Computational Investigation on Electrostatic Loop Mutants Instigating Destabilization and Aggregation on Human SOD1 Protein Causing Amyotrophic Lateral Sclerosis

Mutations in the gene encoding Cu/Zn Superoxide Dismutase 1 (SOD1) protein are contemplated to be a protruding reason for Amyotrophic lateral sclerosis (ALS), which leads towards protein aggregation, misfolding and destabilization. Thus, we investigated the systematic action of entire mutations reported on electrostatic loop of SOD1 protein through thermodynamical and discrete molecular dynamics (DMD) studies. Accordingly, we analyzed the outcomes distinctly for screening the mutant structures having both, deleterious and destabilizing effect. Progressively, the impacts of those mutations on SOD1 were studied using DMD program. Surprisingly, our results predicted that the mutants viz., L126S, N139H and G141A to be the most destabilizing, misfolded and disease-causing compared to other mutants. Besides, the outcomes from secondary structural propensities and free energy landscapes, together assertively suggested that L126S, N139H and G141A tend to increase the formation of aggregates in SOD1 relative to other mutants. Hence, this study could provide an insight into the sprouting neurodegenerative disorder distressing the humans.

Correlating topology and thermodynamics to predict protein structure sensitivity to point mutations

The relation between distribution of hydrophobic amino acids along with protein chains and their structure is far from being completely understood. No reliable method allowsab initioprediction of the folded structure from this distribution of physicochemical properties, even when they are highly degenerated by considering only two classes: hydrophobic and polar. Establishment of long-range hydrophobic three dimension (3D) contacts is essential for the formation of the nucleus, a key process in the early steps of protein folding. Thus, a large number of 3D simulation studies were developed to challenge this issue. They are nowadays evaluated in a specific chapter of the molecular modeling competition, Critical Assessment of Protein Structure Prediction. We present here a simulation of the early steps of the folding process for 850 proteins, performed in a discrete 3D space, which results in peaks in the predicted distribution of intra-chain noncovalent contacts. The residues located at these peak positions tend to be buried in the core of the protein and are expected to correspond to critical positions in the sequence, important both for folding and structural (or similarly, energetic in the thermodynamic hypothesis) stability. The degree of stabilization or destabilization due to a point mutation at the critical positions involved in numerous contacts is estimated from the calculated folding free energy difference between mutated and native structures. The results show that these critical positions are not tolerant towards mutation. This simulation of the noncovalent contacts only needs a sequence as input, and this paper proposes a validation of the method by comparison with the prediction of stability by well-established programs.

A theoretical study on Zn binding loop mutants instigating destabilization and metal binding loss in human SOD1 protein

Mutations in Cu/Zn superoxide dismutase 1 (SOD1) protein are a major cause of the devastating neurodegenerative disorder Amyotrophic lateral sclerosis. Evidence suggests that SOD1 functions as a free radical scavenger in humans. However, neither the mechanism nor a cure for this neurodegenerative disease are yet known. In the present study, we explored the effect of mutations on the mechanistic action on the Zn binding loop of SOD1 through discrete molecular dynamics. The results were analyzed in detail using statistical potential (BACH) to find the mutant structures having the least potential energy. Subsequently, we studied the impact of those mutations on metal ions bound in SOD1 using the program Check My Metal. Remarkably, our results recognized certain mutants, viz. His80Arg and Asp83Gly, that were more damaging to the Zn binding loop than all other mutants, leading to a loss of Zn binding with altered coordination of the Zn ion. Furthermore, the conformational stability, compactness, and secondary structural alteration of the His80Arg and Asp83Gly mutants were monitored using distinct parameters. Hence, at low computational expense, our study provides helpful insight into this emergent neurodegenerative disorder affecting mankind.

Using Wavelet Analysis To Assist in Identification of Significant Events in Molecular Dynamics Simulations

Long time scale molecular dynamics (MD) simulations of biological systems are becoming increasingly commonplace due to the availability of both large-scale computational resources and significant advances in the underlying simulation methodologies. Therefore, it is useful to investigate and develop data mining and analysis techniques to quickly and efficiently extract the biologically relevant information from the incredible amount of generated data. Wavelet analysis (WA) is a technique that can quickly reveal significant motions during an MD simulation. Here, the application of WA on well-converged long time scale (tens of μs) simulations of a DNA helix is described. We show how WA combined with a simple clustering method can be used to identify both the physical and temporal locations of events with significant motion in MD trajectories. We also show that WA can not only distinguish and quantify the locations and time scales of significant motions, but by changing the maximum time scale of WA a more complete characterization of these motions can be obtained. This allows motions of different time scales to be identified or ignored as desired.

Using THz Spectroscopy, Evolutionary Network Analysis Methods, and MD Simulation to Map the Evolution of Allosteric Communication Pathways in c-Type Lysozymes

It is now widely accepted that protein function is intimately tied with the navigation of energy landscapes. In this framework, a protein sequence is not described by a distinct structure but rather by an ensemble of conformations. And it is through this ensemble that evolution is able to modify a protein's function by altering its landscape. Hence, the evolution of protein functions involves selective pressures that adjust the sampling of the conformational states. In this work, we focus on elucidating the evolutionary pathway that shaped the function of individual proteins that make-up the mammalian c-type lysozyme subfamily. Using both experimental and computational methods, we map out specific intermolecular interactions that direct the sampling of conformational states and accordingly, also underlie shifts in the landscape that are directly connected with the formation of novel protein functions. By contrasting three representative proteins in the family we identify molecular mechanisms that are associated with the selectivity of enhanced antimicrobial properties and consequently, divergent protein function. Namely, we link the extent of localized fluctuations involving the loop separating helices A and B with shifts in the equilibrium of the ensemble of conformational states that mediate interdomain coupling and concurrently moderate substrate binding affinity. This work reveals unique insights into the molecular level mechanisms that promote the progression of interactions that connect the immune response to infection with the nutritional properties of lactation, while also providing a deeper understanding about how evolving energy landscapes may define present-day protein function.

Wavelet Analysis of Protein Motion

As high-throughput molecular dynamics simulations of proteins become more common and the databases housing the results become larger and more prevalent, more sophisticated methods to quickly and accurately mine large numbers of trajectories for relevant information will have to be developed. One such method, which is only recently gaining popularity in molecular biology, is the continuous wavelet transform, which is especially well-suited for time course data such as molecular dynamics simulations. We describe techniques for the calculation and analysis of wavelet transforms of molecular dynamics trajectories in detail and present examples of how these techniques can be useful in data mining. We demonstrate that wavelets are sensitive to structural rearrangements in proteins and that they can be used to quickly detect physically relevant events. Finally, as an example of the use of this approach, we show how wavelet data mining has led to a novel hypothesis related to the mechanism of the protein γδ resolvase.