cpSRP43 is both highly flexible and stable: Structural insights using a combined experimental and computational approach

The novel multidomain protein, cpSRP43, is a unique subunit of the post-translational chloroplast signal recognition particle (cpSRP) targeting pathway in higher plants. The cpSRP pathway is responsible for targeting and insertion of light-harvesting chlorophyll a/b binding proteins (LHCPs) to the thylakoid membrane. Nuclear-encoded LHCPs are synthesized in the cytoplasm then imported into the chloroplast. Upon emergence into the stroma, LHCPs form a soluble transit complex with the cpSRP heterodimer, which is composed of cpSRP43 and cpSRP54, a 54 kDa subunit homologous to the universally conserved GTPase in cytosolic SRP pathways. cpSRP43 is irreplaceable as a chaperone to LHCPs in their translocation to the thylakoid membrane and remarkable in its ability to dissolve aggregates of LHCPs without the need for external energy input. In previous studies, cpSRP43 has demonstrated significant flexibility and interdomain dynamics. However, the high flexibility and structural dynamics of cpSRP43 is yet unexplained by current crystal structures of cpSRP43. This is due, in part, to the fact that free full length cpSRP43 is so flexible that it is unable to crystalize. In this study, we explore the structural stability of cpSRP43 under different conditions using various biophysical techniques and find that this protein is concurrently highly stable and flexible. This conclusion is interesting considering that stable proteins typically possess a non-dynamic structure. Molecular dynamics (MD) simulations which correlated with data from biophysical experimentation were used to explain the basis of the extraordinary stability of cpSRP43. This combination of biophysical data and microsecond-level MD simulations allows us to obtain a detailed perspective of the conformational landscape of these proteins.

A deep dive into VDAC1 conformational diversity using all-atom simulations provides new insights into the structural origin of the closed states

The voltage-dependent anion channel 1 (VDAC1) is a crucial mitochondrial transporter which controls the flow of ions and respiratory metabolites entering or exiting mitochondria. As a voltage-gated channel, VDAC1 can switch between a high conducting "open" state and low conducting "closed" states emerging at high transmembrane potential. Although cell homeostasis depends on channel gating to regulate the transport of ions and metabolites, structural hallmarks characterizing the closed states remain unknown. Here we performed microsecond accelerated molecular dynamics to highlight a vast region of VDAC1 conformational landscape accessible at typical voltage known to promote closure. Conformers exhibiting stable subconducting properties inherent to closed states were identified. In all cases, the low conductance was due to the particular positioning of an unfolded part of the N-terminus which obstructed the channel pore. While the N-terminal tail was found to be sensitive to voltage orientation, our low-conducting models suggest that closed states predominantly take place from disordered events and do not result from the displacement of a voltage sensor or a significant change in the pore. In addition, our results were consistent with conductance jumps observed in experiments and corroborates a recent study describing entropy as a key factor for VDAC gating.

Conformational dynamics of the Beta and Kappa SARS-CoV-2 spike proteins and their complexes with ACE2 receptor revealed by cryo-EM

The emergence of SARS-CoV-2 Kappa and Beta variants with enhanced transmissibility and resistance to neutralizing antibodies has created new challenges for the control of the ongoing COVID-19 pandemic. Understanding the structural nature of Kappa and Beta spike (S) proteins and their association with ACE2 is of significant importance. Here we present two cryo-EM structures for each of the Kappa and Beta spikes in the open and open-prone transition states. Compared with wild-type (WT) or G614 spikes, the two variant spikes appear more untwisted/open especially for Beta, and display a considerable population shift towards the open state as well as more pronounced conformational dynamics. Moreover, we capture four conformational states of the S-trimer/ACE2 complex for each of the two variants, revealing an enlarged conformational landscape for the Kappa and Beta S-ACE2 complexes and pronounced population shift towards the three RBDs up conformation. These results implicate that the mutations in Kappa and Beta may modify the kinetics of receptor binding and viral fusion to improve virus fitness. Combined with biochemical analysis, our structural study shows that the two variants are enabled to efficiently interact with ACE2 receptor despite their sensitive ACE2 binding surface is modified to escape recognition by some potent neutralizing MAbs. Our findings shed new light on the pathogenicity and immune evasion mechanism of the Beta and Kappa variants.

Modeling Alternate Conformations with Alphafold2 via Modification of the Multiple Sequence Alignment

The unprecedented performance of Deepmind's Alphafold2 in predicting protein structure in CASP XIV and the creation of a database of structures for multiple proteomes is reshaping structural biology. Moreover, the availability of Alphafold2's architecture and code has stimulated a number of questions on how to harness the capabilities of this remarkable tool. A question of central importance is whether Alphafold2's architecture is amenable to predict the intrinsic conformational heterogeneity of proteins. A general approach presented here builds on a simple manipulation of the multiple sequence alignment, via in silico mutagenesis, and subsequent modeling by Alphafold2. The approach is based in the concept that the multiple sequence alignment encodes for the structural heterogeneity, thus its rational manipulation will enable Alphafold2 to sample alternate conformations and potentially structural alterations due to point mutations. This modeling pipeline is benchmarked against canonical examples of protein conformational flexibility and applied to interrogate the conformational landscape of membrane proteins. This work broadens the applicability of Alphafold2 by generating multiple protein conformations to be tested biologically, biochemically, biophysically, and for use in structure-based drug design.

Computational Insights into the Unfolding of a Destabilized Superoxide Dismutase 1 Mutant

In this work, we investigate the β-barrel of superoxide dismutase 1 (SOD1) in a mutated form, the isoleucine 35 to alanine (I35A) mutant, commonly used as a model system to decipher the role of the full-length apoSOD1 protein in amyotrophic lateral sclerosis (ALS). It is known from experiments that the mutation reduces the stability of the SOD1 barrel and makes it largely unfolded in the cell at 37 degrees Celsius. We deploy state-of-the-art computational machinery to examine the thermal destabilization of the I35A mutant by comparing two widely used force fields, Amber a99SB-disp and CHARMM36m. We find that only the latter force field, when combined with the Replica Exchange with Solute Scaling (REST2) approach, reproduces semi-quantitatively the experimentally observed shift in the melting between the original and the mutated SOD1 barrel. In addition, we analyze the unfolding process and the conformational landscape of the mutant, finding these largely similar to those of the wildtype. Nevertheless, we detect an increased presence of partially misfolded states at ambient temperatures. These states, featuring conformational changes in the region of the β-strands β4−β6, might provide a pathway for nonnative aggregation.

Explore Protein Conformational Space With Variational Autoencoder

Molecular dynamics (MD) simulations have been actively used in the study of protein structure and function. However, extensive sampling in the protein conformational space requires large computational resources and takes a prohibitive amount of time. In this study, we demonstrated that variational autoencoders (VAEs), a type of deep learning model, can be employed to explore the conformational space of a protein through MD simulations. VAEs are shown to be superior to autoencoders (AEs) through a benchmark study, with low deviation between the training and decoded conformations. Moreover, we show that the learned latent space in the VAE can be used to generate unsampled protein conformations. Additional simulations starting from these generated conformations accelerated the sampling process and explored hidden spaces in the conformational landscape.