Identification of Anticancer Peptides from the Genome of Candida albicans: 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 Candida albicans. As a result, four candidates were identified and validated in the HCT116 colon cancer cell line. Among them, PCa1 and PCa2 emerged as the most potent, displaying IC50 values of 3.75 and 56.06 µM, respectively, and demonstrating a 4-fold selectivity for cancer cells over normal cells. In the colon xenograft nude mice model, the administration of both peptides resulted in substantial inhibition of tumor growth without causing significant adverse effects. In conclusion, this work not only contributes a proven computational workflow for ACP discovery but also introduces two peptides, PCa1 and PCa2, as promising candidates poised for further development as targeted therapies for colon cancer. The method as a web service is available at https://app.cbbio.online/acpep/home and the source code at https://github.com/cartercheong/AcPEP_classification.git.

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

Neuropeptides are a group of neuronal signaling molecules that regulate physiological and behavioral processes in animals. Here, we used in silico mining to predict the polypeptide composition of available transcriptomic data of Turbinaria peltata. In total, 118 transcripts encoding putative peptide precursors were discovered. One neuropeptide Y/F-like peptide, named TpNPY, was identified and selected for in silico structural, in silico binding, and pharmacological studies. In our study, the anti-inflammation effect of TpNPY was evaluated using an LPS-stimulated C8-D1A astrocyte cell model. Our results demonstrated that TpNPY, at 0.75-3 µM, inhibited LPS-induced NO production and reduced the expression of iNOS in a dose-dependent manner. Furthermore, TpNPY reduced the secretion of proinflammatory cytokines. Additionally, treatment with TpNPY reduced LPS-mediated elevation of ROS production and the intracellular calcium concentration. Further investigation revealed that TpNPY downregulated the IKK/IκB/NF-κB signaling pathway and inhibited expression of the NLRP3 inflammasome. Through molecular docking and using an NPY receptor antagonist, TpNPY was shown to have the ability to interact with the NPY Y1 receptor. On the basis of these findings, we concluded that TpNPY might prevent LPS-induced injury in astrocytes through activation of the NPY-Y1R.

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. Combined with multiple sequence alignment and phylogenetic analysis, we discovered three polypeptides, we named them AdKuz1, AdKuz2 and AdKuz3, from A. digitifera that showed a close relationship to Kunitz-type peptides. Molecular docking and molecular dynamics simulation indicated that AdKuz1 to 3 could interact with GABAA receptor but AdKuz2-GABAA remained more stable than others. The biological experiments showed that AdKuz1 and AdKuz2 exhibited an anti-inflammatory effect by decreasing the aberrant level of nitric oxide (NO), IL-6, TNF-α and IL-1ß induced by LPS in BV-2 cells. In addition, the pentylenetetrazol (PTZ)-induced epileptic effect on zebrafish was remarkably suppressed by AdKuz1 and AdKuz2. AdKuz2 particularly showed superior anti-epileptic effects compared to the other two peptides. Furthermore, AdKuz2 significantly decreased the expression of c-fos and npas4a, which were up-regulated by PTZ treatment. In addition, AdKuz2 reduced the synthesis of glutamate and enhanced the biosynthesis of gamma-aminobutyric acid (GABA). In conclusion, the results indicated that AdKuz2 may affect the synthesis of glutamate and GABA and enhance the activity of the GABAA receptor to inhibit the symptoms of epilepsy. We believe, AdKuz2 could be a promising anti-epileptic agent and its mechanism of action should be further investigated.

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. The results showed that the initial growth rate and surface coverage of the SAM are dependent on the ligand concentration. At a certain high concentration (about 1.2-1.5 times the minimum concentration), the final surface coverage is optimal. Reducing the chain length and increasing the proportion of hydrophobic diluting molecules are effective ways to improve the surface coverage, but the compositional ligands have to be changed, which may not be desirable for the functional requirements of SAMs. Furthermore, by investigating the behavior of the alkanethiolates and ethanol solvent under an applied external field, we find that a strong electric field with a proper field direction can facilitate the generation of defect-free monolayers. These findings will contribute to the understanding of mixed-SAM formation and provide insight into experimental design for efficient and effective SAM formation.

