Integrating multi-source drug information to cluster drug-drug interaction network.

Characterizing drug-drug interactions is important to improve efficacy and/or slow down the evolution of antimicrobial resistance. Experimental methods are both time-consuming and laborious for characterizing drug-drug interactions. In recent years, many computational methods have been proposed to explore drug-drug interactions. However, these methods failed to effectively integrate multi-source drug information. In this study, we propose a similarity matrix fusion (SMF) method to integrate four drug information (i.e., structural similarity, pharmaceutical similarity, phenotypic similarity and therapeutic similarity). SMF combined with t-distributed stochastic neighbor embedding (t-SNE) and hierarchical clustering algorithm can effectively identify drug groups and group-group interactions are almost monochromatic (purely synergetic or purely antagonistic). To evaluate clustering quality (i.e., monochromaticity), two measures (edge purity and edge normalized mutual information) are proposed, and SMF showed the best performance. In addition, clustered drug-drug interaction network can also be used to predict new drug-drug interactions (accuracy = 0.741). Overall, SMF provides a comprehensive view to understand drug groups and group-group interactions.

The utility of proteases in proteomics, from sequence profiling to structure and function analysis.

In mass spectrometry (MS)-based bottom-up proteomics, protease digestion plays an essential role in profiling both proteome sequences and post-translational modifications (PTMs). Trypsin is the gold standard in digesting intact proteins into small-size peptides, which are more suitable for high-performance liquid chromatography (HPLC) separation and tandem MS (MS/MS) characterization. However, protein sequences lacking Lys and Arg cannot be cleaved by trypsin and may be missed in conventional proteomic analysis. Proteases with cleavage sites complementary to trypsin are widely applied in proteomic analysis to greatly improve the coverage of proteome sequences and PTM sites. In this review, we survey the common and newly emerging proteases used in proteomics analysis mainly in the last 5 years, focusing on their unique cleavage features and specific proteomics applications such as missing protein characterization, new PTM discovery, and de novo sequencing. In addition, we summarize the applications of proteases in structural proteomics and protein function analysis in recent years. Finally, we discuss the future development directions of new proteases and applications in proteomics.

The potential applications of artificially modified exosomes derived from mesenchymal stem cells in tumor therapy.

Mesenchymal stem cells (MSCs) have tumor-homing ability and play critical roles in tumor treatment, but their dual influences on tumor progression limit their therapeutic applications. Exosomes derived from MSCs (MSC-exosomes) exhibit great potential in targeted tumor treatment due to their advantages of high stability, low immunogenicity, good biocompatibility, long circulation time and homing characteristics. Furthermore, the artificial modification of MSC-exosomes could amplify their advantages and their inhibitory effect on tumors and could overcome the limit of tumor-promoting effect. In this review, we summarize the latest therapeutic strategies involving artificially modified MSC-exosomes in tumor treatment, including employing these exosomes as nanomaterials to carry noncoding RNAs or their inhibitors and anticancer drugs, and genetic engineering modification of MSC-exosomes. We also discuss the feasibility of utilizing artificially modified MSC-exosomes as an emerging cell-free method for tumor treatment and related challenges.

Computational models, databases and tools for antibiotic combinations.

Antibiotic combination is a promising strategy to extend the lifetime of antibiotics and thereby combat antimicrobial resistance. However, screening for new antibiotic combinations is both time-consuming and labor-intensive. In recent years, an increasing number of researchers have used computational models to predict effective antibiotic combinations. In this review, we summarized existing computational models for antibiotic combinations and discussed the limitations and challenges of these models in detail. In addition, we also collected and summarized available data resources and tools for antibiotic combinations. This study aims to help computational biologists design more accurate and interpretable computational models.

Lipid-related FABP5 activation of tumor-associated monocytes fosters immune privilege via PD-L1 expression on Treg cells in hepatocellular carcinoma.

