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Efficient identification of drug mechanisms of action remains a challenge. Computational docking approaches have been widely used to predict drug binding targets; yet, such approaches depend on existing protein structures, and accurate structural predictions have only recently become available from AlphaFold2. Here, we combine AlphaFold2 with molecular docking simulations to predict protein‐ligand interactions between 296 proteins spanning Escherichia coli 's essential proteome, and 218 active antibacterial compounds and 100 inactive compounds, respectively, pointing to widespread compound and protein promiscuity. We benchmark model performance by measuring enzymatic activity for 12 essential proteins treated with each antibacterial compound. We confirm extensive promiscuity, but find that the average area under the receiver operating characteristic curve (auROC) is 0.48, indicating weak model performance. We demonstrate that rescoring of docking poses using machine learning‐based approaches improves model performance, resulting in average auROCs as large as 0.63, and that ensembles of rescoring functions improve prediction accuracy and the ratio of true‐positive rate to false‐positive rate. This work indicates that advances in modeling protein‐ligand interactions, particularly using machine learning‐based approaches, are needed to better harness AlphaFold2 for drug discovery. Synopsis Assessing molecular docking simulations based on AlphaFold2‐predicted structures with high‐throughput measurements of protein‐ligand interactions reveals weak model performance. Machine learning‐based approaches improve performance and better harness AlphaFold2 for drug discovery. AlphaFold2‐based molecular docking predictions for 296 Escherichia coli proteins, 218 active antibacterial compounds and 100 inactive compounds predict widespread promiscuity and similar distributions of binding affinities between active and inactive compounds. Quantitative enzymatic inhibition assays for 12 essential E. coli proteins treated with each of the 218 antibacterial compounds confirm extensive promiscuity. The enzymatic inhibition dataset reveals that the performance of the molecular docking model is weak. Rescoring of docking poses using machine learning‐based scoring functions improves model performance. Graphical Abstract Assessing molecular docking simulations based on AlphaFold2‐predicted structures with high‐throughput measurements of protein‐ligand interactions reveals weak model performance. Machine learning‐based approaches improve performance and better harness AlphaFold2 for drug discovery.

Elucidating protein–ligand interaction is crucial for studying the function of proteins and compounds in an organism and critical for drug discovery and design. The problem of protein–ligand interaction is traditionally tackled by molecular docking and simulation, which is based on physical forces and statistical potentials and cannot effectively leverage cryo-EM data and existing protein structural information in the protein–ligand modeling process. In this work, we developed a deep learning bioinformatics pipeline (DeepProLigand) to predict protein–ligand interactions from cryo-EM density maps of proteins and ligands. DeepProLigand first uses a deep learning method to predict the structure of proteins from cryo-EM maps, which is averaged with a reference (template) structure of the proteins to produce a combined structure to add ligands. The ligands are then identified and added into the structure to generate a protein–ligand complex structure, which is further refined. The method based on the deep learning prediction and template-based modeling was blindly tested in the 2021 EMDataResource Ligand Challenge and was ranked first in fitting ligands to cryo-EM density maps. These results demonstrate that the deep learning bioinformatics approach is a promising direction for modeling protein–ligand interactions on cryo-EM data using prior structural information.

In this review article we present a general overview of the recent progress in the newly developing area of the study of protein-ligand interaction by surface enhanced Raman spectroscopy (SERS). Since its first observation in 1977, SERS have been fast developing into an analytical tool for trace detection of molecular entities, particularly in the area of bio-molecule sensing and characterization. Also, with the development of the ability to design a variety of plasmonic structures and to be able to control and tune their plasmonic properties, we have been able to use them as SERS substrates for probing complex materials. Here we describe yet another application of SERS, mainly protein-ligand interaction and its future into drug designing. We start with a general description of the SERS phenomenon. Subsequently we discuss the key spectral features of amino acids, peptides and proteins, and their structural aspects that can be elucidated from the SERS spectra. In the final sections we discuss the application of SERS to the study protein-ligand interaction and its potential role in the area of drug designing.

