Phenylketonuria (PKU) is a genetic disorder caused by variants in the gene encoding phenylalanine hydroxylase (PAH), resulting in accumulation of phenylalanine to neurotoxic levels. Here, we analyzed the cellular stability, localization, and interaction with wild‐type PAH of 20 selected PKU‐linked PAH protein missense variants. Several were present at reduced levels in human cells, and the levels increased in the presence of a proteasome inhibitor, indicating that proteins are proteasome targets. We found that all the tested PAH variants retained their ability to associate with wild‐type PAH, and none formed aggregates, suggesting that they are only mildly destabilized in structure. In all cases, PAH variants were stabilized by the cofactor tetrahydrobiopterin (BH4), a molecule known to alleviate symptoms in certain PKU patients. Biophysical calculations on all possible single‐site missense variants using the full‐length structure of PAH revealed a strong correlation between the predicted protein stability and the observed stability in cells. This observation rationalizes previously observed correlations between predicted loss of protein destabilization and disease severity, a correlation that we also observed using new calculations. We thus propose that many disease‐linked PAH variants are structurally destabilized, which in turn leads to proteasomal degradation and insufficient amounts of cellular PAH protein.

the whole E13,5 brain and in the olfactory bulbs (OB) of E18,5 brain (Fig. 1b, Extended Data Fig. 1d, e). Also, neural stem cells (NSCs) isolated from Ambra1 cKO mice show increased levels of several cell-cycle regulatory proteins (Fig. 1c, Extended Data Fig. 1f, g), together with higher clonogenic potential and replication rate (Fig 1d, Extended Data Fig. 1h). Strikingly, levels of cyclin D1 and D2 proteins and phosphorylated pRb (S807/811) are highly increased both ex and in vivo (Fig. 1c, e, Extended Data Fig. 1g, i-m), suggesting an AMBRA1dependent Cyclin D modulation. Indeed, consistent with our previous results 7 , we find in neural ex vivo and in vitro cell lines that AMBRA1 directly binds and regulates the stability of N-Myc, via the phosphatase PP2A, thereby controlling Cyclin D1 and D2 transcription (Extended Data Fig. 1n-r). Moreover, we noticed that both cyclin D1 and D2 are highly resilient to proteasomal degradation in Ambra1-deficiency conditions (Fig. 1f, Extended Data Fig. 2a, b). In line with the fact that both Myc and D-type cyclins positively regulate G1/S transition 10,11 , Ambra1 cKO NSCs show a shorter G1 phase with faster entry into, and longer residence in S phase (Extended Data Fig. 2c). By reducing cyclin D/CDK kinase activity we could restore proliferation to wt levels (Extended Data Fig. 2d), highlighting the importance of accelerated G1/S transition in the AMBRA1depleted driven phenotype. Additionally, we found that due to Ambra1 deficiency, deregulated cell cycle progression is followed by increased cell death, a phenotype rescued upon cyclin D/CDK activity inhibition (Extended Data Fig. 2e, f). Of note, Ambra1 deficiency in neurodevelopment promotes staminal niche

SCAN domains in zinc-finger transcription factors are crucial mediators of protein-protein interactions. Up to 240 SCAN-domain encoding genes have been identified throughout the human genome. These include cancer-related genes, such as the myeloid zinc finger 1 (MZF1), an oncogenic transcription factor involved in the progression of many solid cancers. The mechanisms by which SCAN homo- and heterodimers assemble and how they alter the transcriptional activity of zinc-finger transcription factors in cancer and other diseases remain to be investigated. Here, we provide the first description of the conformational ensemble of the MZF1 SCAN domain cross-validated against NMR experimental data, which are probes of structure and dynamics on different timescales. We investigated the protein-protein interaction network of MZF1 and how it is perturbed in different cancer types by the analyses of high-throughput proteomics and RNASeq data. Collectively, we integrated many computational approaches, ranging from simple empirical energy functions to all-atom microsecond molecular dynamics simulations and network analyses to unravel the effects of cancer-related substitutions in relation to MZF1 structure and interactions.

Mutations, which result in amino acid substitutions, influence the stability of proteins and their binding to biomolecules. A molecular understanding of the effects of protein mutations is both of biotechnological and medical relevance. Empirical free energy functions that quickly estimate the free energy change upon mutation (ΔΔG) can be exploited for systematic screenings of proteins and protein complexes. In silico saturation mutagenesis can guide the design of new experiments or rationalize the consequences of known mutations. Often software such as FoldX, while fast and reliable, lack the necessary automation features to apply them in a high-throughput manner. We introduce MutateX, a software to automate the prediction of ΔΔGs associated with the systematic mutation of each residue within a protein, or protein complex to all other possible residue types, using the FoldX energy function. MutateX also supports ΔΔG calculations over protein ensembles, upon post-translational modifications and in multimeric assemblies. At the heart of MutateX lies an automated pipeline engine that handles input preparation, parallelization and outputs publication-ready figures. We illustrate the MutateX protocol applied to different case studies. The results of the high-throughput scan provided by our tools can help in different applications, such as the analysis of disease-associated mutations, to complement experimental deep mutational scans, or assist the design of variants for industrial applications. MutateX is a collection of Python tools that relies on open-source libraries. It is available free of charge under the GNU General Public License from https://github.com/ELELAB/mutatex.

Mutations resulting in amino acid substitution influence the stability of proteins along with their binding to other biomolecules. A molecular understanding of the effects induced by protein mutations are both of biotechnological and medical relevance. The availability of empirical free energy functions that quickly estimate the free energy change upon mutation (ΔΔG) can be exploited for systematic screenings of proteins and protein complexes. Indeed, in silico saturation mutagenesis can guide the design of new experiments or rationalize the consequences of already-known mutations at the atomic level. Often software such as FoldX, while fast and reliable, lack the necessary automation features to make them useful in high-throughput scenarios. Here we introduce MutateX, a software which aims to automate the prediction of ΔΔGs associated with the systematic mutation of each available residue within a protein or protein complex to all other possible residue types, by employing the FoldX energy function. MutateX also supports ΔΔG calculations over protein ensembles and the estimation of the changes in free energy upon post-translational modifications. At the heart of MutateX lies an automated pipeline engine that handles input preparation, performs parallel runs with FoldX and outputs publication-ready figures. We here illustrate the MutateX protocol applied to the study of the mutational landscape of cancer-related proteins, industrial enzymes and protein-protein interfaces. The results of the high-throughput scan provided by our tools could help in different applications, such as the analysis of disease-associated mutations, or in the design of protein variants for experimental studies or industrial applications. MutateX is a collection of Python tools that relies on Open Source libraries and requires the FoldX software to be installed beforehand. It is available free of charge and under the GNU General Public License from https://github.com/ELELAB/mutatex.