Monocytes/macrophages, a plastic and heterogeneous cell population of the tumor microenvironment (TME), can constitute a major component of most solid tumors. Under the pressure of rapid proliferation of the tumor, monocytes/macrophages can be educated and foster immune tolerance via metabolic reprogramming. Our studies have shown that the activation of FABP5, a lipid-binding protein, decreases the rate of ß-oxidation causing the accumulation of lipid droplets in monocytes. We found that hepatocellular carcinoma cells (HCC) increased IL-10 secretion by monocytes, which depended on the expression of FABP5 and suppressing of the PPARα pathway. Moreover, the elevated level of IL-10 promotes PD-L1 expression on Treg cells via the JNK-STAT3 pathway activation. We also observed that elevation of FABP5 in monocytes was negatively related to HCC patients' overall survival time. Thus, FABP5 promotes monocyte/macrophage lipid accumulation, fosters immune tolerance formation, and might represent itself as a therapeutic target in both tumor-associated monocytes (TAMs) and cancer cells.

Macrophages and Metabolic Reprograming in the Tumor Microenvironment.

Due to the emergence of traditional drug resistance in tumor treatment, the anti-cancer therapies are facing multiple challenges. Immunotherapy, as a new and universal treatment, has been gradually concerned. The macrophages, as an important part of the immune system, play an important role in it. Many studies have shown that immune state is essential in cancer progression and prognosis, rebuilding the architecture and functional orientation of the tumor region. Most tumors are complex ecosystems that change temporally and spatially under the pressure of proliferation, apoptosis, and extension of every cell in the microenvironment. Here, we review how macrophages states can be dynamically altered in different metabolic states and we also focus on the formation of immune exhaustion. Finally, we look forward to the explorations of clinical treatment for immune metabolism process.

Elucidating the molecular mechanisms of perfluorooctanoic acid-serum protein interactions by structural mass spectrometry.

Perfluorooctanoic acid (PFOA) is a persistent environmental pollutant and will continually accumulate in blood due to its chemical inertness and strong interaction with serum proteins, especially serum albumin (SA), inducing highly adverse health risks. However, the molecular mechanisms of dynamic interactions between PFOA with serum proteins remain unclear, limiting the development of potential therapeutic strategies. Herein, we developed an integrated structural strategy to systematically profile the molecular details of dynamic interactions among PFOA, SA, and ß-cyclodextrin (ß-CD) by combing native mass spectrometry (nMS), lysine reactivity profiling (LRP), and molecular docking (MD) simulation. The SA site 1, site 2 pockets, and cleft nearby are observed as the primary interaction regions of PFOA. Further, ß-CD can disrupt the PFOA combinations with bovine SA regions around sites Lys20, Lys280, Lys350, and Lys431-Lys439, with an overall reversing efficiency of about 26% at an identical concentration to PFOA. The interactome of PFOA with complex human serum proteins is globally profiled with molecular interaction details, including human serum albumin, apolipoprotein A-I, alpha-2-macroglobulin, and complement C3. Our results reveal molecular insights into the detail of the interaction between PFOA and serum proteins, beneficial to understanding PFOA toxicology.

Feedback control of PLK1 by Apolo1 ensures accurate chromosome segregation.

Stable transmission of genetic material during cell division requires accurate chromosome segregation. PLK1 dynamics at kinetochores control establishment of correct kinetochore-microtubule attachments and subsequent silencing of the spindle checkpoint. However, the regulatory mechanism responsible for PLK1 activity in prometaphase has not yet been affirmatively identified. Here we identify Apolo1, which tunes PLK1 activity for accurate kinetochore-microtubule attachments. Apolo1 localizes to kinetochores during early mitosis, and suppression of Apolo1 results in misaligned chromosomes. Using the fluorescence resonance energy transfer (FRET)-based PLK1 activity reporter, we found that Apolo1 sustains PLK1 kinase activity at kinetochores for accurate attachment during prometaphase. Apolo1 is a cognate substrate of PLK1, and the phosphorylation enables PP1γ to inactivate PLK1 by dephosphorylation. Mechanistically, Apolo1 constitutes a bridge between kinase and phosphatase, which governs PLK1 activity in prometaphase. These findings define a previously uncharacterized feedback loop by which Apolo1 provides fine-tuning for PLK1 to guide chromosome segregation in mitosis.

Size-Selective VAILase Proteolysis Provides Dynamic Insights into Protein Structures.

