Pichia pastoris is a widely used host for recombinant protein production because it combines the advantages of microbial cultivation with eukaryotic protein folding and secretion. However, secretion efficiency is often limited by the folding capacity of the endoplasmic reticulum (ER), where recombinant proteins must be translocated, folded, and processed prior to export. When ER folding capacity is exceeded, proteins may be retained, degraded, or secreted in non-native conformations, reducing both yield and product quality. Chaperone engineering and codon optimization represent two promising strategies to address these limitations. Here, we generated stable Pichia strains expressing four model secreted proteins (human serum albumin, interleukin-2, thaumatin-I, and thaumatin-II) using either conventional codon optimization or Epi-MAX codon engineering, which adapts transgene codon usage to stress-responsive translational programs. We also engineered strains containing an additional chromosomal copy of either the ER Hsp70 chaperone Kar2 or protein disulfide isomerase (Pdi1). To assess protein quality, we applied limited proteolysis mass spectrometry (LiP-MS), a structural proteomics approach that can detect subtle conformational differences to secreted proteins. Increased Pdi1 levels improved secretion of all four proteins tested, whereas Kar2 overexpression generally reduced yield. For thaumatin-II, Pdi1 enhanced secretion but promoted release of a non-native conformation, which we could correct through codon engineering. Together, these results demonstrate that maximizing recombinant protein production requires optimization of both yield and structural quality and establish complementary strategies for improving secreted protein expression in Pichia.
The comparison of adopter and non-adopter sample reveals three potential adoption inhibitor, security, data privacy, and portability, which underlines the importance of the technical and security perspectives for research investigating the adoption of technology.
Nattakarn Phaphoom, Xiaofeng Wang, S. Samuel et al.· Journal of Systems and Softw...· 111 citations· ⚡8
This study investigates how Lean internal startup facilitates software product innovation in large companies and identifies its enablers and inhibitors, and shows the potential of the method-in-action framework to investigate the Lean startup approach in non-startup context.
Henry Edison, Nina M. Smørsgård, Xiaofeng Wang et al.· Journal of Systems and Softw...· 78 citations· ⚡6
This paper highlights the challenges to conduct proper affect-related studies with psychology, provides a comprehensive literature review in affect theory, and proposes guidelines for conducting psychoempirical software engineering.
D. Graziotin, Xiaofeng Wang, P. Abrahamsson· SSE@SIGSOFT FSE· 56 citations· ⚡4
This study conducts a multiple case study on twenty European software startups and proposes a prototype-centric learning model in early stage software startups, and identifies factors that occur as barriers but also facilitators for prototyping in earlystage software startups.
Anh Nguyen-Duc, Xiaofeng Wang, P. Abrahamsson· International Conference on...· 44 citations· ⚡5
It is demonstrated that linker-free PROTACs can outperform traditional designs, marking a paradigm shift in PROTAC development for targeted protein degradation.
Pinal, a 16-billion-parameter foundation model that produces protein candidates from natural-language functional descriptions, supports natural language as a high-level interface for candidate generation in protein design, enabling programmable exploration with reduced reliance on manually specified structural or sequence constraints.
A new machine-learning framework aims to improve the success rate of computational protein design while moving away from results that reproduce sequences found in nature.