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. Here, we propose a deep learning method based on convolutional neural networks to predict biological activity (EC50, LC50, IC50, and LD50) against six tumor cells, including breast, colon, cervix, lung, skin, and prostate. We show that models derived with multitask learning achieve better performance than conventional single-task models. In repeated 5-fold cross validation using the CancerPPD data set, the best models with the applicability domain defined obtain an average mean squared error of 0.1758, Pearson's correlation coefficient of 0.8086, and Kendall's correlation coefficient of 0.6156. As a step toward model interpretability, we infer the contribution of each residue in the sequence to the predicted activity by means of feature importance weights derived from the convolutional layers of the model. The present method, referred to as xDeep-AcPEP, will help to identify effective ACPs in rational peptide design for therapeutic purposes. The data, script files for reproducing the experiments, and the final prediction models can be downloaded from http://github.com/chen709847237/xDeep-AcPEP. The web server to directly access this prediction method is at https://app.cbbio.online/acpep/home.

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 Pelophylax fukienensis. It exhibited potent activity against Gram-positive bacteria, but no effect on Gram-negative bacteria. Additionally, TPF exhibited aggregation effects in different solutions. Three analogs were further designed to study the relationship between the aggregation patterns and bioactivities, and the MD simulation was performed for revealing the pattern of the peptide assembly. As the results showed, all peptides were able to aggregate in the standard culture media and salt solutions, especially CaCl2 and MgCl2 buffers, where the aggregation was affected by the concentration of the salts. MD simulation reported that all peptides were able to form oligomers. The parent peptide assembly depended on the hydrophobic interaction via the residues in the middle domain of the sequence. However, the substitution of Trp/D-Trp resulted in an enhanced inter-peptide interaction in the zipper-like domain and eliminated overall biological activities. Our study suggested that introducing aromaticity at the zipper-like domain for temporin may not improve the bioactivities, which might be related to the formation of aggregates via the inter-peptide contacts at the zipper-like motif domain, and it could reduce the binding affinity to the lipid membrane of microorganisms.

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 (CH3-SAM) and compared them to the benchmark experimental data set. Our result revealed that existing biomolecular force fields, GAFF and AMBER ff14sb, cannot reproduce the experimental peptide adsorption free energies by Wei and Latour (Langmuir, 2009, 25, 5637-5646). To obtain the improved force fields, we systematically tuned the Lennard-Jones parameters of selected amino acid sidechains and the functional group of SAM with repeated metadynamics and umbrella sampling simulations. The final parameter set has yielded a significant improvement in the free energy values with R = 0.83 and MSE = 0.65 kcal/mol. We applied the refined force field to predict the initial adsorption orientation of lysozyme on CH3-SAM. Two major orientations-face-down and face-up-were predicted. Our analysis on the protein structure, solvent accessible surface area, and binding of native ligand NAG3 suggested that lysozyme in the face-up orientation can remain active after initial adsorption. However, because of its weaker affinity (ΔΔG = 7.86 kcal/mol) for the ligand, the bioactivity of the protein is expected to reduce. Our work facilitates the use of MD for the study of protein-SAM systems. The refined force field compatible with GROMACS is available at https://cbbio.cis.um.edu.mo/software/SAMFF .

Novel neurotoxic peptides from Protopalythoa variabilis virtually interact with voltage-gated sodium channel and display anti-epilepsy and neuroprotective activities in zebrafish.

We previously reported a novel toxic peptide identified from the anthozoan Protopalythoa variabilis transcriptome which is homologous to a novel structural type of sodium channel toxin isolated from a parental species (Palythoa caribaeorum). The peptide was named, according to its homologous, as Pp V-shape α-helical peptide (PpVα) in the present study. Through molecular docking and dynamics simulation, linear and hairpin folded PpVα peptides were shown to be potential voltage-gated sodium channel blockers. Nowadays, sodium channel blockers have been the mainstream of the pharmacological management of epileptic seizures. Also, sodium channel blockers could promote neuronal survival by reducing sodium influx and reducing the likelihood of calcium importation resulting in suppressing microglial activation and protecting dopaminergic neurons from degeneration. The folded PpVα peptide could decrease pentylenetetrazol (PTZ)-induced c-fos and npas4a expression level leading to reverse PTZ-induced locomotor hyperactivity in zebrafish model. In vitro, the folded PpVα peptide protected PC12 cells against 6-hydroxydopamine (6-OHDA)-induced neurotoxicity via activating heme oxygenase-1 (HO-1) and attenuating inducible nitric oxide synthase (iNOS) expression. In vivo, PpVα peptide suppressed the 6-OHDA-induced neurotoxicity on the locomotive behavior of zebrafish and, importantly, prevented the 6-OHDA-induced excessive ROS generation and subsequent dopaminergic neurons loss. This study indicates that the single S-S bond folded PpVα peptide arises as a new structural template to develop sodium channel blockers and provides an insight on the peptide discovery from cnidarian transcriptome to potentially manage epilepsy and neurodegenerative disorders.

