Targeting intrinsically disordered proteins involved in cancer

Synthetic Peptide Antibodies via a Rational Approach Based on Disulfide-Stabilized α-Helical Peptides, for the Recognition of the Intrinsically Disordered Protein NUPR1

Nuclear Protein 1 (NUPR1) is a ubiquitous protein playing an important role in cancer and acute kidney injury. Its specific targeting by natural or synthetic antibodies like molecularly imprinted polymers (MIPs), is therefore of interest. NUPR1 is an intrinsically disordered protein (IDP), such that it displays a high degree of flexibility and an unstable secondary/tertiary structure, resulting in a continuous fluctuation of its conformation in the free state. These characteristics are not in favor of the creation of homogeneous binding sites during molecular imprinting, so that imprinting using peptide epitopes is investigated. Based on an in silico rational approach, two α-helices from the model structure of NUPR1, as predicted by AlPhaFold, are selected. Two cysteine residues are added at both ends of the epitopes to form a disulfide bond, which provides high stability to the α-helix. The template peptides possess the same 3D structure as the epitope fragments in NUPR1. Consequently, they are effective in producing MIP nanogels that cross-react with high affinity (IC50 1 nm) only with NUPR1. The work indicates that α-helices, besides the preferred flexible loops, can be considered as viable template epitopes for MIPs, opening new opportunities for the obtention of selective antipeptide MIP nanogels for IDPs.

PredIDR2: Improving accuracy of protein intrinsic disorder prediction by updating deep convolutional neural network and supplementing DisProt data

Intrinsically disordered proteins (IDPs) or regions (IDRs) are widespread in proteomes, and involved in several important biological processes and implicated in many diseases. Many computational methods for IDR prediction are being developed to decrease the gap between the low speed of experimental determination of annotated proteins and the rapid increase of non-annotated proteins, and their performances are blindly tested by the community-driven experiment, the Critical Assessment of protein Intrinsic Disorder (CAID). In this paper, we developed PredIDR2 series, an updated version of PredIDR tested in CAID2 in order to accurately predict intrinsically disordered regions from protein sequence. It includes four methods depending on the input features and the producing mode of the negative samples of the training set. PredIDR2 series (AUC_ROC = 0.952) perform remarkably better than our previous PredIDR (AUC_ROC = 0.933) for Disorder-PDB dataset of CAID2, which seems to be mainly attributed to the introduction of a new deep convolutional neural network and the augmentation of the training data, especially from DisProt database. PredIDR2 series outperform the state-of-the-art IDR prediction methods participated in CAID2 in terms of AUC_ROC, AUC_PR and DC_mae and belong to the seven top-performing methods in terms of MCC. PredIDR2 series can be freely used through the CAID Prediction Portal available at https://caid.idpcentral.org/portal or downloaded as a Singularity container from https://biocomputingup.it/shared/caid-predictors/.

Conformational modulation of intrinsically disordered transactivation domains for cancer therapy

Intrinsically disordered proteins are implicated in many diseases, but their overrepresentation among transcription factors, whose deregulation can cause disproportionate expression of oncogenes, suggests an important role in cancer. Targeting disordered transcription factors for therapy is considered challenging, as they undergo dynamic transitions and exist as an ensemble of interconverting states. This enables them to interact with multiple downstream partners, often through their transactivation domains (TADs) by the mechanisms of conformational selection, folding-upon-binding, or formation of "fuzzy" complexes. The TAD interfaces, despite falling outside of what is considered "classical" binding pockets, can be conformationally modulated to interfere with their target recruitment and hence represent potentially druggable sites. Here, we discuss the structure-activity relationship of TADs from p53, c-MYC, and the androgen receptor, and the progresses made in modulating their interactions with small molecules. These recent advances highlight the potential of targeting these so far "undruggable" proteins for cancer therapy.

Intrinsic Disorder and Phase Separation Coordinate Exocytosis, Motility, and Chromatin Remodeling in the Human Acrosomal Proteome

Intrinsic disorder refers to protein regions that lack a fixed three-dimensional structure under physiological conditions, enabling conformational plasticity. This flexibility allows for diverse functions, including transient interactions, signaling, and phase separation via disorder-to-order transitions upon binding. Our study focused on investigating the role of intrinsic disorder and liquid-liquid phase separation (LLPS) in the human acrosome, a sperm-specific organelle essential for fertilization. Using computational prediction models, network analysis, Structural Classification of Proteins (SCOP) functional assessments, and Gene Ontology, we analyzed 250 proteins within the acrosomal proteome. Our bioinformatic analysis yielded 97 proteins with high levels (>30%) of structural disorder. Further analysis of functional enrichment identified associations between disordered regions overlapping with SCOP domains and critical acrosomal processes, including vesicle trafficking, membrane fusion, and enzymatic activation. Examples of disordered SCOP domains include the PLC-like phosphodiesterase domain, the t-SNARE domain, and the P-domain of calnexin/calreticulin. Protein-protein interaction networks revealed acrosomal proteins as hubs in tightly interconnected systems, emphasizing their functional importance. LLPS propensity modeling determined that over 30% of these proteins are high-probability LLPS drivers (>60%), underscoring their role in dynamic compartmentalization. Proteins such as myristoylated alanine-rich C-kinase substrate and nuclear transition protein 2 exhibited both high LLPS propensities and high levels of structural disorder. A significant relationship (p < 0.0001, R² = 0.649) was observed between the level of intrinsic disorder and LLPS propensity, showing the role of disorder in facilitating phase separation. Overall, these findings provide insights into how intrinsic disorder and LLPS contribute to the structural adaptability and functional precision required for fertilization, with implications for understanding disorders associated with the human acrosome reaction.

Deep Learning-Based Comparative Prediction and Functional Analysis of Intrinsically Disordered Regions in SARS-CoV-2

This study explores the role of intrinsically disordered regions (IDRs) in the SARS-CoV-2 proteome and their potential as targets for small-molecule drug discovery. Experimentally validated intrinsic disordered regions from the literature were utilized to assess the prediction of intrinsic disorder across a selection of SARS-CoV-2 proteins. The disorder propensities of proteins using four deep learning-based disorder prediction models: ADOPT, PONDR®VLXT, PONDR®VSL2, and flDPnn, were analyzed. ADOPT, VSL2, and VLXT identified a flexible linker (129-147), while VSL2 and VLXT predicted disorder in the Cu(II) binding region (163-167) of NSP1. ADOPT did not predict disordered regions in NSP11; however, VSL2 and VLXT identified disorder in the experimentally validated regions. The IDR in ORF3a is crucial for protein localization and immune modulation, affecting inflammatory pathways. VSL2 predicted significant disorder in the N-terminal domain (18-23), which aligns with experimental data (1-41), overlapping with the TRAF-binding motif, while ADOPT indicated high disorder in the C-terminal domain (255-275), consistent with VSL2 and flDPnn. All tools identified disorder in the N-terminal (1-68), central linker (181-248), and C-terminal (370-419) regions of the nucleocapsid (N) protein, suggesting flexibility and accuracy. The S2 subunit of the spike protein displayed more predicted disorder than the S1 subunit across ADOPT, VSL2, and flDPnn. These IDRs are essential for viral functions, like protein localization, immune modulation, receptor binding, and membrane fusion. This study highlights the importance of IDR in modulating key inflammatory pathways, suggesting that they could serve as promising targets for small-molecule drug development to combat COVID-19.

