High-resolution visualisation of the states and pathways sampled in molecular dynamics simulations

Disrupting Dimeric β-Amyloid by Electric Fields

The early oligomers of the amyloid Aβ peptide are implicated in Alzheimer's disease, but their transient nature complicates the characterization of their structure and toxicity. Here, we investigate the stability of the minimal toxic species, i.e., β-amyloid dimers, in the presence of an oscillating electric field. We first use deep learning (AlphaFold-multimer) for generating initial models of Aβ42 dimers. The flexibility and secondary structure content of the models are then analyzed by multiple runs of molecular dynamics (MD). Structurally stable models are similar to ensemble representatives from microsecond-long MD sampling. Finally, we employ the validated model as the starting structure of MD simulations in the presence of an external oscillating electric field and observe a fast decay of β-sheet content at high field strengths. Control simulations using the helical dimer of the 42-residue leucine zipper peptide show higher structural stability than the Aβ42 dimer. The simulation results provide evidence that an external electric field (oscillating at 1 GHz) can disrupt amyloid oligomers which should be further investigated by experiments with brain organoids in vitro and eventually in vivo.

Silybins inhibit human IAPP amyloid growth and toxicity through stereospecific interactions

Type 2 Diabetes is a major public health threat, and its prevalence is increasing worldwide. The abnormal accumulation of islet amyloid polypeptide (IAPP) in pancreatic β-cells is associated with the onset of the disease. Therefore, the design of small molecules able to inhibit IAPP aggregation represents a promising strategy in the development of new therapies. Here we employ in vitro, biophysical, and computational methods to inspect the ability of Silybin A and Silybin B, two natural diastereoisomers extracted from milk thistle, to interfere with the toxic self-assembly of human IAPP (hIAPP). We show that Silybin B inhibits amyloid aggregation and protects INS-1 cells from hIAPP toxicity more than Silybin A. Molecular dynamics simulations revealed that the higher efficiency of Silybin B is ascribable to its interactions with precise hIAPP regions that are notoriously involved in hIAPP self-assembly i.e., the S20-S29 amyloidogenic core, H18, the N-terminal domain, and N35. These results highlight the importance of stereospecific ligand-peptide interactions in regulating amyloid aggregation and provide a blueprint for future studies aimed at designing Silybin derivatives with enhanced drug-like properties.

Molecular dynamics analysis of the structural properties of the transglutaminases of Kutzneria albida and Streptomyces mobaraensis

The microbial transglutaminase (TGase) from Streptomyces mobaraensis (MTGase) is widely used for industrial applications. However, in the last decades, TGases from other bacteria have been described. We focused our attention on TGase, from Kutzneria albida (KalbTGase), recently characterized as more selective than MTGase and proposed for applications in drug delivery. By comparison of the crystallographic structures, the volume of the catalytic site results smaller in KalbTGase. We compared KalbTGase and MTGase structural flexibility by molecular dynamics (MD) simulations at different conditions. KalbTGase is more rigid than MTGase at 300 K, but the catalytic site has a preserved conformation in both structures. Preliminary studies at higher temperatures suggest that KalbTGase acquires enhanced conformational flexibility far from the active site region. The volume of the catalytic active site pocket of KalbTGase at room temperature is smaller than that of MTGase, and decreases at 335 K, remaining stable after further temperature increase. On the contrary, in MTGase the pocket volume continues to decrease as the temperature increases. Overall, the results of our study suggest that at room temperature the enhanced specificity of KalbTGase could be related to a more closed catalytic pocket and lower flexibility than MTGase. Moreover, by preliminary results at higher temperature, KalbTGase structural flexibility suggests an adaptability to different substrates not recognized at room temperature. Lower adaptability of MTGase at higher temperature with a reduction of the catalytic pocket, instead, suggests a reduction of its activity.

