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.

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 .

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.

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. In rigid docking, GWOVina showed comparable docking performance to Vina in terms of ligand pose RMSD, success rate, and affinity prediction. In flexible-receptor docking, GWOVina has improved success rate compared to Vina and AutoDockFR. It ran 2 to 7 times faster than Vina and 40 to 100 times faster than AutoDockFR. Therefore, GWOVina can play a role in solving the complex flexible-receptor docking cases and is suitable for virtual screening of compound libraries. GWOVina is freely available at https://cbbio.cis.um.edu.mo/software/gwovina for testing.

LigTMap: ligand and structure-based target identification and activity prediction for small molecular compounds.

Target prediction is a crucial step in modern drug discovery. However, existing experimental approaches to target prediction are time-consuming and costly. Here, we introduce LigTMap, an online server with a fully automated workflow that can identify protein targets of chemical compounds among 17 classes of therapeutic proteins extracted from the PDBbind database. It combines ligand similarity search with docking and binding similarity analysis to predict putative targets. In the validation experiment of 1251 compounds, targets were successfully predicted for more than 70% of the compounds within the top-10 list. The performance of LigTMap is comparable to the current best servers SwissTargetPrediction and SEA. When testing with our newly compiled compounds from recent literature, we get improved top 10 success rate (66% ours vs. 60% SwissTargetPrediction and 64% SEA) and similar top 1 success rate (45% ours vs. 51% SwissTargetPrediction and 41% SEA). LigTMap directly provides ligand docking structures in PDB format, so that the results are ready for further structural studies in computer-aided drug design and drug repurposing projects. The LigTMap web server is freely accessible at https://cbbio.online/LigTMap . The source code is released on GitHub ( https://github.com/ShirleyWISiu/LigTMap ) under the BSD 3-Clause License to encourage re-use and further developments.

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. While traditional machine learning (ML) methods have been used in the field with some success, the limited availability of annotated toxicity data is the major hurdle for further improving model performance. Inspired by the success of semi-supervised learning (SSL) algorithms, we propose a Graph Convolution Neural Network (GCN) to predict chemical toxicity and trained the network by the Mean Teacher (MT) SSL algorithm. Using the Tox21 data, our optimal SSL-GCN models for predicting the twelve toxicological endpoints achieve an average ROC-AUC score of 0.757 in the test set, which is a 6% improvement over GCN models trained by supervised learning and conventional ML methods. Our SSL-GCN models also exhibit superior performance when compared to models constructed using the built-in DeepChem ML methods. This study demonstrates that SSL can increase the prediction power of models by learning from unannotated data. The optimal unannotated to annotated data ratio ranges between 1:1 and 4:1. This study demonstrates the success of SSL in chemical toxicity prediction; the same technique is expected to be beneficial to other chemical property prediction tasks by utilizing existing large chemical databases. Our optimal model SSL-GCN is hosted on an online server accessible through: https://app.cbbio.online/ssl-gcn/home .

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. Vasostatin-1 is one of the most conserved regions of CgA and has diverse inhibitory biological activities. In this study, a novel peptide fragment that contains three typical functional structures of Vasostatin-1 was synthesized. This unique bioengineered Vasostatin-1 Derived Peptide (named V1DP) includes a highly conserved domain between vertebrate species in its N-terminal region, comprising a disulphide bridge formed by two cysteine residues at amino acid positions 17 and 38, respectively. Besides, V1DP contains two significant tripeptide recognition sequences: the amino acid triplets, RGD and KGD. Our data demonstrated that V1DP could induce a dose-dependent relaxation of rat arterial smooth muscle and also increase the contraction activity of rat uterus smooth muscle. More importantly, we found that V1DP inhibits cancer cell proliferation, modulate the HUVEC cell migration, and exhibit anti-angiogenesis effect both in vitro and in vivo. We further investigated the actual mechanism of V1DP, and our results confirmed that V1DP involves inhibiting the vascular endothelial growth factor receptor (VEGFR) signalling. We docked V1DP to the apo structures of VEGFR2 and examined the stability of the peptide in the protein pockets. Our simulation and free energy calculations results indicated that V1DP can bind to the catalytic domain and regulatory domain pockets, depending on whether the conformational state of the protein is JM-in or JM-out. Taken together, our data suggested that V1DP plays a role as the regulator of endothelial cell function and smooth muscle pharmacological homeostasis. V1DP is a water-soluble and biologically stable peptide and could further develop as an anti-angiogenic drug for cancer treatment.

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. Driven by this demand, many structural bioinformatics laboratories have developed methods for estimating model accuracy (EMA). In recent years, EMA by machine learning (ML) have consistently ranked among the top-performing methods in the community-wide CASP challenge. Accordingly, we systematically review all the major ML-based EMA methods developed within the past ten years. The methods are grouped by their employed ML approach-support vector machine, artificial neural networks, ensemble learning, or Bayesian learning-and their significances are discussed from a methodology viewpoint. To orient the reader, we also briefly describe the background of EMA, including the CASP challenge and its evaluation metrics, and introduce the major ML/DL techniques. Overall, this review provides an introductory guide to modern research on protein quality assessment and directions for future research in this area.

In Vitro 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. Bioinformatics algorithms were then performed to design its six mutants (AP1-Z3a, AP1-Z3b, AP1-Z5a, AP1-Z5b, AP1-Z7, and AP1-Z9). After a series of in vitro experiments plus intensive computational analysis, the data demonstrated that AP1-Z5a and AP1-Z5b induced both apoptosis and anti-angiogenic effects to achieve the maximum anticancer activity. Specifically, the most effective mutant, AP1-Z5b, exhibited high selectivity for the charged membrane in molecular dynamics simulations. These findings clearly demonstrated that both charge and hydrophobicity play an important role and are necessary to reach an optimum equilibrium for optimizing the anticancer activity of AMPs. Overall, the present study provides a very crucial theoretical basis and important scientific evidence on the key physicochemical parameters of ACP drugs development.