The UCD nanosafety workshop (03 December 2018): towards developing a consensus on safe handling of nanomaterials within the Irish university labs and beyond - a report.

Careful handling of the nanomaterials (NMs) in research labs is crucial to ensure a safe working environment. As the largest university in Ireland, University College Dublin (UCD) has invested significant resources to update researchers working with NMs. Due to sizes often <100 nm, the NMs including nanoparticles, harbor unprecedented materialistic properties, for example, enhanced reactivity, conductivity, fluorescence, etc. which albeit conferring the NMs an edge over bulk materials regarding the applied aspects; depending on the dose, also render them to be toxic. Thus, a set of regulatory guidelines have emerged regarding safe handling of the NMs within occupational set-ups. Unfortunately, the current regulations based on the toxic chemicals and carcinogens are often confusing, lack clarity, and difficult to apply for the NMs. As a research-intensive university, a diverse range of research activities occur within the UCD labs, and it is difficult, at times impossible, for the UCD Safety, Insurance, Operational Risk & Compliance (SIRC) office to develop a set of common guidelines and cater throughout all its labs conducting research with the NMs. Hence, a necessity for dialog and exchange of ideas was felt across the UCD which encouraged the researchers including early stage researchers (e.g. PhDs, Postdocs) from multiple schools to participate in a workshop held on the 03 December 2018. The workshop tried to follow a pragmatic approach, where apart from discussing both the in vitro and in vivo scenarios, practical cases simulating situations faced frequently in the labs were discussed. This report summarizes the findings made during the workshop by this emerging critical mass in UCD.

SETD1A Methyltransferase Is Physically and Functionally Linked to the DNA Damage Repair Protein RAD18.

SETD1A is a SET domain-containing methyltransferase involved in epigenetic regulation of transcription. It is the main catalytic component of a multiprotein complex that methylates lysine 4 of histone H3, a histone mark associated with gene activation. In humans, six related protein complexes with partly nonredundant cellular functions share several protein subunits but are distinguished by unique catalytic SET-domain proteins. We surveyed physical interactions of the SETD1A-complex using endogenous immunoprecipitation followed by label-free quantitative proteomics on three subunits: SETD1A, RBBP5, and ASH2L. Surprisingly, SETD1A, but not RBBP5 or ASH2L, was found to interact with the DNA damage repair protein RAD18. Reciprocal RAD18 immunoprecipitation experiments confirmed the interaction with SETD1A, whereas size exclusion and protein network analysis suggested an interaction independent of the main SETD1A complex. We found evidence of SETD1A and RAD18 influence on mutual gene expression levels. Further, knockdown of the genes individually showed a DNA damage repair phenotype, whereas simultaneous knockdown resulted in an epistatic effect. This adds to a growing body of work linking epigenetic enzymes to processes involved in genome stability.

Identification of α-helix 4 (α4) of Rab11a as a novel Rab11-binding domain (RBD): Interaction of Rab11a with the Prostacyclin Receptor.

The cellular trafficking of numerous G protein-coupled receptors (GPCRs) is known to be regulated by Rab proteins that involves a direct protein:protein interaction between the receptor and the GTPase. In the case of the human prostacyclin receptor (hIP), it undergoes agonist-induced internalization and subsequent Rab11a-dependent recyclization involving an interaction between a Rab11-binding domain (RBD) localized within its carboxyl-tail domain with Rab11a. However, the GPCR-interacting domain on Rab11a itself is unknown. Hence, we sought to identify the region within Rab11a that mediates its interaction with the RBD of the hIP. The α4 helix region of Rab11 was identified as a novel binding domain for the hIP, a site entirely distinct from the Switch I/Switch II -regions that act as specific binding domain for most other Rab and Ras-like GTPase interactants. Specifically, Glu138 within α4 helix of Rab11a appears to contact with key residues (e.g. Lys304) within the RBD of the hIP, where such contacts differ depending on the agonist-activated versus -inactive status of the hIP. Through mutational studies, supported by in silico homology modelling of the inactive and active hIP:Rab11a complexes, a mechanism is proposed to explain both the constitutive and agonist-induced binding of Rab11a to regulate intracellular trafficking of the hIP. Collectively, these studies are not only the first to identify α4 helix of Rab11a as a protein binding domain on the GTPase but also reveal novel mechanistic insights into the intracellular trafficking of the hIP, and potentially of other members of the GPCR superfamily, involving Rab11-dependent mechanisms.

Toward fast determination of protein stability maps: experimental and theoretical analysis of mutants of a Nocardiopsis prasina serine protease.