Effects of Aging on Intrinsic Protein Disorder in Human Lenses and Zonules

This study aims to compare the levels of intrinsic protein disorder within the human lens and zonule proteomes and investigate the role of aging as a potential influencing factor on disorder levels. A cross-sectional proteomic analysis was employed, utilizing a dataset of 1466 proteins derived from the lens and zonule proteomes previously published by Wang et al. and De Maria et al. Bioinformatics tools, including a composition profiler and a rapid intrinsic disorder analysis online tool, were used to conduct a comparative analysis of protein disorder. Statistical tests such as ANOVA, Tukey's HSD, and chi-squared tests were applied to evaluate differences between groups. The study revealed distinct amino acid compositions for each proteome, showing a direct correlation between aging and increased protein disorder in the zonular proteomes, whereas the lens proteomes exhibited the opposite trend. Findings suggest that age-related changes in intrinsic protein disorder within the lens and zonule proteomes may be linked to structural transformations in these tissues. Understanding how protein disorder evolves with age could enhance knowledge of the molecular basis for age-related conditions such as cataracts and pseudoexfoliation, potentially leading to better therapeutic strategies.

Development of an efficient NUPR1 inhibitor with anticancer activity

Pancreatic cancer is highly lethal and has limited treatment options available. Our team had previously developed ZZW-115, a promising drug candidate that targets the nuclear protein 1 (NUPR1), which is involved in pancreatic cancer development and progression. However, clinical translation of ZZW-115 was hindered due to potential cardiotoxicity caused by its interaction with the human Ether-à-go-go-Related Gene (hERG) potassium channel. To address this, we have performed a high-throughput screening of 10,000 compounds from the HitFinder Chemical Library, and identified AJO14 as a lead compound that binds to NUPR1, without having favorable affinity towards hERG. AJO14 induced cell death through apoptosis, necroptosis, and parthanatos (induced by the poly-ADP ribose polymerase (PARP) overactivation), driven by mitochondrial catastrophe and decreased ATP production. This process seemed to be mediated by the hyperPARylation (an excessive modification of proteins by PARP, leading to cellular dysfunction), as it could be reversed by Olaparib, a PARP inhibitor. In xenografted mice, AJO14 demonstrated a dose-dependent tumor reduction activity. Furthermore, we attempted to improve the anti-cancer properties of AJO14 by molecular modification of the lead compound. Among the 51 candidates obtained and tested, 8 compounds exhibited a significant increase in efficacy and have been retained for further studies, especially LZX-2-73. These AJO14-derived compounds offer potent NUPR1 inhibition for pancreatic cancer treatment, without cardiotoxicity concerns.

Selection of Nucleotide-Encoded Mass Libraries of Macrocyclic Peptides for Inaccessible Drug Targets

Technological advances and breakthrough developments in the pharmaceutical field are knocking at the door of the "undruggable" fortress with increasing insistence. Notably, the 21st century has seen the emergence of macrocyclic compounds, among which cyclic peptides are of particular interest. This new class of potential drug candidates occupies the vast chemical space between classic small-molecule drugs and larger protein-based therapeutics, such as antibodies. As research advances toward clinical targets that have long been considered inaccessible, macrocyclic peptides are well-suited to tackle these challenges in a post-rule of 5 pharmaceutical landscape. Facilitating their discovery is an arsenal of high-throughput screening methods that exploit massive randomized libraries of genetically encoded compounds. These techniques benefit from the incorporation of non-natural moieties, such as non- proteinogenic amino acids or stabilizing hydrocarbon staples. Exploiting these features for the strategic architectural design of macrocyclic peptides has the potential to tackle challenging targets such as protein-protein interactions, which have long resisted research efforts. This Review summarizes the basic principles and recent developments of the main high-throughput techniques for the discovery of macrocyclic peptides and focuses on their specific deployment for targeting undruggable space. A particular focus is placed on the development of new design guidelines and principles for the cyclization and structural stabilization of cyclic peptides and the resulting success stories achieved against well-known inaccessible drug targets.

How to drug a cloud? Targeting intrinsically disordered proteins

Biologically active proteins/regions without stable structure (i.e., intrinsically disordered proteins and regions (IDPs and IDRs)) are commonly found in all proteomes. They have a unique functional repertoire that complements the functionalities of ordered proteins and domains. IDPs/IDRs are multifunctional promiscuous binders capable of folding at interaction with specific binding partners on a template- or context-dependent manner, many of which undergo liquid-liquid phase separation, leading to the formation of membrane-less organelles and biomolecular condensates. Many of them are frequently related to the pathogenesis of various human diseases. All this defines IDPs/IDRs as attractive targets for the development of novel drugs. However, their lack of unique structures, multifunctionality, binding promiscuity, and involvement in unusual modes of action preclude direct use of traditional structure-based drug design approaches for targeting IDPs/IDRs, and make disorder-based drug discovery for these "protein clouds" challenging. Despite all these complexities there is continuing progress in the design of small molecules affecting IDPs/IDRs. This article describes the major structural features of IDPs/IDRs and the peculiarities of the disorder-based functionality. It also discusses the roles of IDPs/IDRs in various pathologies, and shows why the approaches elaborated for finding drugs targeting ordered proteins cannot be directly used for the intrinsic disorder-based drug design, and introduces some novel methodologies suitable for these purposes. Finally, it emphasizes that regardless of their multifunctionality, binding promiscuity, lack of unique structures, and highly dynamic nature, "protein clouds" are principally druggable. Significance Statement Intrinsically disordered proteins and regions are highly abundant in nature, have multiple important biological functions, are commonly involved in the pathogenesis of a multitude of human diseases, and are therefore considered as very attractive drug targets. Although dealing with these unstructured multifunctional protein/regions is a challenging task, multiple innovative approaches have been designed to target them by small molecules.

When an underdog becomes a major player: the role of protein structural disorder in the Atg8 conjugation system

The noncanonical ubiquitin-like conjugation cascade involving the E1 (Atg7), E2 (Atg3, Atg10), and E3 (Atg12-Atg5-Atg16 complex) enzymes is essential for incorporation of Atg8 into the growing phagophore via covalent linkage to PE. This process is an indispensable step in autophagy. Atg8 and E1-E3 enzymes are the first subset from the core autophagy protein machinery structures that were investigated in earlier studies by crystallographic analyses of globular domains. However, research over the past decade shows that many important functions in the conjugation machinery are mediated by intrinsically disordered protein regions (IDPRs) - parts of the protein that do not adopt a stable secondary or tertiary structure, which are inherently dynamic and well suited for protein-membrane interactions but are invisible in protein crystals. Here, we summarize earlier and recent findings on the autophagy conjugation machinery by focusing on the IDPRs. This summary reveals that IDPRs, originally considered dispensable, are in fact major players and a driving force in the function of the autophagy conjugation system. Abbreviation: AD, activation domain of Atg7; AH, amphipathic helix; AIM, Atg8-family interacting motif; CL, catalytic loop (of Atg7); CTD, C-terminal domain; FR, flexible region (of Atg3 or Atg10); GUV, giant unilammelar vesicles; HR, handle region (of Atg3); IDPR, intrinsically disordered protein region; IDPs: intrinsically disordered proteins; LIR, LC3-interacting region; NHD: N-terminal helical domain; NMR, nuclear magnetic resonance; PE, phosphatidylethanolamine; UBL, ubiquitin like.

