Receptor tyrosine kinases (RTKs) control myriads of cellular functions. RTKs are paradigmatic examples of receptors where activity is directly dependent on quaternary structure. In most cases, the monomeric RTK is inactive, and function arises only after a ligand binding event leads the RTK to bind to another copy of itself, activating trans-autophosphorylation of tyrosine residues. Such RTK homodimerization can be accompanied by the formation of homomers of higher stoichiometry. However, RTK monomers can also bind to a second type of RTK, forming heterodimers. RTK heteromerization is believed to result in different signaling than homomerization. Despite its importance, we have a poor understanding of the factors that define if an RTK will form homomers or heteromers. This short review covers recent discoveries on the heteromerization of RTK, in what is called the RTK interactome. We discuss its translational potential, and how ligands and membrane lipids affect heteromer formation.

Structural variability plays a crucial role in enabling biological function, as the ability of proteins to adopt multiple conformations allows them to perform diverse cellular tasks. Cryo-electron tomography combined with subtomogram averaging and classification has emerged as a powerful technique for elucidating the conformational dynamics of proteins in their near-native environment. Increased data availability has provided a driving force for improvements in image classification algorithms which have enabled conformational heterogeneity studies of proteins in situ at higher resolutions than previously possible. In particular, the use of 2D particle projections extracted from raw tilt-series paired with constrained classification strategies of projection sets has emerged as a promising strategy for classifying particles in 3D. Despite these efforts, further method development will be needed to extend the applicability of current strategies for 3D classification to more challenging biological targets, including low-molecular weight complexes and membrane proteins.

Molecular dynamics simulations are crucial for investigating biomolecular mechanisms. The success of these simulations hinges on the accuracy, efficiency, and generalizability of the underlying force field. While classical molecular force fields are efficient yet approximate and quantum mechanics is accurate but computationally prohibitive for large systems, machine learning force fields (MLFFs) have emerged to bridge this gap. We review various MLFFs-from classically parametrized to end-to-end models-evaluating their performance in accuracy and efficiency. However, a significant challenge for MLFFs is generalizability as models trained on specific data often fail to extrapolate to unseen molecules or conformations. To address this, universal MLFFs, such as fragment-based methods like AIBMD designed by Wang et al. and GEMS designed by Unke et al., are being developed. Beyond recent progress, we also discuss the inherent limitations and trade-offs of MLFFs. Looking forward, the integration of MLFFs with virtual cell models and coarse-grained representations is poised to enable whole-cell multiscale simulations.

Cryo-electron microscopy has become the preferred method for determining structures of macromolecular complexes both in isolation, using single particle analysis, and in their cellular contexts, using cryo-electron tomography (Cryo-ET) combined with subvolume averaging (SVA). Collection of tilt series for Cryo-ET introduces challenges such as low signal-to-noise ratios, sample radiation sensitivity, and mechanical imprecision of the microscope stage - particularly at high magnifications. Strategies to improve throughput and resolution include continuous tilt and beam-image-shift parallel acquisition, real-time predictive adjustments, and machine learning-driven targeting. Additionally, montage tomography has increased the observable cellular area, while innovations like rectangular condenser apertures promise improved dose efficiency. Web-based and machine learning-enhanced solutions for automated and remote microscope operation are improving the user experience. Collectively, these advancements represent a critical step towards robust, high-resolution, and user-friendly Cryo-ET, facilitating the visualization of macromolecular assemblies within their authentic biological environments.

The unrealized goal of cryo-electron tomography (cryo-ET) is to visualize every protein within its cellular context. Such capability would enable molecular resolution mapping of three-dimensional protein topography and structure determination within a native context. Current technology limits the proteins identifiable within an individual tomogram to high-molecular-weight complexes. Localization of smaller target proteins requires the use of labeling systems that act as fiducial markers of target protein localization. Several labeling systems have been developed and recently employed, all of which involve trade-offs. The choice of which system to use depends on the biological question of interest. This review outlines considerations for the design and choice of labeling systems for cryo-ET, highlights recent applications, and outlines areas for future development.

