Exploiting the intrinsic misfolding propensity of the KRAS oncoprotein

Heterobifunctional proteomimetic polymers for targeted protein degradation

The burgeoning field of targeted protein degradation (TPD) has opened new avenues for modulating the activity of previously undruggable proteins of interest. To date, TPD has been dominated by small molecules containing separate linked domains for protein engagement and recruitment of cellular degradation machinery. The process of identifying active compounds has required tedious optimization and has been successful largely against a limited set of targets with well-defined, suitable docking pockets. Here we present a polymer chemistry approach termed the HYbrid DegRAding Copolymer (HYDRAC) to overcome standing challenges associated with the development of TPD. These copolymers densely display either peptide-based or small molecule-derived degradation inducers and target-binding peptide sequences for the selective degradation of disease-associated proteins. HYDRACs are synthesized in a facile manner, are modular in design, and are highly selective. Using the intrinsically disordered transcription factor MYC as an initial proof-of-concept, difficult to drug protein target, HYDRACs containing a MYC-inhibitory peptide copolymerized with a validated degron, showed robust and selective degradation of the target protein. Treatment of tumor-bearing mice with MYC-targeted HYDRACs showed decreased cell proliferation and increased tumor apoptosis, leading to significantly suppressed tumor growth in vivo . The versatility of the platform was demonstrated by substituting the degron for recruiters of three different E3 ligases (VHL, KEAP1, and CRBN), which all maintained MYC degradation. To demonstrate generalizability, HYDRACs were further designed against a second elusive target of clinical interest, KRAS, by employing a consensus RAS binding motif. RAS-targeted HYDRACs showed degradation in two cell lines harboring separate KRAS alleles, suggesting potential pan-KRAS activity. We envision the HYDRAC platform as a generalizable approach to developing degraders of proteins of interest, greatly expanding the therapeutic armamentarium for TPD.

Co-translational protein aggregation and ribosome stalling as a broad-spectrum antibacterial mechanism

Drug-resistant bacteria pose an urgent global health threat, necessitating the development of antibacterial compounds with novel modes of action. Protein biosynthesis accounts for up to half of the energy expenditure of bacterial cells, and consequently inhibiting the efficiency or fidelity of the bacterial ribosome is a major target of existing antibiotics. Here, we describe an alternative mode of action that affects the same process: allowing translation to proceed but causing co-translational aggregation of the nascent peptidic chain. We show that treatment with an aggregation-prone peptide induces formation of polar inclusion bodies and activates the SsrA ribosome rescue pathway in bacteria. The inclusion bodies contain ribosomal proteins and ribosome hibernation factors, as well as mRNAs and cognate nascent chains of many proteins in amyloid-like structures, with a bias for membrane proteins with a fold rich in long-range beta-sheet interactions. The peptide is bactericidal against a wide range of pathogenic bacteria in planktonic growth and in biofilms, and reduces bacterial loads in mouse models of Escherichia coli and Acinetobacter baumannii infections. Our results indicate that disrupting protein homeostasis via co-translational aggregation constitutes a promising strategy for development of broad-spectrum antibacterials.

AggNet: Advancing protein aggregation analysis through deep learning and protein language model

Protein aggregation is critical to various biological and pathological processes. Besides, it is also an important property in biotherapeutic development. However, experimental methods to profile protein aggregation are costly and labor-intensive, driving the need for more efficient computational alternatives. In this study, we introduce "AggNet," a novel deep learning framework based on the protein language model ESM2 and AlphaFold2, which utilizes physicochemical, evolutionary, and structural information to discriminate amyloid and non-amyloid peptides and identify aggregation-prone regions (APRs) in diverse proteins. Benchmark comparisons show that AggNet outperforms existing methods and achieves state-of-the-art performance on protein aggregation prediction. Also, the predictive ability of AggNet is stable across proteins with different secondary structures. Feature analysis and visualizations prove that the model effectively captures peptides' physicochemical properties effectively, thereby offering enhanced interpretability. Further validation through a case study on MEDI1912 confirms AggNet's practical utility in analyzing protein aggregation and guiding mutation for aggregation mitigation. This study enhances computational tools for predicting protein aggregation and highlights the potential of AggNet in protein engineering. Finally, to improve the accessibility of AggNet, the source code can be accessed at: https://github.com/Hill-Wenka/AggNet.