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. In this work, transcriptomic and proteomic analyses were combined to identify six groups of expressed peptide toxins in Zoanthus natalensis. These include neurotoxin, hemostatic and hemorrhagic toxin, protease inhibitor, mixed function enzymes, venom auxiliary proteins, allergen peptides, and peptides related to the innate immunity. Molecular docking analysis indicated that one expressed Zoanthus Kunitz-like peptide, ZoaKuz1, could be a voltage-gated potassium channels blocker and, hence, it was selected for functional studies. Functional bioassays revealed that ZoaKuz1 has an intrinsic neuroprotective activity in zebrafish model of Parkinson's disease. Since pharmacological blockade of KV channels is known to induce neuroprotective effects, ZoaKuz1 holds the potential to be developed in a therapeutic tool to control neural dysfunction by slowing or even halting neurodegeneration mediated by ion-channel hyperactivity.

Novel Kunitz-like Peptides Discovered in the Zoanthid Palythoa caribaeorum through Transcriptome Sequencing.

Palythoa caribaeorum (class Anthozoa) is a zoanthid that together jellyfishes, hydra, and sea anemones, which are venomous and predatory, belongs to the Phyllum Cnidaria. The distinguished feature in these marine animals is the cnidocytes in the body tissues, responsible for toxin production and injection that are used majorly for prey capture and defense. With exception for other anthozoans, the toxin cocktails of zoanthids have been scarcely studied and are poorly known. Here, on the basis of the analysis of P. caribaeorum transcriptome, numerous predicted venom-featured polypeptides were identified including allergens, neurotoxins, membrane-active, and Kunitz-like peptides (PcKuz). The three predicted PcKuz isotoxins (1-3) were selected for functional studies. Through computational processing comprising structural phylogenetic analysis, molecular docking, and dynamics simulation, PcKuz3 was shown to be a potential voltage gated potassium-channel inhibitor. PcKuz3 fitted well as new functional Kunitz-type toxins with strong antilocomotor activity as in vivo assessed in zebrafish larvae, with weak inhibitory effect toward proteases, as evaluated in vitro. Notably, PcKuz3 can suppress, at low concentration, the 6-OHDA-induced neurotoxicity on the locomotive behavior of zebrafish, which indicated PcKuz3 may have a neuroprotective effect. Taken together, PcKuz3 figures as a novel neurotoxin structure, which differs from known homologous peptides expressed in sea anemone. Moreover, the novel PcKuz3 provides an insightful hint for biodrug development for prospective neurodegenerative disease treatment.

A Hybrid Cuckoo Search and Differential Evolution Approach to Protein⁻Ligand Docking.

Protein⁻ligand docking is a molecular modeling technique that is used to predict the conformation of a small molecular ligand at the binding pocket of a protein receptor. There are many protein⁻ligand docking tools, among which AutoDock Vina is the most popular open-source docking software. In recent years, there have been numerous attempts to optimize the search process in AutoDock Vina by means of heuristic optimization methods, such as genetic and particle swarm optimization algorithms. This study, for the first time, explores the use of cuckoo search (CS) to solve the protein⁻ligand docking problem. The result of this study is CuckooVina, an enhanced conformational search algorithm that hybridizes cuckoo search with differential evolution (DE). Extensive tests using two benchmark datasets, PDBbind 2012 and Astex Diverse set, show that CuckooVina improves the docking performances in terms of RMSD, binding affinity, and success rate compared to Vina though it requires about 9⁻15% more time to complete a run than Vina. CuckooVina predicts more accurate docking poses with higher binding affinities than PSOVina with similar success rates. CuckooVina's slower convergence but higher accuracy suggest that it is better able to escape from local energy minima and improves the problem of premature convergence. As a summary, our results assure that the hybrid CS⁻DE process to continuously generate diverse solutions is a good strategy to maintain the proper balance between global and local exploitation required for the ligand conformational search.

