DDSBC: A Stacking Ensemble Classifier-Based Approach for Breast Cancer Drug-Pair Cell Synergy Prediction.

Breast cancer (BC) ranks as a leading cause of mortality among women worldwide, with incidence rates continuing to rise. The quest for effective treatments has led to the adoption of drug combination therapy, aiming to enhance drug efficacy. However, identifying synergistic drug combinations remains a daunting challenge due to the myriad of potential drug pairs. Current research leverages machine learning (ML) and deep learning (DL) models for drug-pair synergy prediction and classification. Nevertheless, these models often underperform on specific cancer types, including BC, as they are trained on data spanning various cancers without any specialization. Here, we introduce a stacking ensemble classifier, the drug-drug synergy for breast cancer (DDSBC), tailored explicitly for BC drug-pair cell synergy classification. Unlike existing models that generalize across cancer types, DDSBC is exclusively developed for BC, offering a more focused approach. Our comparative analysis against classical ML methods as well as DL models developed for drug synergy prediction highlights DDSBC's superior performance across test and independent datasets on BC data. Despite certain metrics where other methods narrowly surpass DDSBC by 1-2%, DDSBC consistently emerges as the top-ranked model, showcasing significant differences in scoring metrics and robust performance in ablation studies. DDSBC's performance and practicality position it as a preferred choice or an adjunctive validation tool for identifying synergistic or antagonistic drug pairs in BC, providing valuable insights for treatment strategies.

Exploring microproteins from various model organisms using the mip-mining database.

Microproteins, prevalent across all kingdoms of life, play a crucial role in cell physiology and human health. Although global gene transcription is widely explored and abundantly available, our understanding of microprotein functions using transcriptome data is still limited. To mitigate this problem, we present a database, Mip-mining ( https://weilab.sjtu.edu.cn/mipmining/ ), underpinned by high-quality RNA-sequencing data exclusively aimed at analyzing microprotein functions. The Mip-mining hosts 336 sets of high-quality transcriptome data from 8626 samples and nine representative living organisms, including microorganisms, plants, animals, and humans, in our Mip-mining database. Our database specifically provides a focus on a range of diseases and environmental stress conditions, taking into account chemical, physical, biological, and diseases-related stresses. Comparatively, our platform enables customized analysis by inputting desired data sets with self-determined cutoff values. The practicality of Mip-mining is demonstrated by identifying essential microproteins in different species and revealing the importance of ATP15 in the acetic acid stress tolerance of budding yeast. We believe that Mip-mining will facilitate a greater understanding and application of microproteins in biotechnology. Moreover, it will be beneficial for designing therapeutic strategies under various biological conditions.

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.

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.

Evaluation of human brain hyperthermia using exergy balance equation.

Hyperthermia is caused by disturbance in the thermoregulatory system of the human body and requires emergency treatment to prevent disability or possible mortality. To design any therapeutic device for hyperthermia, an exhaustive effort is required to establish the extremities of such thermal traumas. In this context, the authors have incorporated the human-body exergy-balance equation to compute the hyperthermia thresholds. This is a pioneer attempt to model hyperthermia states. An induced-hyperthermia technique is used to evaluate the extremities of metabolic heat generation and other dependent parameters. Moreover, a case study is also presented to calculate the parameters of prime importance i.e. exergy consumption (EC) and entropy generation rate (δSg) to provide the body's accumulative and exhaustive thermal energy maxima, respectively. Furthermore, the thresholds have been evaluated and simulated by the varying body and/or environmental conditions. The resulting states have been analysed to setup critical ranges to provide the guidelines for rehabilitation therapy. A thermal manikin has also been developed, mimicking the blood circulation in humans, to further substantiate the use of an exergy-based approach. The results indicate that the exergy-based approach is well suited to model hyperthermia at pathophysiological boundaries, contrary to existing approaches which predominantly are limited to the physiological domain.

Computational Screening and Analysis of Lung Cancer Related Non-Synonymous Single Nucleotide Polymorphisms on the Human Kirsten Rat Sarcoma Gene.

The human KRAS (Kirsten rat sarcoma) is an oncogene, involved in the regulation of cell growth and division. The mutations in the KRAS gene have the potential to cause normal cells to become cancerous in human lungs. In the present study, we focus on non-synonymous single nucleotide polymorphisms (nsSNPs), which are point mutations in the DNA sequence leading to the amino acid variants in the encoded protein. To begin with, we developed a pipeline to utilize a set of computational tools in order to obtain the most deleterious nsSNPs (Q22K, Q61P, and Q61R) associated with lung cancer in the human KRAS gene. Furthermore, molecular dynamics simulation and structural analyses of the 3D structures of native and mutant proteins confirmed the impact of these nsSNPs on the stability of the protein. Finally, the experimental results demonstrated that the structural stability of the mutant proteins was worse than that of the native protein. This study provides significant guidance for narrowing down the number of KRAS mutations to be screened as potential diagnostic biomarkers and to better understand the structural and functional mechanisms of the KRAS protein.