Macromolecular complexation leading to coupling of two or more cellular membranes is a crucial step in a number of biological functions of the cell. While other mechanisms may also play a role, adhesion always involves the fluctuations of deformable membranes, the diffusion of proteins and the molecular binding and unbinding. Because these stochastic processes couple over a multitude of time and length scales, theoretical modeling of membrane adhesion has been a major challenge. Here we present an effective Monte Carlo scheme within which the effects of the membrane are integrated into local rates for molecular recognition. The latter step in the Monte Carlo approach enables us to simulate the nucleation and growth of adhesion domains within a system of the size of a cell for tens of seconds without loss of accuracy, as shown by comparison to 106 times more expensive Langevin simulations. To perform this validation, the Langevin approach was augmented to simulate diffusion of proteins explicitly, together with reaction kinetics and membrane dynamics. We use the Monte Carlo scheme to gain deeper insight to the experimentally observed radial growth of micron sized adhesion domains, and connect the effective rate with which the domain is growing to the underlying microscopic events. We thus demonstrate that our technique yields detailed information about protein transport and complexation in membranes, which is a fundamental step toward understanding even more complex membrane interactions in the cellular context.

Accurate identification of ligand-binding pockets in a protein is important for structure-based drug design. In recent years, several deep learning models were developed to learn important physical–chemical and spatial information to predict ligand-binding pockets in a protein. However, ranking the native ligand binding pockets from a pool of predicted pockets is still a hard task for computational molecular biologists using a single web-based tool. Hence, we believe, by using closer to real application data set as training and by providing ligand information, an enhanced model to identify accurate pockets can be obtained. In this article, we propose a new deep learning method called DeepBindPoc for identifying and ranking ligand-binding pockets in proteins. The model is built by using information about the binding pocket and associated ligand. We take advantage of the mol2vec tool to represent both the given ligand and pocket as vectors to construct a densely fully connected layer model. During the training, important features for pocket-ligand binding are automatically extracted and high-level information is preserved appropriately. DeepBindPoc demonstrated a strong complementary advantage for the detection of native-like pockets when combined with traditional popular methods, such as fpocket and P2Rank. The proposed method is extensively tested and validated with standard procedures on multiple datasets, including a dataset with G-protein Coupled receptors. The systematic testing and validation of our method suggest that DeepBindPoc is a valuable tool to rank near-native pockets for theoretically modeled protein with unknown experimental active site but have known ligand. The DeepBindPoc model described in this article is available at GitHub ( https://github.com/haiping1010/DeepBindPoc ) and the webserver is available at ( http://cbblab.siat.ac.cn/DeepBindPoc/index.php ).

Manganese oxidation is an important biogeochemical process that is largely regulated by bacteria through enzymatic reactions. However, the detailed mechanism is poorly understood due to challenges in isolating and characterizing these unknown enzymes. A manganese oxidase, Mnx, from Bacillus sp. PL-12 has been successfully overexpressed in active form as a protein complex with a molecular mass of 211 kDa. We have recently used surface induced dissociation (SID) and ion mobility-mass spectrometry (IM-MS) to release and detect folded subcomplexes for determining subunit connectivity and quaternary structure. The data from the native mass spectrometry experiments led to a plausible structural model of this multicopper oxidase, which has been difficult to study by conventional structural biology methods. It was also revealed that each Mnx subunit binds a variable number of copper ions. Becasue of the heterogeneity of the protein and limited mass resolution, ambiguities in assigning some of the observed peaks remained as a barrier to fully understanding the role of metals and potential unknown ligands in Mnx. In this study, we performed SID in a modified Fourier transform-ion cyclotron resonance (FTICR) mass spectrometer. The high mass accuracy and resolution offered by FTICR unveiled unexpected artificial modifications on the protein that had been previously thought to be iron bound species based on lower resolution spectra. Additionally, isotopically resolved spectra of the released subcomplexes revealed the metal binding stoichiometry at different structural levels. This method holds great potential for in-depth characterization of metalloproteins and protein–ligand complexes. Graphical Abstract ᅟ

