A high-throughput structural dynamics approach for identification of potential agonists of FFAR4 for type 2 diabetes mellitus therapy.

Diabetes mellitus is a metabolic disorder that persists as a global threat to the world. A G-protein coupled receptor (GPCR), free fatty acid receptor 4 (FFAR4), has emerged as a potential target for type 2 diabetes mellitus (T2DM) and obesity-related disorders. The current study has investigated the FFAR4, deploying 3-dimensional structure modeling, molecular docking, machine learning, and high-throughput virtual screening methods to unravel the receptor's crucial and non-crucial binding site residues. We screened four lakh compounds and shortlisted them based on binding energy, stereochemical considerations, non-bonded interactions, and pharmacokinetic profiling. Out of the screened compounds, four compounds were selected for ligand-bound simulations. The molecular dynamic simulations were carried out for 1µs for native FFAR4 and 500 ns each for complexes of FFAR4 with compound 1, compound 2, compound 3, and compound 4. Our findings showed that in addition to reported binding site residues ARG99, ARG183, and VAL98 in known agonists like TUG-891, the amino acids ARG22, ARG24, THR23, TRP305, and GLU43 were also critical binding site residues. These amino acids impart stability to the FFAR4 complexes and contribute to the stronger binding affinity of the compounds. The study also indicated that aromatic residues like PHE211 are crucial for recognizing the active site's pi-pi and C-C double bonds. Since FFAR4 is a membrane protein, the simulation studies give an insight into the mechanisms of the crucial protein-lipid and lipid-water interactions. The analysis of the molecular dynamics trajectories showed all four compounds as potential hit molecules that can be developed further into potential agonists for T2DM therapy. Amongst the four compounds, compound 4 showed relatively better binding affinity, stronger non-bonded interactions, and a stable complex.Communicated by Ramaswamy H. Sarma.

Identification of prognostic markers and potential therapeutic targets using gene expression profiling and simulation studies in pancreatic cancer.

BACKGROUND: Pancreatic ductal adenocarcinoma (PDAC) has a 5-year relative survival rate of less than 10% making it one of the most fatal cancers. A lack of early measures of prognosis, challenges in molecular targeted therapy, ineffective adjuvant chemotherapy, and strong resistance to chemotherapy cumulatively make pancreatic cancer challenging to manage. OBJECTIVE: The present study aims to enhance understanding of the disease mechanism and its progression by identifying prognostic biomarkers, potential drug targets, and candidate drugs that can be used for therapy in pancreatic cancer. METHODS: Gene expression profiles from the GEO database were analyzed to identify reliable prognostic markers and potential drug targets. The disease's molecular mechanism and biological pathways were studied by investigating gene ontologies, KEGG pathways, and survival analysis to understand the strong prognostic power of key DEGs. FDA-approved anti-cancer drugs were screened through cell line databases, and docking studies were performed to identify drugs with high affinity for ARNTL2 and PIK3C2A. Molecular dynamic simulations of drug targets ARNTL2 and PIK3C2A in their native state and complex with nilotinib were carried out for 100 ns to validate their therapeutic potential in PDAC. RESULTS: Differentially expressed genes that are crucial regulators, including SUN1, PSMG3, PIK3C2A, SCRN1, and TRIAP1, were identified. Nilotinib as a candidate drug was screened using sensitivity analysis on CCLE and GDSC pancreatic cancer cell lines. Molecular dynamics simulations revealed the underlying mechanism of the binding of nilotinib with ARNTL2 and PIK3C2A and the dynamic perturbations. It validated nilotinib as a promising drug for pancreatic cancer. CONCLUSION: This study accounts for prognostic markers, drug targets, and repurposed anti-cancer drugs to highlight their usefulness for translational research on developing novel therapies. Our results revealed potential and prospective clinical applications in drug targets ARNTL2, EGFR, and PI3KC2A for pancreatic cancer therapy.

Machine learning-driven exploration of drug therapies for triple-negative breast cancer treatment.

