The aggregation of medin forming aortic medial amyloid is linked to arterial wall degeneration and cerebrovascular dysfunction. Elevated levels of arteriolar medin are correlated with an increased presence of vascular amyloid-β (Aβ) aggregates, a hallmark of Alzheimer's disease (AD) and vascular dementia. The cross-interaction between medin and Aβ results in the formation of heterologous fibrils through co-aggregation and cross-seeding processes both in vitro and in vivo. However, a comprehensive molecular understanding of the cross-interaction between medin and Aβ-two intrinsically disordered proteins-is critically lacking. Here, we employed atomistic discrete molecular dynamics simulations to systematically investigate the self-association, co-aggregation and also the phenomenon of cross-seeding between these two proteins. Our results demonstrated that both Aβ and medin were aggregation prone and their mixture tended to form β-sheet-rich hetero-aggregates. The formation of Aβ-medin hetero-aggregates did not hinder Aβ and medin from recruiting additional Aβ and medin peptides to grow into larger β-sheet-rich aggregates. The β-barrel oligomer intermediates observed in the self-aggregations of Aβ and medin were also present during their co-aggregation. In cross-seeding simulations, preformed Aβ fibrils could recruit isolated medin monomers to form elongated β-sheets. Overall, our comprehensive simulations suggested that the cross-interaction between Aβ and medin may contribute to their pathological aggregation, given the inherent amyloidogenic tendencies of both medin and Aβ. Targeting medin, therefore, could offer a novel therapeutic approach to preserving brain function during aging and AD by improving vascular health.

MOTIVATION: Tyrosine sulfation is a type of post-translational modification (PTM) catalyzed by tyrosylprotein sulfotransferases (TPST). The modification plays a crucial role in mediating protein-protein interactions in many biologically important processes. There is no well-defined sequence motif for TPST sulfation, and the underlying determinants of TPST sulfation specificity remains elusive. Here, we perform molecular modeling to uncover the structural and energetic determinants of TPST sulfation specificity. RESULTS: We estimate the binding affinities between TPST and peptides around tyrosines of both sulfated and non-sulfated proteins to differentiate them. We find that better differentiation is achieved after including energy costs associated with local unfolding of the tyrosine-containing peptide in a host protein, which depends on both the peptide's secondary structures and solvent accessibility. Local unfolding renders buried peptide-with ordered structures-thermodynamically available for TPST binding. Our results suggest that both thermodynamic availability of the peptide and its binding affinity to the enzyme are important for TPST sulfation specificity, and their interplay results into great variations in sequences and structures of sulfated peptides. We expect our method to be useful in predicting potential sulfation sites and transferable to other TPST variants. Our study may also shed light on other PTM systems without well-defined sequence and structural specificities. AVAILABILITY AND IMPLEMENTATION: All the data and scripts used in the work are available at http://dlab.clemson.edu/research/Sulfation.

MOTIVATION: Increasing use of structural modeling for understanding structure-function relationships in proteins has led to the need to ensure that the protein models being used are of acceptable quality. Quality of a given protein structure can be assessed by comparing various intrinsic structural properties of the protein to those observed in high-resolution protein structures. RESULTS: In this study, we present tools to compare a given structure to high-resolution crystal structures. We assess packing by calculating the total void volume, the percentage of unsatisfied hydrogen bonds, the number of steric clashes and the scaling of the accessible surface area. We assess covalent geometry by determining bond lengths, angles, dihedrals and rotamers. The statistical parameters for the above measures, obtained from high-resolution crystal structures enable us to provide a quality-score that points to specific areas where a given protein structural model needs improvement. AVAILABILITY AND IMPLEMENTATION: We provide these tools that appraise protein structures in the form of a web server Gaia (http://chiron.dokhlab.org). Gaia evaluates the packing and covalent geometry of a given protein structure and provides quantitative comparison of the given structure to high-resolution crystal structures. CONTACT: [email protected] SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

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 beta-rich misfolded state and their expansion length. We also find that the flanking regions of XN1 affect its probability to form a beta-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 beta-rich state. Additionally, by lengthening polyQ, the first N-terminal 17 residues are more likely to adopt a beta-sheet conformation rather than an alpha-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.

UNLABELLED: Three-dimensional RNA structure prediction and folding is of significant interest in the biological research community. Here, we present iFoldRNA, a novel web-based methodology for RNA structure prediction with near atomic resolution accuracy and analysis of RNA folding thermodynamics. iFoldRNA rapidly explores RNA conformations using discrete molecular dynamics simulations of input RNA sequences. Starting from simplified linear-chain conformations, RNA molecules (<50 nt) fold to native-like structures within half an hour of simulation, facilitating rapid RNA structure prediction. All-atom reconstruction of energetically stable conformations generates iFoldRNA predicted RNA structures. The predicted RNA structures are within 2-5 A root mean squre deviations (RMSDs) from corresponding experimentally derived structures. RNA folding parameters including specific heat, contact maps, simulation trajectories, gyration radii, RMSDs from native state, fraction of native-like contacts are accessible from iFoldRNA. We expect iFoldRNA will serve as a useful resource for RNA structure prediction and folding thermodynamic analyses. AVAILABILITY: http://iFoldRNA.dokhlab.org.