Exploring Degradation of Intrinsically Disordered Protein Yes-Associated Protein Induced by Proteolysis TArgeting Chimeras

Yes-associated protein (YAP) is a key oncogene in the Hippo tumor suppression pathway, historically challenging to target due to its intrinsically disordered nature. Leveraging recent advances in high-throughput screening that identified several YAP binders, we employed proteolysis-targeting chimera technology to develop a series of YAP degraders. Utilizing NSC682769, a known YAP binder, linked with VHL ligand 2 or pomalidomide via diverse linkers, we synthesized degraders including YZ-6. This degrader not only recruits the E3 ligase VHL for the rapid and sustained degradation of YAP but also effectively inhibits its nuclear localization, curtailing YAP/TEAD-mediated transcription in cancer cell lines such as NCI-H226 and Huh7. This dual action significantly diminishes YAP's oncogenic activity, contributing to the potent antiproliferative effects observed both in vitro and in a Huh7 xenograft mouse model. These results underscore the potential of PROTAC-mediated YAP degradation as a strategy for treating YAP-driven cancers.

flDPnn2: Accurate and Fast Predictor of Intrinsic Disorder in Proteins

Prediction of the intrinsic disorder in protein sequences is an active research area, with well over 100 predictors that were released to date. These efforts are motivated by the functional importance and high levels of abundance of intrinsic disorder, combined with relatively low amounts of experimental annotations. The disorder predictors are periodically evaluated by independent assessors in the Critical Assessment of protein Intrinsic Disorder prediction (CAID) experiments. The recently completed CAID2 experiment assessed close to 40 state-of-the-art methods demonstrating that some of them produce accurate results. In particular, flDPnn2 method, which is the successor of flDPnn that performed well in the CAID1 experiment, secured the overall most accurate results on the Disorder-NOX dataset in CAID2. flDPnn2 implements a number of improvements when compared to its predecessor including changes to the inputs, increased size of the deep network model that we retrained on a larger training set, and addition of an alignment module. Using results from CAID2, we show that flDPnn2 produces accurate predictions very quickly, modestly improving over the accuracy of flDPnn and reducing the runtime by half, to about 27 s per protein. flDPnn2 is freely available as a convenient web server at http://biomine.cs.vcu.edu/servers/flDPnn2/.

Protein tyrosine phosphatase 1B (PTP1B) function, structure, and inhibition strategies to develop antidiabetic drugs

Tyrosine protein phosphatase non-receptor type 1 (PTP1B; also known as protein tyrosine phosphatase 1B) is a member of the protein tyrosine phosphatase (PTP) family and is a soluble enzyme that plays an essential role in different physiological processes, including the regulation of metabolism, specifically in insulin and leptin sensitivity. PTP1B is crucial in the pathogenesis of type 2 diabetes mellitus and obesity. These biological functions have made PTP1B validated as an antidiabetic and anti-obesity, and potentially anticancer, molecular target. Four main approaches aim to inhibit PTP1B: orthosteric, allosteric, bidentate inhibition, and PTPN1 gene silencing. Developing a potent and selective PTP1B inhibitor is still challenging due to the enzyme's ubiquitous expression, subcellular location, and structural properties. This article reviews the main advances in the study of PTP1B since it was first isolated in 1988, as well as recent contextual information related to the PTP family to which this protein belongs. Furthermore, we offer an overview of the role of PTP1B in diabetes and obesity, and the challenges to developing selective, effective, potent, bioavailable, and cell-permeable compounds that can inhibit the enzyme.

Synthesis and Herbicidal Activity of 2-(2-Oxo-3-pyridyl-benzothiazol-6-yloxy)hexanoic Acids

The discovery of a lead compound is fundamental to herbicide innovation, but the limited availability of valuable lead compounds has hindered their development in recent years. By utilizing the structural diversity-oriented inactive group strategy, 3-(2-pyridyl)-benzothiazol-2-one was identified as a promising lead scaffold for herbicides, starting from benzothiazole which is an inactive moiety commonly found in herbicides such as mefenacet, benazolin, benzthiazuron, and fenthiaprop-ethyl. To investigate the structure-activity relationship (SAR) of these chemicals, a series of 2-(2-oxo-3-pyridyl-benzothiazol-6-yloxy)hexanoic acid derivatives (VI01 ∼ VI28) were synthesized through classical nucleophilic SNAr reaction using halogenated pyridines and 6-methoxybenzothiazole-2-one. The chemical structures of all the title compounds were confirmed by NMR and MS analysis. Petri dish assays indicated that many compounds exhibited potent herbicidal activity against both broad-leaf weeds and grass weeds at 1.0 mg/L. The SAR analysis revealed that the presence of a trifluoromethyl group at the 5-position of pyridine is essential for herbicidal activity. Furthermore, carboxylic esters exhibit higher herbicidal activity compared to carboxylic amides and free acids, and the activity decreased with the extension of the carbon chain. The postemergence herbicidal activity of VI03 against 16 species of weeds was tested by pot experiments in a greenhouse. VI03 demonstrated comparable efficacy in controlling broadleaf weeds and superior efficacy in controlling grass weeds compared to carfentrazone ethyl. The present study has unveiled a novel molecular scaffold exhibiting remarkably potent herbicidal activity. These findings are anticipated to provide valuable insights for the advancement of new herbicides and offer an alternative approach for managing resistant weeds.

Proteasome activation: A novel strategy for targeting undruggable intrinsically disordered proteins

Intrinsically disordered proteins (IDPs) are characterized by their inability to adopt well-defined tertiary structures under physiological conditions. Nonetheless, they often play pivotal roles in the progression of various diseases, including cancer, neurodegenerative disorders, and cardiovascular ailments. Owing to their inherent dynamism, conventional drug design approaches based on structural considerations encounter substantial challenges when applied to IDPs. Consequently, the pursuit of therapeutic interventions directed towards IDPs presents a complex endeavor. While there are indeed existing methodologies for targeting IDPs, they are encumbered by noteworthy constrains. Hence, there exists an imminent imperative to investigate more efficacious and universally applicable strategies for modulating IDPs. Here, we present an overview of the latest advancements in the research pertaining to IDPs, along with the indirect regulation approach involving the modulation of IDP degradation through proteasome. By comprehending these advancements in research, novel insights can be generated to facilitate the development of new drugs targeted at addressing the accumulation of IDPs in diverse pathological conditions.

High-throughput assay exploiting disorder-to-order conformational switches: application to the proteasomal Rpn10:E6AP complex

Conformational switching is pervasively driven by protein interactions, particularly for intrinsically disordered binding partners. We developed a dually orthogonal fluorescence-based assay to monitor such events, exploiting environmentally sensitive fluorophores. This assay is applied to E3 ligase E6AP, as its AZUL domain induces a disorder-to-order switch in an intrinsically disordered region of the proteasome, the so-named Rpn10 AZUL-binding domain (RAZUL). By testing various fluorophores, we developed an assay appropriate for high-throughput screening of Rpn10:E6AP-disrupting ligands. We found distinct positions in RAZUL for fluorophore labeling with either acrylodan or Atto610, which had disparate spectral responses to E6AP binding. E6AP caused a hypsochromic shift with increased fluorescence of acrylodan-RAZUL while decreasing fluorescence intensity of Atto610-RAZUL. Combining RAZUL labeled with either acrylodan or Atto610 into a common sample achieved robust and orthogonal measurement of the E6AP-induced conformational switch. This approach is generally applicable to disorder-to-order (or vice versa) transitions mediated by molecular interactions.

Amino acid pattern reveals multi-functionality of ORF3 protein from HEV

The smallest open reading frame (ORF) encoded protein ORF3 of hepatitis E virus (HEV), recently, has been demonstrated to perform multiple functions besides accessory roles. ORF3 could act as a target for vaccine against HEV infections. The IDR (intrinsically disordered region); IDP (ID protein)/IDPR (ID protein region), plays critical role in various regulatory functions of viruses. The dark proteome of HEV-ORF3 protein including its structure and function was systematically examined by computer predictors to explicate its role in viral pathogenesis and drug resistance beyond its functions as accessory viral protein. Amino acid distribution showed ORF3 enrichment with disorder-promoting residues (Ala, Pro, Ser, Gly) while deficiency in order-promoting residues (Asn, Ile, Phe, Tyr and Trp). Initial investigation revealed ORF3 as IDP (entirely disordered protein) or IDPR (proteins consisting of IDRs with structured globular domains). Structural examination revealed preponderance of disordered regions interpreting ORF3 as moderately/highly disordered protein. Further disorder predictors categorized ORF3 as highly disordered protein/IDP. Identified sites and associated-crucial molecular functions revealed ORF3 involvement in diverse biological processes, substantiating them as targets of regulation. As ORF3 functions are yet to completely explored, thus, data on its disorderness could help in elucidating its disorder related functions.