Breast cancer is the second leading cause of cancer death in women among all cancer types. It is highly heterogeneous in nature, which means that the tumors have different morphologies and there is heterogeneity even among people who have the same type of tumor. Several staging and classifying systems have been developed due to the variability of different types of breast cancer. Due to high heterogeneity, personalized treatment has become a new strategy. Out of all breast cancer subtypes, triple-negative breast cancer (TNBC) comprises ∼10%-15%. TNBC refers to the subtype of breast cancer where cells do not express estrogen receptors, progesterone receptors, or human epidermal growth factor receptors (ERs, PRs, and HERs). Tumors in TNBC have a diverse set of genetic markers and prognostic indicators. We scanned the Cancer Cell Line Encyclopedia (CCLE) and Genomics of Drug Sensitivity in Cancer (GDSC) databases for potential drugs using human breast cancer cell lines and drug sensitivity data. Three different machine-learning approaches were used to evaluate the prediction of six effective drugs against the TNBC cell lines. The top biomarkers were then shortlisted on the basis of their involvement in breast cancer and further subjected to testing for radion resistance using data from the Cleveland database. It was observed that Panobinostat, PLX4720, Lapatinib, Nilotinib, Selumetinib, and Tanespimycin were six effective drugs against the TNBC cell lines. We could identify potential derivates that may be used against approved drugs. Only one biomarker (SETD7) was sensitive to all six drugs on the shortlist, while two others (SRARP and YIPF5) were sensitive to both radiation and drugs. Furthermore, we did not find any radioresistance markers for the TNBC. The proposed biomarkers and drug sensitivity analysis will provide potential candidates for future clinical investigation.

Supervised screening of Tecovirimat-like compounds as potential inhibitors for the monkeypox virus E8L protein.

Monkeypox virus (MPXV) is a budding public health threat worldwide, and there lacks a personalized drug availability to treat MPXV infections. Tecovirimat, an antiviral drug against pox viruses, is recently confirmed to be effective against the MPXV in vitro using nanomolar concentrations. Therefore, the current study considers Tecovirimat as a reference compound for a machine learning-based guided screening to scan bioactive compounds from the DrugBank with similar chemical features or moieties as the Tecovirimat to inhibit the MPXV E8L surface binding protein. We used AlphaFold2 to model the E8L's 3D structure, followed by the conformational activity investigation of shortlisted drugs through computational structural biology approaches, including molecular docking and molecular dynamics simulations. As a result, we have shortlisted five drugs named ABX-1431, Alflutinib, Avacopan, Caspitant, and Darapalib that effectively engage the MPXV surface binding protein. Furthermore, the affinity of the proposed drugs is relatively higher than the Tecovirimat by having higher docking scores, establishing more hydrogen and hydrophobic bonds, engaging key residues in the target's structure, and exhibiting stable molecular dynamics.Communicated by Ramaswamy H. Sarma.

Combination of furosemide, gold, and dopamine as a potential therapy for breast cancer.

Breast cancer is one of the leading causes of death in women worldwide. Initially, it develops in the epithelium of the ducts or lobules of the breast glandular tissues with limited growth and the potential to metastasize. It is a highly heterogeneous malignancy; however, the common molecular mechanisms could help identify new targeted drugs for treating its subtypes. This study uses computational drug repositioning approaches to explore fresh drug candidates for breast cancer treatment. We also implemented reversal gene expression and gene expression-based signatures to explore novel drug candidates computationally. The drug activity profiles and related gene expression changes were acquired from the DrugBank, PubChem, and LINCS databases, and then in silico drug screening, molecular dynamics (MD) simulation, replica exchange MD simulations, and simulated annealing molecular dynamics (SAMD) simulations were conducted to discover and verify the valid drug candidates. We have found that compounds like furosemide, gold, and dopamine showed significant outcomes. Furthermore, the expression of genes related to breast cancer was observed to be reversed by these shortlisted drugs. Therefore, we postulate that combining furosemide, gold, and dopamine would be a potential combination therapy measurement for breast cancer patients.

