Protein Unfolding: Denaturant vs. Force

Kelly, Colleen M.; Gage, Matthew J.

doi:10.3390/biomedicines9101395

Cited by 5 publications

(3 citation statements)

References 75 publications

(134 reference statements)

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“…This strategy can principally be used also to conduct various other operations such as physical soundness (e.g., mass conservation), whether a result satisfies one or more principles or a partial differential equation, or constraints or design objectives. The use of natural language, combined with the capabilities of LLMs to effectively deal with numbers, data, code, and its execution, offers a wide array of possibilities in materials science and engineering and beyond, including, but not limited to, interpretability and the possibility of direct engagement with a human expert. − , …”

Section: Discussionmentioning

confidence: 99%

Generative Retrieval-Augmented Ontologic Graph and Multiagent Strategies for Interpretive Large Language Model-Based Materials Design

Buehler

2024

ACS Eng. Au

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Transformer neural networks show promising capabilities, in particular for uses in materials analysis, design, and manufacturing, including their capacity to work effectively with human language, symbols, code, and numerical data. Here, we explore the use of large language models (LLMs) as a tool that can support engineering analysis of materials, applied to retrieving key information about subject areas, developing research hypotheses, discovery of mechanistic relationships across disparate areas of knowledge, and writing and executing simulation codes for active knowledge generation based on physical ground truths. Moreover, when used as sets of AI agents with specific features, capabilities, and instructions, LLMs can provide powerful problem-solution strategies for applications in analysis and design problems. Our experiments focus on using a fine-tuned model, MechGPT, developed based on training data in the mechanics of materials domain. We first affirm how fine-tuning endows LLMs with a reasonable understanding of subject area knowledge. However, when queried outside the context of learned matter, LLMs can have difficulty recalling correct information and may hallucinate. We show how this can be addressed using retrievalaugmented Ontological Knowledge Graph strategies. The graph-based strategy helps us not only to discern how the model understands what concepts are important but also how they are related, which significantly improves generative performance and also naturally allows for injection of new and augmented data sources into generative AI algorithms. We find that the additional feature of relatedness provides advantages over regular retrieval augmentation approaches and not only improves LLM performance but also provides mechanistic insights for exploration of a material design process. Illustrated for a use case of relating distinct areas of knowledge, here, music and proteins, such strategies can also provide an interpretable graph structure with rich information at the node, edge, and subgraph level that provides specific insights into mechanisms and relationships. We discuss other approaches to improve generative qualities, including nonlinear sampling strategies and agent-based modeling that offer enhancements over singleshot generations, whereby LLMs are used to both generate content and assess content against an objective target. Examples provided include complex question answering, code generation, and execution in the context of automated force-field development from actively learned density functional theory (DFT) modeling and data analysis.

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Section: Discussionmentioning

confidence: 99%

Generative Retrieval-Augmented Ontologic Graph and Multiagent Strategies for Interpretive Large Language Model-Based Materials Design

Buehler

2024

ACS Eng. Au

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“…Mechanical methods measure the force that must be exerted on a domain to cause unfolding; single‐molecule atomic force microscopy (AFM) (Anderson et al, 2013; Zuo et al, 2021) has been used to probe the destabilization of domains by missense variants. While protein denaturation methods and protocols vary widely, experiments using chemical, thermal, and mechanical methods on titin domain Ig86 have demonstrated that the unfolding rates were consistent (Kelly & Gage, 2021). Thus, comparison across different methods is possible; however, re‐folding rates differ slightly.…”

Section: Prioritization Of Variantsmentioning

confidence: 99%

Walking with giants: The challenges of variant impact assessment in the giant sarcomeric protein titin

Weston,

Rees,

Gautel

et al. 2023

WIREs Mechanisms of Disease

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Titin, the so‐called “third filament” of the sarcomere, represents a difficult challenge for the determination of damaging genetic variants. A single titin molecule extends across half the length of a sarcomere in striated muscle, fulfilling a variety of vital structural and signaling roles, and has been linked to an equally varied range of myopathies, resulting in a significant burden on individuals and healthcare systems alike. While the consequences of truncating variants of titin are well‐documented, the ramifications of the missense variants prevalent in the general population are less so. We here present a compendium of titin missense variants—those that result in a single amino‐acid substitution in coding regions—reported to be pathogenic and discuss these in light of the nature of titin and the variant position within the sarcomere and their domain, the structural, pathological, and biophysical characteristics that define them, and the methods used for characterization. Finally, we discuss the current knowledge and integration of the multiple fields that have contributed to our understanding of titin‐related pathology and offer suggestions as to how these concurrent methodologies may aid the further development in our understanding of titin and hopefully extend to other, less well‐studied giant proteins.This article is categorized under: Cardiovascular Diseases > Genetics/Genomics/Epigenetics Congenital Diseases > Genetics/Genomics/Epigenetics Congenital Diseases > Molecular and Cellular Physiology

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“…An even more detailed understanding of protein denaturation behavior can be achieved by contrasting the combination of chemical and thermal denaturation experiments ( 25 ) with the behavior of protein denaturation under force ( 26 ). Both types of experiment have been shown to produce similar denaturation pathways and rates ( 27 , 28 ). Computational simulations can add atomistic details ( 29 ) when combined with chemical/thermal denaturation studies.…”

Section: Introductionmentioning

confidence: 99%

Correlation between chemical denaturation and the unfolding energetics of Acanthamoeba actophorin

Thota¹,

Quirk²,

Zhuang³

et al. 2023

Biophysical Journal

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Protein Unfolding: Denaturant vs. Force

Cited by 5 publications

References 75 publications

Generative Retrieval-Augmented Ontologic Graph and Multiagent Strategies for Interpretive Large Language Model-Based Materials Design

Generative Retrieval-Augmented Ontologic Graph and Multiagent Strategies for Interpretive Large Language Model-Based Materials Design

Walking with giants: The challenges of variant impact assessment in the giant sarcomeric protein titin

Correlation between chemical denaturation and the unfolding energetics of Acanthamoeba actophorin

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