Accurately predicting protein-ligand binding affinities is a central task in rational drug design, as it directly influences hit discovery, lead optimization, and compound prioritization. Traditional approaches often suffer from limited accuracy, high computational cost, or dependence on heuristic scoring functions. Recent advances in machine learning (ML) have introduced new paradigms for the binding affinity prediction. In this review, we survey the latest developments in ML-based predictions of protein-ligand binding affinities across various directions, including structure-based approaches that leverage three-dimensional conformational data, ligand-based models that utilize mathematical approaches that employ topological invariants, and hybrid or alternative frameworks addressing diverse prediction scenarios. We highlight representative algorithms ranging from traditional supervised learning to deep learning architectures. Additionally, we discuss the current challenges faced in this domain. Finally, we outline emerging trends and potential future directions, which are poised to further enhance the accuracy and applicability of binding affinity prediction in drug discovery pipelines.

Target engagement (TE) assays are essential for confirming on-target activity, guiding medicinal chemistry, and linking molecular interactions to phenotypic outcomes. Despite their success in human drug discovery, their application to bacterial and protozoan pathogens remains limited due to biological complexity, technical barriers, and lack of high-quality chemical tools and protein reagents. This review surveys current TE strategies and highlights emerging tools such as live-cell bioluminescence resonance energy transfer, cellular thermal shif assay, and chemoproteomics. Expanding TE in pathogen research will deepen mechanistic insights, reduce development risk, and improve the chances of delivering safer, more effective anti-infective therapies.

Sequence-dependent intrinsic conformational dynamics confer specificity to transcriptional factor recognition of genomic DNA. Their genome-scale investigation using all-atom simulations is challenging, and alternative approaches by coarse-graining DNA into beads-and-sticks or polymer models have their own limitations. One parallel approach is what we describe here as a dictionary-based approach. This has been shown to explain several transcriptional events in biological systems but has been inadequately reviewed. These approaches represent studies based on a finite number of DNA fragments and their corresponding conformational properties, scaled up to genomes by pooling nearby fragments and machine learning models. This article aims to organize efforts made in generating these models and their recent successful applications to stimulate further development of this approach.

Protein-nucleic acid interactions are vital to gene regulation and disease, yet have long been considered "undruggable." Recent advances are reshaping this paradigm, enabling therapeutic targeting of DNA- and RNA-binding proteins. In this review, we highlight four major strategies: (1) direct disruption of protein-nucleic acid binding, (2) stabilization of specific complexes or conformations, (3) targeted degradation of interaction partners, and (4) allosteric modulation. We explore key examples across transcription factors, RNA-binding proteins, and DNA repair proteins, and emphasize emerging chemical, structural, and computational techniques that are accelerating discovery. Together, by intervening directly in the gene regulatory machinery, these approaches expand the druggable genome and open new avenues for treating cancer, genetic disorders, and viral infections.

The CRISPR-Cas system provides adaptive immunity for prokaryotes against mobile genetic elements (MGEs) such as bacteriophages and plasmids. As a countermeasure, MGEs have evolved various anti-CRISPR (Acr) mechanisms that neutralize the CRISPR-mediated immunity. Canonical Acr proteins block target binding of Cas proteins in a stoichiometric or enzymatic manner. New findings reveal that Acr also disintegrates functional Cas complexes, induces promiscuous target binding, and mimics Cas proteins and crRNA with defective mutations. Here, we summarize a broad repertoire of structural and functional mechanisms underlying CRISPR-centric competition, highlighting recent discoveries of molecular machinery that modulates CRISPR function.

Protein kinases catalyze the transfer of phosphate groups from ATP to specific substrates, initiating, modulating, or terminating signaling cascades. Generally, the response of these enzymes to stimuli is characterized by ultrasensitive rather than graded responses and mediated by cooperative binding interactions. Here, we provide examples of positive and negative cooperativity processes regulating several protein kinases. We first examine the binding cooperativity between nucleotide and substrate in protein kinase A, showing how dysfunctional cooperativity may be linked to signalopathies. We then illustrate how certain drugs exploit cooperativity to inhibit kinase homo- and hetero-dimerization or select for active and inactive conformational states. A molecular understanding of binding cooperativity could lead to the development of new kinase-specific inhibitors, opening up novel therapeutic possibilities.

Transcription factor (TF) paralogs provide unique insights into how DNA-binding specificity evolves and diversifies. While paralogous TFs share conserved, highly similar DNA-binding domains, they achieve distinct regulatory functions through mechanisms that are now being elucidated. This review examines how sequence variations between paralogs translate into functional diversity, including how mutations distant from the DNA interface can allosterically modulate binding specificity. We focus on competitive binding dynamics when paralogs are co-expressed and discuss emerging evidence that TFs recognize extensive repertoires of lower-affinity binding sites. Differential preferences for lower-affinity binding sites create paralog-specific binding patterns that determine TF genomic occupancy. These insights have important implications for interpreting the impact of coding and noncoding variation on TF-DNA interactions and human disease.

