BINANA 2: Characterizing Receptor/Ligand Interactions in Python and JavaScript

MAGPIE: An interactive tool for visualizing and analyzing protein-ligand interactions

Quantitative tools to compile and analyze biomolecular interactions among chemically diverse binding partners would improve therapeutic design and aid in studying molecular evolution. Here we present Mapping Areas of Genetic Parsimony In Epitopes (MAGPIE), a publicly available software package for simultaneously visualizing and analyzing thousands of interactions between a single protein or small molecule ligand (the "target") and all of its protein binding partners ("binders"). MAGPIE generates an interactive three-dimensional visualization from a set of protein complex structures that share the target ligand, as well as sequence logo-style amino acid frequency graphs that show all the amino acids from the set of protein binders that interact with user-defined target ligand positions or chemical groups. MAGPIE highlights all the salt bridge and hydrogen bond interactions made by the target in the visualization and as separate amino acid frequency graphs. Finally, MAGPIE collates the most common target-binder interactions as a list of "hotspots," which can be used to analyze trends or guide the de novo design of protein binders. As an example of the utility of the program, we used MAGPIE to probe how different antibody fragments bind a viral antigen; how a common metabolite binds diverse protein partners; and how two ligands bind orthologs of a well-conserved glycolytic enzyme for a detailed understanding of evolutionarily conserved interactions involved in its activation and inhibition. MAGPIE is implemented in Python 3 and freely available at https://github.com/glasgowlab/MAGPIE, along with sample datasets, usage examples, and helper scripts to prepare input structures.

FOCUS on NOD2: Advancing IBD Drug Discovery with a User-Informed Machine Learning Framework

In this study, we introduce the Framework for Optimized Customizable User-Informed Synthesis (FOCUS), a generative machine learning model tailored for drug discovery. FOCUS integrates domain expertise and uses Proximal Policy Optimization (PPO) to guide Monte Carlo Tree Search (MCTS) to efficiently explore chemical space. It generates SMILES representations of potential drug candidates, optimizing for druggability and binding efficacy to NOD2, PEP, and MCT1 receptors. The model is highly interpretive, allowing for user-feedback and expert-driven adjustments based on detailed cycle reports. Employing tools like SHAP and LIME, FOCUS provides a transparent analysis of decision-making processes, emphasizing features such as docking scores and interaction fingerprints. Comparative studies with Muramyl Dipeptide (MDP) demonstrate improved interaction profiles. FOCUS merges advanced machine learning with expert insight, accelerating the drug discovery pipeline.

SLiMAn 2.0: meaningful navigation through peptide-protein interaction networks

Among the myriad of protein-protein interactions occurring in living organisms, a substantial amount involves small linear motifs (SLiMs) recognized by structured domains. However, predictions of SLiM-based networks are tedious, due to the abundance of such motifs and a high portion of false positive hits. For this reason, a webserver SLiMAn (Short Linear Motif Analysis) was developed to focus the search on the most relevant SLiMs. Using SLiMAn, one can navigate into a given (meta-)interactome and tune a variety of parameters associated to each type of SLiMs in attempt to identify functional ELM motifs and their recognition domains. The IntAct and BioGRID databases bring experimental information, while IUPred and AlphaFold provide boundaries of folded and disordered regions. Post-translational modifications listed in PhosphoSite+ are highlighted. Links to PubMed accelerate scrutiny into the literature, to support (or not) putative pairings. Dedicated visualization features are also incorporated, such as Cytoscape for macromolecular networks and BINANA for intermolecular contacts within structural models generated by SCWRL 3.0. The use of SLiMAn 2.0 is illustrated on a simple example. It is freely available at https://sliman2.cbs.cnrs.fr.

Exploring Tau Fibril-Disaggregating and Antioxidating Molecules Binding to Membrane-Bound Amyloid Oligomers Using Machine Learning-Enhanced Docking and Molecular Dynamics

