ACPNet: A Deep Learning Network to Identify Anticancer Peptides by Hybrid Sequence Information.

Anti-cancer peptides (ACPs) are a series of short peptides composed of 10–60 amino acids that can inhibit tumour cell proliferation or migration, or suppress the formation of tumour blood vessels, and are less likely to cause drug resistance. The aforementioned merits make ACPs the most promising anti-cancer candidate. However, ACPs may be degraded by proteases, or result in cytotoxicity in many cases. To overcome these drawbacks, a plethora of research has focused on reconstruction or modification of ACPs to improve their anti-cancer activity, while reducing their cytotoxicity. The modification of ACPs mainly includes main chain reconstruction and side chain modification. After summarizing the classification and mechanism of action of ACPs, this paper focuses on recent development and progress about their reconstruction and modification. The information collected here may provide some ideas for further research on ACPs, in particular their modification.

e13665 Background: Cancer is a leading cause of mortality globally, necessitating the development of effective therapies. Anticancer peptides (ACPs) show promises given their selective targeting of cancer cells, low toxicity, and ability to overcome drug resistance. However, traditional discovery and optimization methods are time-consuming and expensive, underscoring the need for efficient computational strategies. Methods: We developed a Deep Hierarchical Conditional Variational Autoencoder (CVAE) for de novo ACP design, using transfer learning by initializing the ESM-2 pre-trained encoder. A comprehensive ACP dataset was collected from 9 different databases. Dataset contained 3,209 confirmed ACP and 4,292 non-ACP sequences. The non-ACP dataset was refined with the CD-HIT program, which clusters and removes redundant sequences, to keep those with < 40% sequence similarity to ACPs. Next, the CVAE encoder used local and global feature extractors to map peptides into a 256-dimensional latent space. The CVAE decoder reconstructed sequences using Gated recurrent unit (GRU) and Transformer layers with nucleus sampling. The CVAE multi-task classifier predicts anticancer and non-anticancer properties, guiding generation of highly active sequences. A gradual fine-tuning strategy was employed, progressively unfreezing the last 6 layers of ESM-2 encoder and applying discriminative learning rates with the AdamW optimizer. The model was trained using a combination of reconstruction loss, Kullback–Leibler (KL) divergence, and classification loss, optimized for balanced performance. For peptide generation, latent vectors were sampled using a Variational Gaussian Mixture Model, decoded into sequences, and filtered based on length, amino acid validity and anticancer probability (> 0.5). Results: The model shows robust performance in different domains. In classification, it achieved 0.89 accuracy, 0.88 precision, 0.87 recall, F1 of 0.87, and an area under receiver operating characteristic curve of 0.94 on the test set, indicating strong discriminative ability between ACPs and non-ACPs. Regarding generative capacity, the model successfully produced 100 unique anticancer peptides from 121 generation attempts, highlighting its ability to create viable candidates. In training, the loss functions consistently converged, the Fine-tune Loss and Reconstruction Loss decreased steadily, and the KL Loss remained stable to maintain meaningful latent representations affirming the model’s enhanced capability to reconstruct peptide sequences accurately. Conclusions: This study advances ACP design with a CVAE model integrating transfer learning and multi-task classification. The model's successful generation of new ACPs emphasize its potential to expedite the clinical translation and development of effective therapies.

iACP-DFSRA: Identification of Anticancer Peptides Based on a Dual-channel Fusion Strategy of ResCNN and Attention

Cancer is a well-known dreadful killer of human being’s health, which has led to countless deaths and misery. Traditional treatment can also affect the normal cells while killing cancer cells. Meanwhile, physical or chemical techniques are costly and inefficient. Fortunately, anticancer peptides are a promising treatment, with specifically targeted, low production cost and other advantages. In order to effectively identify the anticancer peptides, we proposed a stacking heterogeneous ensemble learning model, ACP-SE, for predicting anticancer peptides. More specifically, to fully exploit protein sequence information, we developed an efficient feature representation approach by integrating binary profile feature and conjoint triad feature. Then we use a stacking ensemble strategy to combine the three heterogeneous classifiers and get the final prediction results. It was demonstrated that the proposed ACP-SE remarkably outperformed other comparison methods.