Unveiling the Therapeutic Potential of Paclitaxel Combinations Against Breast Carcinoma and Identification of In Vivo Biomarkers.

Breast cancer (BC) is one of the leading causes of high mortality rates in women worldwide. Although advancements have been made in the design of therapeutic strategies and drug discovery, drug resistance remains one of the key challenges. One of the ways to overcome drug resistance is finding potential drug combinations since the efficacy of combined drugs is higher than their individual efficacies if the combination is a synergistic pair. Therefore, the current study uses a BC patient-derived xenograft (PDX) dataset to evaluate the effects of various cancer drugs on breast cancer in vivo models. The drug effects are further validated by four machine learning models, namely Elastic Net, Least Absolute Shrinkage and Selection (LASSO), Support Vector Machine (SVM), Random Forests (RF), as well as exploring the shortlisted drugs in combination with paclitaxel, a baseline drug for enhanced efficacy on tumor volume reduction. Additionally, the study also shortlists the top 50 in vivo biomarkers correlated with the effects of the drugs. The outcomes could be significantly important for the design of an effective anti-breast cancer therapy.

Molecular dynamic simulation reveals spider antimicrobial peptide Latarcin-1 and human eosinophil cationic protein as peptide inhibitors of SARS-CoV-2 variants.

COVID-19 has rapidly proliferated around 180 countries, and new cases are reported frequently. No peptide medication has been developed that can reliably block SARS-CoV-2 infection. The investigation focuses on the crucial host receptors angiotensin-converting enzyme 2 (ACE2) , which can bind receptor-binding domain (RBD) on the SARS-CoV-2 spike protein (S). To investigate the inhibitory effects of human Eosinophil Cationic Protein (hECP) and Latarcin-1 (L1)on SARS-CoV-2 infection, we have selected them as research subjects. Further, we ran extensive molecular dynamics simulations to bring the docked peptide-ACE2 complex into its equilibrium state. The outcomes were then evaluated with g_MMPBSA and interaction analysis. We have also considered the Delta and Omicron variants to examine these peptides' inhibitory effects. The experimental findings revealed an enhanced capability of L1 and hECP as SARS-CoV-2 inhibitors, occupying hot spots and numerous key residues in ACE2. These include ASP30, ASP38, GLU35 and GLU75, which significantly inhibit the binding of RBD and ACE2 and are effective against two common variants in a similar manner. In addition, this study can serve as a springboard for future research on SARS-CoV-2 inhibitors.Communicated by Ramaswamy H. Sarma.

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.

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.

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.

A Self-attention Graph Convolutional Network for Precision Multi-tumor Early Diagnostics with DNA Methylation Data.

DNA methylation-based precision tumor early diagnostics is emerging as state-of-the-art technology that could capture early cancer signs 3 ~ 5 years in advance, even for clinically homogenous groups. Presently, the sensitivity of early detection for many tumors is ~ 30%, which needs significant improvement. Nevertheless, based on the genome-wide DNA methylation data, one could comprehensively characterize tumors' entire molecular genetic landscape and their subtle differences. Therefore, novel high-performance methods must be modeled by considering unbiased information using excessively available DNA methylation data. To fill this gap, we have designed a computational model involving a self-attention graph convolutional network and multi-class classification support vector machine to identify the 11 most common cancers using DNA methylation data. The self-attention graph convolutional network automatically learns key methylation sites in a data-driven way. Then, multi-tumor early diagnostics is realized by training a multi-class classification support vector machine based on the selected methylation sites. We evaluated our model's performance through several data sets of experiments, and our results demonstrate the effectiveness of the selected key methylation sites, which are highly relevant for blood diagnosis. The pipeline of the self-attention graph convolutional network based computational framework.

Discovering potent inhibitors against the Mpro of the SARS-CoV-2. A medicinal chemistry approach.

The global pandemic caused by a single-stranded RNA (ssRNA) virus known as the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is still at its peak, with new cases being reported daily. Although the vaccines have been administered on a massive scale, the frequent mutations in the viral gene and resilience of the future strains could be more problematic. Therefore, new compounds are always needed to be available for therapeutic approaches. We carried out the present study to discover potential drug compounds against the SARS-CoV-2 main protease (Mpro). A total of 16,000 drug-like small molecules from the ChemBridge database were virtually screened to obtain the top hits. As a result, 1032 hits were selected based on their docking scores. Next, these structures were prepared for molecular docking, and each small molecule was docked into the active site of the Mpro. Only compounds with solid interactions with the active site residues and the highest docking score were subjected to molecular dynamics (MD) simulation. The post-simulation analyses were carried out using the in-built GROMACS tools to gauge the stability, flexibility, and compactness. Principal component analysis (PCA) and hydrogen bonding were also calculated to observe trends and affinity of the drugs towards the target. Among the five top compounds, C1, C3, and C6 revealed strong interaction with the target's active site and remained highly stable throughout the simulation. We believe the predicted compounds in this study could be potential inhibitors in the natural system and can be utilized in designing therapeutic strategies against the SARS-CoV-2.