Intracellular tau fibrils are sources of neurotoxicity and oxidative stress in Alzheimer's. Current drug discovery efforts have focused on molecules with tau fibril disaggregation and antioxidation functions. However, recent studies suggest that membrane-bound tau-containing oligomers (mTCOs), smaller and less ordered than tau fibrils, are neurotoxic in the early stage of Alzheimer's. Whether tau fibril-targeting molecules are effective against mTCOs is unknown. The binding of epigallocatechin-3-gallate (EGCG), CNS-11, and BHT-CNS-11 to in silico mTCOs and experimental tau fibrils was investigated using machine learning-enhanced docking and molecular dynamics simulations. EGCG and CNS-11 have tau fibril disaggregation functions, while the proposed BHT-CNS-11 has potential tau fibril disaggregation and antioxidation functions like EGCG. Our results suggest that the three molecules studied may also bind to mTCOs. The predicted binding probability of EGCG to mTCOs increases with the protein aggregate size. In contrast, the predicted probability of CNS-11 and BHT-CNS-11 binding to the dimeric mTCOs is higher than binding to the tetrameric mTCOs for the homo tau but not for the hetero tau-amylin oligomers. Our results also support the idea that anionic lipids may promote the binding of molecules to mTCOs. We conclude that tau fibril-disaggregating and antioxidating molecules may bind to mTCOs, and that mTCOs may also be useful targets for Alzheimer's drug design.

Guided diffusion for molecular generation with interaction prompt

Molecular generative models have exhibited promising capabilities in designing molecules from scratch with high binding affinities in a predetermined protein pocket, offering potential synergies with traditional structural-based drug design strategy. However, the generative processes of such models are random and the atomic interaction information between ligand and protein are ignored. On the other hand, the ligand has high propensity to bind with residues called hotspots. Hotspot residues contribute to the majority of the binding free energies and have been recognized as appealing targets for designed molecules. In this work, we develop an interaction prompt guided diffusion model, InterDiff to deal with the challenges. Four kinds of atomic interactions are involved in our model and represented as learnable vector embeddings. These embeddings serve as conditions for individual residue to guide the molecular generative process. Comprehensive in silico experiments evince that our model could generate molecules with desired ligand-protein interactions in a guidable way. Furthermore, we validate InterDiff on two realistic protein-based therapeutic agents. Results show that InterDiff could generate molecules with better or similar binding mode compared to known targeted drugs.

Detection and Analysis of Short Linear Motif-Based Protein-Protein Interactions with SLiMAn2 Web Server

Interactomics is bringing a deluge of data regarding protein-protein interactions (PPIs) which are involved in various molecular processes in all types of cells. However, this information does not easily translate into direct and precise molecular interfaces. This limits our understanding of each interaction network and prevents their efficient modulation. A lot of the detected interactions involve recognition of short linear motifs (SLiMs) by a folded domain while others rely on domain-domain interactions. Functional SLiMs hide among a lot of spurious ones, making deeper analysis of interactomes tedious. Hence, actual contacts and direct interactions are difficult to identify.Consequently, there is a need for user-friendly bioinformatic tools, enabling rapid molecular and structural analysis of SLiM-based PPIs in a protein network. In this chapter, we describe the use of the new webserver SLiMAn to help digging into SLiM-based PPIs in an interactive fashion.

Study Models of Drug-Drug Interactions Involving P-Glycoprotein: The Potential Benefit of P-Glycoprotein Modulation at the Kidney and Intestinal Levels

P-glycoprotein (P-gp) is a crucial membrane transporter situated on the cell's apical surface, being responsible for eliminating xenobiotics and endobiotics. P-gp modulators are compounds that can directly or indirectly affect this protein, leading to changes in its expression and function. These modulators can act as inhibitors, inducers, or activators, potentially causing drug-drug interactions (DDIs). This comprehensive review explores diverse models and techniques used to assess drug-induced P-gp modulation. We cover several approaches, including in silico, in vitro, ex vivo, and in vivo methods, with their respective strengths and limitations. Additionally, we explore the therapeutic implications of DDIs involving P-gp, with a special focus on the renal and intestinal elimination of P-gp substrates. This involves enhancing the removal of toxic substances from proximal tubular epithelial cells into the urine or increasing the transport of compounds from enterocytes into the intestinal lumen, thereby facilitating their excretion in the feces. A better understanding of these interactions, and of the distinct techniques applied for their study, will be of utmost importance for optimizing drug therapy, consequently minimizing drug-induced adverse and toxic effects.