Ranking Breast Cancer Drugs and Biomarkers Identification Using Machine Learning and Pharmacogenomics.

Breast cancer is one of the major causes of death in women worldwide. It is a diverse illness with substantial intersubject heterogeneity, even among individuals with the same type of tumor, and customized therapy has become increasingly important in this sector. Because of the clinical and physical variability of different kinds of breast cancers, multiple staging and classification systems have been developed. As a result, these tumors exhibit a wide range of gene expression and prognostic indicators. To date, no comprehensive investigation of model training procedures on information from numerous cell line screenings has been conducted together with radiation data. We used human breast cancer cell lines and drug sensitivity information from Cancer Cell Line Encyclopedia (CCLE) and Genomics of Drug Sensitivity in Cancer (GDSC) databases to scan for potential drugs using cell line data. The results are further validated through three machine learning approaches: Elastic Net, LASSO, and Ridge. Next, we selected top-ranked biomarkers based on their role in breast cancer and tested them further for their resistance to radiation using the data from the Cleveland database. We have identified six drugs named Palbociclib, Panobinostat, PD-0325901, PLX4720, Selumetinib, and Tanespimycin that significantly perform on breast cancer cell lines. Also, five biomarkers named TNFSF15, DCAF6, KDM6A, PHETA2, and IFNGR1 are sensitive to all six shortlisted drugs and show sensitivity to the radiations. The proposed biomarkers and drug sensitivity analysis are helpful in translational cancer studies and provide valuable insights for clinical trial design.

Utilization of Deep Convolutional Neural Networks for Accurate Chest X-Ray Diagnosis and Disease Detection.

Chest radiography is a widely used diagnostic imaging procedure in medical practice, which involves prompt reporting of future imaging tests and diagnosis of diseases in the images. In this study, a critical phase in the radiology workflow is automated using the three convolutional neural network (CNN) models, viz. DenseNet121, ResNet50, and EfficientNetB1 for fast and accurate detection of 14 class labels of thoracic pathology diseases based on chest radiography. These models were evaluated on an AUC score for normal versus abnormal chest radiographs using 112120 chest X-ray14 datasets containing various class labels of thoracic pathology diseases to predict the probability of individual diseases and warn clinicians of potential suspicious findings. With DenseNet121, the AUROC scores for hernia and emphysema were predicted as 0.9450 and 0.9120, respectively. Compared to the score values obtained for each class on the dataset, the DenseNet121 outperformed the other two models. This article also aims to develop an automated server to capture fourteen thoracic pathology disease results using a tensor processing unit (TPU). The results of this study demonstrate that our dataset can be used to train models with high diagnostic accuracy for predicting the likelihood of 14 different diseases in abnormal chest radiographs, enabling accurate and efficient discrimination between different types of chest radiographs. This has the potential to bring benefits to various stakeholders and improve patient care.

Mutational Impacts on the N and C Terminal Domains of the MUC5B Protein: A Transcriptomics and Structural Biology Study.

Cholangiocarcinoma (CCA) involves various epithelial tumors historically linked with poor prognosis because of its aggressive sickness course, delayed diagnosis, and limited efficacy of typical chemotherapy in its advanced stages. In-depth molecular profiling has exposed a varied scenery of genomic alterations as CCA's oncogenic drivers. Previous studies have mainly focused on commonly occurring TP53 and KRAS alterations, but there is limited research conducted to explore other vital genes involved in CCA. We retrieved data from The Cancer Genome Atlas (TCGA) to hunt for additional CCA targets and plotted a mutational landscape, identifying key genes and their frequently expressed variants. Next, we performed a survival analysis for all of the top genes to shortlist the ones with better significance. Among those genes, we observed that MUC5B has the most significant p-value of 0.0061. Finally, we chose two missense mutations at different positions in the vicinity of MUC5B N and C terminal domains. These mutations were further subjected to molecular dynamics (MD) simulation, which revealed noticeable impacts on the protein structure. Our study not only reveals one of the highly mutated genes with enhanced significance in CCA but also gives insights into the influence of its variants. We believe these findings are a good asset for understanding CCA from genomics and structural biology perspectives.

