Skip to content
#protein folding Open access

Quasi-continuous cotranslational compaction and folding of a multidomain protein

Oct 2026 · Nature Communications · Vol 17 · 0 citations · 88 references
Medicine

Abstract

Most proteins start to fold cotranslationally as they come off the ribosome. So far, studies of cotranslational folding have focused mainly on small, single-domain proteins. Here, we have used Force Profile Analysis to study the cotranslational folding of Firefly Luciferase, a complex 550-residue protein composed of an N-terminal domain (NTD) encompassing two split Rossmann folds (RF-1, RF-2) and a β-roll, and a flexibly attached C-terminal domain (CTD). The folding process is characterized by a quasi-continuous series of compaction/folding steps that generate intermediate-size pulling forces on the nascent chain, punctuated by a prominent high-force event that represents the folding of the RF-2 domain, and a few low-force instances that likely indicate the formation of distinct folding intermediates. Trigger Factor interacts extensively with the nascent chain when the central part of RF-2 and the early parts of the CTD are synthesized. Our analysis uncovers a cotranslational compaction/folding process that is rich in detail and not just a simple succession of a few distinct, cooperative folding transitions. Force Profile Analysis show that firefly luciferase folds co-translationally through a continuous compaction process, punctuated by a major subdomain folding event. Trigger Factor binds to the nascent chain at distinct steps in the folding profile.

Read PDF

Similar papers

#computer vision Review Open access May 2015

A survey study on major technical barriers affecting the decision to adopt cloud services

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. · 111 citations · ⚡8
#computer vision Open access Feb 2018

Lean Internal Startups for Software Product Innovation in Large Companies: Enablers and Inhibitors

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. · 78 citations · ⚡6
#computer vision Book Open access Jul 2015

Understanding the affect of developers: theoretical background and guidelines for psychoempirical software engineering

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 · 56 citations · ⚡4
#machine learning Open access May 2017

What Influences the Speed of Prototyping? An Empirical Investigation of Twenty Software Startups

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 · 44 citations · ⚡5
#protein folding Open access Sep 2026

Programmable design of functional proteins from natural language

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...

Fengyuan Dai, Shiyang You, Yudian Zhu et al. · 31 citations · ⚡3

Related blog posts

Google DeepMind Blog Sep 30, 2026

Introducing SynthID Bio

Proof of concept for watermarking AI-generated proteins while preserving biological function.

MIT News · Artificial Intelligence Aug 27, 2026

Looking beyond natural sequences

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.

We use cookies to run the site and, with your consent, for analytics and to show ads. See our Cookie Policy.