Computational and experimental analyses of alanine racemase suggest new avenues for developing allosteric small-molecule antibiotics

Given the ever-present threat of antibacterial resistance, there is an urgent need to identify new antibacterial drugs and targets. One such target is alanine racemase (Alr), an enzyme required for bacterial cell-wall biosynthesis. Alr is an attractive drug target because it is essential for bacterial survival but is absent in humans. Existing drugs targeting Alr lack specificity and have severe side effects. We here investigate alternative mechanisms of Alr inhibition. Alr functions exclusively as an obligate homodimer, so we probed seven conserved interactions on the dimer interface, distant from the enzymatic active site, to identify possible allosteric influences on activity. Using the Alr from Mycobacterium tuberculosis (MT) as a model, we found that the Lys261/Asp135 salt bridge is critical for catalytic activity. The Lys261Ala mutation completely inactivated the enzyme, and the Asp135Ala mutation reduced catalytic activity eight-fold. Further investigation suggested a potential drug-binding site near the Lys261/Asp135 salt bridge that may be useful for allosteric drug discovery.

Changing course: Glucose starvation drives nuclear accumulation of Hexokinase 2 in S. cerevisiae

Glucose is the preferred carbon source for most eukaryotes, and the first step in its metabolism is phosphorylation to glucose-6-phosphate. This reaction is catalyzed by hexokinases or glucokinases. The yeast Saccharomyces cerevisiae encodes three such enzymes, Hxk1, Hxk2, and Glk1. In yeast and mammals, some isoforms of this enzyme are found in the nucleus, suggesting a possible moonlighting function beyond glucose phosphorylation. In contrast to mammalian hexokinases, yeast Hxk2 has been proposed to shuttle into the nucleus in glucose-replete conditions, where it reportedly moonlights as part of a glucose-repressive transcriptional complex. To achieve its role in glucose repression, Hxk2 reportedly binds the Mig1 transcriptional repressor, is dephosphorylated at serine 15 and requires an N-terminal nuclear localization sequence (NLS). We used high-resolution, quantitative, fluorescent microscopy of live cells to determine the conditions, residues, and regulatory proteins required for Hxk2 nuclear localization. Countering previous yeast studies, we find that Hxk2 is largely excluded from the nucleus under glucose-replete conditions but is retained in the nucleus under glucose-limiting conditions. We find that the Hxk2 N-terminus does not contain an NLS but instead is necessary for nuclear exclusion and regulating multimerization. Amino acid substitutions of the phosphorylated residue, serine 15, disrupt Hxk2 dimerization but have no effect on its glucose-regulated nuclear localization. Alanine substation at nearby lysine 13 affects dimerization and maintenance of nuclear exclusion in glucose-replete conditions. Modeling and simulation provide insight into the molecular mechanisms of this regulation. In contrast to earlier studies, we find that the transcriptional repressor Mig1 and the protein kinase Snf1 have little effect on Hxk2 localization. Instead, the protein kinase Tda1 regulates Hxk2 localization. RNAseq analyses of the yeast transcriptome dispels the idea that Hxk2 moonlights as a transcriptional regulator of glucose repression, demonstrating that Hxk2 has a negligible role in transcriptional regulation in both glucose-replete and limiting conditions. Our studies define a new model of cis- and trans-acting regulators of Hxk2 dimerization and nuclear localization. Based on our data, the nuclear translocation of Hxk2 in yeast occurs in glucose starvation conditions, which aligns well with the nuclear regulation of mammalian orthologs. Our results lay the foundation for future studies of Hxk2 nuclear activity.

Engineering Saccharomyces cerevisiae for the de novo Production of Halogenated Tryptophan and Tryptamine Derivatives

The indole scaffold is a recurring structure in multiple bioactive heterocycles and natural products. Substituted indoles like the amino acid tryptophan serve as a precursor for a wide range of natural products with pharmaceutical or agrochemical applications. Inspired by the versatility of these compounds, medicinal chemists have for decades exploited indole as a core structure in the drug discovery process. With the aim of tuning the properties of lead drug candidates, regioselective halogenation of the indole scaffold is a common strategy. However, chemical halogenation is generally expensive, has a poor atom economy, lacks regioselectivity, and generates hazardous waste streams. As an alternative, in this work we engineer the industrial workhorse Saccharomyces cerevisiae for the de novo production of halogenated tryptophan and tryptamine derivatives. Functional expression of bacterial tryptophan halogenases together with a partner flavin reductase and a tryptophan decarboxylase resulted in the production of halogenated tryptophan and tryptamine with chlorine or bromine. Furthermore, by combining tryptophan halogenases, production of di-halogenated molecules was also achieved. Overall, this works paves the road for the production of new-to-nature halogenated natural products in yeast.

