Welcome back to my post-conference note. Let’s jump right into the third session on “Interpreting Experiments Through Molecular Simulations“ including talks by Ceclilia Clementi, Michael Feig and Arianna Fornili on Saturday afternoon and with Shang-Te Danny Hsu, Jana Selent and Massimiliano Bonomi on Sunday morning.
The challenges of computational biophysics aiming to bridge molecular and cellular studies is among others due to the characterization of macromolecular systems by sets of different timescales, separated by large gaps. Celilia Clementi tackles this challenge by combining coherent state analysis and Markov State Modeling. Furthermore, she introduced a theoretical framework for the optimal combination of simulation and experiments in the definition of simplified coarse-grained Hamiltonian protein models. As those simplified models loose information, the combination with experimental data makes them more realistic and provides larger time-scales. She further illustrated the method and mentioned as application the coarse-graine model of FIP35.
Next, Michael Feig investigated protein dynamics and stability with crowders in simulations and experiments. He states protein destabilization in Villin due to crowding, in order to explain the differing results from NMR vs. MD. With Villin under dilute conditions, he observed oligomer formation in MD, whereas transient oligomers form on timescales longer than rotational translation diffusion. Rotational diffusion is slowed down by additional factors. In addition to his study on Villin, he could identify transitions between conformations connecting all states of Bacterial genomic DNA, based on targeted MD.
The last talk of this Saturday was given by Arianna Fornili about identification of rescue sites for protein function. As rescue mutations can be mimicked by drugs, their locations is of interest for drug design wherefore she developed a method. Double Force Scanning (DFS) mimics mutations by external forces, using an elastic network model to represent protein dynamics. In oder to model structural perturbations, linear response theory is used. In detail, she used the Fibonacci lattice for uniform distribution for force vectors. The performance of DFS was tested on p53, predicting 80% non-rescue sides correctly, and on an evolutionary dataset, where 79% of evolutionary rescue sites where predicted correctly.
Subsequently after Ariannas talk and a short coffee break, the first poster session was started. Although it was very crowed (which might have been a live experiments from the organizers, fitting to the last session), posters of high interests have been presented, leaving the presenters no break for a small sip of water. Afterwards, the program was open for all those aiming to explore the city of Berlin. On Sunday morning, the third session was continued with talks from Shang-Te Danny Hsu, Jana Selent and Massimiliano Bonomi.
The first session of this Sunday started with a rather biological topic on the structural basis of substrate recognition and chaperone activity of ribosome-associated trigger factor (TF) regulated by monomer-dimer-equilibrium from Shang-Te Danny Hsu. As the structure contains highly dynamic regions, those parts could not be easily resolved. This was also confirmed by solid state NMR studies, showing ribosome binding-induced conformational change in the ribosome binding domain of TF (TF-RBD). Shang-Te revealed TF substrate specificity by peptide array analysis, originating from recognition of averaged property rather than an exact sequence. Produced SAXS data showed a misfit between those and the crystal structure of TF. Further studies using pulse dipolar ESR spectroscopy revealed multiple dimer configurations of TF, which could be identified by chemical cross-linking. Furthermore, he modeled a dynamical system using several different techniques such as crystallography, NMR, SAXS. Comment: good luck for your PhD student 😉
Next, Jana Selent presented her work on the functional dynamics of the distal C-tail of arrestin. As introduced by her, phosphorylation of the GPCR C-tail triggers the arrestin pre-complex, including a partial C-tail displacement of arrestin. To investigate the role of the distal C-tail, she performed all-atom MD simulations and site-directed mutagenesis studies. She described its conformational space with preference to bind to positively charged residues. Tryptophan-induced dynamic quenching was increased for some residues. Furthermore, she investigated the mechanism of IP6-induced displacement, where IP6 displaces residue 393 to 400 but not further down upon binding to GPCR. As second topic, she investigated the C-edge loop of arrestin. By MD simulations, she showed that C-edge loop of pre-activated arrestin was able to penetrate the membrane, in contrast to activated arrestin. This was confirmed by quenching mutagenesis experiments. Finally, she postulates a step-wise binding process from unbound GPCR over an arrestin-GPCR pre-complex including the displacement of the distal C-tail and the penetration of the C-edge into the membrane, followed by the high affinity complex.
The last talk for this session was hold by Massimiliano Bonomi on integrative structural and dynamical biology with PLUMED-ISDB. As computational and experimental technique have their challenges or errors, a hybrid or integrative method could provide a more realistic view. By providing the module PLUMED-ISDB, he presents a way to investigate heterogeneous systems by including experimental data with a priori information. It uses a Bayesian inference method which accounts for data noise and averaged ensembles. He applied this metainference approach to cryo-EM data, able to explain the data better with a lower resolution as a mixture of dynamics and noise.
At last, he addresses challenges to the community, to those I could not agree more and therefore will end my post with his wishes: More distribution of ensembles of the community like structural models, model populations and protocols; the establishment of robust methods for ensemble comparison and validation; and a way to facilitate comparison of different ensemble modeling approaches by sharing methodologies.