Molecular simulations integrated with experiments for probing the interaction dynamics and binding mechanisms of intrinsically disordered proteins

Intrinsically disordered proteins (IDPs) exploit their plasticity to deploy a rich panoply of soft interactions and binding phenomena. Advances in tailoring molecular simulations for IDPs combined with experimental cross-validation offer an atomistic view of the mechanisms that control IDP binding, function, and dysfunction. The emerging theme is that unbound IDPs autonomously form transient local structures and self-interactions that determine their binding behavior. Recent results have shed light on whether and how IDPs fold, stay disordered or drive condensation upon binding; how they achieve binding specificity and select among competing partners. The disorder-binding paradigm is now being proactively used by researchers to target IDPs for rational drug design and engineer molecular responsive elements for biosensing applications.

The Proteomic Analysis of Cancer-Related Alterations in the Human Unfoldome

Many proteins lack stable 3D structures. These intrinsically disordered proteins (IDPs) or hybrid proteins containing ordered domains with intrinsically disordered protein regions (IDPRs) often carry out regulatory functions related to molecular recognition and signal transduction. IDPs/IDPRs constitute a substantial portion of the human proteome and are termed "the unfoldome". Herein, we probe the human breast cancer unfoldome and investigate relations between IDPs and key disease genes and pathways. We utilized bottom-up proteomics, MudPIT (Multidimensional Protein Identification Technology), to profile differentially expressed IDPs in human normal (MCF-10A) and breast cancer (BT-549) cell lines. Overall, we identified 2271 protein groups in the unfoldome of normal and cancer proteomes, with 148 IDPs found to be significantly differentially expressed in cancer cells. Further analysis produced annotations of 140 IDPs, which were then classified to GO (Gene Ontology) categories and pathways. In total, 65% (91 of 140) IDPs were related to various diseases, and 20% (28 of 140) mapped to cancer terms. A substantial portion of the differentially expressed IDPs contained disordered regions, confirmed by in silico characterization. Overall, our analyses suggest high levels of interactivity in the human cancer unfoldome and a prevalence of moderately and highly disordered proteins in the network.

Unraveling the Behavior of Intrinsically Disordered Protein c-Myc: A Study Utilizing Gaussian-Accelerated Molecular Dynamics

The protein c-Myc is a transcription factor that remains largely intrinsically disordered and is known to be involved in various biological processes and is overexpressed in various cancers, making it an attractive drug target. However, intrinsically disordered proteins such as c-Myc do not show funnel-like basins in their free-energy landscapes; this makes their druggability a challenge. For the first time, we propose a heterodimer model of c-Myc/Max in full length in this work. We used Gaussian-accelerated molecular dynamics (GaMD) simulations to explore the behavior of c-Myc and its various regions, including the transactivation domain (TAD) and the basic helix-loop-helix-leucine-zipper (bHLH-Zipper) motif in three different conformational states: (a) monomeric c-Myc, (b) c-Myc when bound to its partner protein, Max, and (c) when Max was removed after binding. We analyzed the GaMD trajectories using root-mean-square deviation (RMSD), radius of gyration, root-mean-square fluctuation, and free-energy landscape (FEL) calculations to elaborate the behaviors of these regions. The results showed that the monomeric c-Myc structure showed a higher RMSD fluctuation as compared with the c-Myc/Max heterodimer in the bHLH-Zipper motif. This indicated that the bHLH-Zipper motif of c-Myc is more stable when it is bound to Max. The TAD region in both monomeric and Max-bound states showed similar plasticity in terms of RMSD. We also conducted residue decomposition calculations and showed that the c-Myc and Max interaction could be driven mainly by electrostatic interactions and the residues Arg299, Ile403, and Leu420 seemed to play important roles in the interaction. Our work provides insights into the behavior of c-Myc and its regions that could support the development of drugs that target c-Myc and other intrinsically disordered proteins.

Using NMR diffusion data to validate MD models of disordered proteins: Test case of N-terminal tail of histone H4

MD simulations can provide uniquely detailed models of intrinsically disordered proteins (IDPs). However, these models need careful experimental validation. The coefficient of translational diffusion Dtr, measurable by pulsed field gradient NMR, offers a potentially useful piece of experimental information related to the compactness of the IDP's conformational ensemble. Here, we investigate, both experimentally and via the MD modeling, the translational diffusion of a 25-residue N-terminal fragment from histone H4 (N-H4). We found that the predicted values of Dtr, as obtained from mean-square displacement of the peptide in the MD simulations, are largely determined by the viscosity of the MD water (which has been reinvestigated as a part of our study). Beyond that, our analysis of the diffusion data indicates that MD simulations of N-H4 in the TIP4P-Ew water give rise to an overly compact conformational ensemble for this peptide. In contrast, TIP4P-D and OPC simulations produce the ensembles that are consistent with the experimental Dtr result. These observations are supported by the analyses of the 15N spin relaxation rates. We also tested a number of empirical methods to predict Dtr based on IDP's coordinates extracted from the MD snapshots. In particular, we show that the popular approach involving the program HYDROPRO can produce misleading results. This happens because HYDROPRO is not intended to predict the diffusion properties of highly flexible biopolymers such as IDPs. Likewise, recent empirical schemes that exploit the relationship between the small-angle x-ray scattering-informed conformational ensembles of IDPs and the respective experimental Dtr values also prove to be problematic. In this sense, the first-principle calculations of Dtr from the MD simulations, such as demonstrated in this work, should provide a useful benchmark for future efforts in this area.

Conformational Stability of the N-Terminal Region of MDM2

MDM2 is an E3 ubiquitin ligase which is crucial for the degradation and inhibition of the key tumor-suppressor protein p53. In this work, we explored the stability and the conformational features of the N-terminal region of MDM2 (N-MDM2), through which it binds to the p53 protein as well as other protein partners. The isolated domain possessed a native-like conformational stability in a narrow pH range (7.0 to 10.0), as shown by intrinsic and 8-anilinonapthalene-1-sulfonic acid (ANS) fluorescence, far-UV circular dichroism (CD), and size exclusion chromatography (SEC). Guanidinium chloride (GdmCl) denaturation followed by intrinsic and ANS fluorescence, far-UV CD and SEC at physiological pH, and differential scanning calorimetry (DSC) and thermo-fluorescence experiments showed that (i) the conformational stability of isolated N-MDM2 was very low; and (ii) unfolding occurred through the presence of several intermediates. The presence of a hierarchy in the unfolding intermediates was also evidenced through DSC and by simulating the unfolding process with the help of computational techniques based on constraint network analysis (CNA). We propose that the low stability of this protein is related to its inherent flexibility and its ability to interact with several molecular partners through different routes.

The Intrinsically Disordered N Terminus in Atg12 from Yeast Is Necessary for the Functional Structure of the Protein

The Atg12 protein in yeast is an indispensable polypeptide in the highly conserved ubiquitin-like conjugation system operating in the macroautophagy/autophagy pathway. Atg12 is covalently conjugated to Atg5 through the action of Atg7 and Atg10; the Atg12-Atg5 conjugate binds Atg16 to form an E3 ligase that functions in a separate conjugation pathway involving Atg8. Atg12 is comprised of a ubiquitin-like (UBL) domain preceded at the N terminus by an intrinsically disordered protein region (IDPR), a domain that comprises a major portion of the protein but remains elusive in its conformation and function. Here, we show that the IDPR in unconjugated Atg12 is positioned in proximity to the UBL domain, a configuration that is important for the functional structure of the protein. A major deletion in the IDPR disrupts intactness of the UBL domain at the unconjugated C terminus, and a mutation in the predicted α0 helix in the IDPR prevents Atg12 from binding to Atg7 and Atg10, which ultimately affects the protein function in the ubiquitin-like conjugation cascade. These findings provide evidence that the IDPR is an indispensable part of the Atg12 protein from yeast.