ProtPOS: a python package for the prediction of protein preferred orientation on a surface.

UNLABELLED: Atomistic molecular dynamics simulation is a promising technique to investigate the energetics and dynamics in the protein-surface adsorption process which is of high relevance to modern biotechnological applications. To increase the chance of success in simulating the adsorption process, favorable orientations of the protein at the surface must be determined. Here, we present ProtPOS which is a lightweight and easy-to-use python package that can predict low-energy protein orientations on a surface of interest. It combines a fast conformational sampling algorithm with the energy calculation of GROMACS. The advantage of ProtPOS is it allows users to select any force fields suitable for the system at hand and provide structural output readily available for further simulation studies. AVAILABILITY AND IMPLEMENTATION: ProtPOS is freely available for academic and non-profit uses at http://cbbio.cis.umac.mo/software/protpos SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online. CONTACT: shirleysiu@umac.mo.

TMDIM: an improved algorithm for the structure prediction of transmembrane domains of bitopic dimers.

[Formula: see text]-Helical transmembrane proteins are the most important drug targets in rational drug development. However, solving the experimental structures of these proteins remains difficult, therefore computational methods to accurately and efficiently predict the structures are in great demand. We present an improved structure prediction method TMDIM based on Park et al. (Proteins 57:577-585, 2004) for predicting bitopic transmembrane protein dimers. Three major algorithmic improvements are introduction of the packing type classification, the multiple-condition decoy filtering, and the cluster-based candidate selection. In a test of predicting nine known bitopic dimers, approximately 78% of our predictions achieved a successful fit (RMSD <2.0 Å) and 78% of the cases are better predicted than the two other methods compared. Our method provides an alternative for modeling TM bitopic dimers of unknown structures for further computational studies. TMDIM is freely available on the web at https://cbbio.cis.umac.mo/TMDIM . Website is implemented in PHP, MySQL and Apache, with all major browsers supported.

Identification of novel natural compound inhibitors for human complement component 5a receptor by homology modeling and virtual screening.

ABSTRACT: Neuropathic pain and inflammatory pain are two common types of pathological pain in human health problems. To date, normal painkillers are only partially effective in treating such pain, leading to a tremendous demand to develop new chemical entities to combat pain and inflammation. A promising pharmacological treatment is to control signal transduction via the inflammatory mediator-coupled receptor protein C5aR by finding antagonists to inhibit C5aR activation. Here, we report the first computational study on the identification of non-peptide natural compound inhibitors for C5aR by homology modeling and virtual screening. Our study revealed a novel natural compound inhibitor Acteoside with better docking scores than all four existing non-peptidic natural compounds. The MM-GBSA binding free energy calculations confirmed that Acteoside has a decrease of ~39 kcal/mol in the free energy of binding compared to the strongest binding reference compound. Main contributions to the higher affinity of Acteoside to C5aR are the exceptionally strong lipophilic interaction, enhanced electrostatics and hydrogen bond interactions. Detailed analysis on the physiochemical properties of Acteoside suggests further directions in lead optimization. Taken together, our study proposes that Acteoside is a potential lead molecule targeting the C5aR allosteric site and provides helpful information for further experimental studies.

Membrane-proximal tryptophans of synaptobrevin II stabilize priming of secretory vesicles.

Trans-soluble N-ethylmaleimide-sensitive factor attachment protein (SNAP) receptor (SNARE) complexes formed between the SNARE motifs of synaptobrevin II, SNAP-25, and syntaxin play an essential role in Ca(2+)-regulated exocytosis. Apart from the well studied interactions of the SNARE domains, little is known about the functional relevance of other evolutionarily conserved structures in the SNARE proteins. Here, we show that substitution of two highly conserved tryptophan residues within the juxtamembrane domain (JMD) of the vesicular SNARE Synaptobrevin II (SybII) profoundly impairs priming of granules in mouse chromaffin cells without altering catecholamine release from single vesicles. Using molecular dynamic simulations of membrane-embedded SybII, we show that Trp residues of the JMD influence the electrostatic surface potential by controlling the position of neighboring lysine and arginine residues at the membrane-water interface. Our observations indicate a decisive role of the tryptophan moiety of SybII in keeping the vesicles in the release-ready state and support a model wherein tryptophan-mediated protein-lipid interactions assist in bridging the apposing membranes before fusion.