Nuclear receptor ligand binding domains (LBDs) convert ligand binding events into changes in gene expression by recruiting transcriptional coregulators to a conserved activation function-2 (AF-2) surface. While most nuclear receptor LBDs form homo- or heterodimers, the human nuclear receptor pregnane X receptor (PXR) forms a unique and essential homodimer and is proposed to assemble into a functional heterotetramer with the retinoid X receptor (RXR). How the homodimer interface, which is located 30 A from the AF-2, would affect function at this critical surface has remained unclear. By using 20- to 30-ns molecular dynamics simulations on PXR in various oligomerization states, we observed a remarkably high degree of correlated motion in the PXR-RXR heterotetramer, most notably in the four helices that create the AF-2 domain. The function of such correlation may be to create "active-capable" receptor complexes that are ready to bind to transcriptional coactivators. Indeed, we found in additional simulations that active-capable receptor complexes involving other orphan or steroid nuclear receptors also exhibit highly correlated AF-2 domain motions. We further propose a mechanism for the transmission of long-range motions through the nuclear receptor LBD to the AF-2 surface. Taken together, our findings indicate that long-range motions within the LBD scaffold are critical to nuclear receptor function by promoting a mobile AF-2 state ready to bind coactivators.

UNLABELLED: We built a novel web-based platform for performing discrete molecular dynamics simulations of proteins. In silico protein folding involves searching for minimal frustration in the vast conformational landscape. Conventional approaches for simulating protein folding insufficiently address the problem of simulations in relevant time and length scales necessary for a mechanistic understanding of underlying biomolecular phenomena. Discrete molecular dynamics (DMD) offers an opportunity to bridge the size and timescale gaps and uncover the structural and biological properties of experimentally undetectable protein dynamics. The iFold server supports large-scale simulations of protein folding, thermal denaturation, thermodynamic scan, simulated annealing and p(fold) analysis using DMD and coarse-grained protein model with structure-based Gō-interactions between amino acids. AVAILABILITY: http://ifold.dokhlab.org

Diverse proteins with similar structures are grouped into families of homologs and analogs, if their sequence similarity is higher or lower, respectively, than 20%-30%. It was suggested that protein homologs and analogs originate from a common ancestor and diverge in their distinct evolutionary time scales, emerging as a consequence of the physical properties of the protein sequence space. Although a number of studies have determined key signatures of protein family organization, the sequence-structure factors that differentiate the two evolution-related protein families remain unknown. Here, we stipulate that subtle structural changes, which appear due to accumulating mutations in the homologous families, lead to distinct packing of the protein core and, thus, novel compositions of core residues. The latter process leads to the formation of distinct families of homologs. We propose that such differentiation results in the formation of analogous families. To test our postulate, we developed a molecular modeling and design toolkit, Medusa, to computationally design protein sequences that correspond to the same fold family. We find that analogous proteins emerge when a backbone structure deviates only 1-2 angstroms root-mean-square deviation from the original structure. For close homologs, core residues are highly conserved. However, when the overall sequence similarity drops to approximately 25%-30%, the composition of core residues starts to diverge, thereby forming novel families of protein homologs. This direct observation of the formation of protein homologs within a specific fold family supports our hypothesis. The conservation of amino acids in designed sequences recapitulates that of the naturally occurring sequences, thereby validating our computational design methodology.

In PLoS Computational Biology, volume 2, issue 7: DOI: 10.1371/journal.pcbi.0020085 The references to the figure parts in the legend of figure 3 were incorrect. The correct caption is as follows: Figure 3. The Sequence Identity for the Constructed Homologous Structures Three different protein folds are studied: HPR domain (A,D), ROSSMAN fold (B,E), and SH3 domain (C,F). (A,B,C) The sequence identities of the redesigned proteins using the flexible-backbone design simulation are presented as the function of the backbone-RMSD from the reference protein. (D,E,F) The sequence identity of the core is also plotted against the overall sequence identity. The “twilight zone” of sequence identity (20%–30%) corresponds to regions between horizontal (A,B,C) or vertical (D,E,F) lines.

Expansion of polyglutamine (polyQ) tracts in proteins results in protein aggregation and is associated with cell death in at least nine neurodegenerative diseases. Disease age of onset is correlated with the polyQ insert length above a critical value of 35-40 glutamines. The aggregation kinetics of isolated polyQ peptides in vitro also shows a similar critical-length dependence. While recent experimental work has provided considerable insights into polyQ aggregation, the molecular mechanism of aggregation is not well understood. Here, using computer simulations of isolated polyQ peptides, we show that a mechanism of aggregation is the conformational transition in a single polyQ peptide chain from random coil to a parallel beta-helix. This transition occurs selectively in peptides longer than 37 glutamines. In the beta-helices observed in simulations, all residues adopt beta-strand backbone dihedral angles, and the polypeptide chain coils around a central helical axis with 18.5 +/- 2 residues per turn. We also find that mutant polyQ peptides with proline-glycine inserts show formation of antiparallel beta-hairpins in their ground state, in agreement with experiments. The lower stability of mutant beta-helices explains their lower aggregation rates compared to wild type. Our results provide a molecular mechanism for polyQ-mediated aggregation.