Machine learning reveals hidden stability code in protein native fluorescence

Crystal structures and molecular dynamics simulations of a humanised antibody fragment at acidic to basic pH

Antibody-fragment (Fab) therapy development has the potential to be accelerated by computational modelling and simulations that predict their target binding, stability, formulation, manufacturability, and the impact of further protein engineering. Such approaches are currently predicated on starting with good crystal structures that closely represent those found under the solution conditions to be simulated. A33 Fab, is an undeveloped immunotherapeutic antibody candidate that was targeted to the human A33 antigen homogeneously expressed in 95% cases of primary and metastatic colorectal cancers. It is now used as a very well characterised testing ground for developing analytics, formulation and protein engineering strategies, and to gain a deeper understanding of mechanisms of destabilisation, representative of the wider therapeutic Fab platform. In this article, we report the structure of A33 Fab in two different crystal forms obtained at acidic and basic pH. The structures overlapped with RMSD of 1.33 Å overall, yet only 0.5 Å and 0.76 Å for the variable- and constant regions alone. While most of the differences were within experimental error, the switch linker between the variable and the constant regions showed some small differences between the two pHs. The two structures then enabled a direct evaluation of the impact of initial crystal structure selection on the outcomes of molecular dynamics simulations under different conditions, and their subsequent use for determining best fit solution structures using previously obtained small-angle x-ray scattering (SAXS) data. The differences in the two structures did not have a major impact on MD simulations regardless of the pH, other than a slight persistence of structure affecting the solvent accessibility of one of the predicted APR regions of A33 Fab. Interestingly, despite being obtained at pH 4 and pH 9, the two crystal structures were more similar to the SAXS solution structures obtained at pH 7, than to those at pH 4 or pH 9. Furthermore, the P65 crystal structure from pH 4 was also a better representation of the solution structures at any other pH, than was the P1 structure obtained at pH 9. Thus, while obtained at different pH, the two crystal structures may represent highly (P65) and lesser (P1) populated species that both exist at pH 7 in solution. These results now lay the foundation for confident MD simulations of A33 Fab that rationalise or predict behaviours in a range of conditions.

In-depth analysis of biocatalysts by microfluidics: An emerging source of data for machine learning

Nowadays, the vastly increasing demand for novel biotechnological products is supported by the continuous development of biocatalytic applications which provide sustainable green alternatives to chemical processes. The success of a biocatalytic application is critically dependent on how quickly we can identify and characterize enzyme variants fitting the conditions of industrial processes. While miniaturization and parallelization have dramatically increased the throughput of next-generation sequencing systems, the subsequent characterization of the obtained candidates is still a limiting process in identifying the desired biocatalysts. Only a few commercial microfluidic systems for enzyme analysis are currently available, and the transformation of numerous published prototypes into commercial platforms is still to be streamlined. This review presents the state-of-the-art, recent trends, and perspectives in applying microfluidic tools in the functional and structural analysis of biocatalysts. We discuss the advantages and disadvantages of available technologies, their reproducibility and robustness, and readiness for routine laboratory use. We also highlight the unexplored potential of microfluidics to leverage the power of machine learning for biocatalyst development.

NMR Reveals Functionally Relevant Thermally Induced Structural Changes within the Native Ensemble of G-CSF

Structure–function relationships in proteins refer to a trade-off between stability and bioactivity, molded by evolution of the molecule. Identifying which protein amino acid residues jeopardize global or local stability for the benefit of bioactivity would reveal residues pivotal to this structure–function trade-off. Here, we use ¹⁵N–¹H heteronuclear single quantum coherence (HSQC) nuclear magnetic resonance (NMR) spectroscopy to probe the microenvironment and dynamics of residues in granulocyte colony-stimulating factor (G-CSF) through thermal perturbation. From this analysis, we identified four residues (G4, A6, T133, and Q134) that we classed as significant to global stability, given that they all experienced large environmental and dynamic changes and were closely correlated to each other in their NMR characteristics. Additionally, we observe that roughly four structural clusters are subject to localized conformational changes or partial unfolding prior to global unfolding at higher temperature. Combining NMR observables with structure relaxation methods reveals that these structural clusters concentrate around loop AB (binding site III inclusive). This loop has been previously implicated in conformational changes that result in an aggregation prone state of G-CSF. Residues H43, V48, and S63 appear to be pivotal to an opening motion of loop AB, a change that is possibly also important for function. Hence, we present here an approach to profiling residues in order to highlight their potential roles in the two vital characteristics of proteins: stability and bioactivity.

Protein Engineering and HDX Identify Structural Regions of G-CSF Critical to Its Stability and Aggregation

The protein engineering and formulation of therapeutic proteins for prolonged shelf-life remain a major challenge in the biopharmaceutical industry. Understanding the influence of mutations and formulations on the protein structure and dynamics could lead to more predictive approaches to their improvement. Previous intrinsic fluorescence analysis of the chemically denatured granulocyte colony-stimulating factor (G-CSF) suggested that loop AB could subtly reorganize to form an aggregation-prone intermediate state. Hydrogen deuterium exchange mass spectrometry (HDX-MS) has also revealed that excipient binding increased the thermal unfolding transition midpoint (T_m) by stabilizing loop AB. Here, we have combined protein engineering with biophysical analyses and HDX-MS to reveal that increased exchange in a core region of the G-CSF comprising loop AB (ABI, a small helix, ABII) and loop CD packed onto helix B and the beginning of loop BC leads to a decrease in T_m and higher aggregation rates. Furthermore, some mutations can increase the population of the aggregation-prone conformation within the native ensemble, as measured by the greater local exchange within this core region.