CORDAX web server: an online platform for the prediction and 3D visualization of aggregation motifs in protein sequences

MOTIVATION

Proteins, the molecular workhorses of biological systems, execute a multitude of critical functions dictated by their precise three-dimensional structures. In a complex and dynamic cellular environment, proteins can undergo misfolding, leading to the formation of aggregates that take up various forms, including amorphous and ordered aggregation in the shape of amyloid fibrils. This phenomenon is closely linked to a spectrum of widespread debilitating pathologies, such as Alzheimer's disease, Parkinson's disease, type-II diabetes, and several other proteinopathies, but also hampers the engineering of soluble agents, as in the case of antibody development. As such, the accurate prediction of aggregation propensity within protein sequences has become pivotal due to profound implications in understanding disease mechanisms, as well as in improving biotechnological and therapeutic applications.

RESULTS

We previously developed Cordax, a structure-based predictor that utilizes logistic regression to detect aggregation motifs in protein sequences based on their structural complementarity to the amyloid cross-beta architecture. Here, we present a dedicated web server interface for Cordax. This online platform combines several features including detailed scoring of sequence aggregation propensity, as well as 3D visualization with several customization options for topology models of the structural cores formed by predicted aggregation motifs. In addition, information is provided on experimentally determined aggregation-prone regions that exhibit sequence similarity to predicted motifs, scores, and links to other predictor outputs, as well as simultaneous predictions of relevant sequence propensities, such as solubility, hydrophobicity, and secondary structure propensity.

AVAILABILITY AND IMPLEMENTATION

The Cordax webserver is freely accessible at https://cordax.switchlab.org/.

KRAS: Biology, Inhibition, and Mechanisms of Inhibitor Resistance

KRAS is a small GTPase that is among the most commonly mutated oncogenes in cancer. Here, we discuss KRAS biology, therapeutic avenues to target it, and mechanisms of resistance that tumors employ in response to KRAS inhibition. Several strategies are under investigation for inhibiting oncogenic KRAS, including small molecule compounds targeting specific KRAS mutations, pan-KRAS inhibitors, PROTACs, siRNAs, PNAs, and mutant KRAS-specific immunostimulatory strategies. A central challenge to therapeutic effectiveness is the frequent development of resistance to these treatments. Direct resistance mechanisms can involve KRAS mutations that reduce drug efficacy or copy number alterations that increase the expression of mutant KRAS. Indirect resistance mechanisms arise from mutations that can rescue mutant KRAS-dependent cells either by reactivating the same signaling or via alternative pathways. Further, non-mutational forms of resistance can take the form of epigenetic marks, transcriptional reprogramming, or alterations within the tumor microenvironment. As the possible strategies to inhibit KRAS expand, understanding the nuances of resistance mechanisms is paramount to the development of both enhanced therapeutics and innovative drug combinations.

Mechanisms and pathology of protein misfolding and aggregation

Despite advances in machine learning-based protein structure prediction, we are still far from fully understanding how proteins fold into their native conformation. The conventional notion that polypeptides fold spontaneously to their biologically active states has gradually been replaced by our understanding that cellular protein folding often requires context-dependent guidance from molecular chaperones in order to avoid misfolding. Misfolded proteins can aggregate into larger structures, such as amyloid fibrils, which perpetuate the misfolding process, creating a self-reinforcing cascade. A surge in amyloid fibril structures has deepened our comprehension of how a single polypeptide sequence can exhibit multiple amyloid conformations, known as polymorphism. The assembly of these polymorphs is not a random process but is influenced by the specific conditions and tissues in which they originate. This observation suggests that, similar to the folding of native proteins, the kinetics of pathological amyloid assembly are modulated by interactions specific to cells and tissues. Here, we review the current understanding of how intrinsic protein conformational propensities are modulated by physiological and pathological interactions in the cell to shape protein misfolding and aggregation pathology.