NMR-assisted protein structure prediction with MELDxMD.

We describe the performance of MELD-accelerated molecular dynamics (MELDxMD) in determining protein structures in the NMR-data-assisted category in CASP13. Seeded from web server predictions, MELDxMD was found best in the NMR category, over 17 targets, outperforming the next-best groups by a factor of ~4 in z-score. MELDxMD gives ensembles, not single structures; succeeds on a 326-mer, near the current upper limit for NMR structures; and predicts structures that match experimental residual dipolar couplings even though the only NMR-derived data used in the simulations was NOE-based ambiguous atom-atom contacts and backbone dihedrals. MELD can use noisy and ambiguous experimental information to reduce the MD search space. We believe MELDxMD is a promising method for determining protein structures from NMR data.

Inverse Correlation between Amyloid Stiffness and Size.

We reveal that the axial stiffness of amyloid fibrils is inversely correlated with their cross-sectional area. Because amyloid fibrils' stiffness is determined by hydrogen bond (H-bond) density with a linear correlation, our finding implies that amyloid fibrils with larger radial sizes are generally softer and have lower density H-bond networks. In silico calculations show that the stiffness-size relationship of amyloid fibrils is, indeed, driven by the packing densities of residues and H-bonds. Our results suggest that polypeptide chains which form amyloid fibrils with narrow cross sections can optimize packing densities in the fibrillar core structure, in contrast to those forming wide amyloid fibrils. Consequently, the density of residues and H-bonds that contribute to mechanical stability is higher in amyloid fibrils with narrow cross sections. This size dependence of nanomechanics appears to be a global property of amyloid fibrils, just like the well-known cross-ß sheet topology.

Mapping the Broad Structural and Mechanical Properties of Amyloid Fibrils.

Amyloids are fibrillar nanostructures of proteins that are assembled in several physiological processes in human cells (e.g., hormone storage) but also during the course of infectious (prion) and noninfectious (nonprion) diseases such as Creutzfeldt-Jakob and Alzheimer's diseases, respectively. How the amyloid state, a state accessible to all proteins and peptides, can be exploited for functional purposes but also have detrimental effects remains to be determined. Here, we measure the nanomechanical properties of different amyloids and link them to features found in their structure models. Specifically, we use shape fluctuation analysis and sonication-induced scission in combination with full-atom molecular dynamics simulations to reveal that the amyloid fibrils of the mammalian prion protein PrP are mechanically unstable, most likely due to a very low hydrogen bond density in the fibril structure. Interestingly, amyloid fibrils formed by HET-s, a fungal protein that can confer functional prion behavior, have a much higher Young's modulus and tensile strength than those of PrP, i.e., they are much stiffer and stronger due to a tighter packing in the fibril structure. By contrast, amyloids of the proteins RIP1/RIP3 that have been shown to be of functional use in human cells are significantly stiffer than PrP fibrils but have comparable tensile strength. Our study demonstrates that amyloids are biomaterials with a broad range of nanomechanical properties, and we provide further support for the strong link between nanomechanics and ß-sheet characteristics in the amyloid core.

Accelerating Protein Folding Molecular Dynamics Using Inter-Residue Distances from Machine Learning Servers.

Recently, predicting the native structures of proteins has become possible using computational molecular physics (CMP)─physics-based force fields sampled with proper statistics─but only for small proteins. Algorithms with better scaling are needed. We describe ML x MELD x MD, a molecular dynamics (MD) method that inputs residue contacts derived from machine learning (ML) servers into MELD, a Bayesian accelerator that preserves detailed-balance statistics. Contacts are derived from trRosetta-predicted distance histograms (distograms) and are integrated into MELD's atomistic MD as spatial restraints through parametrized potential functions. In the CASP14 blind prediction event, ML x MELD x MD predicted 13 native structures to better than 4.5 Šerror, including for 10 proteins in the range of 115-250 amino acids long. Also, the scaling of simulation time vs protein length is much better than unguided MD: tsim ∼ e0.023N for ML x MELD x MD vs tsim ∼ e0.168N for MD alone. This shows how machine learning information can be leveraged to advance physics-based modeling of proteins.

The Protein Folding Problem: The Role of Theory.

The protein folding problem was first articulated as question of how order arose from disorder in proteins: How did the various native structures of proteins arise from interatomic driving forces encoded within their amino acid sequences, and how did they fold so fast? These matters have now been largely resolved by theory and statistical mechanics combined with experiments. There are general principles. Chain randomness is overcome by solvation-based codes. And in the needle-in-a-haystack metaphor, native states are found efficiently because protein haystacks (conformational ensembles) are funnel-shaped. Order-disorder theory has now grown to encompass a large swath of protein physical science across biology.

Mechanical Anisotropy in GNNQQNY Amyloid Crystals.

