Skip to content
#protein folding Open access

Diagnostic cancellation and benchmark mismatch in SOD1 folding free energy calculations

Oct 2026 · Zenodo (CERN European Organization for Nuclear Research)
Protein Structure and Dynamics

Abstract

Alchemical free-energy calculations are increasingly used to predict how missense mutations change protein folding stability, and their reliability is usually judged by aggregate convergence diagnostics and agreement with experimental controls. This working paper reports a validation campaign for the apo, disulfide-reduced monomer of human Cu/Zn superoxide dismutase 1 (SOD1), the protein behind a subset of familial ALS, computed with GROMACS, pmx hybrid topologies and equilibrium λ-window sampling analyzed by MBAR. Gate thresholds (Pearson r ≥ 0.70, RMSE ≤ 1.5 kcal/mol, median net hysteresis ≤ 0.75 kcal/mol against eight experimental controls) were recorded before the first evaluation. The gate failed (r = 0.326, RMSE = 2.123 kcal/mol across seven usable variants), and prospective predictions were withheld. Post-verdict audits identified two limitations of the validation design itself. First, the standard net forward/reverse hysteresis metric sums signed discrepancies and can conceal large local disagreement: in one F64A ladder, net hysteresis was 0.023 kcal/mol while the summed absolute discrepancy was 1.678 kcal/mol (98.6% cancellation), and separately specified continuations showed the estimate moving by −2.08 kcal/mol between disjoint 3-ns blocks. Second, the experimental references were not state-matched to the simulated species in redox state, construct, oligomeric transition, or temperature. These are reported as limitations of one validation design, not as a validated diagnostic or an accuracy estimate for SOD1. This record contains the manuscript (PDF and Word) and the S1 Appendix (A4V sampling-extension and G93A disulfide sensitivity studies). Code, configuration, and analysis workflows are available at https://github.com/Meowsers4/sod1-free-energy-validation. This is a working paper and has not been peer reviewed. AI tools assisted with code development, auditing, manuscript drafting and formatting; the authors are responsible for the scientific content

View source

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.