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Microtubule-associated protein 2 (MAP2) is the predominant cytoskeletal regulator within neuronal dendrites, abundant and specific enough to serve as a robust somatodendritic marker. It influences microtubule dynamics and microtubule/actin interactions to control neurite outgrowth and synaptic functions, similarly to the closely related MAP Tau. Though pathology of Tau has been well appreciated in the context of neurodegenerative disorders, the consequences of pathologically dysregulated MAP2 have been little explored, despite alterations in its immunoreactivity, expression, splicing and/or stability being observed in a variety of neurodegenerative and neuropsychiatric disorders including Huntington’s disease, prion disease, schizophrenia, autism, major depression and bipolar disorder. Here we review the understood structure and functions of MAP2, including in neurite outgrowth, synaptic plasticity, and regulation of protein folding/transport. We also describe known and potential mechanisms by which MAP2 can be regulated via post-translational modification. Then, we assess existing evidence of its dysregulation in various brain disorders, including from immunohistochemical and (phospho) proteomic data. We propose pathways by which MAP2 pathology could contribute to endophenotypes which characterize these disorders, giving rise to the concept of a “MAP2opathy”—a series of disorders characterized by alterations in MAP2 function.

Glioblastoma is a highly malignant and incurable brain tumor characterized by intrinsic and adaptive resistance to immunotherapies. However, how glioma cells induce tumor immunosuppression and escape immunosurveillance remains poorly understood. Here, we find upregulation of cancer-intrinsic chitinase-3-like 1 (CHI3L1) signaling modulating an immunosuppressive microenvironment by reprogramming tumor-associated macrophages (TAMs). Mechanistically, CHI3L1 binding with galectin 3 (Gal3) selectively promotes TAM migration and infiltration with a protumor M2-like, but not an antitumor M1-like, phenotype in vitro and in vivo, governed by a transcriptional program of NF-[kappa]B/CEBP[beta] in the CHI3L1/Gal3-PI3K/AKT/mTOR axis. Conversely, galectin 3-binding protein (Gal3BP) negatively regulates this process by competing with Gal3 to bind CHI3L1. Administration of a Gal3BP mimetic peptide in syngeneic glioblastoma mouse models reverses immune suppression and attenuates tumor progression. These results shed light on the role of CHI3L1 protein complexes in immune evasion by glioblastoma and as a potential immunotherapeutic target for this devastating disease.

Recent evidence demonstrates that novel protein-coding genes can arise de novo from non-genic loci. This evolutionary innovation is thought to be facilitated by the pervasive translation of non-genic transcripts, which exposes a reservoir of variable polypeptides to natural selection. Here, we systematically characterize how these de novo emerging coding sequences impact fitness in budding yeast. Disruption of emerging sequences is generally inconsequential for fitness in the laboratory and in natural populations. Overexpression of emerging sequences, however, is enriched in adaptive fitness effects compared to overexpression of established genes. We find that adaptive emerging sequences tend to encode putative transmembrane domains, and that thymine-rich intergenic regions harbor a widespread potential to produce transmembrane domains. These findings, together with in-depth examination of the de novo emerging YBR196C-A locus, suggest a novel evolutionary model whereby adaptive transmembrane polypeptides emerge de novo from thymine-rich non-genic regions and subsequently accumulate changes molded by natural selection. There is increasing evidence that protein-coding genes can emerge de novo from noncoding genomic regions. Vakirlis et al. propose that sequences encoding transmembrane polypeptides can emerge de novo in thymine-rich genomic regions and provide organisms with fitness benefits.

