Inhibition of FAM46/TENT5 activity by BCCIPα adopting a unique fold

Dissecting the Puzzling Roles of FAM46C: A Multifaceted Pan-Cancer Tumour Suppressor with Increasing Clinical Relevance

FAM46C is a well-established tumour suppressor with a role that is not completely defined or universally accepted. Although FAM46C expression is down-modulated in several tumours, significant mutations in the FAM46C gene are only found in multiple myeloma (MM). Consequently, its tumour suppressor activity has primarily been studied in the MM context. However, emerging evidence suggests that FAM46C is involved also in other cancer types, namely colorectal, prostate and gastric cancer and squamous cell and hepatocellular carcinoma, where FAM46C expression was found to be significantly reduced in tumoural versus non-tumoural tissues and where FAM46C was shown to possess anti-proliferative properties. Accordingly, FAM46C was recently proposed to function as a pan-cancer prognostic marker, bringing FAM46C under the spotlight and attracting growing interest from the scientific community in the pathways modulated by FAM46C and in its mechanistic activity. Here, we will provide the first comprehensive review regarding FAM46C by covering (1) the intracellular pathways regulated by FAM46C, namely the MAPK/ERK, PI3K/AKT, β-catenin and TGF-β/SMAD pathways; (2) the models regarding its mode of action, specifically the poly(A) polymerase, intracellular trafficking modulator and inhibitor of centriole duplication models, focusing on connections and interdependencies; (3) the regulation of FAM46C expression in different environments by interferons, IL-4, TLR engagement or transcriptional modulators; and, lastly, (4) how FAM46C expression levels associate with increased/decreased tumour cell sensitivity to anticancer agents, such as bortezomib, dexamethasone, lenalidomide, pomalidomide, doxorubicin, melphalan, SK1-I, docetaxel and norcantharidin.

AlphaFold2 has more to learn about protein energy landscapes

Recent work suggests that AlphaFold2 (AF2)-a deep learning-based model that can accurately infer protein structure from sequence-may discern important features of folded protein energy landscapes, defined by the diversity and frequency of different conformations in the folded state. Here, we test the limits of its predictive power on fold-switching proteins, which assume two structures with regions of distinct secondary and/or tertiary structure. Using several implementations of AF2, including two published enhanced sampling approaches, we generated >280,000 models of 93 fold-switching proteins whose experimentally determined conformations were likely in AF2's training set. Combining all models, AF2 predicted fold switching with a modest success rate of ~25%, indicating that it does not readily sample both experimentally characterized conformations of most fold switchers. Further, AF2's confidence metrics selected against models consistent with experimentally determined fold-switching conformations in favor of inconsistent models. Accordingly, these confidence metrics-though suggested to evaluate protein energetics reliably-did not discriminate between low and high energy states of fold-switching proteins. We then evaluated AF2's performance on seven fold-switching proteins outside of its training set, generating >159,000 models in total. Fold switching was accurately predicted in one of seven targets with moderate confidence. Further, AF2 demonstrated no ability to predict alternative conformations of two newly discovered targets without homologs in the set of 93 fold switchers. These results indicate that AF2 has more to learn about the underlying energetics of protein ensembles and highlight the need for further developments of methods that readily predict multiple protein conformations.

SSDraw: Software for generating comparative protein secondary structure diagrams

The program SSDraw generates publication-quality protein secondary structure diagrams from three-dimensional protein structures. To depict relationships between secondary structure and other protein features, diagrams can be colored by conservation score, B-factor, or custom scoring. Diagrams of homologous proteins can be registered according to an input multiple sequence alignment. Linear visualization allows the user to stack registered diagrams, facilitating comparison of secondary structure and other properties among homologous proteins. SSDraw can be used to compare secondary structures of homologous proteins with both conserved and divergent folds. It can also generate one secondary structure diagram from an input protein structure of interest. The source code can be downloaded (https://github.com/ncbi/SSDraw) and run locally for rapid structure generation, while a Google Colab notebook allows easy use.

SSDraw: software for generating comparative protein secondary structure diagrams

The program SSDraw generates publication-quality protein secondary structure diagrams from three-dimensional protein structures. To depict relationships between secondary structure and other protein features, diagrams can be colored by conservation score, B-factor, or custom scoring. Diagrams of homologous proteins can be registered according to an input multiple sequence alignment. Linear visualization allows the user to stack registered diagrams, facilitating comparison of secondary structure and other properties among homologous proteins. SSDraw can be used to compare secondary structures of homologous proteins with both conserved and divergent folds. It can also generate one secondary structure diagram from an input protein structure of interest. The source code can be downloaded (https://github.com/ethanchen1301/SSDraw) and run locally for rapid structure generation, while a Google Colab notebook allows easy use.