Intrinsic disorder may drive the interaction of PROS1 and MERTK in uveal melanoma

BACKGROUND

Class 2 uveal melanomas are associated with the inactivation of the BRCA1 ((breast cancer type 1 susceptibility protein)-associated protein 1 (BAP1)) gene. Inactivation of BAP1 promotes the upregulation of vitamin K-dependent protein S (PROS1), which interacts with the tyrosine-protein kinase Mer (MERTK) receptor on M2 macrophages to induce an immunosuppressive environment.

METHODS

We simulated the interaction of PROS1 with MERTK with ColabFold. We evaluated PROS1 and MERTK for the presence of intrinsically disordered protein regions (IDPRs) and disorder-to-order (DOT) regions to understand their protein-protein interaction (PPI). We first evaluated the structure of each protein with AlphaFold. We then analyzed specific sequence-based features of the PROS1 and MERTK with a suite of bioinformatics tools.

RESULTS

With high-resolution, moderate confidence, we successfully modeled the interaction between PROS1 and MERTK (predicted local distance difference test score (pDLLT) = 70.68). Our structural analysis qualitatively demonstrated IDPRs (i.e., spaghetti-like entities) in PROS1 and MERK. PROS1 was 23.37 % disordered, and MERTK was 23.09 % disordered, classifying them as moderately disordered and flexible proteins. PROS1 was significantly enriched in cysteine, the most order-promoting residue (p-value <0.05). Our IUPred analysis demonstrated that there are two disorder-to-order transition (DOT) regions in PROS1. MERTK was significantly enriched in proline, the most disorder-promoting residue (p-value <0.05), but did not contain DOT regions. Our STRING analysis demonstrated that the PPI network between PROS1 and MERTK is more complex than their assumed one-to-one binding (p-value <2.0 × 10-6).

CONCLUSION

Our findings present a novel prediction for the interaction between PROS1 and MERTK. Our findings show that PROS1 and MERTK contain elements of intrinsic disorder. PROS1 has two DOT regions that are attractive immunotherapy targets. We recommend that IDPRs and DOT regions found in PROS1 and MERTK should be considered when developing immunotherapies targeting this PPI.

Structure, Function, and Allosteric Regulation of the 20S Proteasome by the 11S/PA28 Family of Proteasome Activators

The proteasome, a complex multi-catalytic protease machinery, orchestrates the protein degradation essential for maintaining cellular homeostasis, and its dysregulation also underlies many different types of diseases. Its function is regulated by many different mechanisms that encompass various factors such as proteasome activators (PAs), adaptor proteins, and post-translational modifications. This review highlights the unique characteristics of proteasomal regulation through the lens of a distinct family of regulators, the 11S, REGs, or PA26/PA28. This ATP-independent family, spanning from amoebas to mammals, exhibits a common architectural structure; yet, their cellular biology and criteria for protein degradation remain mostly elusive. We delve into their evolution and cellular biology, and contrast their structure and function comprehensively, emphasizing the unanswered questions regarding their regulatory mechanisms and broader roles in proteostasis. A deeper understanding of these processes will illuminate the roles of this regulatory family in biology and disease, thus contributing to the advancement of therapeutic strategies.

Model-Dependent Solvation of the K-18 Domain of the Intrinsically Disordered Protein Tau

A known imbalance between intra-protein and protein-water interactions in many empirical force fields results in collapsed conformational ensembles of intrinsically disordered proteins in explicit solvent simulations that disagree with experiments. Multiple strategies have been introduced in the literature to modify protein-water interactions, which improve agreement between experiments and simulations. In this work, we combine simulations with standard and modified force fields with a spatially resolved analysis of solvation free energy contributions and compare the consequences of each strategy. We find that enhanced Lennard-Jones (LJ) interactions between protein atoms and water oxygens primarily improve the solvation of nonpolar functional groups of the protein. In contrast, modified electrostatics in the water model or strengthened LJ interactions between the protein and water hydrogens mainly affect the hydration of polar functional groups. Modified electrostatics further impact the average orientation of water molecules in the hydration shell. As a result, protein-water interactions with the first hydration layers are strengthened, while interactions with water molecules in higher hydration shells are weakened. Hence, distinct strategies to balance intra-protein and protein-water interactions in simulations have qualitatively different effects on protein solvation. These differences are not necessarily captured by comparisons to experiments that report on global parameters describing protein conformational ensembles, e.g., the radius of gyration, but will influence the tendency of a protein to form aggregates or phase-separated droplets.

The global landscape and research trend of phase separation in cancer: a bibliometric analysis and visualization

Background

Cancer as a deathly disease with high prevalence has impelled researchers to investigate its causative mechanisms in the search for effective therapeutics. Recently, the concept of phase separation has been introduced to biological science and extended to cancer research, which helps reveal various pathogenic processes that have not been identified before. As a process of soluble biomolecules condensed into solid-like and membraneless structures, phase separation is associated with multiple oncogenic processes. However, there are no bibliometric characteristics for these results. To provide future trends and identify new frontiers in this field, a bibliometric analysis was conducted in this study.

Methods

The Web of Science Core Collection (WoSCC) was used to search for literature on phase separation in cancer from 1/1/2009 to 31/12/2022. After screening the literature, statistical analysis and visualization were carried out by the VOSviewer software (version 1.6.18) and Citespace software (Version 6.1.R6).

Results

A total of 264 publications, covering 413 organizations and 32 countries, were published in 137 journals, with an increasing trend in publication and citation numbers per year. The USA and China were the two countries with the largest number of publications, and the University of Chinese Academy of Sciences was the most active institution based on the number of articles and cooperations. Molecular Cell was the most frequent publisher with high citations and H-index. The most productive authors were Fox AH, De Oliveira GAP, and Tompa P. Overlay, whilst few authors had a strong collaboration with each other. The combined analysis of concurrent and burst keywords revealed that the future research hotspots of phase separation in cancer were related to tumor microenvironments, immunotherapy, prognosis, p53, and cell death.

Conclusion

Phase separation-related cancer research remained in the hot streak period and exhibited a promising outlook. Although inter-agency collaboration existed, cooperation among research groups was rare, and no author dominated this field at the current stage. Investigating the interfaced effects between phase separation and tumor microenvironments on carcinoma behaviors, and constructing relevant prognoses and therapeutics such as immune infiltration-based prognosis and immunotherapy might be the next research trend in the study of phase separation and cancer.

ADOPT: intrinsic protein disorder prediction through deep bidirectional transformers

Intrinsically disordered proteins (IDPs) are important for a broad range of biological functions and are involved in many diseases. An understanding of intrinsic disorder is key to develop compounds that target IDPs. Experimental characterization of IDPs is hindered by the very fact that they are highly dynamic. Computational methods that predict disorder from the amino acid sequence have been proposed. Here, we present ADOPT (Attention DisOrder PredicTor), a new predictor of protein disorder. ADOPT is composed of a self-supervised encoder and a supervised disorder predictor. The former is based on a deep bidirectional transformer, which extracts dense residue-level representations from Facebook's Evolutionary Scale Modeling library. The latter uses a database of nuclear magnetic resonance chemical shifts, constructed to ensure balanced amounts of disordered and ordered residues, as a training and a test dataset for protein disorder. ADOPT predicts whether a protein or a specific region is disordered with better performance than the best existing predictors and faster than most other proposed methods (a few seconds per sequence). We identify the features that are relevant for the prediction performance and show that good performance can already be gained with <100 features. ADOPT is available as a stand-alone package at https://github.com/PeptoneLtd/ADOPT and as a web server at https://adopt.peptone.io/.