Monitoring the dynamic alterations of protein structures within an aqueous solution remains enormously challenging. In this study, we describe a size-selective VAILase proteolysis (SVP)-mass spectrometry (MS) strategy to probe the protein structure changes without strict control of the proteolysis kinetics. The unique conformation selectivity of SVP depends on the uniform nano-sized entrance pores of the VAILase hexameric cage as well as the six inherent molecular rulers in the VAILase-substrate recognition and cleavage. The dynamic insights into subtle conformation alterations of both myoglobin unfolding transition and Aurora kinase A-inhibitor binding are successfully captured using the SVP strategy, which matches well with the results in the molecular dynamics simulation. Our work provides a new paradigm of size-selective native proteolysis for exploring the aqueous protein structure-function relationships.

Improving the performance of proteomic analysis via VAILase cleavage and 193-nm ultraviolet photodissociation.

Further improving the proteomic identification coverage and reliability is still challenging in the mass spectrometry (MS)-based proteomics. Herein, we combine VAILase and trypsin digestion with 193-nm ultraviolet photodissociation (UVPD) and higher-energy collision dissociation (HCD) to improve the performance of bottom-up proteomics. As VAILase exhibits high complementarity to trypsin, the proteome sequence coverage is improved obviously whether with HCD or 193-nm UVPD. The high diversity of fragment ion types produced by UVPD contributes to the improvements of identification reliability for both trypsin- and VAILase-digested peptides with an average XCorr score improvement of 10%.

Probing the Proteomics Dark Regions by VAILase Cleavage at Aliphatic Amino Acids.

Proteomics emerges from the protein identification to protein functional elucidation, which depends to a large extent on the characterization of protein sequences. However, a large part of proteome sequences remains unannotated due to the limitation in proteolytic digestion by golden standard protease trypsin. Herein, we demonstrated that a cyanobacterial protease VAILase could specifically cleave at the short-chain aliphatic amino acids valine, alanine, leucine, isoleucine and threonine with cleavage specificity about 92% in total for proteomic analysis. The unique features of VAILase cleavage facilitate the characterization of most proteins and exhibit high complementarity to trypsin, and 22% of the covered sequences by VAILase are unique. VAILase can greatly improve the coverages of sequences with abundant aliphatic residues that are usually dark regions in conventional proteomic analysis, such as the transmembrane regions within anion exchanger 1 and photosystem II.

Probing conformational hotspots for the recognition and intervention of protein complexes by lysine reactivity profiling.

Probing the conformational and functional hotspot sites within aqueous native protein complexes is still a challenging task. Herein, a mass spectrometry (MS)-based two-step isotope labeling-lysine reactivity profiling (TILLRP) strategy is developed to quantify the reactivities of lysine residues and probe the molecular details of protein-protein interactions as well as evaluate the conformational interventions by small-molecule active compounds. The hotspot lysine sites that are crucial to the SARS-CoV-2 S1-ACE2 combination could be successfully probed, such as S1 Lys417 and Lys444. Significant alteration of the reactivities of lysine residues at the interaction interface of S1-RBD Lys386-Lys462 was observed during the formation of complexes, which might be utilized as indicators for investigating the S1-ACE2 dynamic recognition and intervention at the molecular level in high throughput.

Analysis of oligonucleotides by ion-pair reversed-phase liquid chromatography coupled with positive mode electrospray ionization mass spectrometry.

Oligonucleotides are usually analyzed by ion-pair reversed-phase liquid chromatography (IP-RPLC) coupled with negative mode electrospray ionization mass spectrometry (ESI-MS) due to their highly negative charged phosphodiester backbones. Herein, the signal suppression effect of triethylamine (TEA) adducts caused the ion-pair reagent TEA/hexafluoroisopropanol (HFIP) is greatly alleviated after improving the in-source energy in positive mode ESI-MS. This strategy is applied for different RNA sequencing through analyzing their formic acid hydrolysates via IP-RPLC MS. Comparing with negative ion mode, we demonstrate that IP-RPLC MS analysis in positive ion mode is more suitable for RNA sequencing with fewer contaminant interferences. Finally, simultaneous online separation and detection of oligonucleotides and protein digests are achieved in positive ion mode IP-RPLC MS analysis with little interference to each other.