Characterization of Piezoelectric Properties of Ag-NPs Doped PVDF Nanocomposite Fibres Membrane Prepared by Near Field Electrospinning.

BACKGROUND: In this study, Near-field electrospinning (NFES) technique is used with a cylindrical collector to fabricate a large area permanent piezoelectric micro and nanofibers by a prepared solution. NFES requires a small electric field to fabricate fibers Objective: The objective of this paper to investigate silver nanoparticle (Ag-NP)/ Polyvinylidene fluoride (PVDF) composite as the best piezoelectric material with improved properties to produced tremendously flexible and sensitive piezoelectric material with pertinent conductance Methods: In this paper, we used controllable electrospinning technique based on Near-field electrospinning (NFES). The process parameter for Ag-NP/PVDF composite electrospun fiber based on pure PVDF fiber. A PVDF solution concentration of 18 wt.% and 6 wt.% silver nitrate, which is relative to the weight of PVDF wt.% with 1058 µS conductivity fibers, have been directly written on a rotating cylindrical collector for aligned fiber PVDF/Ag-NP fibers are patterned on fabricated copper (Cu) interdigitated electrodes were implemented on a thin flexible polyethylene terephthalate (PET) substrate and Polydimethylsiloxane (PDMS) used as a package to enhance the durability of the PVDF/ Ag-NP device. RESULTS: A notable effect on the piezoelectric response has been observed after Ag-NP addition, confirmed by XRD characterization and tapping test of Ag-NP/PVDF composite fiber. The morphology of the PVDF/Ag-NP fibers and measure diameter by scanning electron microscopy (SEM) and Optical micrograph (OM), of fiber. Finally, a diameter of PVDF/Ag-NP fibers up to ~7 µm. The high diffraction peak at 2θ = 20.5˚ was investigated by X-ray diffraction (XRD) in the piezoelectric crystal ß-phase structure. Further addition of silver nanoparticles (Ag- NPs) in the PVDF solution resulted in enhancing the electromechanical conversion of the fibers from ~0.1 V to ~1 V. CONCLUSION: In conclusion, we can say that confirmed and validated the addition of Ag-NP in PVDF could enhance the piezoelectric property by using NFES technique with improved crystalline phase content can be useful for a wide range of power and sensing applications like biomedical devices and energy harvesting, among others.

Computational Methods for Structure-Based Drug Design Through System Biology.

The advances in computational chemistry and biology, computer science, structural biology, and molecular biology go in parallel with the rapid progress in target-based systems. This technique has become a powerful tool in medicinal chemistry for the identification of hit molecules. The recent developments in target-based systems have played a major role in the creation of libraries of compounds, and it has also been widely applied for the design of molecular docking methods. The main advantage of this method is that it hits the fragment that has the strongest binding, has relatively small size, and leads to better compounds in terms of pharmacokinetic properties when compared with virtual screening (VS) and high-throughput screening (HTS) hits. De novo design is an essential aspect of target-based systems and requires the synthesis of chemical to allow the design of promising compound.

Prediction of Protein Solubility Based on Sequence Feature Fusion and DDcCNN.

