In this review, we focus on the biophysical and structural aspects of the oligomeric states of physiologically intrinsically disordered proteins and peptides tau, amyloid-β and α-synuclein and partly disordered prion protein and their isolations from animal models and human brains. These protein states may be the most toxic agents in the pathogenesis of Alzheimer's and Parkinson's disease. It was shown that oligomers are important players in the aggregation cascade of these proteins. The structural information about these structural states has been provided by methods such as solution and solid-state NMR, cryo-EM, crosslinking mass spectrometry, AFM, TEM, etc., as well as from hybrid structural biology approaches combining experiments with computational modelling and simulations. The reliable structural models of these protein states may provide valuable information for future drug design and therapies.

In this review, we focus on the biophysical and structural aspects of the oligomeric states of physiologically intrinsically disordered proteins and peptides tau, amyloid-β and α-synuclein and partly disordered prion protein and their isolations from animal models and human brains. These protein states may be the most toxic agents in the pathogenesis of Alzheimer’s and Parkinson’s disease. It was shown that oligomers are important players in the aggregation cascade of these proteins. The structural information about these structural states has been provided by methods such as solution and solid-state NMR, cryo-EM, crosslinking mass spectrometry, AFM, TEM, etc., as well as from hybrid structural biology approaches combining experiments with computational modelling and simulations. The reliable structural models of these protein states may provide valuable information for future drug design and therapies.

Coarse-grained (CG) molecular dynamics simulations of integral membrane proteins have gained wide popularity because they provide a cost-effective but still accurate description of the protein-membrane interactions as a whole and on the role of individual lipidic species. Therefore, they can provide biologically meaningful information at a resolution comparable to those accessible to experimental techniques. However, the simulation of membrane proteins remains a challenging task that requires specific expertise, as external pressures and solvation need to be carefully handled. CG simulations that lump several water molecules into one single supramolecular moiety may present further intricacies due to bulkier solvent representations or model-dependent compressibilities. This chapter provides a detailed protocol for setting up, running, and analyzing CG simulations of membrane proteins using the SIRAH force field for CG simulations within the AMBER package.

The small soluble aggregates of Aβ1-42 are broadly documented as potential targets for the development of new compounds with the capacity to inhibit the early stages of Alzheimer´s disease. Nevertheless, Aβ1-42 peptides show an intrinsically disordered character with a high propensity for aggregation, which complicates the identification of conserved structural patterns. Because of this, experimental techniques find substantial difficulties in the characterization of such soluble oligomers. Theoretical techniques, such as molecular dynamics (MD) simulations, provide a possible workaround for this problem. However, the computational cost associated with comprehensively sampling the vast conformational space accessible to these peptides might become prohibitive. In this sense, coarse-grained (CG) simulations can effectively overcome that hurdle at a fraction of the computational cost. In this dataset, we furnish an extensive collection of Aβ1-42 peptides in dimeric conformation generated with the SIRAH force field for CG MD simulation. It comprises 25 independent trajectories in .xtc (gromacs) format of Aβ1-42 couples of peptides that evolve towards dimeric states along eleven µs-long unbiased simulations. Thanks to the backmapping capabilities of our force field, pseudo atomistic coordinates can be straightforwardly recovered from MD trajectories reported here and analyzed with popular molecular editing programs. This set of simulations performed at room conditions and physiological salt concentrations may furnish a complete collection of inter-peptide interfaces that can be used in high-throughput docking or as new starting states for peptide oligomerization seeding of Aβ1-42 dimerization.