Mapping the nanomechanical properties of amyloids can provide valuable insights into structure and assembly mechanisms of protein aggregates that underlie the development of various human diseases. Although it is well-known that amyloids exhibit an intrinsic stiffness comparable to that of silk (1-10 GPa), a detailed understanding of the directional dependence (anisotropy) of the stiffness of amyloids and how it relates to structural features in these protein aggregates is missing. Here we used steered molecular dynamics (SMD) simulations and amplitude modulation-frequency modulation (AM-FM) atomic force microscopy to measure the directional variation in stiffness of GNNQQNY amyloid crystals. We reveal that individual crystals display significant mechanical anisotropy and relate this anisotropy to subtle but mechanically important differences in interactions between interfaces that define the crystal architecture. Our results provide detailed insights into the structure-mechanics relationship of amyloid that may help in designing amyloid-based nanomaterials with tailored mechanical properties.

The [PSI +] yeast prion does not wildly affect proteome composition whereas selective pressure exerted on [PSI +] cells can promote aneuploidy.

The yeast Sup35 protein is a subunit of the translation termination factor, and its conversion to the [PSI +] prion state leads to more translational read-through. Although extensive studies have been done on [PSI +], changes at the proteomic level have not been performed exhaustively. We therefore used a SILAC-based quantitative mass spectrometry approach and identified 4187 proteins from both [psi -] and [PSI +] strains. Surprisingly, there was very little difference between the two proteomes under standard growth conditions. We found however that several [PSI +] strains harbored an additional chromosome, such as chromosome I. Albeit, we found no evidence to support that [PSI +] induces chromosomal instability (CIN). Instead we hypothesized that the selective pressure applied during the establishment of [PSI +]-containing strains could lead to a supernumerary chromosome due to the presence of the ade1-14 selective marker for translational read-through. We therefore verified that there was no prevalence of disomy among newly generated [PSI +] strains in absence of strong selection pressure. We also noticed that low amounts of adenine in media could lead to higher levels of mitochondrial DNA in [PSI +] in ade1-14 cells. Our study has important significance for the establishment and manipulation of yeast strains with the Sup35 prion.

Computational Identification of MoRFs in Protein Sequences Using Hierarchical Application of Bayes Rule.

MOTIVATION: Intrinsically disordered regions of proteins play an essential role in the regulation of various biological processes. Key to their regulatory function is often the binding to globular protein domains via sequence elements known as molecular recognition features (MoRFs). Development of computational tools for the identification of candidate MoRF locations in amino acid sequences is an important task and an area of growing interest. Given the relative sparseness of MoRFs in protein sequences, the accuracy of the available MoRF predictors is often inadequate for practical usage, which leaves a significant need and room for improvement. In this work, we introduce MoRFCHiBi_Web, which predicts MoRF locations in protein sequences with higher accuracy compared to current MoRF predictors. METHODS: Three distinct and largely independent property scores are computed with component predictors and then combined to generate the final MoRF propensity scores. The first score reflects the likelihood of sequence windows to harbour MoRFs and is based on amino acid composition and sequence similarity information. It is generated by MoRFCHiBi using small windows of up to 40 residues in size. The second score identifies long stretches of protein disorder and is generated by ESpritz with the DisProt option. Lastly, the third score reflects residue conservation and is assembled from PSSM files generated by PSI-BLAST. These propensity scores are processed and then hierarchically combined using Bayes rule to generate the final MoRFCHiBi_Web predictions. RESULTS: MoRFCHiBi_Web was tested on three datasets. Results show that MoRFCHiBi_Web outperforms previously developed predictors by generating less than half the false positive rate for the same true positive rate at practical threshold values. This level of accuracy paired with its relatively high processing speed makes MoRFCHiBi_Web a practical tool for MoRF prediction. AVAILABILITY: http://morf.chibi.ubc.ca:8080/morf/.

Structure and intrinsic disorder in protein autoinhibition.

Autoinhibition plays a significant role in the regulation of many proteins. By analyzing autoinhibited proteins, we demonstrate that these proteins are enriched in intrinsic disorder because of the properties of their inhibitory modules (IMs). A comparison of autoinhibited proteins with structured and intrinsically disordered IMs revealed that in the latter group (1) multiple phosphorylation sites are highly abundant; (2) splice variants occur in greater number than in their structured cousins; and (3) activation is often associated with changes in secondary structure in the IM. Analyses of families of autoinhibited proteins revealed that the levels of disorder in IMs can vary significantly throughout homologous proteins, whereas residues located at the interfaces between the IMs and inhibited domains are conserved. Our findings suggest that intrinsically disordered IMs provide advantages over structured ones that are likely to be exploited in the fine-tuning of the equilibrium between active and inactive states of autoinhibited proteins.