Anticancer peptides (ACPs) have natural cationic properties and can act on the anionic cell membrane of cancer cells to kill cancer cells. Therefore, ACPs have become a potential anticancer drug with good research value and prospect. In this article, we propose AACFlow, an end-to-end model for identification of ACPs based on deep learning. End-to-end models have more room to automatically adjust according to the data, making the overall fit better and reducing error propagation. The combination of attention augmented convolutional neural network (AAConv) and multi-layer convolutional neural network (CNN) forms a deep representation learning module, which is used to obtain global and local information on the sequence. Based on the concept of flow network, multi-head flow-attention mechanism is introduced to mine the deep features of the sequence to improve the efficiency of the model. On the independent test dataset, the ACC, Sn, Sp, and AUC values of AACFlow are 83.9%, 83.0%, 84.8%, and 0.892, respectively, which are 4.9%, 1.5%, 8.0%, and 0.016 higher than those of the baseline model. The MCC value is 67.85%. In addition, we visualize the features extracted by each module to enhance the interpretability of the model. Various experiments show that our model is more competitive in predicting ACPs.

Cancer, a significant global public health issue, resulted in about 10 million deaths in 2022. Anticancer peptides (ACPs), as a category of bioactive peptides, have emerged as a focal point in clinical cancer research due to their potential to inhibit tumor cell proliferation with minimal side effects. However, the recognition of ACPs through wet-lab experiments still faces challenges of low efficiency and high cost. Our work proposes a recognition method for ACPs named ACP-DRL based on deep representation learning, to address the challenges associated with the recognition of ACPs in wet-lab experiments. ACP-DRL marks initial exploration of integrating protein language models into ACPs recognition, employing in-domain further pre-training to enhance the development of deep representation learning. Simultaneously, it employs bidirectional long short-term memory networks to extract amino acid features from sequences. Consequently, ACP-DRL eliminates constraints on sequence length and the dependence on manual features, showcasing remarkable competitiveness in comparison with existing methods.

We previously showed that interleukin (IL)-18 produced by NFSA cells induced the M1 type of macrophages in NFSA tumors, caused the destruction of endothelial cells in vitro and may have resulted in the necrosis of NFSA tumors by enhancing macrophage phagocytosis and cytotoxicity. However, the effect of IL-18 on blood vessel formation in vivo has not been elucidated. MS-K cells do not express il-18, and they form tumors with well-developed blood vessels. Here, we established IL-18-over-expressing MS-K cell clones (MS-K-IL-18) to address the roles of IL-18 in angiogenesis. The over-expression of IL-18 inhibited the proliferation rate of the MS-K-IL-18 cells in vitro and blood vessel formation in the MS-K-IL-18 tumors. Interestingly, CD14-positive cells from the MS-K-IL-18 tumor had up-regulated expression of the M1-type macrophage marker il-6 and down-regulated expression of interferon (ifn)-γ. Furthermore, FACS analysis showed more accumulation of CD11b+/CD80+ M1 macrophages in the MS-K-IL-18 tumors than in the parental MS-K tumor. Moreover, an in vitro coculture assay showed that MS-K-IL-18-conditioned medium (CM) stimulated macrophages to induce the apoptosis of endothelial cells. Cumulatively, our data showed that IL-18 inhibited tumor blood vessel formation in vivo.

The prevalence of cancer as a threat to human life, responsible for 9.6 million deaths worldwide in 2018, motivates the search for new anticancer agents. While many options are currently available for treatment, these are often expensive and impact the human body unfavourably. Anticancer peptides represent a promising emerging field of anticancer therapeutics, which are characterized by favourable toxicity profile. The development of accurate in silico methods for anticancer peptide prediction is of paramount importance, as the amount of available sequence data is growing each year. This study leverages advances in machine learning research to produce a novel sequence-based deep neural network classifier for anticancer peptide activity. The classifier achieves performance comparable to the best-in-class, with a cross-validated accuracy of 98.3%, Matthews correlation coefficient of 0.91 and an Area Under the Curve of 0.95. This innovative classifier is available as a web server at https://research.timmons.eu/ennaact, facilitating in silico screening and design of new anticancer peptide chemotherapeutics by the research community.

Cancer is the second leading cause of death globally, and use of therapeutic peptides to target and kill cancer cells has received considerable attention in recent years. Identification of anticancer peptides (ACPs) through wet-lab experimentation is expensive and often time consuming; therefore, development of an efficient computational method is essential to identify potential ACP candidates prior to in vitro experimentation. In this study, we developed support vector machine- and random forest-based machine-learning methods for the prediction of ACPs using the features calculated from the amino acid sequence, including amino acid composition, dipeptide composition, atomic composition, and physicochemical properties. We trained our methods using the Tyagi-B dataset and determined the machine parameters by 10-fold cross-validation. Furthermore, we evaluated the performance of our methods on two benchmarking datasets, with our results showing that the random forest-based method outperformed the existing methods with an average accuracy and Matthews correlation coefficient value of 88.7% and 0.78, respectively. To assist the scientific community, we also developed a publicly accessible web server at www.thegleelab.org/MLACP.html.

