Sequence and structural determinants of Cu, Zn superoxide dismutase aggregation

Diverse point mutations in the enzyme Cu, Zn superoxide dismutase (SOD1) are linked to its aggregation in the familial form of the disease amyotrophic lateral sclerosis. The disease-associated mutations are known to destabilize the protein, but the structural basis of the aggregation of the destabilized protein and the structure of aggregates are not well understood. Here, we investigate in silico the sequence and structural determinants of SOD1 aggregation: (1) We identify sequence fragments in SOD1 that have a high aggregation propensity, using only the sequence of SOD1, and (2) we perform molecular dynamics simulations of the SOD1 dimer folding and misfolding. In both cases, we identify identical regions of the protein as having high propensity to form intermolecular interactions. These regions correspond to the N- and C-termini, and two crossover loops and two beta-strands in the Greek-key native fold of SOD1. Our results suggest that the high aggregation propensity of mutant SOD1 may result from a synergy of two factors: the presence of highly amyloidogenic sequence fragments ("hot spots"), and the presence of these fragments in regions of the protein that are structurally most likely to form intermolecular contacts under destabilizing conditions. Therefore, we postulate that the balance between the self-association of aggregation-prone sequences and the specific structural context of these sequences in the native state determines the aggregation propensity of proteins.     

Submillisecond Elastic Recoil Reveals Molecular Origins of Fibrin Fiber Mechanics

Fibrin fibers form the structural scaffold of blood clots. Thus, their mechanical properties are of central importance to understanding hemostasis and thrombotic disease. Recent studies have revealed that fibrin fibers are elastomeric despite their high degree of molecular ordering. These results have inspired a variety of molecular models for fibrin’s elasticity, ranging from reversible protein unfolding to rubber-like elasticity. An important property that has not been explored is the timescale of elastic recoil, a parameter that is critical for fibrin’s mechanical function and places a temporal constraint on molecular models of fiber elasticity. Using high-frame-rate imaging and atomic force microscopy-based nanomanipulation, we measured the recoil dynamics of individual fibrin fibers and found that the recoil was orders of magnitude faster than anticipated from models involving protein refolding. We also performed steered discrete molecular-dynamics simulations to investigate the molecular origins of the observed recoil. Our results point to the unstructured αC regions of the otherwise structured fibrin molecule as being responsible for the elastic recoil of the fibers.     

Polyglutamine Induced Misfolding of Huntingtin Exon1 is Modulated by the Flanking Sequences

Polyglutamine (polyQ) expansion in exon1 (XN1) of the huntingtin protein is linked to Huntington''s disease. When the number of glutamines exceeds a threshold of approximately 36–40 repeats, XN1 can readily form amyloid aggregates similar to those associated with disease. Many experiments suggest that misfolding of monomeric XN1 plays an important role in the length-dependent aggregation. Elucidating the misfolding of a XN1 monomer can help determine the molecular mechanism of XN1 aggregation and potentially help develop strategies to inhibit XN1 aggregation. The flanking sequences surrounding the polyQ region can play a critical role in determining the structural rearrangement and aggregation mechanism of XN1. Few experiments have studied XN1 in its entirety, with all flanking regions. To obtain structural insights into the misfolding of XN1 toward amyloid aggregation, we perform molecular dynamics simulations on monomeric XN1 with full flanking regions, a variant missing the polyproline regions, which are hypothesized to prevent aggregation, and an isolated polyQ peptide (Q_n). For each of these three constructs, we study glutamine repeat lengths of 23, 36, 40 and 47. We find that polyQ peptides have a positive correlation between their probability to form a β-rich misfolded state and their expansion length. We also find that the flanking regions of XN1 affect its probability to^x_page_count=28 form a β-rich state compared to the isolated polyQ. Particularly, the polyproline regions form polyproline type II helices and decrease the probability of the polyQ region to form a β-rich state. Additionally, by lengthening polyQ, the first N-terminal 17 residues are more likely to adopt a β-sheet conformation rather than an α-helix conformation. Therefore, our molecular dynamics study provides a structural insight of XN1 misfolding and elucidates the possible role of the flanking sequences in XN1 aggregation.     

Computational approaches to understanding protein aggregation in neurodegeneration

The generation of toxic non-native protein conformers has emerged as a unifying thread among disorders such as Alzheimer＇s disease, Parkinson＇s disease, and amyotrophic lateral sclerosis. Atomic-level detail regarding dynamical changes that facilitate protein aggre- gation, as well as the structural features of large-scale ordered aggregates and soluble non-native oligomers, would contribute signifi- cantly to current understanding of these complex phenomena and offer potential strategies for inhibiting formation of cytotoxic species. However, experimental limitations often preclude the acquisition of high-resolution structural and mechanistic information for aggregating systems. Computational methods, particularly those combine both aU-atom and coarse-grained simulations to cover a wide range of time and length scales, have thus emerged as crucial tools for investigating protein aggregation. Here we review the current state of computational methodology for the study of protein self-assembly, with a focus on the application of these methods toward understanding of protein aggregates in human neurodegenerative disorders.     

