A Computational Approach to Investigate TDP-43 RNA-Recognition Motif 2 C-Terminal Fragments Aggregation in Amyotrophic Lateral Sclerosis

Abstract

Many of the molecular mechanisms underlying the pathological aggregation of proteins observed in neurodegenerative diseases are still not fully understood. Among the aggregate-associated diseases, Amyotrophic Lateral Sclerosis (ALS) is of relevant importance. In fact, although understanding the processes that cause the disease is still an open challenge, its relationship with protein aggregation is widely known. In particular, human TDP-43, an RNA/DNA binding protein, is a major component of the pathological cytoplasmic inclusions observed in ALS patients. Indeed, the deposition of the phosphorylated full-length TDP-43 in spinal cord cells has been widely studied. Moreover, it has also been shown that the brain cortex presents an accumulation of phosphorylated C-terminal fragments (CTFs). Even if it is debated whether the aggregation of CTFs represents a primary cause of ALS, it is a hallmark of TDP-43 related neurodegeneration in the brain. Here, we investigate the CTFs aggregation process, providing a computational model of interaction based on the evaluation of shape complementarity at the molecular interfaces. To this end, extensive Molecular Dynamics (MD) simulations were conducted for different types of protein fragments, with the aim of exploring the equilibrium conformations. Adopting a newly developed approach based on Zernike polynomials, able to find complementary regions in the molecular surface, we sampled a large set of solvent-exposed portions of CTFs structures as obtained from MD simulations. Our analysis proposes and assesses a set of possible association mechanisms between the CTFs, which could drive the aggregation process of the CTFs. To further evaluate the structural details of such associations, we perform molecular docking and additional MD simulations to propose possible complexes and assess their stability, focusing on complexes whose interacting regions are both characterized by a high shape complementarity and involve β3 and β5 strands at their interfaces.

Electrostatic complementarity at the interface drives transient protein-protein interactions

“…Finally, we propose a novel method to assess electrostatic complementarity without the need of having complex structures. Indeed, we already developed a novel computational protocol based on the Zernike polynomials to describe the shape of portions of the molecular surface in the form of a vector of numbers 16,[37][38][39][40] . Here, the method is extended to molecular surfaces for which the electrostatic potential has been calculated through the Poisson-Boltzmann equation 44 .…”

“…Following these results, we propose a new method able to quickly distinguish between interacting and non-interacting patches by describing their electrostatic potential projections with vectors and looking at the difference between these descriptors. This computational approach has been developed starting from the 2D Zernike method, that we proposed to quickly evaluate the shape complementarity at interfaces 16,[37][38][39][40][41] ; for what concerns shape complementarity, our method has already been demonstrated to be able to efficiently identify interacting regions by measuring the shape complementarity in terms of the Euclidean distance between the Zernike invariant descriptors associated with the projections of the molecular surfaces patches (see Methods for more details).Here, the Zernike invariant vectors describe the electrostatic potential by considering in the same function both positive and negative values. As a final step, we show that the Zernike descriptions of the electrostatic allow for fast and superposition-free discrimination between transient and permanent interactions.…”

Computational evidences of a misfolding event in an aggregation‐prone light chain preceding the formation of the non‐native pathogenic dimer

“…7 In the majority of these diseases, the aggregative molecular species can be identified in a small set of proteins whose features are welldefined. This is the case, for instance, of the amyloid β peptide in AD, 8 Superoxide Dismutase 1, TAR DNA-binding protein 43 and RNA-binding protein FUS in ALS [9][10][11] and Transthyretin in ATTR amyloidosis. 12 Interestingly, AL amyloidosis differs from this paradigm because the molecular aggregating species varies in each patient.…”

Dynamical changes of SARS‐CoV‐2 spike variants in the highly immunogenic regions impact the viral antibodies escaping

“…In this method, each protein surface region is associated with an ordered set of numerical descriptors, defining the shape geometry of that molecular patch. Such characterization, independent of the orientation of the protein, permits an easy comparison between molecular patches and has already proven its efficacy in the evaluation of patch similarity or complementarity 40–49 . In addition to this, we studied the local changes in chemical properties of S surface regions.…”

“…Such characterization, independent of the orientation of the protein, permits an easy comparison between molecular patches and has already proven its efficacy in the evaluation of patch similarity or complementarity. [40][41][42][43][44][45][46][47][48][49] In addition to this, we studied the local changes in chemical properties of S surface regions. Using the residue hydropathy scale introduced by Di Rienzo et al 50 each surface point can be indeed labeled with the value of hydrophobicity of the residues generating it.…”