Machine Learning Generation of Dynamic Protein Conformational Ensembles

Machine learning has achieved remarkable success across a broad range of scientific and engineering disciplines, particularly its use for predicting native protein structures from sequence information alone. However, biomolecules are inherently dynamic, and there is a pressing need for accurate predictions of dynamic structural ensembles across multiple functional levels. These problems range from the relatively well-defined task of predicting conformational dynamics around the native state of a protein, which traditional molecular dynamics (MD) simulations are particularly adept at handling, to generating large-scale conformational transitions connecting distinct functional states of structured proteins or numerous marginally stable states within the dynamic ensembles of intrinsically disordered proteins. Machine learning has been increasingly applied to learn low-dimensional representations of protein conformational spaces, which can then be used to drive additional MD sampling or directly generate novel conformations. These methods promise to greatly reduce the computational cost of generating dynamic protein ensembles, compared to traditional MD simulations. In this review, we examine recent progress in machine learning approaches towards generative modeling of dynamic protein ensembles and emphasize the crucial importance of integrating advances in machine learning, structural data, and physical principles to achieve these ambitious goals.

Intrinsic disorder in the open reading frame 2 of hepatitis E virus: a protein with multiple functions beyond viral capsid

BACKGROUND

Hepatitis E virus (HEV) is the cause of a liver disease hepatitis E. The translation product of HEV ORF2 has recently been demonstrated as a protein involved in multiple functions besides performing its major role of a viral capsid. As intrinsically disordered regions (IDRs) are linked to various essential roles in the virus's life cycle, we analyzed the disorder pattern distribution of the retrieved ORF2 protein sequences by employing different online predictors. Our findings might provide some clues on the disorder-based functions of ORF2 protein that possibly help us in understanding its behavior other than as a HEV capsid protein.

RESULTS

The modeled three dimensional (3D) structures of ORF2 showed the predominance of random coils or unstructured regions in addition to major secondary structure components (alpha helix and beta strand). After initial scrutinization, the predictors VLXT and VSL2 predicted ORF2 as a highly disordered protein while the predictors VL3 and DISOPRED3 predicted ORF2 as a moderately disordered protein, thus categorizing HEV-ORF2 into IDP (intrinsically disordered protein) or IDPR (intrinsically disordered protein region) respectively. Thus, our initial predicted disorderness in ORF2 protein 3D structures was in excellent agreement with their predicted disorder distribution patterns (evaluated through different predictors). The abundance of MoRFs (disorder-based protein binding sites) in ORF2 was observed that signified their interaction with binding partners which might further assist in viral infection. As IDPs/IDPRs are targets of regulation, we carried out the phosphorylation analysis to reveal the presence of post-translationally modified sites. Prevalence of several disordered-based phosphorylation sites further signified the involvement of ORF2 in diverse and significant biological processes. Furthermore, ORF2 structure-associated functions revealed its involvement in several crucial functions and biological processes like binding and catalytic activities.

CONCLUSIONS

The results predicted ORF2 as a protein with multiple functions besides its role as a capsid protein. Moreover, the occurrence of IDPR/IDP in ORF2 protein suggests that its disordered region might serve as novel drug targets via functioning as potential interacting domains. Our data collectively might provide significant implication in HEV vaccine search as disorderness in viral proteins is related to mechanisms involved in immune evasion.

Computational Prediction of Protein Intrinsically Disordered Region Related Interactions and Functions

Intrinsically Disordered Proteins (IDPs) and Regions (IDRs) exist widely. Although without well-defined structures, they participate in many important biological processes. In addition, they are also widely related to human diseases and have become potential targets in drug discovery. However, there is a big gap between the experimental annotations related to IDPs/IDRs and their actual number. In recent decades, the computational methods related to IDPs/IDRs have been developed vigorously, including predicting IDPs/IDRs, the binding modes of IDPs/IDRs, the binding sites of IDPs/IDRs, and the molecular functions of IDPs/IDRs according to different tasks. In view of the correlation between these predictors, we have reviewed these prediction methods uniformly for the first time, summarized their computational methods and predictive performance, and discussed some problems and perspectives.

Portability of a Small-Molecule Binding Site between Disordered Proteins

Intrinsically disordered proteins (IDPs) are important in both normal and disease states. Small molecules can be targeted to disordered regions, but we currently have only a limited understanding of the nature of small-molecule binding sites in IDPs. Here, we show that a minimal small-molecule binding sequence of eight contiguous residues derived from the Myc protein can be ported into a different disordered protein and recapitulate small-molecule binding activity in the new context. We also find that the residue immediately flanking the binding site can have opposing effects on small-molecule binding in the different disordered protein contexts. The results demonstrate that small-molecule binding sites can act modularly and are portable between disordered protein contexts but that residues outside of the minimal binding site can modulate binding affinity.

Intrinsically Disordered Proteins: An Overview

Many proteins and protein segments cannot attain a single stable three-dimensional structure under physiological conditions; instead, they adopt multiple interconverting conformational states. Such intrinsically disordered proteins or protein segments are highly abundant across proteomes, and are involved in various effector functions. This review focuses on different aspects of disordered proteins and disordered protein regions, which form the basis of the so-called "Disorder-function paradigm" of proteins. Additionally, various experimental approaches and computational tools used for characterizing disordered regions in proteins are discussed. Finally, the role of disordered proteins in diseases and their utility as potential drug targets are explored.

Anchor Residues Govern Binding and Folding of an Intrinsically Disordered Domain

Minimal protein mimics have yielded novel classes of protein-protein interaction inhibitors; however, this success has not been extended to targeting intrinsically disordered proteins, which represent a significant proportion of important therapeutic targets. We sought to determine the requirements for binding an intrinsically disordered region (IDR) by its native binding partner as a prelude to developing minimal protein mimics that regulate IDR interactions. Our analysis reinforces the hypothesis that IDRs reside on a fulcrum between unfolded and folded states and that a handful of key binding residues on partner protein surfaces dictate their folding. Our studies also suggest that minimal mimics of protein surfaces may not offer specific ligands for IDRs and that it would be more judicious to target the globular protein partners of IDRs.

Intrinsic Disorder in BAP1 and Its Association with Uveal Melanoma

Background: Specific subvariants of uveal melanoma (UM) are associated with increased rates of metastasis compared to other subvariants. BRCA1 (BReast CAncer gene 1)-associated protein-1 (BAP1) is encoded by a gene that has been linked to aggressive behavior in UM. Methods: We evaluated BAP1 for the presence of intrinsically disordered protein regions (IDPRs) and its protein−protein interactions (PPI). We evaluated specific sequence-based features of the BAP1 protein using a set of bioinformatic databases, predictors, and algorithms. Results: We show that BAP1’s structure contains extensive IDPRs as it is highly enriched in proline residues (the most disordered amino acid; p-value < 0.05), the average percent of predicted disordered residues (PPDR) was 57.34%, and contains 9 disorder-based binding sites (ie. molecular recognition features (MoRFs)). BAP1’s intrinsic disorder allows it to engage in a complex PPI network with at least 49 partners (p-value < 1.0 × 10−16). Conclusion: These findings show that BAP1 contains IDPRs and an intricate PPI network. Mutations in UM that are associated with the BAP1 gene may alter the function of the IDPRs embedded into its structure. These findings develop the understanding of UM and may provide a target for potential novel therapies to treat this aggressive neoplasm.

