Biology subjects

Jandova, Z.

Publications and source records attributed to Jandova, Z..

3 recordsLinked to original sources

bioRxiv2024

Frustration in the Protein-Protein interface Plays a Central Role in the Cooperativity of PROTAC Ternary Complexes

Targeted protein degradation of a protein of interest (POI) by Proteolysis Targeting Chimeras (PROTACs) is an attractive approach for dealing with formerly undruggable protein targets. PROTACs are heterobifunctional molecules that connect a POI-binding and an E3-ligase (E3) binding motif with a linker. The simultaneous binding and formation of a ternary POI::PROTAC::E3 complex (TC) induces proximity between the POI and the E3, allowing for POI-ubiquitination and subsequent induction of proteasomal degradation. Despite the availability of many three-dimensional structures of TCs, unveiling the structure-function relationships for the design of PROTACs remains a challenge. This is because the TCs can be dynamic with a complex conformational landscape that individual crystal structures may not capture. In this work, we used SMARCA2 as the POI and VHL as the E3-ligase and solved the X-ray crystal structures of the respective ternary complexes with four different PROTACs. Molecular dynamics (MD) simulations were used to show that the SMARCA2-VHL interface is flexible with multiple energy minima. The protein-protein (POI-E3) interactions are largely formed by residues located in structurally disordered loops in both VHL and SMARCA2. The residue pairs in the SMARCA2-VHL interface are frustrated, i.e., adopt a suboptimal energetic state. The number of frustrated residue pairs averaged over the MD ensemble shows a positive correlation with the experimentally determined cooperativity of the PROTACs. This indicates that protein-protein interface frustration can play an important role in PROTAC function. The TC ensembles of VHL, SMARCA2 and 11 different PROTACs were modeled by comparative modeling followed by MD. The frustration was subsequently calculated from the MD trajectories and correlated with the cooperativity. We found that identification of the dynamic protein-protein contacts and frustrated residue pairs in the interface can provide a rational framework for the structure-based design of PROTACs.

biophysics↗

bioRxiv2024

Integrative modeling in the age of machine learning: a summary of HADDOCK strategies in CAPRI rounds 47-55

The HADDOCK team participated in CAPRI rounds 47-55 as both server, manual predictor, and scorers. Throughout these CAPRI rounds, we used a plethora of computational strategies to predict the structure of protein complexes. Of the 10 targets comprising 24 interfaces, we achieved acceptable or better models for 3 targets in the human category and 1 in the server category. Our performance in the scoring challenge was slightly better, with our simple scoring protocol being the only one capable of identifying an acceptable model for Target 234. This result highlights the robustness of the simple, fully physics-based HADDOCK scoring function, especially when applied to highly flexible antibody-antigen complexes. Inspired by the significant advances in machine learning for structural biology and the dramatic improvement in our success rates after the public release of Alphafold2, we identify the integration of classical approaches like HADDOCK with AI-driven structure prediction methods as a key strategy for improving the accuracy of model generation and scoring.

bioinformatics↗

bioRxiv2021

Native or non-native protein-protein docking models? Molecular dynamics to the rescue.

Molecular docking excels at creating a plethora of potential models of protein-protein complexes. To correctly distinguish the favourable, native-like models from the remaining ones remains, however, a challenge. We assessed here if a protocol based on molecular dynamics (MD) simulations would allow to distinguish native from non-native models to complement scoring functions used in docking. To this end, first models for 25 protein-protein complexes were generated using HADDOCK. Next, MD simulations complemented with machine learning were used to discriminate between native and non-native complexes based on a combination of metrics reporting on the stability of the initial models. Native models showed higher stability in almost all measured properties, including the key ones used for scoring in the CAPRI competition, namely the positional root mean square deviations and fraction of native contacts from the initial docked model. A Random Forest classifier was trained, reaching 0.85 accuracy in correctly distinguishing native from non-native complexes. Reasonably modest simulation lengths in the order of 50 to 100 ns are already sufficient to reach this accuracy, which makes this approach applicable in practice.

bioinformatics↗