In silico assessment of potential druggable pockets on the surface of α1-Antitrypsin conformers

The search for druggable pockets on the surface of a protein is often performed on a single conformer, treated as a rigid body. Transient druggable pockets may be missed in this approach. Here, we describe a methodology for systematic in silico analysis of surface clefts across multiple conformers of the metastable protein α1-antitrypsin (A1AT). Pathological mutations disturb the conformational landscape of A1AT, triggering polymerisation that leads to emphysema and hepatic cirrhosis. Computational screens for small molecule inhibitors of polymerisation have generally focused on one major druggable site visible in all crystal structures of native A1AT. In an alternative approach, we scan all surface clefts observed in crystal structures of A1AT and in 100 computationally produced conformers, mimicking the native solution ensemble. We assess the persistence, variability and druggability of these pockets. Finally, we employ molecular docking using publicly available libraries of small molecules to explore scaffold preferences for each site. Our approach identifies a number of novel target sites for drug design. In particular one transient site shows favourable characteristics for druggability due to high enclosure and hydrophobicity. Hits against this and other druggable sites achieve docking scores corresponding to a Kd in the µM–nM range, comparing favourably with a recently identified promising lead. Preliminary ThermoFluor studies support the docking predictions. In conclusion, our strategy shows considerable promise compared with the conventional single pocket/single conformer approach to in silico screening. Our best-scoring ligands warrant further experimental investigation

An entropic safety catch controls Hepatitis C virus entry and antibody resistance

E1 and E2 (E1E2), the fusion proteins of Hepatitis C Virus (HCV), are unlike that of any other virus yet described, and the detailed molecular mechanisms of HCV entry/fusion remain unknown. Hypervariable region-1 (HVR-1) of E2 is a putative intrinsically disordered protein tail. Here, we demonstrate that HVR-1 has an autoinhibitory function that suppresses the activity of E1E2 on free virions; this is dependent on its conformational entropy. Thus, HVR-1 is akin to a safety catch that prevents premature triggering of E1E2 activity. Crucially, this mechanism is turned off by host receptor interactions at the cell surface to allow entry. Mutations that reduce conformational entropy in HVR-1, or genetic deletion of HVR-1, turn off the safety catch to generate hyper-reactive HCV that exhibits enhanced virus entry but is thermally unstable and acutely sensitive to neutralising antibodies. Therefore, the HVR-1 safety catch controls the efficiency of virus entry and maintains resistance to neutralising antibodies. This discovery provides an explanation for the ability of HCV to persist in the face of continual immune assault and represents a novel regulatory mechanism that is likely to be found in other viral fusion machinery

A Multilaboratory Comparison of Calibration Accuracy and the Performance of External References in Analytical Ultracentrifugation

Analytical ultracentrifugation (AUC) is a first principles based method to determine absolute sedimentation coefficients and buoyant molar masses of macromolecules and their complexes, reporting on their size and shape in free solution. The purpose of this multi-laboratory study was to establish the precision and accuracy of basic data dimensions in AUC and validate previously proposed calibration techniques. Three kits of AUC cell assemblies containing radial and temperature calibration tools and a bovine serum albumin (BSA) reference sample were shared among 67 laboratories, generating 129 comprehensive data sets. These allowed for an assessment of many parameters of instrument performance, including accuracy of the reported scan time after the start of centrifugation, the accuracy of the temperature calibration, and the accuracy of the radial magnification. The range of sedimentation coefficients obtained for BSA monomer in different instruments and using different optical systems was from 3.655 S to 4.949 S, with a mean and standard deviation of (4.304 ± 0.188) S (4.4%). After the combined application of correction factors derived from the external calibration references for elapsed time, scan velocity, temperature, and radial magnification, the range of s-values was reduced 7-fold with a mean of 4.325 S and a 6-fold reduced standard deviation of ± 0.030 S (0.7%). In addition, the large data set provided an opportunity to determine the instrument-to-instrument variation of the absolute radial positions reported in the scan files, the precision of photometric or refractometric signal magnitudes, and the precision of the calculated apparent molar mass of BSA monomer and the fraction of BSA dimers. These results highlight the necessity and effectiveness of independent calibration of basic AUC data dimensions for reliable quantitative studies

A Multilaboratory Comparison of Calibration Accuracy and the Performance of External References in Analytical Ultracentrifugation

Analytical ultracentrifugation (AUC) is a first principles based method to determine absolute sedimentation coefficients and buoyant molar masses of macromolecules and their complexes, reporting on their size and shape in free solution. The purpose of this multi-laboratory study was to establish the precision and accuracy of basic data dimensions in AUC and validate previously proposed calibration techniques. Three kits of AUC cell assemblies containing radial and temperature calibration tools and a bovine serum albumin (BSA) reference sample were shared among 67 laboratories, generating 129 comprehensive data sets. These allowed for an assessment of many parameters of instrument performance, including accuracy of the reported scan time after the start of centrifugation, the accuracy of the temperature calibration, and the accuracy of the radial magnification. The range of sedimentation coefficients obtained for BSA monomer in different instruments and using different optical systems was from 3.655 S to 4.949 S, with a mean and standard deviation of (4.304 ± 0.188) S (4.4%). After the combined application of correction factors derived from the external calibration references for elapsed time, scan velocity, temperature, and radial magnification, the range of s-values was reduced 7-fold with a mean of 4.325 S and a 6-fold reduced standard deviation of ± 0.030 S (0.7%). In addition, the large data set provided an opportunity to determine the instrument-to-instrument variation of the absolute radial positions reported in the scan files, the precision of photometric or refractometric signal magnitudes, and the precision of the calculated apparent molar mass of BSA monomer and the fraction of BSA dimers. These results highlight the necessity and effectiveness of independent calibration of basic AUC data dimensions for reliable quantitative studies

A multilaboratory comparison of calibration accuracy and the performance of external references in analytical ultracentrifugation.

