Interpreting a black box predictor to gain insights into early folding mechanisms

Grau, Isel; Nowé, Ann; Vranken, Wim

doi:10.1016/j.csbj.2021.08.041

Cited by 6 publications

(2 citation statements)

References 35 publications

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“…With the breakthroughs in protein structure prediction techniques, the exploration and prediction of protein folding pathways have garnered substantial attention from the computational structure biology community. Various methods have been proposed, including the simulation of an inverse folding pathway from native state to unfolded state, , prediction of early folding residues using machine learning , and the prediction of protein folding intermediates based on templates . Although these methods have shown promising results to some extent, accurately predicting protein folding pathways remains a major challenge.…”

Section: Challenges For Protein Structure Prediction Methodsmentioning

confidence: 99%

Recent Advances and Challenges in Protein Structure Prediction

Peng,

Liang,

Xia

et al. 2023

J. Chem. Inf. Model.

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Artificial intelligence has made significant advances in the field of protein structure prediction in recent years. In particular, DeepMind's end-to-end model, AlphaFold2, has demonstrated the capability to predict three-dimensional structures of numerous unknown proteins with accuracy levels comparable to those of experimental methods. This breakthrough has opened up new possibilities for understanding protein structure and function as well as accelerating drug discovery and other applications in the field of biology and medicine. Despite the remarkable achievements of artificial intelligence in the field, there are still some challenges and limitations. In this Review, we discuss the recent progress and some of the challenges in protein structure prediction. These challenges include predicting multidomain protein structures, protein complex structures, multiple conformational states of proteins, and protein folding pathways. Furthermore, we highlight directions in which further improvements can be conducted.

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Section: Challenges For Protein Structure Prediction Methodsmentioning

confidence: 99%

Recent Advances and Challenges in Protein Structure Prediction

Peng,

Liang,

Xia

et al. 2023

J. Chem. Inf. Model.

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“…By and large, post-hoc explanation methods can either rely on the inner structures of the explained model or be completely model-agnostic. Prominent approaches in the latter category include feature attribution methods, such as Shapley additive explanations (SHAP) [1], feature importance [2], [3], local interpretable model-agnostic explanations (LIME) [4], or global surrogate models [5]- [7]. On the other hand, posthoc explanation methods specifically designed for neural networks cover multilayer [8], [9], convolutional [10]- [13], graph [14], [15], as well as recurrent [16], [17] architectures.…”

Section: Introductionmentioning

confidence: 99%

Forward Composition Propagation for Explainable Neural Reasoning

Grau,

Nápoles,

Bello

et al. 2024

IEEE Comput. Intell. Mag.

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Protein structuromics: A new method for protein structure–function crosstalk in glioma

et al. 2023

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Glioma is a type of tumor that starts in the glial cells of the brain or spine. Since the 1800s, when the disease was first named, its survival rates have always been unsatisfactory. Despite great advances in molecular biology and traditional treatment methods, many questions regarding cancer occurrence and the underlying mechanism remain to be answered. In this study, we assessed the protein structural features of 20 oncogenes and 20 anti‐oncogenes via protein structure and dynamic analysis methods and 3D structural and systematic analyses of the structure–function relationships of proteins. All of these results directly indicate that unfavorable group proteins show more complex structures than favorable group proteins. As the tumor cell microenvironment changes, the balance of oncogene‐related and anti‐oncogene‐related proteins is disrupted, and most of the structures of the two groups of proteins will be disrupted. However, more unfavorable group proteins will maintain and refold to achieve their correct shape faster and perform their functions more quickly than favorable group proteins, and the former thus support cancer development. We hope that these analyses will help promote mechanistic research and the development of new treatments for glioma.

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Interpreting a black box predictor to gain insights into early folding mechanisms

Abstract: Graphical abstract

Cited by 6 publications

References 35 publications

Recent Advances and Challenges in Protein Structure Prediction

Recent Advances and Challenges in Protein Structure Prediction

Forward Composition Propagation for Explainable Neural Reasoning

Protein structuromics: A new method for protein structure–function crosstalk in glioma

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