Bioinformatics toolbox for exploring target mutation-induced drug resistance

Systematic investigation of machine learning on limited data: A study on predicting protein-protein binding strength

The application of machine learning techniques in biological research, especially when dealing with limited data availability, poses significant challenges. In this study, we leveraged advancements in method development for predicting protein-protein binding strength to conduct a systematic investigation into the application of machine learning on limited data. The binding strength, quantitatively measured as binding affinity, is vital for understanding the processes of recognition, association, and dysfunction that occur within protein complexes. By incorporating transfer learning, integrating domain knowledge, and employing both deep learning and traditional machine learning algorithms, we mitigated the impact of data limitations and made significant advancements in predicting protein-protein binding affinity. In particular, we developed over 20 models, ultimately selecting three representative best-performing ones that belong to distinct categories. The first model is structure-based, consisting of a random forest regression and thirteen handcrafted features. The second model is sequence-based, employing an architecture that combines transferred embedding features with a multilayer perceptron. Finally, we created an ensemble model by averaging the predictions of the two aforementioned models. The comparison with other predictors on three independent datasets confirms the significant improvements achieved by our models in predicting protein-protein binding affinity. The programs for running these three models are available at https://github.com/minghuilab/BindPPI.

Exploring the Target Genes of Fucosylated Chondroitin Sulfate in Treating Lung Adenocarcinoma Based on the Integration of Bioinformatics Analysis, Molecular Docking, and Experimental Verification

Fucosylated chondroitin sulfate (FCS), extracted from sea cucumbers' body walls, has been found to inhibit the proliferation of lung adenocarcinoma (LUAD) cells. However, there have been few studies of the associated drug targets. This study combined bioinformatics analysis and molecular docking to screen the main targets of FCS intervention in LUAD. Moreover, an experimental validation was performed. First, we downloaded the LUAD gene data set from The Cancer Genome Atlas (TCGA) database and the cisplatin (DDP) resistance gene data set of LUAD A549 cells from the Gene Expression Omnibus (GEO) database. Nine significant genes (PLK1, BUB1, CDK1, CDC20, CCNB1, BUB1B, KIF11, CCNB2, and DLAGP5) were identified by bioinformatics analysis, and these nine genes overlapped in both data sets. Then, molecular docking results showed that FCS had a better affinity with target proteins BUB1 and PLK1. Further experimental verification revealed that FCS inhibited the growth of A549 cells and increased the sensitivity of A549 cells to DDP. Quantitative real-time polymerase chain reaction (qRT-PCR) revealed that A549 cells treated with FCS exhibited down-regulated BUB1 and PLK1 mRNA expression. At the same time, FCS+DDP treatment resulted in a more significant reduction in BUB1 and PLK1 mRNA expression than DDP or FCS treatment alone. These findings reveal potential targets of FCS for LUAD and provide clues for the development of FCS as a potential anticancer agent.

Dysregulated Signalling Pathways Driving Anticancer Drug Resistance

One of the leading causes of death worldwide, in both men and women, is cancer. Despite the significant development in therapeutic strategies, the inevitable emergence of drug resistance limits the success and impedes the curative outcome. Intrinsic and acquired resistance are common mechanisms responsible for cancer relapse. Several factors crucially regulate tumourigenesis and resistance, including physical barriers, tumour microenvironment (TME), heterogeneity, genetic and epigenetic alterations, the immune system, tumour burden, growth kinetics and undruggable targets. Moreover, transforming growth factor-beta (TGF-β), Notch, epidermal growth factor receptor (EGFR), integrin-extracellular matrix (ECM), nuclear factor kappa-light-chain-enhancer of activated B cells (NF-κB), phosphoinositol-3-kinase/protein kinase B/mammalian target of rapamycin (PI3K/Akt/mTOR), wingless-related integration site (Wnt/β-catenin), Janus kinase/signal transducers and activators of transcription (JAK/STAT) and RAS/RAF/mitogen-activated protein kinase (MAPK) signalling pathways are some of the key players that have a pivotal role in drug resistance mechanisms. To guide future cancer treatments and improve results, a deeper comprehension of drug resistance pathways is necessary. This review covers both intrinsic and acquired resistance and gives a comprehensive overview of recent research on mechanisms that enable cancer cells to bypass barriers put up by treatments, and, like "satellite navigation", find alternative routes by which to carry on their "journey" to cancer progression.

Degradation strategies of pesticide residue: From chemicals to synthetic biology

The past 50 years have witnessed a massive expansion in the demand and application of pesticides. However, pesticides are difficult to be completely degraded without intervention hence the pesticide residue could pose a persistent threat to non-target organisms in many aspects. To aim at the problem of the abuse of pesticide products and excessive pesticide residues in the environment, chemical and biological degradation methods are widely developed but are scaled and insufficient to solve such a pollution. In recent years, bio-degradative tools instructed by synthetic biological principles have been further studied and have paved a way for pesticide degradation. Combining the customized design strategy and standardized assembly mode, the engineering bacteria for multi-dimensional degradation has become an effective tool for pesticide residue degradation. This review introduces the mechanisms and hazards of different pesticides, summarizes the methods applied in the degradation of pesticide residues, and discusses the advantages, applications, and prospects of synthetic biology in degrading pesticide residues.