Approaches for probing the sequence space of substrates recognized by molecular chaperones

Neurodegeneration, the progressive loss of function in neurons that eventually leads to their death, is the cause of many neurodegenerative disorders including Alzheimer's, Parkinson's, and Huntington's diseases. Protein aggregation is a hallmark of most neurodegenerative diseases, where unfolded proteins form intranuclear, cytosolic, and extracellular insoluble aggregates in neurons. Mounting evidence from studies in neurodegenerative disease models shows that molecular chaperones, key regulators of protein aggregation and degradation, play critical roles in the progression of neurodegeneration. Although chaperones exhibit promiscuity in their substrate specificity, specific molecular features are required for substrate recognition. Understanding the basis for substrate recognition by chaperones will aid in the development of therapeutic strategies that regulate chaperone expression levels in order to combat neurodegeneration. Many experimental techniques, including alanine scanning mutagenesis and phage display library screening, have been developed and applied to understand the basis of substrate recognition by chaperones. Here, we present computational algorithms that can be applied to rapidly screen the sequence space of potential substrates to determine the sequence and structural requirements for substrate recognition by chaperones.     

Thermodynamic stability of histone H3 is a necessary but not sufficient driving force for its evolutionary conservation

Determining the forces that conserve amino acid positions in proteins across species is a fundamental pursuit of molecular evolution. Evolutionary conservation is driven by either a protein's function or its thermodynamic stability. Highly conserved histone proteins offer a platform to evaluate these driving forces. While the conservation of histone H3 and H4 "tail" domains and surface residues are driven by functional importance, the driving force behind the conservation of buried histone residues has not been examined. Using a computational approach, we determined the thermodynamically preferred amino acids at each buried position in H3 and H4. In agreement with what is normally observed in proteins, we find a significant correlation between thermodynamic stability and evolutionary conservation in the buried residues in H4. In striking contrast, we find that thermodynamic stability of buried H3 residues does not correlate with evolutionary conservation. Given that these H3 residues are not post-translationally modified and only regulate H3-H3 and H3-H4 stabilizing interactions, our data imply an unknown function responsible for driving conservation of these buried H3 residues.     

Flanking bases influence the nature of DNA distortion by platinum 1,2-intrastrand (GG) cross-links

The differences in efficacy and molecular mechanisms of platinum anti-cancer drugs cisplatin (CP) and oxaliplatin (OX) are thought to be partially due to the differences in the DNA conformations of the CP and OX adducts that form on adjacent guanines on DNA, which in turn influence the binding of damage-recognition proteins that control downstream effects of the adducts. Here we report a comprehensive comparison of the structural distortion of DNA caused by CP and OX adducts in the TGGT sequence context using nuclear magnetic resonance (NMR) spectroscopy and molecular dynamics (MD) simulations. When compared to our previous studies in other sequence contexts, these structural studies help us understand the effect of the sequence context on the conformation of Pt-GG DNA adducts. We find that both the sequence context and the type of Pt-GG DNA adduct (CP vs. OX) play an important role in the conformation and the conformational dynamics of Pt-DNA adducts, possibly explaining their influence on the ability of many damage-recognition proteins to bind to Pt-DNA adducts.     

Structural mechanism of S-adenosyl methionine binding to catechol O-methyltransferase

Methyltransferases possess a homologous domain that requires both a divalent metal cation and S-adenosyl-L-methionine (SAM) to catalyze its reactions. The kinetics of several methyltransferases has been well characterized; however, the details regarding their structural mechanisms have remained unclear to date. Using catechol O-methyltransferase (COMT) as a model, we perform discrete molecular dynamics and computational docking simulations to elucidate the initial stages of cofactor binding. We find that COMT binds SAM via an induced-fit mechanism, where SAM adopts a different docking pose in the absence of metal and substrate in comparison to the holoenzyme. Flexible modeling of the active site side-chains is essential for observing the lowest energy state in the apoenzyme; rigid docking tools are unable to recapitulate the pose unless the appropriate side-chain conformations are given a priori. From our docking results, we hypothesize that the metal reorients SAM in a conformation suitable for donating its methyl substituent to the recipient ligand. The proposed mechanism enables a general understanding of how divalent metal cations contribute to methyltransferase function.     

Automated minimization of steric clashes in protein structures

Molecular modeling of proteins including homology modeling, structure determination, and knowledge-based protein design requires tools to evaluate and refine three-dimensional protein structures. Steric clash is one of the artifacts prevalent in low-resolution structures and homology models. Steric clashes arise due to the unnatural overlap of any two nonbonding atoms in a protein structure. Usually, removal of severe steric clashes in some structures is challenging since many existing refinement programs do not accept structures with severe steric clashes. Here, we present a quantitative approach of identifying steric clashes in proteins by defining clashes based on the Van der Waals repulsion energy of the clashing atoms. We also define a metric for quantitative estimation of the severity of clashes in proteins by performing statistical analysis of clashes in high-resolution protein structures. We describe a rapid, automated, and robust protocol, Chiron, which efficiently resolves severe clashes in low-resolution structures and homology models with minimal perturbation in the protein backbone. Benchmark studies highlight the efficiency and robustness of Chiron compared with other widely used methods. We provide Chiron as an automated web server to evaluate and resolve clashes in protein structures that can be further used for more accurate protein design.