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Gram-negative bacteria, especially the ones with multidrug resistance, post dire challenges to antibiotic treatments due to the presence of the outer membrane (OM), which blocks the entry of many antibiotics. Current solutions for such permeability issues, namely lipophilic-cationic derivatization of antibiotics and sensitization with membrane-active agents, cannot effectively potentiate the large, globular, and hydrophilic antibiotics such as vancomycin, due to ineffective disruption of the OM. Here, we present our solution for high-degree OM binding of vancomycin via a hybrid "derivatization-for-sensitization" approach, which features a combination of LPS-targeting lipo-cationic modifications on vancomycin and OM disruption activity from a sensitizing adjuvant. 10

Nucleic acid (NA)–ligand interactions have crucial roles in many cellular processes and, thus, are increasingly attracting therapeutic interest in drug discovery. Molecular docking is a valuable tool for studying molecular interactions. However, because NAs differ significantly from proteins in both their physical and chemical properties, traditional docking algorithms and scoring functions for protein–ligand interactions might not be applicable to NA–ligand docking. Therefore, various sampling strategies and scoring functions for NA–ligand interactions have been developed. Here, we review the basic principles and current status of docking algorithms and scoring functions for DNA/RNA–ligand interactions. We also discuss challenges and limitations of current docking and scoring approaches.

Site-specific modification of proteins has important applications in biological research and drug development. Reactive tags such as azide, alkyne, and tetrazine have been used extensively to achieve the abovementioned goal. However, bulky side-chain "ligation scars" are often left after the labeling and may hinder the biological application of such engineered protein products. Conjugation chemistry via dehydroalanine (Dha) may provide an opportunity for "traceless" ligation because the activated alkene moiety on Dha can then serve as an electrophile to react with radicalophile, thiol/amine nucleophile, and reactive phosphine probe to introduce a minimal linker in the protein post-translational modifications. In this report, we present a mild and highly efficient enzymatic approach to incorporate Dha with phosphothreonine/serine lyases, OspF and SpvC. These lyases originally catalyze an irreversible elimination reaction that converts a doubly phosphorylated substrate with phosphothreonine (pT) or phosphoserine (pS) to dehydrobutyrine (Dhb) or Dha. To generate a simple monophosphorylated tag for these lyases, we conducted a systematic approach to profile the substrate specificity of OspF and SpvC using peptide arrays and self-assembled monolayers for matrix-assisted laser desorption/ionization mass spectrometry. The optimized tag, [F/Y/W]-pT/pS-[F/Y/W] (where [F/Y/W] indicates an aromatic residue), results in a ∼10-fold enhancement of the overall peptide labeling efficiency via Dha chemistry and enables the first demonstration of protein labeling as well as live cell labeling with a minimal ligation linker via enzyme-mediated incorporation of Dha.

Protein–protein interactions play an important role in many biological processes. However, although structure prediction for monomer proteins has achieved great progress with the advent of advanced deep learning algorithms like AlphaFold, the structure prediction for protein–protein complexes remains an open question. Taking advantage of the Transformer model of ESM-MSA, we have developed a deep learning-based model, named DeepHomo2.0, to predict protein–protein interactions of homodimeric complexes by leveraging the direct-coupling analysis (DCA) and Transformer features of sequences and the structure features of monomers. DeepHomo2.0 was extensively evaluated on diverse test sets and compared with eight state-of-the-art methods including protein language model-based, DCA-based and machine learning-based methods. It was shown that DeepHomo2.0 achieved a high precision of >70% with experimental monomer structures and >60% with predicted monomer structures for the top 10 predicted contacts on the test sets and outperformed the other eight methods. Moreover, even the version without using structure information, named DeepHomoSeq, still achieved a good precision of >55% for the top 10 predicted contacts. Integrating the predicted contacts into protein docking significantly improved the structure prediction of realistic Critical Assessment of Protein Structure Prediction homodimeric complexes. DeepHomo2.0 and DeepHomoSeq are available at http://huanglab.phys.hust.edu.cn/DeepHomo2/.

Nucleic acid-ligand interactions play an important role in numerous cellular processes such as gene function expression and regulation. Therefore, nucleic acids such as RNAs have become more and more important drug targets, where the structural determination of nucleic acid-ligand complexes is pivotal for understanding their functions and thus developing therapeutic interventions. Molecular docking has been a useful computational tool in predicting the complex structure between molecules. However, although a number of docking algorithms have been developed for protein-ligand interactions, only a few docking programs were presented for nucleic acid-ligand interactions. Here, we have developed a fast nucleic acid-ligand docking algorithm, named NLDock, by implementing our intrinsic scoring function ITScoreNL for nucleic acid-ligand interactions into a modified version of the MDock program. NLDock was extensively evaluated on four test sets and compared with five other state-of-the-art docking algorithms including AutoDock, DOCK 6, rDock, GOLD, and Glide. It was shown that our NLDock algorithm obtained a significantly better performance than the other docking programs in binding mode predictions and achieved the success rates of 73%, 36%, and 32% on the largest test set of 77 complexes for local rigid-, local flexible-, and global flexible-ligand docking, respectively. In addition, our NLDock approach is also computationally efficient and consumed an average of as short as 0.97 and 2.08 min for a local flexible-ligand docking job and a global flexible-ligand docking job, respectively. These results suggest the good performance of our NLDock in both docking accuracy and computational efficiency.