Discovery of a Natural Product with Potent Efficacy Against SARS-CoV-2 by Drug Screening.

The novel coronavirus disease 2019 (COVID-19) caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has spread worldwide for almost 2 years. It starts from viral adherence to host cells through an interaction between spike glycoprotein 1 (S1) containing a receptor-binding domain (RBD) and human angiotensin-converting enzyme-2 (ACE2). One of the useful strategies to prevent SARS-CoV-2 infection is to inhibit the attachment of RBD to ACE2. Therefore, the current work proposed potent peptides against SARS-CoV-2 infection by carrying out MM-PBSA calculation based on the binding of 52 antiviral peptides (AVPs) to RBD. Considering the binding free energies of AVPs to RBD, cyanovirin-N (CV-N) showed the strongest RBD binding affinity among 52 AVPs. Upon structural analysis of RBD complex with CV-N, it was observed that 12 of the 13 key residues of RBD binding to ACE2 were hijacked by CV-N. CV-N bound to RBD at a smaller affinity of 14.9 nM than that of ACE2 and inhibited the recruitment of S1 to human alveolar epithelial cells. Further analysis revealed that CV-N suppressed SARS-CoV-2 S pseudovirion infection with a half-maximal inhibitory concentration (IC50) of 18.52 µg/mL. This study demonstrated a drug screening for AVPs against SARS-CoV-2 and discovered a peptide with inspiring antiviral properties, which provided a promising strategy for the COVID-19 therapeutic approach.

HD5 and LL-37 Inhibit SARS-CoV and SARS-CoV-2 Binding to Human ACE2 by Molecular Simulation.

The coronavirus (COVID-19) pandemic is still spreading all over the world. As reported, angiotensin-converting enzyme-2 (ACE2) is a receptor of SARS-CoV-2 spike protein that initializes viral entry into host cells. Previously, the human defensin 5 (HD5) has been experimentally confirmed to be functional against the SARS-CoV-2. The present study proposes a human cathelicidin known as LL37 that strongly binds to the carboxypeptidase domain of human ACE2 compared to HD5. Therefore, LL37 bears a great potential to be tested as an anti-SARS-CoVD-2 peptide. We investigated the molecular interactions formed between the LL37 and ACE2 as well as HD5 and ACE2 tailed by their thermodynamic stability. The MM-PBSA and free energy landscape analysis outcomes confirmed its possible inhibitory effect against the SARS-CoV-2. The results obtained here could help propose a promising therapeutic strategy against the havoc caused by SARS-CoV-2 infections.

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.

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.

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.

Pan-Cancer Analysis and Drug Formulation for GPR139 and GPR142.

GPR (G protein receptor) 139 and 142 are novel foundling GPCRs (G protein-coupled receptors) in the class "A" of the GPCRs family and are suitable targets for various biological conditions. To engage these targets, validated pharmacophores and 3D QSAR (Quantitative structure-activity relationship) models are widely used because of their direct fingerprinting capability of the target and an overall accuracy. The current work initially analyzes GPR139 and GPR142 for its genomic alteration via tumor samples. Next to that, the pharmacophore is developed to scan the 3D database for such compounds that can lead to potential agonists. As a result, several compounds have been considered, showing satisfactory performance and a strong association with the target. Additionally, it is gripping to know that the obtained compounds were observed to be responsible for triggering pan-cancer. This suggests the possible role of novel GPR139 and GPR142 as the substances for initiating a physiological response to handle the condition incurred as a result of cancer.

A comparative chemogenic analysis for predicting Drug-Target Pair via Machine Learning Approaches.

A computational technique for predicting the DTIs has now turned out to be an indispensable job during the process of drug finding. It tapers the exploration room for interactions by propounding possible interaction contenders for authentication through experiments of wet-lab which are known for their expensiveness and time consumption. Chemogenomics, an emerging research area focused on the systematic examination of the biological impact of a broad series of minute molecular-weighting ligands on a broad raiment of macromolecular target spots. Additionally, with the advancement in time, the complexity of the algorithms is increasing which may result in the entry of big data technologies like Spark in this field soon. In the presented work, we intend to offer an inclusive idea and realistic evaluation of the computational Drug Target Interaction projection approaches, to perform as a guide and reference for researchers who are carrying out work in a similar direction. Precisely, we first explain the data utilized in computational Drug Target Interaction prediction attempts like this. We then sort and explain the best and most modern techniques for the prediction of DTIs. Then, a realistic assessment is executed to show the projection performance of several illustrative approaches in various situations. Ultimately, we underline possible opportunities for additional improvement of Drug Target Interaction projection enactment and also linked study objectives.