PseKRAAC reduced amino acids composition and convolutional neural network.

Machine Learning Approaches for Quality Assessment of Protein Structures.

Protein structures play a very important role in biomedical research, especially in drug discovery and design, which require accurate protein structures in advance. However, experimental determinations of protein structure are prohibitively costly and time-consuming, and computational predictions of protein structures have not been perfected. Methods that assess the quality of protein models can help in selecting the most accurate candidates for further work.

and MD Simulation Study to Explore Physicochemical Parameters for Antibacterial Peptide to Become Potent Anticancer Peptide.

Although the physicochemical properties of antimicrobial peptides (AMPs) and anticancer peptides (ACPs) are very similar, it remains unclear which specific parameter(s) of ACPs confer the major anticancer activity. By answering how to construct a short AMP/ACP that could easily be synthesized in the most cost effective way plus conferring a maximum anticancer effect is a very important scientific breakthrough in the development of protein/peptide drugs. In this study, an 18-amino-acids antimicrobial peptide, AcrAP1 (named AP1-Z1), was used as a template.

Refined Empirical Force Field to Model Protein-Self-Assembled Monolayer Interactions Based on AMBER14 and GAFF.

Understanding protein interaction with material surfaces is important for the development of nanotechnological devices. The structures and dynamics of proteins can be studied via molecular dynamics (MD) if the protein-surface interactions can be accurately modeled. To answer this question, we computed the adsorption free energies of peptides (representing eleven different amino acids) on a hydrophobic self-assembled monolayer (CH-SAM) and compared them to the benchmark experimental data set.

Combined transcriptomic and proteomic analysis reveals a diversity of venom-related and toxin-like peptides expressed in the mat anemone Zoanthus natalensis (Cnidaria, Hexacorallia).

Venoms from marine animals have been recognized as a new emerging source of peptide-based therapeutics. Several peptide toxins from sea anemone have been investigated as therapeutic leads or pharmacological tools. Venom complexity should be further highlighted using combined strategies of large-scale sequencing and data analysis which integrated transcriptomics and proteomics to elucidate new proteins or peptides to be compared among species.

Structure-Based in Silico Screening Identifies a Potent Ebolavirus Inhibitor from a Traditional Chinese Medicine Library.

Potent Ebolavirus (EBOV) inhibitors will help to curtail outbreaks such as that which occurred in 2014-16 in West Africa. EBOV has on its surface a single glycoprotein (GP) critical for viral entry and membrane fusion. Recent high-resolution complexes of EBOV GP with a variety of approved drugs revealed that binding to a common cavity prevented fusion of the virus and endosomal membranes, inhibiting virus infection.