The stability of serine proteases is of major importance for their application in industrial processes. Here we study the determinants of the stability of a Nocardiopsis prasina serine protease using fast residual activity assays, a feature classification algorithm, and structure-based energy calculation algorithms for 121 micropurified mutant enzyme clones containing multiple point mutations. Using a multivariate regression analysis, we deconvolute the data for the mutant clones and find that mutations of residues Asn47 and Pro124 are deleterious to the stability of the enzyme. Both of these residues are situated in loops that are known to be important for the stability of the highly homologous α-lytic protease. Structure-based energy calculations with PEATSA give a good general agreement with the trend of experimentally measured values but also identify a number of clones that the algorithm fails to predict correctly. We discuss the significance of the results in relation to the structure and function of closely related proteases, comment on the optimal experimental design when performing high-throughput experiments for characterizing the determinants of protein stability, and discuss the performance of structure-based energy calculations with complex data sets such as the one presented here.

Highly perturbed pKa values in the unfolded state of hen egg white lysozyme.

The majority of pK(a) values in protein unfolded states are close to the amino acid model pK(a) values, thus reflecting the weak intramolecular interactions present in the unfolded ensemble of most proteins. We have carried out thermal denaturation measurements on the WT and eight mutants of HEWL from pH 1.5 to pH 11.0 to examine the unfolded state pK(a) values and the pH dependence of protein stability for this enzyme. The availability of accurate pK(a) values for the folded state of HEWL and separate measurements of mutant-induced effects on the folded state pK(a) values, allows us to estimate the pK(a) values of seven acidic residues in the unfolded state of HEWL. Asp-48 and Asp-66 display pK(a) values of 2.9 and 3.1 in our analysis, thus representing the most depressed unfolded state pK(a) values observed to date. We observe a strong correlation between the folded state pK(a) values and the unfolded state pK(a) values of HEWL, thus suggesting that the unfolded state of HEWL possesses a large degree of native state characteristics.

Remeasuring HEWL pK(a) values by NMR spectroscopy: methods, analysis, accuracy, and implications for theoretical pK(a) calculations.

Site-specific pK(a) values measured by NMR spectroscopy provide essential information on protein electrostatics, the pH-dependence of protein structure, dynamics and function, and constitute an important benchmark for protein pK(a) calculation algorithms. Titration curves can be measured by tracking the NMR chemical shifts of several reporter nuclei versus sample pH. However, careful analysis of these curves is needed to extract residue-specific pK(a) values since pH-dependent chemical shift changes can arise from many sources, including through-bond inductive effects, through-space electric field effects, and conformational changes. We have re-measured titration curves for all carboxylates and His 15 in Hen Egg White Lysozyme (HEWL) by recording the pH-dependent chemical shifts of all backbone amide nitrogens and protons, Asp/Glu side chain protons and carboxyl carbons, and imidazole protonated carbons and protons in this protein. We extracted pK(a) values from the resulting titration curves using standard fitting methods, and compared these values to each other, and with those measured previously by ¹H NMR (Bartik et al., Biophys J 1994;66:1180­1184). This analysis gives insights into the true accuracy associated with experimentally measured pK(a) values. We find that apparent pK(a) values frequently differ by 0.5­1.0 units depending upon the nuclei monitored, and that larger differences occasionally can be observed. The variation in measured pK(a) values, which reflects the difficulty in fitting and assigning pH-dependent chemical shifts to specific ionization equilibria, has significant implications for the experimental procedures used for measuring protein pK(a) values, for the benchmarking of protein pK(a) calculation algorithms, and for the understanding of protein electrostatics in general.

Capturing, sharing and analysing biophysical data from protein engineering and protein characterization studies.

Large amounts of data are being generated annually on the connection between the sequence, structure and function of proteins using site-directed mutagenesis, protein design and directed evolution techniques. These data provide the fundamental building blocks for our understanding of protein function, molecular biology and living organisms in general. However, much experimental data are never deposited in databases and is thus 'lost' in journal publications or in PhD theses. At the same time theoretical scientists are in need of large amounts of experimental data for benchmarking and calibrating novel predictive algorithms, and theoretical progress is therefore often hampered by the lack of suitable data to validate or disprove a theoretical assumption. We present PEAT (Protein Engineering Analysis Tool), an application that integrates data deposition, storage and analysis for researchers carrying out protein engineering projects or biophysical characterization of proteins. PEAT contains modules for DNA sequence manipulation, primer design, fitting of biophysical characterization data (enzyme kinetics, circular dichroism spectroscopy, NMR titration data, etc.), and facilitates sharing of experimental data and analyses for a typical university-based research group. PEAT is freely available to academic researchers at http://enzyme.ucd.ie/PEAT.