A deep learning method for predicting the minimum inhibitory concentration of antimicrobial peptides against Escherichia coli 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 Escherichia coli. The optimal MBC-Attention model achieved an average Pearson correlation coefficient (PCC) of 0.775 and a root mean squared error (RMSE) of 0.533 (log µM) in three independent tests of randomly drawn sequences from the data set. This results in a 5-12% improvement in PCC and a 6-13% improvement in RMSE compared to 17 traditional machine learning models and 2 optimally tuned models using random forest and support vector machine. Ablation studies confirmed that the two proposed attention mechanisms, global attention and local attention, contributed largely to performance improvement. IMPORTANCE Antimicrobial peptides (AMPs) are potential candidates for replacing conventional antibiotics to combat drug resistance in pathogenic bacteria. Therefore, it is necessary to evaluate the antimicrobial activity of AMPs quantitatively. However, wet-lab experiments are labor-intensive and time-consuming. To accelerate the evaluation process, we develop a deep learning method called MBC-Attention to regress the experimental minimum inhibitory concentration of AMPs against Escherichia coli. The proposed model outperforms traditional machine learning methods. Data, scripts to reproduce experiments, and the final production models are available on GitHub.

Discovery and analysis of a novel antimicrobial peptide B1AW from the skin secretion of Amolops wuyiensis 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, Amolops wuyiensis (A. wuyiensisi), named B1AW (FLPLLAGLAANFLPQIICKIARKC). B1AW displayed anti-bacterial activity against Gram-positive bacteria Staphylococcus aureus (S. aureus), methicillin-resistant Staphylococcus aureus (MRSA), and Enterococcus faecalis (E. faecalis). B1AW-K was designed to broaden the antimicrobial spectrum of B1AW. The introduction of a lysine residue generated an AMP with enhanced broad-spectrum antibacterial activity. It also displayed the ability to inhibit the growth of human prostatic cancer PC-3, non-small lung cancer H838, and glioblastoma cancer U251MG cell lines. In molecular dynamic (MD) simulations, B1AW-K had a faster approach and adsorption to the anionic membrane than B1AW. Therefore, B1AW-K was considered a drug prototype with a dual effect, which deserves further clinical investigation and validation.

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. However, experimental approaches used to discover and design new AMPs are very expensive and time-consuming. In recent years, there has been considerable interest in using in silico methods, including traditional machine learning (ML) and deep learning (DL) approaches, to drug discovery. While there are a few papers summarizing computational AMP prediction methods, none of them focused on DL methods. In this review, we aim to survey the latest AMP prediction methods achieved by DL approaches. First, the biology background of AMP is introduced, then various feature encoding methods used to represent the features of peptide sequences are presented. We explain the most popular DL techniques and highlight the recent works based on them to classify AMPs and design novel peptide sequences. Finally, we discuss the limitations and challenges of AMP prediction.

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. As for its real-world applications, prediction models that are able to recognize novel sequences having high or low similarities to training sequences are required. To this end, we tested our models on two test sets: a general test set including sequences spanning different similarity levels to those of the training set, and a novel-test set consisting of only sequences that bear little resemblance to sequences from the training set. Our experiments showed that the Multi-Branch-CNN method performs better than thirteen traditional ML algorithms (TML13), yielding an improvement in accuracy of 3.2%, 1.2%, and 2.3% on the test sets as well as 8.8%, 14.3%, and 14.6% on the novel-test sets for sodium, potassium, and calcium ion channels, respectively. We confirmed the effectiveness of Multi-Branch-CNN by comparing it to the standard CNN method with one input branch (Single-Branch-CNN) and an ensemble method (TML13-Stack). The data sets, script files to reproduce the experiments, and the final predictive models are freely available at https://github.com/jieluyan/Multi-Branch-CNN.

Novel Dihydropyrimidinone Derivatives as Potential P-Glycoprotein Modulators.

P-glycoprotein (Pgp), an ATP binding cassette (ABC) transporter, is an ATP-dependent efflux pump responsible for cancer multidrug resistance. As part of efforts to identify human Pgp (hPgp) inhibitors, we prepared a series of novel triazole-conjugated dihydropyrimidinones using a synthetic approach that is well suited for obtaining compound libraries. Several of these dihydropyrimidinone derivatives modulate human P-glycoprotein (hPgp) activity with low micromolar EC50 values. Molecular docking studies suggest that these compounds bind to the M-site of the transporter.