Protein-nucleic acid interactions are involved in various cellular processes. Therefore, determining the structures of protein-nucleic acid complexes can provide insights into the mechanisms of the interactions and thus guide the rational drug design to modulate these interactions. Due to the high cost and technical difficulties of solving complex structures experimentally, computational modeling such as molecular docking has been playing an important role in the study of molecular interactions. In order to make it easier for researchers to obtain biomolecular complex structures through molecular docking, we developed the HDOCK server for protein-protein and protein-RNA/DNA docking (accessed at http://hdock.phys.hust.edu.cn/). Since its first release in 2017, HDOCK has been widely used in the scientific community. As nucleic acids may include single-stranded (ss) RNA/DNA and double-stranded (ds) RNA/DNA, we now present an updated version of HDOCK, which offers new options for structural modeling of ssRNA, ssDNA, dsRNA, and dsDNA. We hope this update will better help the scientific community solve important biological problems, thereby advancing the field. In this article, we describe the general protocol of HDOCK with emphasis on the new functions on RNA/DNA modeling. Several application examples are also given to illustrate the usage of the new functions.

Covalent inhibitors have received extensive attentions in the past few decades because of their long residence time, high binding efficiency and strong selectivity. Therefore, it is valuable to develop computational tools like molecular docking for modeling of covalent protein–ligand interactions or screening of potential covalent drugs. Meeting the needs, we have proposed HCovDock, an efficient docking algorithm for covalent protein–ligand interactions by integrating a ligand sampling method of incremental construction and a scoring function with covalent bond-based energy. Tested on a benchmark containing 207 diverse protein–ligand complexes, HCovDock exhibits a significantly better performance than seven other state-of-the-art covalent docking programs (AutoDock, Cov_DOX, CovDock, FITTED, GOLD, ICM-Pro and MOE). With the criterion of ligand root-mean-squared distance < 2.0 Å, HCovDock obtains a high success rate of 70.5% and 93.2% in reproducing experimentally observed structures for top 1 and top 10 predictions. In addition, HCovDock is also validated in virtual screening against 10 receptors of three proteins. HCovDock is computationally efficient and the average running time for docking a ligand is only 5 min with as fast as 1 sec for ligands with one rotatable bond and about 18 min for ligands with 23 rotational bonds. HCovDock can be freely assessed at http://huanglab.phys.hust.edu.cn/hcovdock/.

Extracellular cytokines are enriched in the tumor microenvironment and regulate various important properties of cancers, including autophagy. However, the precise molecular mechanisms underlying the link between autophagy and extracellular cytokines remain to be elucidated. In the present study, we demonstrate that IL-6 activates autophagy through the IL-6/JAK2/BECN1 pathway and promotes chemotherapy resistance in colorectal cancer (CRC). Mechanistically, IL-6 triggers the interaction between JAK2 and BECN1, where JAK2 phosphorylates BECN1 at Y333. We demonstrate that BECN1 Y333 phosphorylation is crucial for BECN1 activation and IL-6-induced autophagy by regulating PI3KC3 complex formation. Furthermore, we investigate BECN1 Y333 phosphorylation as a predictive marker for poor CRC prognosis and chemotherapy resistance. Combination treatment with autophagy inhibitors or pharmacological agents targeting the IL-6/JAK2/BECN1 signaling pathway may represent a potential strategy for CRC cancer therapy., IL-6 is an important cytokine in the tumour microenvironment, but its role in regulating autophagy in cancer cells is unclear. Here the authors show that IL-6 activates autophagy in colorectal cancer through the interaction between JAK2 and autophagy regulator, BECN1, which leads to chemotherapeutic resistance.

Rapid identification of inhibitors for a family of proteins and prediction of ligand specificity are highly desirable for structure-based drug design. However, sequentially docking ligands into each protein target with conventional single-target docking methods is too computationally expensive to achieve these two goals, especially when the number of the targets is large. In this work, we use an efficient ensemble docking algorithm for simultaneous docking of ligands against multiple protein targets. We use protein kinases, a family of proteins that are highly important for many cellular processes and for rational drug design, as an example to demonstrate the feasibility of investigating ligand selectivity with this algorithm. Specifically, 14 human protein kinases were selected. First, native docking calculations were performed to test the ability of our energy scoring function to reproduce the experimentally determined structures of the ligand-protein kinase complexes. Next, cross-docking calculations were conducted using our ensemble docking algorithm to study ligand selectivity, based on the assumption that the native target of an inhibitor should have a more negative (i.e., favorable) energy score than the non-native targets. Staurosporine and Gleevec were studied as examples of nonselective and selective binding, respectively. Virtual ligand screening was also performed against five protein kinases that have at least seven known inhibitors. Our quantitative analysis of the results showed that the ensemble algorithm can be effective on screening for inhibitors and investigating their selectivities for multiple target proteins.

Protein-protein docking plays an important role in the computational prediction of the complex structure between two proteins. For years, a variety of docking algorithms have been developed, as witnessed by the critical assessment of prediction interactions (CAPRI) experiments. However, despite their successes, many docking algorithms often require a series of manual operations like modeling structures from sequences, incorporating biological information, and selecting final models. The difficulties in these manual steps have significantly limited the applications of protein-protein docking, as most of the users in the community are nonexperts in docking. Therefore, automated docking like a web server, which can give a comparable performance to human docking protocol, is pressingly needed. As such, we have participated in the blind CAPRI experiments for Rounds 38-45 and CASP13-CAPRI challenge for Round 46 with both our HDOCK automated docking web server and human docking protocol. It was shown that our HDOCK server achieved an "acceptable" or higher CAPRI-rated model in the top 10 submitted predictions for 65.5% and 59.1% of the targets in the docking experiments of CAPRI and CASP13-CAPRI, respectively, which are comparable to 66.7% and 54.5% for human docking protocol. Similar trends can also be observed in the scoring experiments. These results validated our HDOCK server as an efficient automated docking protocol for nonexpert users. Challenges and opportunities of automated docking are also discussed.