BACKGROUND: Prediction of protein solubility is an indispensable prerequisite for pharmaceutical research and production. The general and specific objective of this work is to design a new model for predicting protein solubility by using protein sequence feature fusion and deep dual-channel convolutional neural networks (DDcCNN) to improve the performance of existing prediction models. METHODS: The redundancy of raw protein is reduced by CD-HIT. The four subsequences are built from protein sequence: one global and three locals. The global subsequence is the entire protein sequence, and these local subsequences are obtained by moving a sliding window with some rules. Using G-gap to extract the features of the above four subsequences, a mixed matrix is constructed as the input of one channel which is composed of three-layer convolutional operating. Additional features are extracted by SCRATCH tool as input of another channel, which is consist of a single convolution in order to find hidden relationships and improve the accuracy of predictor. The outputs of two parallel channels are concatenated as the input of the hidden layer. And the prediction of protein solubility is obtained in the output layer. The best protein solubility prediction model is obtained by doing some comparative experiments of different frameworks. RESULTS: The performance indicators of DDcCNN model (our designed) are as follows: accuracy of 77.82%, Matthew's correlation coefficient of 0.57, sensitivity of 76.13% and specificity of 79.32%. The results of some comparative experiments show that the overall performance of DDcCNN model is better than existing models (GCNN, LCNN and PCNN). The related models and data are publicly deposited at http://www.ddccnn.wang . CONCLUSION: The satisfactory performance of DDcCNN model reveals that these features and flexible computational methodologies can reinforce the existing prediction models for better prediction of protein solubility could be applied in several applications, such as to preselect initial targets that are soluble or to alter solubility of target proteins, thus can help to reduce the production cost.

Exosomal ncRNAs profiling of mycobacterial infection identified miRNA-185-5p as a novel biomarker for tuberculosis.

BACKGROUND: There are ever increasing researches implying that noncoded RNAs (ncRNAs) specifically circular RNAs (circRNAs) and microRNAs (miRNAs) in exosomes play vital roles in respiratory disease. However, the detailed mechanisms persist to be unclear in mycobacterial infection. METHODS: In order to detect circRNAs and miRNAs expression pattern and potential biological function in tuberculosis, we performed immense parallel sequencing for exosomal ncRNAs from THP-1-derived macrophages infected by Mycobacterium tuberculosis H37Ra, Mycobacterium bovis BCG and control Streptococcus pneumonia, respectively and uninfected normal cells. Besides, THP-1-derived macrophages were used to verify the validation of differential miRNAs, and monocytes from PBMCs and clinical plasma samples were used to further validate differentially expressed miR-185-5p. RESULTS: Many exosomal circRNAs and miRNAs associated with tuberculosis infection were recognized. Extensive enrichment analyses were performed to illustrate the major effects of altered ncRNAs expression. Moreover, the miRNA-mRNA and circRNA-miRNA networks were created and expected to reveal their interrelationship. Further, significant differentially expressed miRNAs based on Exo-BCG, Exo-Ra and Exo-Control, were evaluated, and the potential target mRNAs and function were analyzed. Eventually, miR-185-5p was collected as a promising potential biomarker for tuberculosis. CONCLUSION: Our findings provide a new vision for exploring biological functions of ncRNAs in mycobacterial infection and screening novel potential biomarkers. To sum up, exosomal ncRNAs might represent useful functional biomarkers in tuberculosis pathogenesis and diagnosis.

Insight into the drug resistance whole genome of Mycobacterium tuberculosis isolates from Khyber Pakhtunkhwa, Pakistan.

Whole genome sequencing (WGS) is one of the most reliable methods for detection of drug resistance, genetic diversity in other virulence factor and also evolutionary dynamics of Mycobacterium tuberculosis complex (MTBC). First-line anti-tuberculosis drugs are the major weapons against Mycobacterium tuberculosis (MTB). However, the emergence of drug resistance remained a major obstacle towards global tuberculosis (TB) control program 2030, especially in high burden countries including Pakistan. To overcome the resistance and design potent drugs, genomic variations in drugs targets as well as in the virulence and evolutionary factors might be useful for better understanding and designing potential inhibitors. Here we aimed to find genomic variations in the first-line drugs targets, along with other virulence and evolutionary factors among the circulating isolates in Khyber Pakhtunkhwa, Pakistan. Samples were collected and drug susceptibility testing (DST) was performed as per WHO standard. The resistance samples were subjected to WGS. Among the five whole genome sequences, three samples (NCBI BioProject Accession: PRJNA629298, PRJNA629388) harbored 1997, 1162, and 2053 mutations. Some novel mutations have been detected in drugs targets. Similarly, numerous novel variants have also been detected in virulency and evolutionary factors, PE, PPE, and secretory system of MTB isolates. Exploring the genomic variations among the circulating isolates in geographical specific locations might be useful for future drug designing. To the best of our knowledge, this is the first study that provides useful data regarding the insight genomic variations in virulency, evolutionary factors including ESX and PE/PPE as well as drug targets, for better understanding and management of TB in a WHO declared high burden country.