DATA REPORT article Front. Med. Technol., 15 February 2021Sec. Pharmaceutical Innovation Volume 3 - 2021 | https://doi.org/10.3389/fmedt.2021.644039

Apolipoprotein A-I (apoA-I) has a key function in the reverse cholesterol transport. However, aggregation of apoA-I single point mutants can lead to hereditary amyloid pathology. Although several studies have tackled the biophysical and structural consequences introduced by these mutations, there is little information addressing the relationship between the evolutionary and structural features that contribute to the amyloid behavior of apoA-I. We combined evolutionary studies, in silico mutagenesis and molecular dynamics (MD) simulations to provide a comprehensive analysis of the conservation and pathogenic role of the aggregation-prone regions (APRs) present in apoA-I. Sequence analysis demonstrated that among the four amyloidogenic regions described for human apoA-I, only two (APR1 and APR4) are evolutionary conserved across different species of Sarcopterygii. Moreover, stability analysis carried out with the FoldX engine showed that APR1 contributes to the marginal stability of apoA-I. Structural properties of full-length apoA-I models suggest that aggregation is avoided by placing APRs into highly packed and rigid portions of its native fold. Compared to silent variants extracted from the gnomAD database, the thermodynamic and pathogenic impact of amyloid mutations showed evidence of a higher destabilizing effect. MD simulations of the amyloid variant G26R evidenced the partial unfolding of the alpha-helix bundle with the concomitant exposure of APR1 to the solvent, suggesting an insight into the early steps involved in its aggregation. Our findings highlight APR1 as a relevant component for apoA-I structural integrity and emphasize a destabilizing effect of amyloid variants that leads to the exposure of this region.

Poly glutamine and glutamine-rich peptides play a central role in a plethora of pathological aggregation events. However, biophysical characterization of soluble oligomers -the most toxic species involved in these processes- remains elusive due to their structural heterogeneity and dynamical nature. Here, we exploit the high spatio-temporal resolution of coarse-grained simulations as a computational microscope to characterize the aggregation propensity and morphology of a series of polyglutamine and glutamine-rich peptides. Comparative analysis of ab-initio aggregation pinpointed a double role for glutamines. In the first phase, glutamines mediate seeding by pairing monomeric peptides, which serve as primers for higher-order nucleation. According to the glutamine content, these low molecular-weight oligomers may then proceed to create larger aggregates. Once within the aggregates, buried glutamines continue to play a role in their maturation by optimizing solvent-protected hydrogen bonds networks.

The challenges posed by intrinsically disordered proteins (IDPs) to atomistic and coarse-grained (CG) simulations are boosting efforts to develop and reparametrize current force fields. An assessment of the dynamical behavior of IDPs' and unstructured peptides with the CG SIRAH force field suggests that the current version achieves a fair description of IDPs' conformational flexibility. Moreover, we found a remarkable capability to capture the effect of point mutations in loosely structured peptides.

Protein-lipid interactions modulate a plethora of physiopathologic processes and have been the subject of countless studies. However, these kinds of interactions in the context of viral envelopes have remained relatively unexplored, partially because the intrinsically small dimensions of the molecular systems escape to the current resolution of experimental techniques. However, coarse-grained and multiscale simulations may fill that niche, providing nearly atomistic resolution at an affordable computational price. Here we use multiscale simulations to characterize the lipid-protein interactions in the envelope of the Zika Virus, a prominent member of the Flavivirus genus. Comparisons between the viral envelope and simpler molecular systems indicate that the viral membrane is under extreme pressures and asymmetric forces. Furthermore, the dense net of protein-protein contacts established by the envelope proteins creates poorly solvated regions that destabilize the external leaflet leading to a decoupled dynamics between both membrane layers. These findings lead to the idea that the Flaviviral membrane may store a significant amount of elastic energy, playing an active role in the membrane fusion process.

This dataset contains a collection of molecular dynamics (MD) simulations of polyglutamine (polyQ) and glutamine-rich (Q-rich) peptides in the multi-microsecond timescale. Primary data from coarse-grained simulations performed using the SIRAH force field has been processed to provide fully atomistic coordinates. The dataset encloses MD trajectories of polyQs of 4 (Q4), 11 (Q11), and 36 (Q36) amino acids long. In the case of Q11, simulations in presence of Q5 and QEQQQ peptides, which modulate aggregation, are also included. The dataset also comprises MD trajectories of the gliadin related p31-43 peptide, and Insulin's C-peptide at pH=7 and pH=3.2, which constitute examples of Q-rich and Q-poor aggregating peptides. The dataset grants molecular insights on the role of glutamines in spontaneous and unbiased ab-initio aggregation of a series of peptides using a homogeneous set of simulations [1]. The trajectory files are provided in Protein Data Bank (PDB) format containing the Cartesian coordinates of all heavy atoms in the aggregating peptides. Further analyses of the trajectories can be performed directly using any molecular visualization/analysis software suites.