Background Exosomes are progressively known as significant mediators of cell-to-cell communication. They convey active biomolecules to target cells and have vital functions in several physiological and pathological processes, and show substantial promise as novel treatment strategies for diseases. Methods In this review study, we studied numerous articles over the past two decades published on application of exosomes in different diseases as well as on perspective and challenges in this field. Results The main clinical application of exosomes are using them as a biomarker, cell-free therapeutic agents, drug delivery carriers, basic analysis for exosome kinetics, and cancer vaccine. Different exosomes from human or plant sources are utilized in various clinical trials. Most researchers used exosomes from the circulatory system for biomarker experiments. Mesenchymal stem cells (MSCs) and dendritic cells (DCs) are two widely held cell sources for exosome use. MSCs-derived exosomes are commonly used for inflammation treatment and drug delivery, while DCs-exosomes are used to induce inflammation response in cancer patients. However, the clinical application of exosomes faces various questions and challenges. In addition, translation of exosome-based clinical trials is required to conform to specific good manufacturing practices (GMP). In this review, we summarize exosomes in the clinical trials according to the type of application and disease. We also address the main questions and challenges regarding exosome kinetics and clinical applications. Conclusions Exosomes are promising platforms for treatment of many diseases in clinical trials. This exciting field is developing hastily, understanding of the underlying mechanisms that direct the various observed roles of exosomes remains far from complete and needs further multidisciplinary research in working with these small vesicles. 8nqLYBgiTpLR_8Q311G-7h Video Abstract

The use of exosomes in clinical settings is progressively becoming a reality, as clinical trials testing exosomes for diagnostic and therapeutic applications are generating remarkable interest from the scientific community and investors. Exosomes are small extracellular vesicles secreted by all cell types playing intercellular communication roles in health and disease by transferring cellular cargoes such as functional proteins, metabolites and nucleic acids to recipient cells. An in-depth understanding of exosome biology is therefore essential to ensure clinical development of exosome based investigational therapeutic products. Here we summarise the most up-to-date knowkedge about the complex biological journey of exosomes from biogenesis and secretion, transport and uptake to their intracellular signalling. We delineate the major pathways and molecular players that influence each step of exosome physiology, highlighting the routes of interest, which will be of benefit to exosome manipulation and engineering. We highlight the main controversies in the field of exosome research: their adequate definition, characterisation and biogenesis at plasma membrane. We also delineate the most common identified pitfalls affecting exosome research and development. Unravelling exosome physiology is key to their ultimate progression towards clinical applications. 6VAGYNgLFyqkhnacPGvJhW Video Abstract

A new crystal structure is reported here for the adduct formed in the reaction between NH 4 [Au(Sac)2], AuSac2, a cytotoxic homoleptic gold(I) complex with the saccharinate ligand, and the model protein hen egg white lysozyme. To produce this adduct, AuSac2 breaks down and releases both saccharinate ligands. The resulting Au(I) ions bind the protein to ND1 and NE2 atoms of His15 but also to SD atom of the zero-solvent accessible Met105 side chain, which is located in the protein hydrophobic box. The unexpected existence of this secondary gold(I) binding site is confirmed by spectroscopic and spectrometric measurements in solution. Graphical Abstract

The dynamic interactions of cancer cells with their microenvironment consisting of stromal cells (cellular part) and extracellular matrix (ECM) components (non-cellular) is essential to stimulate the heterogeneity of cancer cell, clonal evolution and to increase the multidrug resistance ending in cancer cell progression and metastasis. The reciprocal cell-cell/ECM interaction and tumor cell hijacking of non-malignant cells force stromal cells to lose their function and acquire new phenotypes that promote development and invasion of tumor cells. Understanding the underlying cellular and molecular mechanisms governing these interactions can be used as a novel strategy to indirectly disrupt cancer cell interplay and contribute to the development of efficient and safe therapeutic strategies to fight cancer. Furthermore, the tumor-derived circulating materials can also be used as cancer diagnostic tools to precisely predict and monitor the outcome of therapy. This review evaluates such potentials in various advanced cancer models, with a focus on 3D systems as well as lab-on-chip devices. CrkSwiS3n3u2qrY4n_t2ne Video abstract