Analytical ultracentrifugation (AUC) is a first principles based method to determine absolute sedimentation coefficients and buoyant molar masses of macromolecules and their complexes, reporting on their size and shape in free solution. The purpose of this multi-laboratory study was to establish the precision and accuracy of basic data dimensions in AUC and validate previously proposed calibration techniques. Three kits of AUC cell assemblies containing radial and temperature calibration tools and a bovine serum albumin (BSA) reference sample were shared among 67 laboratories, generating 129 comprehensive data sets. These allowed for an assessment of many parameters of instrument performance, including accuracy of the reported scan time after the start of centrifugation, the accuracy of the temperature calibration, and the accuracy of the radial magnification. The range of sedimentation coefficients obtained for BSA monomer in different instruments and using different optical systems was from 3.655 S to 4.949 S, with a mean and standard deviation of (4.304 ± 0.188) S (4.4%). After the combined application of correction factors derived from the external calibration references for elapsed time, scan velocity, temperature, and radial magnification, the range of s-values was reduced 7-fold with a mean of 4.325 S and a 6-fold reduced standard deviation of ± 0.030 S (0.7%). In addition, the large data set provided an opportunity to determine the instrument-to-instrument variation of the absolute radial positions reported in the scan files, the precision of photometric or refractometric signal magnitudes, and the precision of the calculated apparent molar mass of BSA monomer and the fraction of BSA dimers. These results highlight the necessity and effectiveness of independent calibration of basic AUC data dimensions for reliable quantitative studies

Protein sample characterization

Most biophysical experiments require protein samples of high quality and accurately determined concentration. This chapter attempts to compile basic information on the most common methods to assess the purity, dispersity, and stability of protein samples. It also reminds of methods to measure protein concentration and of their limits. The idea is to make aware and remind of the range of methods available and of commonly overlooked pitfalls. The aim is to enable experimenters to fully characterize their preparations of soluble or membrane proteins and gain reliable and reproducible results from their experimental work

Properties of surface pockets in crystal structures and <i>in silico</i> conformers of α₁-antitrypsin.

<p>Persistence of clefts A–I among A1AT crystal structures (A) and computationally produced conformers (B). Where the sites C and E overlapped, the data are presented under the label “C_E”. The distribution of SiteMap calculated properties for the 100 <i>in silico</i> conformers are shown as boxplots: SiteScore (C), DScore (D), site volume (E) and hydrophobic vs. hydrophilic character balance (F). The corresponding data for crystal structures are shown as red symbols superimposed on the boxplots; 1qlp (circle), 2qug (plus sign), 3cwm (square), 1hp7 (diamond), 3drm (triangle point up), 1oph (triangle point down). Data are shown only for sites identified within PDB entries for native (stressed, ‘S’) forms of A1AT, as these are likely to be the appropriate target states for the design of polymerization inhibitors.</p

Shifts in melting temperature of A1AT in the presence of selected small molecule ligands (ThermoFluor assay).

<p>The quoted <i>p</i>-values are the result of a Welch two-sample t-test (performed using the R statistical software) testing the null hypothesis that the difference in the mean values of the distribution of the thermal shift values for DMSO and the distribution of the thermal shift values observed for each ligand is zero. The null hypothesis was rejected for <i>p</i>-values <0.01.</p

Fragment docking to the A site targets the pharmacophore defined by Asn104, Thr114, and His139.

<p>Best poses of the top-scoring 20 fragments (coloured sticks) from the ZINC dataset docked in the A site of A1AT (cartoon, blue). The majority of these fragments fill the pocket defined by Thr114 and Asn104 at the top, and His139 at the bottom (thin sticks, cyan), identified in our previous study as a potential allosteric site for targeting A1AT polymerization. Some of the fragments take advantage of hydrogen bonding opportunities presented by His139 and Thr114.</p

Results from docking the DrugBank collection against nine pockets on α₁-antitrypsin.

<p>(A) Boxplot distributions of docking scores for DrugBank molecules docked to each of the nine sites A to I. Only the top-ranking pose is included for each ligand and only ligands of molecular weight less than 500 Daltons are included in this plot. (B) The best-scoring ligand for each site is assigned a worse score when docked against each of the other sites. The red diamonds represent the best docking score for each ligand depicted in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0036612#pone-0036612-t002" target="_blank">Table 2</a>, when docked to the site where it is ranked top. The black diamonds correspond to the scores for each of these ligands when docked to all other sites. The x-axis labels correspond to the DrugBank ID of the ligand and, in brackets, the site against which it is selected as “best-scoring”, e.g. 07124(A) refers to DrugBank entry DB07124 which achieves its best score against site A.</p