Interplay between Folding and Assembly of Fibril-Forming Polypeptides.

Polypeptides can self-assemble into hierarchically organized fibrils consisting of a stack of individually folded polypeptides driven together by hydrophobic interaction. Using a coarse grained model, we systematically studied this self-assembly as a function of temperature and hydrophobicity of the residues on the outside of the building block. We find the self-assembly can occur via two different pathways - a random aggregation-folding route, and a templated-folding process - thus indicating a strong coupling between folding and assembly. The simulation results can explain experimental evidence that assembly through stacking of folded building blocks is rarely observed, at the experimental concentrations. The model thus provides a generic picture of hierarchical fibril formation.Comment: Accepted in Physical Review Letter

Multi-task learning to leverage partially annotated data for PPI interface prediction

Protein protein interactions (PPI) are crucial for protein functioning, nevertheless predicting residues in PPI interfaces from the protein sequence remains a challenging problem. In addition, structure-based functional annotations, such as the PPI interface annotations, are scarce: only for about one-third of all protein structures residue-based PPI interface annotations are available. If we want to use a deep learning strategy, we have to overcome the problem of limited data availability. Here we use a multi-task learning strategy that can handle missing data. We start with the multi-task model architecture, and adapted it to carefully handle missing data in the cost function. As related learning tasks we include prediction of secondary structure, solvent accessibility, and buried residue. Our results show that the multi-task learning strategy significantly outperforms single task approaches. Moreover, only the multi-task strategy is able to effectively learn over a dataset extended with structural feature data, without additional PPI annotations. The multi-task setup becomes even more important, if the fraction of PPI annotations becomes very small: the multi-task learner trained on only one-eighth of the PPI annotations—with data extension—reaches the same performances as the single-task learner on all PPI annotations. Thus, we show that the multi-task learning strategy can be beneficial for a small training dataset where the protein’s functional properties of interest are only partially annotated

Design characteristics for facilities which process hazardous particulate

Los Alamos National Laboratory is establishing a research and processing capability for beryllium. The unique properties of beryllium, including light weight, rigidity, thermal conductivity, heat capacity, and nuclear properties make it critical to a number of US defense and aerospace programs. Concomitant with the unique engineering properties are the health hazards associated with processing beryllium in a particulate form and the potential for worker inhalation of aerosolized beryllium. Beryllium has the lowest airborne standard for worker protection compared to all other nonradioactive metals by more than an order of magnitude. This paper describes the design characteristics of the new beryllium facility at Los Alamos as they relate to protection of the workforce. Design characteristics to be reviewed include; facility layout, support systems to minimize aerosol exposure and spread, and detailed review of the ventilation system design for general room air cleanliness and extraction of particulate at the source

An efficient basis set representation for calculating electrons in molecules

The method of McCurdy, Baertschy, and Rescigno, J. Phys. B, 37, R137 (2004) is generalized to obtain a straightforward, surprisingly accurate, and scalable numerical representation for calculating the electronic wave functions of molecules. It uses a basis set of product sinc functions arrayed on a Cartesian grid, and yields 1 kcal/mol precision for valence transition energies with a grid resolution of approximately 0.1 bohr. The Coulomb matrix elements are replaced with matrix elements obtained from the kinetic energy operator. A resolution-of-the-identity approximation renders the primitive one- and two-electron matrix elements diagonal; in other words, the Coulomb operator is local with respect to the grid indices. The calculation of contracted two-electron matrix elements among orbitals requires only O(N log(N)) multiplication operations, not O(N^4), where N is the number of basis functions; N = n^3 on cubic grids. The representation not only is numerically expedient, but also produces energies and properties superior to those calculated variationally. Absolute energies, absorption cross sections, transition energies, and ionization potentials are reported for one- (He^+, H_2^+ ), two- (H_2, He), ten- (CH_4) and 56-electron (C_8H_8) systems.Comment: Submitted to JC

Deciphering Protein Secretion from the Brain to Cerebrospinal Fluid for Biomarker Discovery

Cerebrospinal fluid (CSF) is an essential matrix for the discovery of neurological disease biomarkers. However, the high dynamic range of protein concentrations in CSF hinders the detection of the least abundant protein biomarkers by untargeted mass spectrometry. It is thus beneficial to gain a deeper understanding of the secretion processes within the brain. Here, we aim to explore if and how the secretion of brain proteins to the CSF can be predicted. By combining a curated CSF proteome and the brain elevated proteome of the Human Protein Atlas, brain proteins were classified as CSF or non-CSF secreted. A machine learning model was trained on a range of sequence-based features to differentiate between CSF and non-CSF groups and effectively predict the brain origin of proteins. The classification model achieves an area under the curve of 0.89 if using high confidence CSF proteins. The most important prediction features include the subcellular localization, signal peptides, and transmembrane regions. The classifier generalized well to the larger brain detected proteome and is able to correctly predict novel CSF proteins identified by affinity proteomics. In addition to elucidating the underlying mechanisms of protein secretion, the trained classification model can support biomarker candidate selection