CoronaPep: An Anti-Coronavirus Peptide Generation Tool.

The novel coronavirus (COVID-19) infections have adopted the shape of a global pandemic now, demanding an urgent vaccine design. The current work reports contriving an anti-coronavirus peptide scanner tool to discern anti-coronavirus targets in the embodiment of peptides. The proffered CoronaPep tool features the fast fingerprinting of the anti-coronavirus target serving supreme prominence in the current bioinformatics research. The anti-coronavirus target protein sequences reported from the current outbreak are scanned against the anti-coronavirus target data-sets via CORONAPEP which provides precision-based anti-coronavirus peptides. This tool is specifically for the coronavirus data, which can predict peptides from the whole genome, or a gene or protein's list. Besides it is relatively fast, accurate, userfriendly and can generate maximum output from the limited information. The availability of tools like CORONAPEP will immeasurably perquisite researchers in the discipline of oncology and structure-based drug design.

Structures of SARS-CoV-2 RNA-Binding Proteins and Therapeutic Targets.

BACKGROUND: The severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) epidemic has resulted in thousands of infections and deaths worldwide. Several therapies are currently undergoing clinical trials for the treatment of SARS-CoV-2 infection. However, the development of new drugs and the repositioning of existing drugs can only be achieved after the identification of potential therapeutic targets within structures, as this strategy provides the most precise solution for developing treatments for sudden epidemic infectious diseases. SUMMARY: In the current investigation, crystal and cryo-electron microscopy structures encoded by the SARS-CoV-2 genome were systematically examined for the identification of potential drug targets. These structures include nonstructural proteins (Nsp-9; Nsp-12; and Nsp-15), nucleocapsid (N) proteins, and the main protease (Mpro). Key Message: The structural information reveals the presence of many potential alternative therapeutic targets, primarily involved in interaction between N protein and Nsp3, forming replication-transcription complexes (RTCs) which might be a potential drug target for effective control of current SARS-CoV-2 pandemic. RTCs consist of 16 nonstructural proteins (Nsp1-16) that play the most essential role in the synthesis of viral RNA. Targeting the physical linkage between the envelope and single-stranded positive RNA, a process facilitated by matrix proteins may provide a good alternative strategy. Our current study provides useful information for the development of new lead compounds against SARS-CoV-2 infections.

Bringing Structural Implications and Deep Learning-Based Drug Identification for KRAS Mutants.

Colorectal cancer is considered one of the leading causes of death that is linked with the Kirsten Rat Sarcoma (KRAS) harboring codons 13 and 61 mutations. The objective for this study is to search for clinically important codon 61 mutations and analyze how they affect the protein structural dynamics. Additionally, a deep-learning approach is used to carry out a similarity search for potential compounds that might have a comparatively better affinity. Public databases like The Cancer Genome Atlas and Genomic Data Commons were accessed for obtaining the data regarding mutations that are associated with colon cancer. Multiple analysis such as genomic alteration landscape, survival analysis, and systems biology-based kinetic simulations were carried out to predict dynamic changes for the selected mutations. Additionally, a molecular dynamics simulation of 100 ns for all the seven shortlisted codon 61 mutations have been conducted, which revealed noticeable deviations. Finally, the deep learning-based predicted compounds were docked with the KRAS 3D conformer, showing better affinity and good docking scores as compared to the already existing drugs. Taking together the outcomes of systems biology and molecular dynamics, it is observed that the reported mutations in the SII region are highly detrimental as they have an immense impact on the protein sensitive sites' native conformation and overall stability. The drugs reported in this study show increased performance and are encouraged to be used for further evaluation regarding the situation that ascends as a result of KRAS mutations.