The structural basis for HIV-1 Vif antagonism of human APOBEC3G

The APOBEC3 (A3) proteins are host antiviral cellular proteins that hypermutate the viral genome of diverse viral families. In retroviruses, this process requires A3 packaging into viral particles1-4. The lentiviruses encode a protein, Vif, that antagonizes A3 family members by targeting them for degradation. Diversification of A3 allows host escape from Vif whereas adaptations in Vif enable cross-species transmission of primate lentiviruses. How this 'molecular arms race' plays out at the structural level is unknown. Here, we report the cryogenic electron microscopy structure of human APOBEC3G (A3G) bound to HIV-1 Vif, and the hijacked cellular proteins that promote ubiquitin-mediated proteolysis. A small surface explains the molecular arms race, including a cross-species transmission event that led to the birth of HIV-1. Unexpectedly, we find that RNA is a molecular glue for the Vif-A3G interaction, enabling Vif to repress A3G by ubiquitin-dependent and -independent mechanisms. Our results suggest a model in which Vif antagonizes A3G by intercepting it in its most dangerous form for the virus-when bound to RNA and on the pathway to packaging-to prevent viral restriction. By engaging essential surfaces required for restriction, Vif exploits a vulnerability in A3G, suggesting a general mechanism by which RNA binding helps to position key residues necessary for viral antagonism of a host antiviral gene.

Prot2Prot: a deep learning model for rapid, photorealistic macromolecular visualization

Molecular visualization is a cornerstone of structural biology, providing insights into the form and function of biomolecules that are difficult to achieve any other way. Scientific analysis, publication, education, and outreach often benefit from photorealistic molecular depictions rendered using advanced computer-graphics programs such as Maya, 3ds Max, and Blender. However, setting up molecular scenes in these programs is laborious even for expert users, and rendering often requires substantial time and computer resources. We have created a deep-learning model called Prot2Prot that quickly imitates photorealistic visualization styles, given a much simpler, easy-to-generate molecular representation. The resulting images are often indistinguishable from images rendered using industry-standard 3D graphics programs, but they can be created in a fraction of the time, even when running in a web browser. To the best of our knowledge, Prot2Prot is the first example of image-to-image translation applied to macromolecular visualization. Prot2Prot is available free of charge, released under the terms of the Apache License, Version 2.0. Users can access a Prot2Prot-powered web app without registration at http://durrantlab.com/prot2prot .

FPocketWeb: protein pocket hunting in a web browser

Detecting macromolecular (e.g., protein) cavities where small molecules bind is an early step in computer-aided drug discovery. Multiple pocket-detection algorithms have been developed over the past several decades. Among them, fpocket, created by Schmidtke and Le Guilloux, is particularly popular. Like many programs used in computational-biology research, fpocket requires users to download and install an executable file. That file must also be run via a command-line interface, further complicating use. An existing fpocket server application effectively addresses these challenges, but it requires users to upload their possibly proprietary structures to a third-party server. The FPocketWeb web app builds on this prior work. It runs the fpocket3 executable entirely in a web browser without requiring installation. The pocket-finding calculations occur on the user's computer rather than on a remote server. A working version of the open-source FPocketWeb app can be accessed free of charge from http://durrantlab.com/fpocketweb .

Open-Source Browser-Based Tools for Structure-Based Computer-Aided Drug Discovery

We here outline the importance of open-source, accessible tools for computer-aided drug discovery (CADD). We begin with a discussion of drug discovery in general to provide context for a subsequent discussion of structure-based CADD applied to small-molecule ligand discovery. Next, we identify usability challenges common to many open-source CADD tools. To address these challenges, we propose a browser-based approach to CADD tool deployment in which CADD calculations run in modern web browsers on users’ local computers. The browser app approach eliminates the need for user-initiated download and installation, ensures broad operating system compatibility, enables easy updates, and provides a user-friendly graphical user interface. Unlike server apps—which run calculations “in the cloud” rather than on users’ local computers—browser apps do not require users to upload proprietary information to a third-party (remote) server. They also eliminate the need for the difficult-to-maintain computer infrastructure required to run user-initiated calculations remotely. We conclude by describing some CADD browser apps developed in our lab, which illustrate the utility of this approach. Aside from introducing readers to these specific tools, we are hopeful that this review highlights the need for additional browser-compatible, user-friendly CADD software.