It is now widely accepted that allosteric signaling is beyond signal transmission to, or conformational change triggered at, a distal point within a structure; it also affects different cellular pathways and functions depending on the specific allosteric modulators. A family of signaling molecules that has attracted wide attention in recent years is the Class B1 G protein-coupled receptors (GPCRs). In the classic view of GPCR signaling, cyclic adenosine monophosphate (cAMP) production is accepted to be uniquely associated with signaling events at the plasma membrane. However, a growing number of studies over the past decade, especially for the parathyroid hormone type 1 receptor (PTHR), demonstrate that cAMP is also produced at the endosomes through a different pathway after receptor internalization. Recent advances in the structural and computational characterization of this family of allosteric receptors provide new insights into the mechanisms of their activation or inhibition, as well as determinants of differential allosteric signaling. We focus on PTHR as a prototypical member of the family and present our current understanding of the role of selected ligands in acting as positive or negative allosteric modulators and eliciting signaling location biases in the cell.

Epigenetic memory-the stable inheritance of a cellular state over cell generations-has long been associated with chromatin modifications. But individual modifications are very dynamic. How can they carry information across cell generations? Recent theoretical work suggests the answer might lie, in part, in the three-dimensional organization of the genome. Cooperation between marks brought together by genome folding can correct epigenetic errors, making stable memory units out of unstable marks. If marks direct the phase separation of chromatin, the resulting bidirectional coupling between marks and structure provides a mechanism for many of these units to operate independently along the genome. Models of bidirectional coupling have helped identify elements, such as formation of a dense compartment, 3D mark spreading, and limited enzyme, which may be key to stable epigenetic memory. An analogy between these 3D models and a classic model of associative memory hints at a way chromatin could perform sophisticated information processing.

Though allostery is a well-established research field, its importance for biomedical applications, spanning from the allosteric effects of mutations in diagnostics to the design of innovative allosteric drugs, is yet to be fully appreciated. The potential of allostery in resolving issues of current drug design and in targeting proteins considered difficult or undruggable is the main topic of this review. In particular, we discuss how the non-conservatism of allosteric sites enables selective targeting of individual members in conserved families, thereby minimizing off-target toxicity. The multiplicity of allosteric sites in any structure allows alleviation of challenges posed by emerging drug resistance driven by allosteric and orthosteric mutations. The modulatory action of allosteric drugs provides, at the same time, an important therapeutic advantage in gradual activation/inhibition of enzymatic function and signaling in receptors. We also discuss here an approach for rational design of allosteric drugs, illustrating its process and output in obtaining effectors with the above-mentioned advantages and characteristics. Reviewing recent advances in the development of allosteric effectors, we show that allostery is undoubtedly becoming an integral part of the drug discovery paradigm. We, therefore, anticipate that the pharmaceutical industry will be prompted to systematically incorporate allostery as an important complement to existing drug design strategies in the near future.

Cells have evolved multiple pathways to preserve genome integrity, with homologous recombination (HR) playing a central role in the accurate repair of DNA-double strand breaks (DSBs) by using homologous templates. Several proteins are involved in HR, and their mutations have been associated with cancer initiation and progression. In this review, we present an overview of recent structural insights into the HR pathway, highlighting the pivotal role of structural approaches in elucidating this complex and finely regulated DNA repair machinery, with the aim of advancing understanding and informing future research in the field.

DNA-encoded library (DEL) technology has enabled efficient discovery of both non-covalent and covalent inhibitors, with covalent binders typically identified via covalent DELs (CoDELs) containing diverse electrophilic warheads. Recent developments have expanded CoDEL applications beyond cysteine to residues like lysine, tyrosine, arginine, and glutamic acid. The integration of CoDEL with activity-based protein profiling (ABPP) has further enabled the identification of potential protein targets for CoDEL screening using residue-selective warheads. Additionally, proteome profiling with fully-functionalized tags has guided target identification for focused DELs with privileged structures. This review highlights recent advances in CoDEL technologies for targeting both cysteine and non-cysteine residues, and discusses how proteomics facilitates hit discovery through CoDELs and focused DELs.