Celiac disease (CeD) is a highly prevalent chronic immune‐mediated enteropathy developed in genetically predisposed individuals after ingestion of a group of wheat proteins (called gliadins and glutenins). The 13mer α‐gliadin peptide, p31‐43, induces proinflammatory responses, observed by in vitro assays and animal models, that may contribute to innate immune mechanisms of CeD pathogenesis. Since a cellular receptor for p31‐43 has not been identified, this raises the question of whether this peptide could mediate different biological effects. In this work, we aimed to characterize the p31‐43 secondary structure by different biophysical and in silico techniques. By dynamic light scattering and using an oligomer/fibril‐sensitive fluorescent probe, we showed the presence of oligomers of this peptide in solution. Furthermore, atomic force microscopy analysis showed p31‐43 oligomers with different height distribution. Also, peptide concentration had a very strong influence on peptide self‐organization process. Oligomers gradually increased their size at lower concentration. Whereas, at higher ones, oligomers increased their complexity, forming branched structures. By CD, we observed that p31‐43 self‐organized in a polyproline II conformation in equilibrium with β‐sheets‐like structures, whose pH remained stable in the range of 3–8. In addition, these findings were supported by molecular dynamics simulation. The formation of p31‐43 nanostructures with increased β‐sheet structure may help to explain the molecular etiopathogenesis in the induction of proinflammatory effects and subsequent damage at the intestinal mucosa in CeD.

This dataset contains the trajectory of a 10 microseconds-long coarse-grained molecular dynamics simulation of SARS-CoV2 Nucleocapsid protein N-terminal RNA binding domain (PDB id:6M3M). Simulations have been performed using the SIRAH force field running with the Amber18 package at the Uruguayan National Center for Supercomputing (ClusterUY) under the conditions reported in Machado et al. JCTC 2019, adding 150 mM NaCl according to Machado & Pantano JCTC 2020. The files 6M3M_SIRAHcg_rawdata.tar contains all the raw information required to visualize (on VMD), analyze, backmap, and eventually continue the simulations using Amber18 or higher. Step-By-Step tutorials for running, visualizing, and analyzing CG trajectories using SirahTools can be found at www.sirahff.com. Additionally, the file 6M3M_SIRAHcg_10us_prot.tar contains only the protein coordinates, while 6M3M_SIRAHcg_10us_prot_skip10ns.tar contains one frame every 10ns. To take a quick look at the trajectory: 1- Untar the file 6M3M_SIRAHcg_10us_prot_skip10ns.tar 2- Open the trajectory on VMD using the command line: vmd 6W4B_SIRAHcg_prot.prmtop 6W4B_SIRAHcg_prot.ncrst 6W4B_SIRAHcg_prot_10us_skip10ns.nc -e sirah_vmdtk.tcl Note that you can use normal VMD drawing methods as vdw, licorice, etc., and coloring by restype, element, name, etc. This dataset is part of the SIRAH-CoV2 initiative. For further details, please contact Florencia Klein (fklein@pasteur.edu.uy) or Sergio Pantano (spantano@pasteur.edu.uy).

