l-Peptide functionalized dual-responsive nanoparticles for controlled paclitaxel release and enhanced apoptosis in breast cancer cells

Nanoparticles and macromolecular carriers have been widely used to increase the efficacy of chemotherapeutics, largely through passive accumulation provided by their enhanced permeability and retention effect. However, the therapeutic efficacy of nanoscale anticancer drug delivery systems is severely truncated by their low tumor-targetability and inefficient drug release at the target site. Here, the design and development of novel l-peptide functionalized dual-responsive nanoparticles (l-CS-g-PNIPAM-PTX) for active targeting and effective treatment of GRP78-overexpressing human breast cancer in vitro and in vivo are reported. l-CS-g-PNIPAM-PTX NPs have a relative high drug loading (13.5%) and excellent encapsulation efficiency (74.3%) and an average diameter of 275 nm. The release of PTX is slow at pH 7.4 and 25 °C but greatly accelerated at pH 5.0 and 37 °C. MTT assays and confocal experiments showed that the l-CS-g-PNIPAM-PTX NPs possessed high targetability and antitumor activity toward GRP78 overexpressing MDA-MB-231 human breast cancer cells. As expected, l-CS-g-PNIPAM-PTX NPs could effectively treat mice bearing MDA-MB-231 human breast tumor xenografts with little side effects, resulting in complete inhibition of tumor growth and a high survival rate over an experimental period of 60 days. These results indicate that l-peptide-functionalized acid - and thermally activated - PTX prodrug NPs have a great potential for targeted chemotherapy in breast cancer.</p

Predicting mutational effects on protein-protein binding via a side-chain diffusion probabilistic model

Many crucial biological processes rely on networks of protein-protein interactions. Predicting the effect of amino acid mutations on protein-protein binding is vital in protein engineering and therapeutic discovery. However, the scarcity of annotated experimental data on binding energy poses a significant challenge for developing computational approaches, particularly deep learning-based methods. In this work, we propose SidechainDiff, a representation learning-based approach that leverages unlabelled experimental protein structures. SidechainDiff utilizes a Riemannian diffusion model to learn the generative process of side-chain conformations and can also give the structural context representations of mutations on the protein-protein interface. Leveraging the learned representations, we achieve state-of-the-art performance in predicting the mutational effects on protein-protein binding. Furthermore, SidechainDiff is the first diffusion-based generative model for side-chains, distinguishing it from prior efforts that have predominantly focused on generating protein backbone structures

A novel scoring schema for peptide identification by searching protein sequence databases using tandem mass spectrometry data

BACKGROUND: Tandem mass spectrometry (MS/MS) is a powerful tool for protein identification. Although great efforts have been made in scoring the correlation between tandem mass spectra and an amino acid sequence database, improvements could be made in three aspects, including characterization ofpeaks in spectra, adoption of effective scoring functions and access to thereliability of matching between peptides and spectra. RESULTS: A novel scoring function is presented, along with criteria to estimate the performance confidence of the function. Through learning the typesof product ions and the probability of generating them, a hypothetic spectrum was generated for each candidate peptide. Then relative entropy was introduced to measure the similarity between the hypothetic and the observed spectra. Based on the extreme value distribution (EVD) theory, a threshold was chosen to distinguish a true peptide assignment from a random one. Tests on a public MS/MS dataset demonstrated that this method performs better than the well-known SEQUEST. CONCLUSION: A reliable identification of proteins from the spectra promises a more efficient application of tandem mass spectrometry to proteomes with high complexity

A multifunctional nanoplatform based on MoS2-nanosheets for targeted drug delivery and chemo-photothermal therapy

Synergistic tumor treatment has recently attracted more and more attention due to its remarkable therapeutic effect. Herein, a multifunctional drug delivery system based on hyaluronic acid (HA) targeted dual stimulation responsive MoS2 nanosheets (HA-PEI-LA-MoS2-PEG, HPMP) for active interaction with CD44 receptor positive MCF-7 cells is reported. Melanin (Mel), a new type of photothermal agent and doxorubicin (DOX) are both loaded onto the HPMP nanocomposite and can be released by mild acid or hyperthermia. The prepared HPMP nanocomposite has a uniform hydrodynamic diameter (104 nm), a high drug loading (944.3 mg.g-1 HPMP), a remarkable photothermal effect (photothermal conversion efficiency: 55.3%) and excellent biocompatibility. The DOX release from HPMP@(DOX/Mel) can be precisely controlled by the dual stimuli of utilizing the acidic environment in the tumor cells and external laser irradiation. Meanwhile, loading of Mel onto the surface can enhance the photothermal effect of the MoS2 nanosheets. In vitro experiments showed that the HPMP@(DOX/Mel) nanoplatform could efficiently deliver DOX into MCF-7 cells and demonstrated enhanced cytotoxicity compared to that of the non-targeted nanoplatform. In vivo experiments in a breast cancer model of nude mice further confirmed that the HPMP@(DOX/Mel) significantly inhibited tumor growth under near infrared (NIR) laser irradiation, which is superior to any single therapy. In summary, this flexible nanoplatform, based on multi-faceted loaded MoS2 nanosheets, exhibits considerable potential for efficient pH/NIR-responsive targeted drug delivery and chemo-photothermal synergistic tumor therapy

Co-delivery of doxorubicin and oleanolic acid by triple-sensitive nanocomposite based on chitosan for effective promoting tumor apoptosis

Nanocomposites as “stevedores” for co-delivery of multidrugs hold great promise in addressing the drawbacks of traditional cancer chemotherapy. In this work, our strategy presents a new avenue for the stepwise release of two co-delivered agents into the tumor cells. The hybrid nanocomposite consists of a pH-responsive chitosan (CS), a thermosensitive poly(N-vinylcaprolactam) (PNVCL) and a functionalized cell-penetrating peptide (H6R6). Doxorubicin (DOX) and oleanolic acid (OA) are loaded into the nanocomposite (H6R6-CS-g-PNVCL). The system displayed a suitable size (∼190 nm), a high DOX loading (13.2%) and OA loading efficiency (7.3%). The tumor microenvironment triggered the nanocomposite to be selectively retained in tumor cells, then releasing the drugs. Both in vitro and in vivo studies showed a significant enhancement in antitumor activity of the co-delivered system in comparison to mono-delivery. This approach which relies on redox, pH and temperature effects utilizing co-delivery nanosystems may be beneficial for future applications in cancer chemotherapy

Towards Long-term Annotators: A Supervised Label Aggregation Baseline

Relying on crowdsourced workers, data crowdsourcing platforms are able to efficiently provide vast amounts of labeled data. Due to the variability in the annotation quality of crowd workers, modern techniques resort to redundant annotations and subsequent label aggregation to infer true labels. However, these methods require model updating during the inference, posing challenges in real-world implementation. Meanwhile, in recent years, many data labeling tasks have begun to require skilled and experienced annotators, leading to an increasing demand for long-term annotators. These annotators could leave substantial historical annotation records on the crowdsourcing platforms, which can benefit label aggregation, but are ignored by previous works. Hereby, in this paper, we propose a novel label aggregation technique, which does not need any model updating during inference and can extensively explore the historical annotation records. We call it SuperLA, a Supervised Label Aggregation method. Inside this model, we design three types of input features and a straightforward neural network structure to merge all the information together and subsequently produce aggregated labels. Based on comparison experiments conducted on 22 public datasets and 11 baseline methods, we find that SuperLA not only outperforms all those baselines in inference performance but also offers significant advantages in terms of efficiency