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1. Machine-Learned Free Energy Surfaces for Capillary Condensation and Evaporation in Mesopores

   https://doi.org/10.3390/e24010097  

   Desgranges, Caroline; Delhommelle, Jerome (January 2022, Entropy)

   Using molecular simulations, we study the processes of capillary condensation and capillary evaporation in model mesopores. To determine the phase transition pathway, as well as the corresponding free energy profile, we carry out enhanced sampling molecular simulations using entropy as a reaction coordinate to map the onset of order during the condensation process and of disorder during the evaporation process. The structural analysis shows the role played by intermediate states, characterized by the onset of capillary liquid bridges and bubbles. We also analyze the dependence of the free energy barrier on the pore width. Furthermore, we propose a method to build a machine learning model for the prediction of the free energy surfaces underlying capillary phase transition processes in mesopores. 

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2. One-Pot Assembly of Drug-Eluting Silk Coatings with Applications for Nerve Regeneration

   https://doi.org/10.1021/acsbiomaterials.3c01042  

   Fink, Tanner D.; Funnell, Jessica L.; Gilbert, Ryan J.; Zha, R. Helen (January 2024, ACS Biomaterials Science & Engineering)

   Clinical use of polymeric scaffolds for tissue engineering often suffers from their inability to promote strong cellular interactions. Functionalization with biomolecules may improve outcomes; however, current functionalization approaches using covalent chemistry or physical adsorption can lead to loss of biomolecule bioactivity. Here, we demonstrate a novel bottom-up approach for enhancing the bioactivity of poly(l-lactic acid) electrospun scaffolds though interfacial coassembly of protein payloads with silk fibroin into nanothin coatings. In our approach, protein payloads are first added into an aqueous solution with Bombyx mori-derived silk fibroin. Phosphate anions are then added to trigger coassembly of the payload and silk fibroin, as well as noncovalent formation of a payload-silk fibroin coating at poly(l-lactic) acid fiber surfaces. Importantly, the coassembly process results in homogeneous distribution of protein payloads, with the loading quantity depending on payload concentration in solution and coating time. This coassembly process yields greater loading capacity than physical adsorption methods, and the payloads can be released over time in physiologically relevant conditions. We also demonstrate that the coating coassembly process can incorporate nerve growth factor and that coassembled coatings lead to significantly more neurite extension than loading via adsorption in a rat dorsal root ganglia explant culture model. 

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3. Non-native entanglement protein misfolding observed in all-atom simulations and supported by experimental structural ensembles

   https://doi.org/10.1126/sciadv.adt8974  

   Vu, Quyen V; Sitarik, Ian; Jiang, Yang; Xia, Yingzi; Sharma, Piyoosh; Yadav, Divya; Song, Hyebin; Li, Mai Suan; Fried, Stephen D; O’Brien, Edward P (August 2025, Science Advances)

   Several mechanisms are known to cause monomeric protein misfolding. Coarse-grained simulations have predicted an additional mechanism exists involving off-pathway, noncovalent lasso entanglements, which are long-lived kinetic traps and structurally resemble the native state. Here, we examine whether such misfolded states occur in long-timescale, all-atom folding simulations of ubiquitin and λ-repressor. We find that these entangled misfolded states are populated in higher-resolution models. However, because of the small size of ubiquitin and λ-repressor, these states are short-lived. In contrast, coarse-grained simulations of a larger protein, IspE, predict that it populates long-lived misfolded states. Using an Arrhenius extrapolation applied to all-atom simulations, we estimate that these IspE misfolded states have lifetimes similar to the native state while remaining soluble. We further show that these misfolded states are consistent with the structural changes inferred from limited proteolysis and cross-linking mass spectrometry experiments. Our results indicate that misfolded states composed of non-native entanglements can persist for long timescales in both all-atom simulations and experiments. 

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4. Exploring adsorption of neutral aromatic pollutants onto graphene nanomaterials via molecular dynamics simulations and theoretical linear solvation energy relationships

   https://doi.org/10.1039/C8EN00575C  

   Wang, Ya; Comer, Jeffrey; Chen, Zhongfang; Chen, Jingwen; Gumbart, James C. (January 2018, Environmental Science: Nano)

   Predicting adsorption of organic pollutants onto graphene nanomaterials is not only useful for exploring their potential adsorbent applications, but also helpful for better understanding their fate and risks in aquatic environments. Herein molecular dynamics (MD) simulations and theoretical linear solvation energy relationships (TLSERs) were employed to construct prediction models for adsorption of neutral organic pollutants onto graphene and graphene oxides. The MD simulations for adsorption of 43 aromatic compounds onto graphene and diverse models of graphene oxides with various functional groups (hydroxyl, epoxy and carbonyl) demonstrate that graphene has a stronger affinity for the aromatic compounds than graphene oxides. The hydroxyl and carbonyl groups of graphene oxides were found to form hydrogen bonds with the aromatic adsorbates, while epoxy groups did not. TLSER models were developed for predicting the adsorption equilibrium coefficients ( K ) onto graphene and graphene oxide nanosheets. In the graphene prediction model, H-donating ability ( ε α ) and dispersion/hydrophobic interactions ( V ) have significant effects on log  K values, while in the graphene oxide model, ε α is the most influential factor on log  K values. The models provide in silico approaches for predicting adsorption affinities onto graphenic nanomaterials. 

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5. UbE3-APA: A Bioinformatic Strategy to Elucidate Ubiquitin E3 Ligase Activities in Quantitative Proteomics Study

   https://doi.org/10.1093/bioinformatics/btac069  

   Gong, Yao; Chen, Yue (February 2022, Bioinformatics)

   Vitek, Olga (Ed.)

   Abstract Motivation Ubiquitination is widely involved in protein homeostasis and cell signaling. Ubiquitin E3 ligases are critical regulators of ubiquitination that recognize and recruit specific ubiquitination targets for the final rate-limiting step of ubiquitin transfer reactions. Understanding the ubiquitin E3 ligase activities will provide knowledge in the upstream regulator of the ubiquitination pathway and reveal potential mechanisms in biological processes and disease progression. Recent advances in mass spectrometry-based proteomics have enabled deep profiling of ubiquitylome in a quantitative manner. Yet, functional analysis of ubiquitylome dynamics and pathway activity remains challenging. Results Here, we developed a UbE3-APA, a computational algorithm and stand-alone python-based software for Ub E3 ligase Activity Profiling Analysis. Combining an integrated annotation database with statistical analysis, UbE3-APA identifies significantly activated or suppressed E3 ligases based on quantitative ubiquitylome proteomics datasets. Benchmarking the software with published quantitative ubiquitylome analysis confirms the genetic manipulation of SPOP enzyme activity through overexpression and mutation. Application of the algorithm in the re-analysis of a large cohort of ubiquitination proteomics study revealed the activation of PARKIN and the co-activation of other E3 ligases in mitochondria depolarization-induced mitophagy process. We further demonstrated the application of the algorithm in the DIA-based quantitative ubiquitylome analysis. Availability Source code and binaries are freely available for download at URL: https://github.com/Chenlab-UMN/Ub-E3-ligase-Activity-Profiling-Analysis, implemented in python and supported on Linux and MS Windows Supplementary information Supplementary data are available at Bioinformatics online. 

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