This dataset contains the trajectory of a 10 microseconds-long coarse-grained molecular dynamics simulation of the co-factor complex of NSP7 and the C-terminal domain of NSP8 from SARS CoV-2 (PDBid:6WIQ). Simulations have been performed using the SIRAH force field running with the Amber18 package at the Uruguayan National Center for Supercomputing (ClusterUY) under the conditions reported in Machado et al. JCTC 2019, adding 150 mM NaCl according to Machado & Pantano JCTC 2020. The files 6WIQ_SIRAHcg_rawdata_0-5us.tar, and 6WIQ_SIRAHcg_rawdata_5-10us.tar, contain all the raw information required to visualize (on VMD), analyze, backmap, and eventually continue the simulations using Amber18 or higher. Step-By-Step tutorials for running, visualizing, and analyzing CG trajectories using SirahTools can be found at www.sirahff.com. Additionally, the file 6WIQ_SIRAHcg_10us_prot.tar contains only the protein coordinates, while 6WIQ_SIRAHcg_10us_prot_skip10ns.tar contains one frame every 10ns. To take a quick look at the trajectory: 1- Untar the file 6WIQ_SIRAHcg_10us_prot_skip10ns.tar 2- Open the trajectory on VMD using the command line: vmd 6WIQ_SIRAHcg_prot.prmtop 6WIQ_SIRAHcg_prot.ncrst 6WIQ_SIRAHcg_10us_prot_skip10ns.nc -e sirah_vmdtk.tcl Note that you can use normal VMD drawing methods as vdw, licorice, etc., and coloring by restype, element, name, etc. This dataset is part of the SIRAH-CoV2 initiative. For further details, please contact Florencia Klein (fklein@pasteur.edu.uy) or Sergio Pantano (spantano@pasteur.edu.uy).

This dataset contains the trajectory of a 10 microseconds-long coarse-grained molecular dynamics simulation of SARS-CoV2 ORF7a encoded accessory protein (PDB id: 6W37). Simulations have been performed using the SIRAH force field running with the Amber18 package at the Uruguayan National Center for Supercomputing (ClusterUY) under the conditions reported in Machado et al. JCTC 2019, adding 150 mM NaCl according to Machado & Pantano JCTC 2020. The file 6W37_SIRAHcg_rawdata.tar contains all the raw information required to visualize (on VMD), analyze, backmap, and eventually, continue the simulations using Amber18 or higher. Step-By-Step tutorials for running, visualizing, and analyzing CG trajectories using SirahTools can be found at www.sirahff.com. Additionally, the file 6W37_SIRAHcg_10us_prot.tar contains only the protein coordinates, while 6W37_SIRAHcg_10us_prot_skip10ns.tar contains one frame every 10ns. To take a quick look at the trajectory: 1- Untar the file 6W37_SIRAHcg_10us_prot_skip10ns.tar 2- Open the trajectory on VMD using the command line: vmd 6w37_SIRAHcg_prot.prmtop 6w37_SIRAHcg_prot.ncrst 6w37_SIRAHcg_prot_10us_skip10ns.nc -e sirah_vmdtk.tcl Note that you can use normal VMD drawing methods as vdw, licorice, etc., and coloring by restype, element, name, etc. This dataset is part of the SIRAH-CoV2 initiative. For further details, please contact Martín Soñora (msonora@pasteur.edu.uy) or Sergio Pantano (spantano@pasteur.edu.uy).

