Searching for Structure: Characterizing the Protein Conformational Landscape with Clustering-Based Algorithms.

The identification and characterization of the main conformations from a protein population are a challenging and inherently high-dimensional problem. Here, we evaluate the performance of the Secondary sTructural Ensembles with machine LeArning (StELa) double-clustering method, which clusters protein structures based on the relationship between the φ and ψ dihedral angles in a protein backbone and the secondary structure of the protein, thus focusing on the local properties of protein structures. The classification of states as vectors composed of the clusters' indices arising naturally from the Ramachandran plot is followed by the hierarchical clustering of the vectors to allow for the identification of the main features of the corresponding free energy landscape (FEL). We compare the performance of StELa with the established root-mean-squared-deviation (RMSD)-based clustering algorithm, which focuses on global properties of protein structures and with Combinatorial Averaged Transient Structure (CATS), the combinatorial averaged transient structure clustering method based on distributions of the φ and ψ dihedral angle coordinates. Using ensembles of conformations from molecular dynamics simulations of intrinsically disordered proteins (IDPs) of various lengths (tau protein fragments) or short fragments from a globular protein, we show that StELa is the clustering method that identifies many of the minima and relevant energy states around the minima from the corresponding FELs. In contrast, the RMSD-based algorithm yields a large number of clusters that usually cover most of the FEL, thus being unable to distinguish between states, while CATS does not sample well the FELs for long IDPs and fragments from globular proteins.

The quaternary question: Determining allostery in spastin through dynamics classification learning and bioinformatics.

The nanomachine from the ATPases associated with various cellular activities superfamily, called spastin, severs microtubules during cellular processes. To characterize the functionally important allostery in spastin, we employed methods from evolutionary information, to graph-based networks, to machine learning applied to atomistic molecular dynamics simulations of spastin in its monomeric and the functional hexameric forms, in the presence or absence of ligands. Feature selection, using machine learning approaches, for transitions between spastin states recognizes all the regions that have been proposed as allosteric or functional in the literature. The analysis of the composition of the Markov State Model macrostates in the spastin monomer, and the analysis of the direction of change in the top machine learning features for the transitions, indicate that the monomer favors the binding of ATP, which primes the regions involved in the formation of the inter-protomer interfaces for binding to other protomer(s). Allosteric path analysis of graph networks, built based on the cross-correlations between residues in simulations, shows that perturbations to a hub specific for the pre-hydrolysis hexamer propagate throughout the structure by passing through two obligatory regions: the ATP binding pocket, and pore loop 3, which connects the substrate binding site to the ATP binding site. Our findings support a model where the changes in the terminal protomers due to the binding of ligands play an active role in the force generation in spastin. The secondary structures in spastin, which are found to be highly degenerative within the network paths, are also critical for feature transitions of the classification models, which can guide the design of allosteric effectors to enhance or block allosteric signaling.

Design, Rationalization, and Automation of a Catalytic Sensing Mechanism for Homogeneous SERS Biosensors.

The current pandemic has shown that we need sensitive and deployable diagnostic technologies. Surface-enhanced Raman scattering (SERS) sensors can be an ideal solution for developing such advanced point-of-need (PON) diagnostic tests. Homogeneous (reagentless) SERS sensors work by directly responding to the target without any processing step, making them capable for simple one-pot assays, but their limitation is the achievable sensitivity, insufficient compared to what is needed for sensing of viral biomarkers. Noncovalent DNA catalysis mechanisms have been recently exploited for catalytic amplification in SERS assays. These advances used catalytic hairpin assembly (CHA) and other DNA self-assembly processes to develop sensing mechanisms with improved sensitivities. However, these mechanisms have not been used in OFF-to-ON homogeneous sensors, and they often target the same biomarker, likely due to the complexity of the mechanism design. There is still a strong need for a catalytic SERS sensor with a homogeneous mechanism and a rationalization of the catalytic sensing mechanism to translate this sensing strategy to different targets and applications. We developed and investigated a homogeneous SERS sensing mechanism that uses catalytic amplification based on DNA self-assembly. We systematically investigated the role of three domains in the fuel strand (internal loop, stem, and toehold), which drives the catalytic mechanism. The thermodynamic parameters determined in our studies were used to build an algorithm for automated design of catalytic sensors that we validated on target sequences associated with malaria and SARS-CoV-2 strains. With our mechanism, we were able to achieve an amplification level of 20-fold for conventional DNA and of 36-fold using locked nucleic acids (LNAs), with corresponding improvements observed in the sensor limit of detection (LOD). We also show a single-base sequence specificity for a sensor targeting a sequence associated with the omicron variant, tested against a delta variant target. This work on catalytic amplification of homogeneous SERS sensors has the potential to enable the use of this sensing modality in new applications, such as infectious disease surveillance, by improving the LOD while conserving the sensor's homogeneous character.

Microtubule Severing Enzymes Oligomerization and Allostery: A Tale of Two Domains.

Severing proteins are nanomachines from the AAA+ (ATPases associated with various cellular activities) superfamily whose function is to remodel the largest cellular filaments, microtubules. The standard AAA+ machines adopt hexameric ring structures for functional reasons, while being primarily monomeric in the absence of the nucleotide. Both major severing proteins, katanin and spastin, are believed to follow this trend. However, studies proposed that they populate lower-order oligomers in the presence of cofactors, which are functionally relevant. Our simulations show that the preferred oligomeric assembly is dependent on the binding partners and on the type of severing protein. Essential dynamics analysis predicts that the stability of an oligomer is dependent on the strength of the interface between the helical bundle domain (HBD) of a monomer and the convex face of the nucleotide binding domain (NBD) of a neighboring monomer. Hot spots analysis found that the region consisting of the HBD tip and the C-terminal (CT) helix is the only common element between the allosteric networks responding to nucleotide, substrate, and intermonomer binding. Clustering analysis indicates the existence of multiple pathways for the transition between the secondary structure of the HBD tip in monomers and the structure(s) it adopts in oligomers.

Molecular investigations into the unfoldase action of severing enzymes on microtubules.

Microtubule (MT)-associated proteins regulate the dynamic behavior of MTs during cellular processes. MT severing enzymes are the associated proteins which destabilize MTs by removing subunits from the lattice. One model for how severing enzymes remove tubulin dimers from the MT lattice is by unfolding its subunits through pulling on the carboxy-terminal tails of tubulin dimers. This model stems from the fact that severing enzymes are AAA+ unfoldases. To test this mechanism, we apply pulling forces on the carboxy-terminal regions of MT subunits using coarse grained molecular simulations. In our simulations, we used different MT lattices and concentrations of severing enzymes. We compare our simulation results with data from in vitro severing assays and find that the experimental data is best fit by a model of cooperative removal of protofilament fragments by severing enzymes, which depends on the severing enzyme concentration and placement on the MT lattice.