NUPR1 protects against hyperPARylation-dependent cell death

Proteomic, cellular and biochemical analysis of the stress protein NUPR1 reveals that it binds to PARP1 into the nucleus and inhibits PARP1 activity in vitro. Mutations on residues Ala33 or Thr68 of NUPR1 or treatment with its inhibitor ZZW-115 inhibits this effect. PARylation induced by 5-fluorouracil (5-FU) treatment is strongly enhanced by ZZW-115 and associated with a decrease of NAD⁺/NADH ratio and rescued by the PARP inhibitor olaparib. Cell death induced by ZZW-115 treatment of pancreas cancer-derived cells is rescued by olaparib and improved with PARG inhibitor PDD00017273. The mitochondrial catastrophe induced by ZZW-115 treatment or by genetic inactivation of NUPR1 is associated to a hyperPARylation of the mitochondria, disorganization of the mitochondrial network, mitochondrial membrane potential decrease, and with increase of superoxide production, intracellular level of reactive oxygen species (ROS) and cytosolic levels of Ca²⁺. These features are rescued by olaparib or NAD⁺ precursor nicotinamide mononucleotide in a dose-dependent manner and partially by antioxidants treatments. In conclusion, inactivation of NUPR1 induces a hyperPARylation, which in turn, induces a mitochondrial catastrophe and consequently a cell death through a non-canonical Parthanatos, since apoptosis inducing-factor (AIF) is not translocated out of the mitochondria.

Targeting Protein Interaction Hotspots Using Structured and Disordered Chimeric Peptide Inhibitors

The main challenge in inhibiting protein-protein interactions (PPI) for therapeutic purposes is designing molecules that bind specifically to the interaction hotspots. Adding to the complexity, such hotspots can be within both structured and disordered interaction interfaces. To address this, we present a strategy for inhibiting the structured and disordered hotspots of interactions using chimeric peptides that contain both structured and disordered parts. The chimeric peptides we developed are comprised of a cyclic structured part and a disordered part, which target both disordered and structured hotspots. We demonstrate our approach by developing peptide inhibitors for the interactions of the antiapoptotic iASPP protein. First, we developed a structured, α-helical stapled peptide inhibitor, derived from the N-terminal domain of MDM2. The peptide bound two hotspots on iASPP at the low micromolar range and had a cytotoxic effect on A2780 cancer cells with a half-maximal inhibitory concentration (IC₅₀) value of 10 ± 1 μM. We then developed chimeric peptides comprising the structured stapled helical peptide and the disordered p53-derived LinkTer peptide that we previously showed to inhibit iASPP by targeting its disordered RT loop. The chimeric peptide targeted both structured and disordered domains in iASPP with higher affinity compared to the individual structured and disordered peptides and caused cancer cell death. Our strategy overcomes the inherent difficulty in inhibiting the interactions of proteins that possess structured and disordered regions. It does so by using chimeric peptides derived from different interaction partners that together target a much wider interface covering both the structured and disordered domains. This paves the way for developing such inhibitors for therapeutic purposes.

AlphaFold illuminates half of the dark human proteins

We investigate the use of confidence scores to evaluate the accuracy of a given AlphaFold (AF2) protein model for drug discovery. Prediction of accuracy is improved by not considering confidence scores below 80 due to the effects of disorder. On a set of recent crystal structures, 95% are likely to have accurate folds. Conformational discordance in the training set has a much more significant effect on accuracy than sequence divergence. We propose criteria for models and residues that are possibly useful for virtual screening. Based on these criteria, AF2 provides models for half of understudied (dark) human proteins and two-thirds of residues in those models.

Energetics of a protein disorder-order transition in small molecule recognition

Many proteins recognise other proteins via mechanisms that involve the folding of intrinsically disordered regions upon complex formation. Here we investigate how the selectivity of a drug-like small molecule arises from its modulation of a protein disorder-to-order transition. Binding of the compound AM-7209 has been reported to confer order upon an intrinsically disordered ‘lid’ region of the oncoprotein MDM2. Calorimetric measurements revealed that truncation of the lid region of MDM2 increases the apparent dissociation constant of AM-7209 250-fold. By contrast, lid truncation has little effect on the binding of the ligand Nutlin-3a. Insights into these differential binding energetics were obtained via a complete thermodynamic analysis that featured adaptive absolute alchemical free energy of binding calculations with enhanced-sampling molecular dynamics simulations. The simulations reveal that in apo MDM2 the ordered lid state is energetically disfavoured. AM-7209, but not Nutlin-3a, shows a significant energetic preference for ordered lid conformations, thus shifting the balance towards ordering of the lid in the AM-7209/MDM2 complex. The methodology reported herein should facilitate broader targeting of intrinsically disordered regions in medicinal chemistry.

Molecular simulations and biophysical measurements elucidate why the ligand AM-7209 orders a disordered region of the protein MDM2 on binding. This work expands strategies available to medicinal chemists for targeting disordered proteins.

Fusion-associated carcinomas of the breast: Diagnostic, prognostic, and therapeutic significance

Recurrent gene fusions comprise a class of viable genetic targets in solid tumors that have culminated several recent breakthrough cancer therapies. Their role in breast cancer, however, remains largely underappreciated due to the complexity of genomic rearrangements in breast malignancy. Just recently, we and others have identified several recurrent gene fusions in breast cancer with important clinical and biological implications. Examples of the most significant recurrent gene fusions to date include (1) ESR1::CCDC170 gene fusions in luminal B and endocrine-resistant breast cancer that exert oncogenic function via modulating the HER2/HER3/SRC Proto-Oncogene (SRC) complex, (2) ESR1 exon 6 fusions in metastatic disease that drive estrogen-independent estrogen-receptor transcriptional activity, (3) BCL2L14::ETV6 fusions in a more aggressive form of the triple-negative subtype that prime epithelial-mesenchymal transition and endow paclitaxel resistance, (4) the ETV6::NTRK3 fusion in secretory breast carcinoma that constitutively activates NTRK3 kinase, (5) the oncogenic MYB-NFIB fusion as a genetic driver underpinning adenoid cystic carcinomas of the breast that activates MYB Proto-Oncogene (MYB) pathway, and (6) the NOTCH/microtubule-associated serine-threonine (MAST) kinase gene fusions that activate NOTCH and MAST signaling. Importantly, these fusions are enriched in more aggressive and lethal breast cancer presentations and appear to confer therapeutic resistance. Thus, these gene fusions could be utilized as genetic biomarkers to identify patients who require more intensive treatment and surveillance. In addition, kinase fusions are currently being evaluated in breast cancer clinical trials and ongoing mechanistic investigation is exposing therapeutic vulnerabilities in patients with fusion-positive disease.

Development of Low-Molecular-Weight Compounds Targeting the Cancer-Associated KLF5 Transcription Factor

Krüppel-like factor 5 (KLF5) is a potential target for anticancer drugs. However, as an intrinsically disordered protein (IDP) whose tertiary structure cannot be solved, innovative strategies are needed. We focused on its hydrophobic α-helix structure, defined as an induced helical motif (IHM), which is a possible interface for protein-protein interaction. Using mathematical analyses predicting the α-helix's structure and hydrophobicity, a 4-amino-acid site (V-A-I-F) was identified as an IHM. Low-molecular-weight compounds that mimic the main chain conformation of the α-helix with the four side chains of V-A-I-F were synthesized using bicyclic pyrazinooxadiazine-4,7-dione. These compounds selectively suppressed the proliferation and survival of cancer cells but not noncancer cells and decreased the protein but not mRNA levels of KLF5 in addition to reducing proteins of Wnt signaling. The compounds further suppressed transplanted colorectal cancer cells in vivo without side effects. Our approach appears promising for developing drugs against key IDPs.