This dataset contains the trajectory of a 10 microseconds-long coarse-grained molecular dynamics simulation of the SARS-CoV2 ORF3a dimeric transmembrane protein (PDB id: 6XDC, Bioassembly 1) embedded in a membrane patch containing POPE, POPC, and POPS phospholipids in a 2:1:1 proportion. Simulations have been performed using the SIRAH force field running with the Amber18 package at the Uruguayan National Center for Supercomputing (ClusterUY) under the conditions reported in Barrera et al. JCTC 2019, adding 150 mM NaCl according to Machado & Pantano JCTC 2020. The files contain all the raw information required to visualize (on VMD), analyze, backmap, and eventually continue the simulations using Amber18 or higher. Step-By-Step tutorials for running, visualizing, and analyzing CG trajectories using SirahTools can be found at www.sirahff.com. Additionally, The files 6xdc_SIRAHcg_rawdata_0-2us.tar, 6xdc_SIRAHcg_rawdata_2-4us.tar, 6xdc_SIRAHcg_rawdata_4-6us.tar, 6xdc_SIRAHcg_rawdata_6-8us.tar, and 6xdc_SIRAHcg_rawdata_8-10us.tar contain all the raw information required to visualize (on VMD), analyze, backmap, and eventually continue the simulations using Amber18 or higher. Step-By-Step tutorials for running, visualizing, and analyzing CG trajectories using SirahTools can be found at www.sirahff.com. Additionally, the file 6XDC_SIRAHcg_10us_prot-memb_skip10ns.tar contains only the protein and phospholipids´ coordinates, with one frame every 10ns. To take a quick look at the trajectory: 1- Untar the file 6xdc_SIRAHcg_10us_prot-memb_skip10ns.tar 2- Open the trajectory on VMD using the command line: vmd 6xdc_SIRAHcg_prot-memb.prmtop 6xdc_SIRAHcg_prot-memb.ncrst 6xdc_SIRAHcg_10us_prot-memb_skip10ns.nc -e sirah_vmdtk.tcl Note that you can use normal VMD drawing methods as vdw, licorice, etc., and coloring by restype, element, name, etc. This dataset is part of the SIRAH-CoV2 initiative. For further details, please contact Exequiel Barrera (ebarrera@pasteur.edu.uy) or Sergio Pantano (spantano@pasteur.edu.uy).

This dataset contains the trajectory of a 10 microseconds-long coarse-grained molecular dynamics simulation of a Spike's RBD from SARS-CoV2 glycosylated at Asn331, 343, and 481 with Man9 glycosylation trees. The initial coordinates correspond to amino acids 327 to 532 taken from the PDB structure 6XEY. Missing loops and glycosylation trees were added with CHARMM-GUI (http://www.charmm-gui.org). Simulations have been performed using the SIRAH force field running with the Amber18 package at the Uruguayan National Center for Supercomputing (ClusterUY) under the conditions reported in Machado et al. JCTC 2019, adding 150 mM NaCl according to Machado & Pantano JCTC 2020. Glycan parameters correspond to those reported by Garay et al. The files 6XEY-RBD-3Man9_SIRAHcg_0-4us.tar, 6XEY-RBD-3Man9_SIRAHcg_4-8us.tar, and 6XEY-RBD-3Man9_SIRAHcg_8-10us.tar, contain all the raw information required to visualize (on VMD), analyze, backmap, and eventually continue the simulations using Amber18 or higher. Step-By-Step tutorials for running, visualizing, and analyzing CG trajectories using SirahTools can be found at www.sirahff.com. Additionally, the file 6XEY-RBD-3Man9_SIRAHcg_glycoprot_10us.tar contains only the protein coordinates, while 6XEY-RBD-3Man9_SIRAHcg_glycoprot_skip10ns.tar contains one frame every 10ns. To take a quick look at the trajectory: 1- Untar the file 6XEY-RBD-3Man9_SIRAHcg_glycoprot_skip10ns.tar 2- Open the trajectory on VMD using the command line: vmd 6XEY-RBD-3Man9_SIRAHcg_glycoprot.prmtop 6XEY-RBD-3Man9_SIRAHcg_glycoprot.ncrst 6XEY-RBD-3Man9_SIRAHcg_10us_skip10ns.nc -e sirah_vmdtk.tcl Note that you can use normal VMD drawing methods as vdw, licorice, etc., and coloring by restype, element, name, etc. This dataset is part of the SIRAH-CoV2 initiative. For further details, please contact Pablo Garay (pgaray@pasteur.edu.uy) or Sergio Pantano (spantano@pasteur.edu.uy).