An expanded chemical space is essential for improved identification of small molecules for emerging therapeutic targets. However, the identification of targets for novel compounds is biased towards the synthesis of known scaffolds that bind familiar protein families, limiting the exploration of chemical space. To change this paradigm, we validated a new pipeline that identifies small molecule-protein interactions and works even for compounds lacking similarity to known drugs. Based on differential mRNA profiles in multiple cell types exposed to drugs and in which gene knockdowns (KD) were conducted, we showed that drugs induce gene regulatory networks that correlate with those produced after silencing protein-coding genes. Next, we applied supervised machine learning to exploit drug-KD signature correlations and enriched our predictions using an orthogonal structure-based screen. As a proof-of-principle for this regimen, top-10/top-100 target prediction accuracies of 26% and 41%, respectively, were achieved on a validation of set 152 FDA-approved drugs and 3104 potential targets. We then predicted targets for 1680 compounds and validated chemical interactors with four targets that have proven difficult to chemically modulate, including non-covalent inhibitors of HRAS and KRAS. Importantly, drug-target interactions manifest as gene expression correlations between drug treatment and both target gene KD and KD of genes that act up- or down-stream of the target, even for relatively weak binders. These correlations provide new insights on the cellular response of disrupting protein interactions and highlight the complex genetic phenotypes of drug treatment. With further refinement, our pipeline may accelerate the identification and development of novel chemical classes by screening compound-target interactions.

Target-centric drug development strategies prioritize single-target potency in vitro and do not account for connectivity and multi-target effects within a signal transduction network. Here, we present a systems biology approach that combines transcriptomic and structural analyses with live-cell imaging to predict small molecule inhibitors of TNF-induced NF-κB signaling and elucidate the network response. We identify two first-in-class small molecules that inhibit the NF-κB signaling pathway by preventing the maturation of a rate-limiting multiprotein complex necessary for IKK activation. Our findings suggest that a network-centric drug discovery approach is a promising strategy to evaluate the impact of pharmacologic intervention in signaling. Chemical perturbation of specific protein–protein interactions is notoriously difficult, yet necessary when complete inhibition of a signalling pathway is detrimental to the cell. Here, the authors use a systems approach and identify two first-in-class small molecules that specifically inhibit TNF-induced NF-κB activation.

FOXM1 transcription factor is an oncogene and a master regulator of chemoresistance in multiple cancers. Pharmacological inhibition of FOXM1 is a promising approach but has proven to be challenging. We performed a network-centric transcriptomic analysis to identify a novel compound STL427944 that selectively suppresses FOXM1 by inducing the relocalization of nuclear FOXM1 protein to the cytoplasm and promoting its subsequent degradation by autophagosomes. Human cancer cells treated with STL427944 exhibit increased sensitivity to cytotoxic effects of conventional chemotherapeutic treatments (platinum-based agents, 5-fluorouracil, and taxanes). RNA-seq analysis of STL427944-induced gene expression changes revealed prominent suppression of gene signatures characteristic for FOXM1 and its downstream targets but no significant changes in other important regulatory pathways, thereby suggesting high selectivity of STL427944 toward the FOXM1 pathway. Collectively, the novel autophagy-dependent mode of FOXM1 suppression by STL427944 validates a unique pathway to overcome tumor chemoresistance and improve the efficacy of treatment with conventional cancer drugs.

Although there is no shortage of potential drug targets, there are only a handful known low-molecular-weight inhibitors of protein-protein interactions (PPIs). One problem is that current efforts are dominated by low-yield high-throughput screening, whose rigid framework is not suitable for the diverse chemotypes present in PPIs. Here, we developed a novel pharmacophore-based interactive screening technology that builds on the role anchor residues, or deeply buried hot spots, have in PPIs, and redesigns these entry points with anchor-biased virtual multicomponent reactions, delivering tens of millions of readily synthesizable novel compounds. Application of this approach to the MDM2/p53 cancer target led to high hit rates, resulting in a large and diverse set of confirmed inhibitors, and co-crystal structures validate the designed compounds. Our unique open-access technology promises to expand chemical space and the exploration of the human interactome by leveraging in-house small-scale assays and user-friendly chemistry to rationally design ligands for PPIs with known structure.

A large number of proteins are sufficiently unstable that their full 3D structure cannot be resolved. The origins of this intrinsic disorder are not well understood, but its ubiquitous presence undercuts the principle that a protein's structure determines its function. Here we present a quantitative theory that makes predictions regarding the role of intrinsic disorder in protein structure and function. In particular, we discuss the implications of analytical solutions of a series of fundamental thermodynamic models of protein interactions in which disordered proteins are characterized by positive folding free energies. We validate our predictions by assigning protein function by using the gene ontology classification--in which \"protein binding\", \"catalytic activity\", and \"transcription regulator activity\" are the three largest functional categories--and by performing genome-wide surveys of both the amount of disorder in these functional classes and binding affinities for both prokaryotic and eukaryotic genomes. Specifically, without assuming any a priori structure-function relationship, the theory predicts that both catalytic and low-affinity binding (Kd [greater, similar]10⁻⁷ M) proteins prefer ordered structures, whereas only high-affinity binding proteins (found mostly in eukaryotes) can tolerate disorder. Relevant to both transcription and signal transduction, the theory also explains how increasing disorder can tune the binding affinity to maximize the specificity of promiscuous interactions. Collectively, these studies provide insight into how natural selection acts on folding stability to optimize protein function.