Unsupervised Methods for Detection of Neural States: Case Study of Hippocampal-Amygdala Interactions

The hippocampus and amygdala are functionally coupled brain regions that play a crucial role in processes involving memory and learning. Because interareal communication has been reported both during specific sleep stages and in awake, behaving animals, these brain regions can serve as an archetype to establish that measuring functional interactions is important for comprehending neural systems. To this end, we analyze here a public dataset of local field potentials (LFPs) recorded in rats simultaneously from the hippocampus and amygdala during different behaviors. Employing a specific, time-lagged embedding technique, named topological causality (TC), we infer directed interactions between the LFP band powers of the two regions across six frequency bands in a time-resolved manner. The combined power and interaction signals are processed with our own unsupervised tools developed originally for the analysis of molecular dynamics simulations to effectively visualize and identify putative, neural states that are visited by the animals repeatedly. Our proposed methodology minimizes impositions onto the data, such as isolating specific epochs, or averaging across externally annotated behavioral stages, and succeeds in separating internal states by external labels such as sleep or stimulus events. We show that this works better for two of the three rats we analyzed, and highlight the need to acknowledge individuality in analyses of this type. Importantly, we demonstrate that the quantification of functional interactions is a significant factor in discriminating these external labels, and we suggest our methodology as a general tool for large, multisite recordings.

Volume-scaled common nearest neighbor clustering algorithm with free-energy hierarchy

The combination of Markov state modeling (MSM) and molecular dynamics (MD) simulations has been shown in recent years to be a valuable approach to unravel the slow processes of molecular systems with increasing complexity. While the algorithms for intermediate steps in the MSM workflow such as featurization and dimensionality reduction have been specifically adapted to MD datasets, conventional clustering methods are generally applied to the discretization step. This work adds to recent efforts to develop specialized density-based clustering algorithms for the Boltzmann-weighted data from MD simulations. We introduce the volume-scaled common nearest neighbor (vs-CNN) clustering that is an adapted version of the common nearest neighbor (CNN) algorithm. A major advantage of the proposed algorithm is that the introduced density-based criterion directly links to a free-energy notion via Boltzmann inversion. Such a free-energy perspective allows a straightforward hierarchical scheme to identify conformational clusters at different levels of a generally rugged free-energy landscape of complex molecular systems.

Unsupervised identification of states from voltage recordings of neural networks

BACKGROUND

Modern techniques for multi-neuronal recording produce large amounts of data. There is no automatic procedure for the identification of states in recurrent voltage patterns.

NEW METHOD

We propose NetSAP (Network States And Pathways), a data-driven analysis method that is able to recognize multi-neuron voltage patterns (states). To capture the subtle differences between snapshots in voltage recordings, NetSAP infers the underlying functional neural network in a time-resolved manner with a sliding window approach. Then NetSAP identifies states from a reordering of the time series of inferred networks according to a user-defined metric. The procedure for unsupervised identification of states was developed originally for the analysis of molecular dynamics simulations of proteins.

RESULTS

We tested NetSAP on neural network simulations of GABAergic inhibitory interneurons. Most simulation parameters are chosen to reproduce literature observations, and we keep noise terms as control parameters to regulate the coherence of the simulated signals. NetSAP is able to identify multiple states even in the case of high internal noise and low signal coherence. We provide evidence that NetSAP is robust for networks with up to about 50% of the neurons spiking randomly. NetSAP is scalable and its code is open source.

COMPARISON WITH EXISTING METHODS

NetSAP outperforms common analysis techniques, such as PCA and k-means clustering, on a simulated recording of voltage traces of 50 neurons.

CONCLUSIONS

NetSAP analysis is an efficient tool to identify voltage patterns from neuronal recordings.

Disorder at the Tips of a Disease-Relevant Aβ42 Amyloid Fibril: A Molecular Dynamics Study

We present a simulation study of the early events of peptide dissociation from a fibril of the Alzheimer's Aβ42 peptide. The fibril consists of layers of two adjacent Aβ42 peptides each folded in an S-shaped structure which has been determined by solid state NMR spectroscopy of a monomorphic disease-relevant species. Multiple molecular dynamics runs (16 at 310 K and 15 at 370 K) were carried out starting from an 18-peptide protofibril for a cumulative sampling of about 15 μs. The simulations show structural stability of the fibrillar core and an overall increase in the twist to about 3 degrees. The N-terminal segment 1-14 is disordered in all peptides. At both ends of the fibril, the central segment 21-29, which includes part of the β₂ strand, dissociates in some of the simulations. The β₁ and β₃ strands, residues 15-20 and 35-41, respectively, are structurally stable. The transient binding of the N-terminal stretch to the β₃ strand of the adjacent peptide at the tip is likely to contribute to the arrest phase of the stop-and-go mechanism.