This dataset contains the trajectory of a 10 microseconds-long coarse-grained molecular dynamics simulation of SARS-CoV2 Papain-like protease in its APO form with Zn ions bound (PDB id: 6W9C). Simulations have been performed using the SIRAH force field running with the Amber18 package at the Uruguayan National Center for Supercomputing (ClusterUY) under the conditions reported in Machado et al. JCTC 2019, adding 150 mM NaCl according to Machado & Pantano JCTC 2020. Zinc ions were parameterized as reported in Klein et al. 2020. The files 6W9C_SIRAHcg_rawdata_0-5us.tar, 6W9C_SIRAHcg_rawdata_5-10us.tar, contain all the raw information required to visualize (on VMD), analyze, backmap, and eventually continue the simulations using Amber18 or higher. Step-By-Step tutorials for running, visualizing, and analyzing CG trajectories using SirahTools can be found at www.sirahff.com. Additionally, the file 6W9C_SIRAHcg_10us_prot.tar contains only the protein coordinates, while 6W9C_SIRAHcg_10us_prot_skip10ns.tar contains one frame every 10ns. To take a quick look at the trajectory: 1- Untar the file 6W9C_SIRAHcg_10us_prot_skip10ns.tar 2- Open the trajectory on VMD using the command line: vmd 6W9C_SIRAHcg_prot.prmtop 6W9C_SIRAHcg_prot.ncrst 6W9C_SIRAHcg_10us_prot_skip10ns.nc -e sirah_vmdtk.tcl Note that you can use normal VMD drawing methods as vdw, licorice, etc., and coloring by restype, element, name, etc. This dataset is part of the SIRAH-CoV2 initiative. For further details, please contact Florencia Klein (fklein@pasteur.edu.uy) or Sergio Pantano (spantano@pasteur.edu.uy).

Trypanothione synthetase (TryS) produces N1,N8-bis(glutathionyl)spermidine (or trypanothione) at the expense of ATP. Trypanothione is a metabolite unique and essential for survival and drug-resistance of trypanosomatid parasites. In this study, we report the mechanistic and biological characterisation of optimised N5-substituted paullone analogues with anti-TryS activity. Several of the new derivatives retained submicromolar IC50 against leishmanial TryS. The binding mode to TryS of the most potent paullones has been revealed by means of kinetic, biophysical and molecular modelling approaches. A subset of analogues showed an improved potency (EC50 0.5–10 µM) and selectivity (20–35) against the clinically relevant stage of Leishmania braziliensis (mucocutaneous leishmaniasis) and L. infantum (visceral leishmaniasis). For a selected derivative, the mode of action involved intracellular depletion of trypanothione. Our findings shed light on the molecular interaction of TryS with rationally designed inhibitors and disclose a new set of compounds with on-target activity against different Leishmania species.

This dataset contains the trajectory of a 10 microseconds-long coarse-grained molecular dynamics simulation of SARS-CoV2 NSP9 RNA binding protein (PDB id: 6W4B, Bioassembly 1). Simulations have been performed using the SIRAH force field running with the Amber18 package at the Uruguayan National Center for Supercomputing (ClusterUY) under the conditions reported in Machado et al. JCTC 2019, adding 150 mM NaCl according to Machado & Pantano JCTC 2020. The file 6W4B_SIRAHcg_rawdata.tar contains all the raw information required to visualize (on VMD), analyze, backmap, and eventually continue the simulations using Amber18 or higher. Step-By-Step tutorials for running, visualizing, and analyzing CG trajectories using SirahTools can be found at www.sirahff.com. Additionally, the file 6W4B_SIRAHcg_10us_prot.tar contains only the protein coordinates, while 6W4B_SIRAHcg_10us_prot_skip10ns.tar contains one frame every 10ns. To take a quick look at the trajectory: 1- Untar the file 6W4B_SIRAHcg_10us_prot_skip10ns.tar 2- Open the trajectory on VMD using the command line: vmd 6W4B_SIRAHcg_prot.prmtop 6W4B_SIRAHcg_prot.ncrst 6W4B_SIRAHcg_prot_10us_skip10ns.nc -e sirah_vmdtk.tcl Note that you can use normal VMD drawing methods as vdw, licorice, etc., and coloring by restype, element, name, etc. This dataset is part of the SIRAH-CoV2 initiative. For further details, please contact Sergio Pantano (spantano@pasteur.edu.uy).