We show that the mechanism for molecular recognition requires one of the interacting proteins, usually the smaller of the two, to anchor a specific side chain in a structurally constrained binding groove of the other protein, providing a steric constraint that helps to stabilize a native-like bound intermediate. We identify the anchor residues in 39 protein-protein complexes and verify that, even in the absence of their interacting partners, the anchor side chains are found in conformations similar to those observed in the bound complex. These ready-made recognition motifs correspond to surface side chains that bury the largest solvent-accessible surface area after forming the complex (≥ 100 Å2). The existence of such anchors implies that binding pathways can avoid kinetically costly structural rearrangements at the core of the binding interface, allowing for a relatively smooth recognition process. Once anchors are docked, an induced fit process further contributes to forming the final high-affinity complex. This later stage involves flexible (solvent-exposed) side chains that latch to the encounter complex in the periphery of the binding pocket. Our results suggest that the evolutionary conservation of anchor side chains applies to the actual structure that these residues assume before the encounter complex and not just to their loci. Implications for protein docking are also discussed.

Many eukaryotic regulatory proteins adopt distinct bound and unbound conformations, and use this structural flexibility to bind specifically to multiple partners. However, we lack an understanding of how an interface can select some ligands, but not others. Here, we present a molecular dynamics approach to identify and quantitatively evaluate the interactions responsible for this selective promiscuity. We apply this approach to the anticancer target PD-1 and its ligands PD-L1 and PD-L2. We discover that while unbound PD-1 exhibits a hard-to-drug hydrophilic interface, conserved specific triggers encoded in the cognate ligands activate a promiscuous binding pathway that reveals a flexible hydrophobic binding cavity. Specificity is then established by additional contacts that stabilize the PD-1 cavity into distinct bound-like modes. Collectively, our studies provide insight into the structural basis and evolution of multiple binding partners, and also suggest a biophysical approach to exploit innate binding pathways to drug seemingly undruggable targets. Many proteins need to interact with other proteins to carry out their various tasks in cells. Such interactions are essential for almost all biological processes and are often disrupted in disease. Cells have thousands of different types of proteins and each has a unique shape that determines which other proteins it can bind to. It was previously thought that two proteins bind to each other in a manner similar to that of a lock and a key, in which the rigid shape of one protein meshes perfectly with the rigid shape of its partner. However, many proteins are flexible and adopt different shapes depending on whether they are attached to their partner, or not. Moreover, an individual protein may also bind to several different partners, each requiring that protein to adopt several different shapes. These observations have challenged the lock and key model and suggest that flexibility in the structure of a protein plays a key role in its binding to other proteins. However, it is not clear how structural flexibility enables a protein to bind to several different partners while being selective enough to prevent the protein from binding to the wrong ones. A protein called PD-1 is involved in immune responses in humans and is an emerging target for drugs to treat cancer. Pabon and Camacho used computer simulations to model PD-1’s structural flexibility and to find out how this enables the protein to form different shapes when it binds to different partners. The experiments show that the region of PD-1 that binds to other proteins adopts a different shape in the absence and presence of its partners. The binding partners make initial contact with PD-1 via specific features that they share in common. This causes PD-1 to change shape, uncovering a surface of PD-1 that is flexible and is able to accommodate a variety of partners. After this, the binding partners form additional contacts with PD-1 that are specific to each partner. These findings suggest that the ability of a protein to bind to several different partners is unlocked by certain structures that are present in the binding partners. These structures are found in proteins produced by many different organisms, suggesting that this mechanism is likely to be widespread in nature. This work may open up new avenues for designing drugs to target PD-1 and other proteins that contribute to disease but have so far been impossible to target with drugs.