Automated optimization of the solubility of a hyper-stable α-amylase.

Most successes in computational protein engineering to date have focused on enhancing one biophysical trait, while multi-trait optimization remains a challenge. Different biophysical properties are often conflicting, as mutations that improve one tend to worsen the others. In this study, we explored the potential of an automated computational design strategy, called CamSol Combination, to optimize solubility and stability of enzymes without affecting their activity. Specifically, we focus on Bacillus licheniformis α-amylase (BLA), a hyper-stable enzyme that finds diverse application in industry and biotechnology. We validate the computational predictions by producing 10 BLA variants, including the wild-type (WT) and three designed models harbouring between 6 and 8 mutations each. Our results show that all three models have substantially improved relative solubility over the WT, unaffected catalytic rate and retained hyper-stability, supporting the algorithm's capacity to optimize enzymes. High stability and solubility embody enzymes with superior resilience to chemical and physical stresses, enhance manufacturability and allow for high-concentration formulations characterized by extended shelf lives. This ability to readily optimize solubility and stability of enzymes will enable the rapid and reliable generation of highly robust and versatile reagents, poised to contribute to advancements in diverse scientific and industrial domains.

Risk willingness in multiple system atrophy and Parkinson's disease understanding patient preferences.

Disease-modifying therapeutics in the α-synucleinopathies multiple system atrophy (MSA) and Parkinson's Disease (PD) are in early phases of clinical testing. Involving patients' preferences including therapy-associated risk willingness in initial stages of therapy development has been increasingly pursued in regulatory approval processes. In our study with 49 MSA and 38 PD patients, therapy-associated risk willingness was quantified using validated standard gamble scenarios for varying severities of potential drug or surgical side effects. Demonstrating a non-gaussian distribution, risk willingness varied markedly within, and between groups. MSA patients accepted a median 1% risk [interquartile range: 0.001-25%] of sudden death for a 99% [interquartile range: 99.999-75%] chance of cure, while PD patients reported a median 0.055% risk [interquartile range: 0.001-5%]. Contrary to our hypothesis, a considerable proportion of MSA patients, despite their substantially impaired quality of life, were not willing to accept increased therapy-associated risks. Satisfaction with life situation, emotional, and nonmotor disease burden were associated with MSA patients' risk willingness in contrast to PD patients, for whom age, and disease duration were associated factors. An individual approach towards MSA and PD patients is crucial as direct inference from disease (stage) to therapy-associated risk willingness is not feasible. Such studies may be considered by regulatory agencies in their approval processes assisting with the weighting of safety aspects in a patient-centric manner. A systematic quantitative assessment of patients' risk willingness and associated features may assist physicians in conducting individual consultations with patients who have MSA or PD by facilitating communication of risks and benefits of a treatment option.