A growing body of evidence suggests that synonymous substitutions, DNA sequence changes that do not alter the encoded amino acid and so should be selectively neutral, can be under strong selection. Yet our ability to identify putative non-neutral synonymous substitutions from comparative sequence data is limited. Here, we use Bayesian stochastic character mapping to contrast substitution rates and patterns for each position along two bacterial genes, followed by hierarchical clustering on principal components (HCPC) and consensus clustering to robustly identify synonymous sites showing signatures of selection. Specifically, we compare gtsB from Pseudomonas fluorescens, known from experimental studies to harbour synonymous mutations with strongly beneficial fitness effects, with a more conserved gene in the same operon, gtsD. We find that synonymous four-fold degenerate sites can vary from highly constrained, characterized by low rates and constrained patterns of substitution comparable to neighboring nonsynonymous second codon positions, to weakly constrained, exhibiting high rates and diverse patterns of substitution indicative of weak purifying selection characteristic of nearly neutrally evolving sites. Highly constrained sites account for 8% and 7.5% of the four-fold degenerate sites in gtsB and gtsD, respectively, and include some positions previously characterized through experiments as beneficial, suggesting they are sites that experience non-neutral selection. Patterns of codon usage, protein structure, or local sequence motifs do not explain the variation in substitution patterns between constrained and unconstrained sites. Combined, our site-specific approach suggests non-neutral synonymous mutations can occur at an appreciable frequency and may often contribute to adaptation.
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 seque...
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.