E3 ligase ligand chemistries: from building blocks to protein degraders

In recent years, proteolysis-targeting chimeras (PROTACs), capable of achieving targeted protein degradation, have proven their great therapeutic potential and usefulness as molecular biology tools. These heterobifunctional compounds are comprised of a protein-targeting ligand, an appropriate linker, and a ligand binding to the E3 ligase of choice. A successful PROTAC induces the formation of a ternary complex, leading to the E3 ligase-mediated ubiquitination of the targeted protein and its proteasomal degradation. In over 20 years since the concept was first demonstrated, the field has grown substantially, mainly due to the advancements in the discovery of non-peptidic E3 ligase ligands. Development of small-molecule E3 binders with favourable physicochemical profiles aided the design of PROTACs, which are known for breaking the rules of established guidelines for discovering small molecules. Synthetic accessibility of the ligands and numerous successful applications led to the prevalent use of cereblon and von Hippel-Lindau as the hijacked E3 ligase. However, the pool of over 600 human E3 ligases is full of untapped potential, which is why expanding the artillery of E3 ligands could contribute to broadening the scope of targeted protein degradation. In this comprehensive review, we focus on the chemistry aspect of the PROTAC design process by providing an overview of liganded E3 ligases, their chemistries, appropriate derivatisation, and synthetic approaches towards their incorporation into heterobifunctional degraders. By covering syntheses of both established and underexploited E3 ligases, this review can serve as a chemistry blueprint for PROTAC researchers during their future ventures into the complex field of targeted protein degradation.

Targeting key proteins involved in transcriptional regulation for cancer therapy: Current strategies and future prospective

The key proteins involved in transcriptional regulation play convergent roles in cellular homeostasis, and their dysfunction mediates aberrant gene expressions that underline the hallmarks of tumorigenesis. As tumor progression is dependent on such abnormal regulation of transcription, it is important to discover novel chemical entities as antitumor drugs that target key tumor-associated proteins involved in transcriptional regulation. Despite most key proteins (especially transcription factors) involved in transcriptional regulation are historically recognized as undruggable targets, multiple targeting approaches at diverse levels of transcriptional regulation, such as epigenetic intervention, inhibition of DNA-binding of transcriptional factors, and inhibition of the protein-protein interactions (PPIs), have been established in preclinically or clinically studies. In addition, several new approaches have recently been described, such as targeting proteasomal degradation and eliciting synthetic lethality. This review will emphasize on accentuating these developing therapeutic approaches and provide a thorough conspectus of the drug development to target key proteins involved in transcriptional regulation and their impact on future oncotherapy.

The PentUnFOLD algorithm as a tool to distinguish the dark and the light sides of the structural instability of proteins

Intrinsically disordered proteins are frequently involved in important regulatory processes in the cell thanks to their ability to bind several different targets performing sometimes even opposite functions. The PentUnFOLD algorithm is a physicochemical method that is based on new propensity scales for disordered, nonstable and stable elements of secondary structure and on the counting of stabilizing and destabilizing intraprotein contacts. Unlike other methods, it works with a PDB file, and it can determine not only those fragments of alpha helices, beta strands, and random coils that can turn into disordered state (the “dark” side of the disorder), but also nonstable regions of alpha helices and beta strands which are able to turn into random coils (the “light” side), and vice versa (H ↔ C, E ↔ C). The scales have been obtained from structural data on disordered regions from the middle parts of amino acid sequences only, and not on their expectedly disordered N- and C-termini. Among other tendencies we have found that regions of both alpha helices and beta strands that can turn into the disordered state are relatively enriched in residues of Ala, Met, Asp, and Lys, while regions of both alpha helices and beta strands that can turn into random coil are relatively enriched in hydrophilic residues, and Cys, Pro, and Gly. Moreover, PentUnFOLD has the option to determine the effect of secondary structure transitions on the stability of a given region of a protein. The PentUnFOLD algorithm is freely available at http://3.17.12.213/pent-un-fold and http://chemres.bsmu.by/PentUnFOLD.htm.

Deep learning in prediction of intrinsic disorder in proteins

Intrinsic disorder prediction is an active area that has developed over 100 predictors. We identify and investigate a recent trend towards the development of deep neural network (DNN)-based methods. The first DNN-based method was released in 2013 and since 2019 deep learners account for majority of the new disorder predictors. We find that the 13 currently available DNN-based predictors are diverse in their topologies, sizes of their networks and the inputs that they utilize. We empirically show that the deep learners are statistically more accurate than other types of disorder predictors using the blind test dataset from the recent community assessment of intrinsic disorder predictions (CAID). We also identify several well-rounded DNN-based predictors that are accurate, fast and/or conveniently available. The popularity, favorable predictive performance and architectural flexibility suggest that deep networks are likely to fuel the development of future disordered predictors. Novel hybrid designs of deep networks could be used to adequately accommodate for diversity of types and flavors of intrinsic disorder. We also discuss scarcity of the DNN-based methods for the prediction of disordered binding regions and the need to develop more accurate methods for this prediction.

Intrinsically disordered proteins: Ensembles at the limits of Anfinsen's dogma

Intrinsically disordered proteins (IDPs) are proteins that lack rigid 3D structure. Hence, they are often misconceived to present a challenge to Anfinsen's dogma. However, IDPs exist as ensembles that sample a quasi-continuum of rapidly interconverting conformations and, as such, may represent proteins at the extreme limit of the Anfinsen postulate. IDPs play important biological roles and are key components of the cellular protein interaction network (PIN). Many IDPs can interconvert between disordered and ordered states as they bind to appropriate partners. Conformational dynamics of IDPs contribute to conformational noise in the cell. Thus, the dysregulation of IDPs contributes to increased noise and "promiscuous" interactions. This leads to PIN rewiring to output an appropriate response underscoring the critical role of IDPs in cellular decision making. Nonetheless, IDPs are not easily tractable experimentally. Furthermore, in the absence of a reference conformation, discerning the energy landscape representation of the weakly funneled IDPs in terms of reaction coordinates is challenging. To understand conformational dynamics in real time and decipher how IDPs recognize multiple binding partners with high specificity, several sophisticated knowledge-based and physics-based in silico sampling techniques have been developed. Here, using specific examples, we highlight recent advances in energy landscape visualization and molecular dynamics simulations to discern conformational dynamics and discuss how the conformational preferences of IDPs modulate their function, especially in phenotypic switching. Finally, we discuss recent progress in identifying small molecules targeting IDPs underscoring the potential therapeutic value of IDPs. Understanding structure and function of IDPs can not only provide new insight on cellular decision making but may also help to refine and extend Anfinsen's structure/function paradigm.

Molecular simulations of proteins: From simplified physical interactions to complex biological phenomena

Molecular dynamics simulation is the most popular computational technique for investigating the structural and dynamical behaviour of proteins, in search of the molecular basis of their function. Far from being a completely settled field of research, simulations are still evolving to best capture the essential features of the atomic interactions that govern a protein's inner motions. Modern force fields are becoming increasingly accurate in providing a physical description adequate to this purpose, and allow us to model complex biological systems under fairly realistic conditions. Furthermore, the use of accelerated sampling techniques is improving our access to the observation of progressively larger molecular structures, longer time scales, and more hidden functional events. In this review, the basic principles of molecular dynamics simulations and a number of key applications in the area of protein science are summarized, and some of the most important results are discussed. Examples include the study of the structure, dynamics and binding properties of 'difficult' targets, such as intrinsically disordered proteins and membrane receptors, and the investigation of challenging phenomena like hydration-driven processes and protein aggregation. The findings described provide an overall picture of the current state of this research field, and indicate new perspectives on the road ahead to the upcoming future of molecular simulations.