Biology subjects

Kempf, G.

Publications and source records attributed to Kempf, G..

5 recordsLinked to original sources

bioRxiv2024

ChAHP2 and ChAHP control diverse retrotransposons by complementary activities

Retrotransposon control in mammals is an intricate process that is effectuated by a broad network of chromatin regulatory pathways. We previously discovered ChAHP, a protein complex with repressive activity against SINE retrotransposons, composed of the transcription factor ADNP, chromatin remodeler CHD4, and HP1 proteins. Here we identify ChAHP2, a protein complex homologous to ChAHP, wherein ADNP is replaced by ADNP2. ChAHP2 is predominantly targeted to ERVs and LINEs, via HP1{beta}-mediated binding of H3K9 trimethylated histones. We further demonstrate that ChAHP also binds these elements in a mechanistically equivalent manner to ChAHP2, and distinct from DNA sequence-specific recruitment at SINEs. Genetic ablation of ADNP2 alleviates ERV and LINE1 repression, which is synthetically exacerbated by additional depletion of ADNP. Together, our results reveal that the ChAHP and ChAHP2 complexes function to control both non-autonomous and autonomous retrotransposons by complementary activities, further adding to the complexity of mammalian transposon control.

molecular biology↗

bioRxiv2023

Massively parallel mapping of substrate cleavage sites defines dipeptidyl peptidase four subsite cooperativity

Substrate specificity determines protease functions in physiology and in clinical and biotechnological application, yet quantitative cleavage information is often unavailable, biased, or limited to a small number of events. Here, we develop qPISA (quantitative Protease specificity Inference from Substrate Analysis) to study Dipeptidyl Peptidase Four (DPP4), a key regulator of blood glucose levels. We use mass spectrometry to quantify its effects on >40,000 peptides from a complex, commercially available peptide mixture. By determining substrate turnover instead of focusing on product identification, we can reveal cooperative interactions within DPP4s active pocket and derive a sequence motif that predicts activity quantitatively. qPISA distinguishes DPP4 from the related C. elegans DPF-3 (a DPP8/9 orthologue), and we relate the differences to structural features of the two enzymes. We demonstrate that qPISA can direct protein engineering efforts like stabilization of GLP-1, a key DPP4 substrate used in treatment of diabetes and obesity. Thus, qPISA offers a versatile approach for profiling protease and especially exopeptidase specificity, facilitating insight into enzyme mechanisms and biotechnological and clinical applications.

biochemistry↗

bioRxiv2023

Structural insights into viral hijacking of p53 by E6 and E6AP

The E3-ubiquitin ligase E6AP degrades p53 when complexed with the viral protein E6 from human papilloma virus (HPV), which contributes to the transformation of cells in HPV-related cancers. Previous crystal structures of the E6AP-E6-p53 ternary complex have implicated a peptide containing an LxxLL motif from E6AP as the interface between the three proteins. However, the contributions to the ternary complex from the remainder of the E6AP protein remain unknown. We reexamined this complex using cryo-EM and full-length proteins and find additional protein interaction interfaces involving a previously uncharacterized domain of E6AP. Additionally, we observe that the ternary complex forms both 1:1:1 and 2:2:2 stochiometric complexes comprised of E6AP, E6 and p53.

biophysics↗

bioRxiv2023

The genetic architecture of protein interaction affinity and specificity

Proteins function in crowded cellular environments in which they must bind to specific target proteins but also avoid binding to many other off-target proteins. In large protein families this task is particularly challenging because many off-target proteins have very similar structures. How this specificity of physical protein-protein interactions in cellular networks is encoded and evolves is not very well understood. Here we address the question of specificity-encoding by comprehensively quantifying the effects of all mutations in one protein, JUN, on its binding to all other members of a protein family, the 54 human basic leucine zipper transcription factors. Fitting a global thermodynamic model to the data reveals that most affinity changing mutations equally affect JUNs propensity to bind to all its interaction partners. Mutations that alter the specificity of binding are much rarer. These specificity-altering mutations are, however, distributed throughout the JUN interaction interface. JUNs interaction specificity is encoded by both positive determinants that promote on-target interactions and negative determinants that prevent off-target interactions. Indeed, about half of the specificity-defining residues in JUN have dual functions and both promote on-target binding and prevent off-target binding. Whereas nearly all mutations that alter specificity are pleiotropic and also alter the affinity of binding to all interaction partners, the converse is not true with mutations outside of the interface able to tune affinity without affecting specificity. Our results provide the first global view of how mutations in a protein affect binding to all its potential interaction partners and reveal the distributed encoding of specificity and affinity in an interaction interface. They also show how the modular architecture of coiled-coils provides an elegant solution to the challenge of optimising specificity and affinity in a large protein family.

systems biology↗

bioRxiv2023

GUIFold - A graphical user interface for local AlphaFold2

GUIFold is a graphical user interface for the open-source structure prediction pipeline AlphaFold2. A particular emphasis lies on tracking prediction jobs in a database as well as user-friendly submission to queueing systems. GUIFold is built on top of a modified AlphaFold2 pipeline that primarily allows more control of the feature generation step. Additionally, the application provides an evaluation pipeline to rank predictions and visualize confidence metrics. GUIFold can be installed along with AlphaFold2 in a virtual environment and, after initial setup, allows running jobs without particular technical expertise.

bioinformatics↗