The ATAD2 bromodomain binds different acetylation marks on the histone H4 in similar fuzzy complexes

Bromodomains are protein modules adopting conserved helix bundle folds. Some bromodomain-containing proteins, such as ATPase family AAA domain-containing protein 2 (ATAD2), isoform A, have attracted much interest because they are overexpressed in many types of cancer. Bromodomains bind to acetylated lysine residues on histone tails and thereby facilitate the reading of the histone code. Epigenetic regulators in general have been implicated as indicators, mediators, or causes of a large number of diseases and disorders. To interfere with or modulate these processes, it is therefore of fundamental interest to understand the molecular mechanisms by which epigenetic regulation occurs. Here, we present results from molecular dynamics simulations of a doubly acetylated histone H4 peptide bound to the bromodomain of ATAD2 (hereafter referred to as ATAD2A). These simulations revealed how the flexibility of ATAD2A's major loop, the so-called ZA loop, creates an adaptable interface that preserves the disorder of both peptide and loop in the bound state. We further demonstrate that the binding involves an almost identical average pattern of interactions irrespective of which acetyl mark is inserted into the pocket. In conjunction with a likely mechanism of electrostatically driven recruitment, our simulation results highlight how the bromodomain is built toward promiscuous binding with low specificity. In conclusion, the simulations indicate that disorder and electrostatic steering function jointly to recruit ATAD2A to the histone core and that these fuzzy interactions may promote cooperativity between nearby epigenetic marks.

Discovery of CREBBP Bromodomain Inhibitors by High-Throughput Docking and Hit Optimization Guided by Molecular Dynamics

We have identified two chemotypes of CREBBP bromodomain ligands by fragment-based high-throughput docking. Only 17 molecules from the original library of two-million compounds were tested in vitro. Optimization of the two low-micromolar hits, the 4-acylpyrrole 1 and acylbenzene 9, was driven by molecular dynamics results which suggested improvement of the polar interactions with the Arg1173 side chain at the rim of the binding site. The synthesis of only two derivatives of 1 yielded the 4-acylpyrrole 6 which shows a single-digit micromolar affinity for the CREBBP bromodomain and a ligand efficiency of 0.34 kcal/mol per non-hydrogen atom. Optimization of the acylbenzene hit 9 resulted in a series of derivatives with nanomolar potencies, good ligand efficiency and selectivity (see Unzue, A.; Xu, M.; Dong, J.; Wiedmer, L.; Spiliotopoulos, D.; Caflisch, A.; Nevado, C.Fragment-Based Design of Selective Nanomolar Ligands of the CREBBP Bromodomain. J. Med. Chem. 2015, DOI: 10.1021/acs.jmedchem.5b00172). The in silico predicted binding mode of the acylbenzene derivative 10 was validated by solving the structure of the complex with the CREBBP bromodomain.

Peptide Binding to a PDZ Domain by Electrostatic Steering via Nonnative Salt Bridges

We have captured the binding of a peptide to a PDZ domain by unbiased molecular dynamics simulations. Analysis of the trajectories reveals on-pathway encounter complex formation, which is driven by electrostatic interactions between negatively charged carboxylate groups in the peptide and positively charged side chains surrounding the binding site. In contrast, the final stereospecific complex, which matches the crystal structure, features completely different interactions, namely the burial of the hydrophobic side chain of the peptide C-terminal residue and backbone hydrogen bonds. The simulations show that nonnative salt bridges stabilize kinetically the encounter complex during binding. Unbinding follows the inverse sequence of events with the same nonnative salt bridges in the encounter complex. Thus, in contrast to protein folding, which is driven by native interactions, the binding of charged peptides can be steered by nonnative interactions, which might be a general mechanism, e.g., in the recognition of histone tails by bromodomains.