A-CaMP: a tool for anti-cancer and antimicrobial peptide generation.

Anti-cancer peptides (ACPs) play a vital role in the cell signaling process. Antimicrobial peptides (AMPs) provide immunity against pathogenic microbes, AMPs present activity against pathogenic microbes. Some of them are known to possess both anticancer and antimicrobial activity. However, so far, no tools have been developed that could predict potential ACPs from wild and mutated cancerous protein sequences in the numerous public databases. In the present study, we developed a A-CaMP tool that allows rapid fingerprinting of the anti-cancer and antimicrobial peptides, which play a crucial role in current bioinformatics research. Besides, we compared the performance and functionality of our A-CaMP tool with those of other methods available online. A-CaMP scans the target protein sequences provided by the user against the datasets. It possesses a robust coding architecture, has been developed in PERL language and is scalable of therefore has extensive applications in bioinformatics. It was observed to achieve a prediction accuracy of 93.4%, which is much higher than that of any of the existing tools. Sequence alignment studies also highlight the potential use of A-CaMP as a tool for the identification of AMPs. A-CaMP is the first open source tool that uses clinical data and proposes final peptides along with the necessary information; this includes wild and mutant sequence and peptides, which lays the foundation for its application in therapies for cancer and bacterial infections. Communicated by Ramaswamy H. Sarma.

DTI-CDF: a cascade deep forest model towards the prediction of drug-target interactions based on hybrid features.

Drug-target interactions (DTIs) play a crucial role in target-based drug discovery and development. Computational prediction of DTIs can effectively complement experimental wet-lab techniques for the identification of DTIs, which are typically time- and resource-consuming. However, the performances of the current DTI prediction approaches suffer from a problem of low precision and high false-positive rate. In this study, we aim to develop a novel DTI prediction method for improving the prediction performance based on a cascade deep forest (CDF) model, named DTI-CDF, with multiple similarity-based features between drugs and the similarity-based features between target proteins extracted from the heterogeneous graph, which contains known DTIs. In the experiments, we built five replicates of 10-fold cross-validation under three different experimental settings of data sets, namely, corresponding DTI values of certain drugs (SD), targets (ST), or drug-target pairs (SP) in the training sets are missed but existed in the test sets. The experimental results demonstrate that our proposed approach DTI-CDF achieves a significantly higher performance than that of the traditional ensemble learning-based methods such as random forest and XGBoost, deep neural network, and the state-of-the-art methods such as DDR. Furthermore, there are 1352 newly predicted DTIs which are proved to be correct by KEGG and DrugBank databases. The data sets and source code are freely available at https://github.com//a96123155/DTI-CDF.

ACPS: An accurate bioinformatics tool for precision-based anti-cancer peptide generation via omics data.

The anti-cancer targets play a crucial role in the signaling processes of cells, and therefore, it becomes nearly impossible to engage these targets without affecting the native cellular function. Thus, an approach has been taken to develop an anti-cancer Scanner (ACPS) tool aimed toward the recognition of anti-cancer marks in the form of peptides. The proposed ACPS tool allows fast fingerprinting of the anti-cancer targets having extreme significance in the current bioinformatics research. There already exist some tools that offer these features on a single platform; however, the performance of ACPS was compared with the preexisting online tools and was observed that ACPS offers greater than 95% accuracy that is comparatively much higher. The anti-cancer marked sequences of proteins supplied by the operators are scanned against the anti-cancer target datasets via ACPS and provide precision-based anti-cancer peptides. The proposed tool has been contrived in PERL programming language, and this tool is the extended version of A-CaMP codes, which are highly scalable having an extensible application in cancer biology with robust coding architecture. The availability of tools like ACPS will greatly benefit researchers in the field of oncology and structure-based drug design.