Patient's thoughts and expectations about centres of expertise for PKU

Background: In the Netherlands (NL) the government assigned 2 hospitals as centres of expertise (CE) for Phenylketonuria (PKU), while in the United Kingdom (UK) and Germany no centres are assigned specifically as PKU CE's. Methods: To identify expectations of patients/caregivers with PKU of CEs, a web-based survey was distributed through the national Phenylketonuria societies of Germany, NL and UK. Results: In total, 105 responded (43 patients, 56 parents, 4 grandparents, 2 other) of whom 59 were from NL, 33 from UK and 13 from Germany. All participants (n = 105) agreed that patients and/or practitioners would benefit from CEs. The frequency patients would want to visit a CE, when not treated in a CE (n = 83) varied: every hospital visit (24%, n = 20), annual or bi-annual (45%, n = 37), at defined patient ages (6%, n = 5), one visit only (22%, n = 18), or never (4%, n = 3). Distance was reported as a major barrier (42%, n = 35). 78% (n = 65) expected CE physicians and dieticians to have a higher level of knowledge than in non-CE centres. For participants already treated in a CE (n = 68), 66% requested a more extensive annual or bi-annual review. In general, psychology review and neuropsychologist assessment were identified as necessary by approximately half of the 105 participants. In addition, 66% (n = 68) expected a strong collaboration with patient associations. Conclusion: In this small study, most participants expected that assigning CEs will change the structure of and delivery of Phenylketonuria care

Structural flexibility and heterogeneity of recombinant human glial fibrillary acidic protein (GFAP)

Glial fibrillary acidic protein (GFAP) is a promising biomarker for brain and spinal cord disorders. Recent studies have highlighted the differences in the reliability of GFAP measurements in different biological matrices. The reason for these discrepancies is poorly understood as our knowledge of the protein's 3-dimensional conformation, proteoforms, and aggregation remains limited. Here, we investigate the structural properties of GFAP under different conditions. For this, we characterized recombinant GFAP proteins from various suppliers and applied hydrogen-deuterium exchange mass spectrometry (HDX-MS) to provide a snapshot of the conformational dynamics of GFAP in artificial cerebrospinal fluid (aCSF) compared to the phosphate buffer. Our findings indicate that recombinant GFAP exists in various conformational species. Furthermore, we show that GFAP dimers remained intact under denaturing conditions. HDX-MS experiments show an overall decrease in H-bonding and an increase in solvent accessibility of GFAP in aCSF compared to the phosphate buffer, with clear indications of mixed EX2 and EX1 kinetics. To understand possible structural interface regions and the evolutionary conservation profiles, we combined HDX-MS results with the predicted GFAP-dimer structure by AlphaFold-Multimer. We found that deprotected regions with high structural flexibility in aCSF overlap with predicted conserved dimeric 1B and 2B domain interfaces. Structural property predictions combined with the HDX data show an overall deprotection and signatures of aggregation in aCSF. We anticipate that the outcomes of this research will contribute to a deeper understanding of the structural flexibility of GFAP and ultimately shed light on its behavior in different biological matrices

Mapping the Protein Fold Universe Using the CamTube Force Field in Molecular Dynamics Simulations

It has been recently shown that the coarse-graining of the structures of polypeptide chains as self-avoiding tubes can provide an effective representation of the conformational space of proteins. In order to fully exploit the opportunities offered by such a \u2018tube model\u2019 approach, we present here a strategy to combine it with molecular dynamics simulations. This strategy is based on the incorporation of the \u2018CamTube\u2019 force field into the Gromacs molecular dynamics package. By considering the case of a 60-residue polyvaline chain, we show that CamTube molecular dynamics simulations can comprehensively explore the conformational space of proteins. We obtain this result by a 20 \u3bcs metadynamics simulation of the polyvaline chain that recapitulates the currently known protein fold universe. We further show that, if residue-specific interaction potentials are added to the CamTube force field, it is possible to fold a protein into a topology close to that of its native state. These results illustrate how the CamTube force field can be used to explore efficiently the universe of protein folds with good accuracy and very limited computational cost