Bone Morphogenetic Protein-2 (BMP-2) is highly overexpressed in the majority of patient-derived lung carcinomas. However, a mechanism revealing its role in cancer has not been established. Here we report that BMP-2 enhances the neovascularization of developing tumors. Recombinant BMP-2 stimulated blood vessel formation in tumors formed from A549 cells injected s.c. into thymic nude mice. Recombinant BMP-2 also enhanced angiogenesis in Matrigel plugs containing A549 cells in nude mice. The BMP-2 antagonist noggin abrogated BMP-2-induced angiogenic response. Furthermore, antisense transfection of BMP-2 cDNA resulted in a decrease in blood vessel formation in the Matrigel assays. BMP-2 induced tube formation in both human aortic endothelial cells (HAEC) and umbilical vein endothelial cells. BMP-2 also stimulated proliferation of HAEC. The ability of BMP-2 to activate endothelial cells was further demonstrated by its ability to phosphorylate Smad 1/5/8 and ERK-1/2 and to increase expression of Id1. This study reveals that BMP-2 enhanced the angiogenic response in developing tumors. Furthermore, these data suggest that BMP-2 stimulation of angiogenesis may involve the activation of endothelial cells.

Robust multi-class model constructed for rapid quality control of Cordyceps sinensis

Gastric cancer has become one of the major diseases threatening human health. This study aimed to investigate the mechanism of an anticancer bioactive peptide (ACBP) combined with oxaliplatin (OXA) on MKN-45, SGC7901, and NCI-N87 differentiated human gastric cancer cells and GES-1 immortalized human gastric mucosal epithelial cells. The therapeutic effect and action mechanism of short-term intermittent ACBP combined with OXA on nude mice with human gastric cancer were also investigated. The half-maximal inhibitory concentrations of these agents in these cells were measured by an MTT assay, and cell morphological changes were observed by H&E staining. The expression of Lin28, miR-107, miR-609, and Let-7 in these four cell lines was determined by q-PCR after drug treatment. Lin28 protein expression in these four cell lines treated with these drugs was measured by western blotting. Furthermore, activity and quality of life were observed daily in all tumor-bearing nude mice, and the expression of Lin28 in tumor tissue was determined by immunohistochemistry and RT-PCR. The results showed that ACBP inhibited the proliferation of MKN-45, SGC7901, and NCI-N87 gastric cancer cells in a dose-dependent manner and weakly suppressed the proliferation of GES-1 cells. Moreover, its inhibitory effect on proliferation was stronger in poorly differentiated gastric cancer cells. ACBP, OXA, and the combination upregulated Lin28 gene expression in MKN-45 cells and downregulated it in SGC7901 and GES-1 cells. ACBP and the combination therapy downregulated Let-7 expression in MKN-45 cells and upregulated Let-7 expression in SGC7901 cells. The combination of ACBP with OXA demonstrated significant anticancer sensitization. Moreover, it also significantly improved the quality of life of tumor-bearing nude mice and reduced the toxic side effects of chemotherapeutic drugs on nude mice. ACBP alone and in combination with oxaliplatin influenced the expression of tumor stem cell marker gene Lin28 and regulated the expression of microRNAs specifically regulated by Lin28. In addition, the anticancer effects and attenuated sensitization effects of ACBP may be related to the Lin28/miRNA-107 signaling pathway, acting by inhibiting the proliferation of cancerous stem cells. The findings of this study provide a scientific basis for exploring the antitumor mechanism of ACBP alone and combined with chemotherapeutic drugs.

DNA-binding proteins are fundamentally important in understanding cellular processes. Thus, the identification of DNA-binding proteins has the particularly important practical application in various fields, such as drug design. We have proposed a novel approach method for predicting DNA-binding proteins using only sequence information. The prediction model developed in this study is constructed by support vector machine-sequential minimal optimization (SVM-SMO) algorithm in conjunction with a hybrid feature. The hybrid feature is incorporating evolutionary information feature, physicochemical property feature, and two novel attributes. These two attributes use DNA-binding residues and nonbinding residues in a query protein to obtain DNA-binding propensity and nonbinding propensity. The results demonstrate that our SVM-SMO model achieves 0.67 Matthew's correlation coefficient (MCC) and 89.6% overall accuracy with 88.4% sensitivity and 90.8% specificity, respectively. Performance comparisons on various features indicate that two novel attributes contribute to the performance improvement. In addition, our SVM-SMO model achieves the best performance than state-of-the-art methods on independent test dataset.

Keynote address: Bioreductive therapy

Recent advancements in the small-molecule drugs for hepatocellular carcinoma (HCC): Structure-activity relationships, pharmacological activities, and the clinical trials