Pointwise prediction of protein diffusive properties using machine learning
The understanding of cellular mechanisms benefits substantially from accurate determination of protein diffusive properties. Prior work in this field primarily focuses on traditional methods, such as mean square displacements, for calculation of protein diffusion coefficients and biological states....
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IOP Publishing
2025-01-01
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| Series: | JPhys Photonics |
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| Online Access: | https://doi.org/10.1088/2515-7647/adede9 |
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| author | Rasched Haidari Achillefs N Kapanidis |
| author_facet | Rasched Haidari Achillefs N Kapanidis |
| author_sort | Rasched Haidari |
| collection | DOAJ |
| description | The understanding of cellular mechanisms benefits substantially from accurate determination of protein diffusive properties. Prior work in this field primarily focuses on traditional methods, such as mean square displacements, for calculation of protein diffusion coefficients and biological states. This proves difficult and error-prone for proteins undergoing heterogeneous behaviour, particularly in complex environments, limiting the exploration of new biological behaviours. The importance of determining protein diffusion coefficients, anomalous exponents, and biological behaviours led to the Anomalous Diffusion Challenge 2024, exploring machine learning methods to infer these variables in heterogeneous trajectories with time-dependent changepoints. In response to the challenge, we present M3, a machine learning method for pointwise inference of diffusive coefficients, anomalous exponents, and states along noisy heterogenous protein trajectories. M3 makes use of long short-term memory cells to achieve small mean absolute errors for the diffusion coefficient and anomalous exponent alongside high state accuracies (>90%). Subsequently, we implement changepoint detection to determine timepoints at which protein behaviour changes. M3 removes the need for expert fine-tuning required in most conventional statistical methods while being computationally inexpensive to train. The model finished in the Top 5 of the Anomalous Diffusive Challenge 2024, with small improvements made since challenge closure. |
| format | Article |
| id | doaj-art-108f3f62e3b545248b2b8502fcab653d |
| institution | Kabale University |
| issn | 2515-7647 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IOP Publishing |
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| series | JPhys Photonics |
| spelling | doaj-art-108f3f62e3b545248b2b8502fcab653d2025-08-20T03:30:33ZengIOP PublishingJPhys Photonics2515-76472025-01-017303502510.1088/2515-7647/adede9Pointwise prediction of protein diffusive properties using machine learningRasched Haidari0https://orcid.org/0009-0003-2371-7397Achillefs N Kapanidis1https://orcid.org/0000-0001-6699-136XGene Machines Group, Clarendon Laboratory, Department of Physics, University of Oxford , Oxford, United Kingdom; Kavli Institute of Nanoscience Discovery, Dorothy Crowfoot Hodgkin Building, University of Oxford , Oxford, United KingdomGene Machines Group, Clarendon Laboratory, Department of Physics, University of Oxford , Oxford, United Kingdom; Kavli Institute of Nanoscience Discovery, Dorothy Crowfoot Hodgkin Building, University of Oxford , Oxford, United KingdomThe understanding of cellular mechanisms benefits substantially from accurate determination of protein diffusive properties. Prior work in this field primarily focuses on traditional methods, such as mean square displacements, for calculation of protein diffusion coefficients and biological states. This proves difficult and error-prone for proteins undergoing heterogeneous behaviour, particularly in complex environments, limiting the exploration of new biological behaviours. The importance of determining protein diffusion coefficients, anomalous exponents, and biological behaviours led to the Anomalous Diffusion Challenge 2024, exploring machine learning methods to infer these variables in heterogeneous trajectories with time-dependent changepoints. In response to the challenge, we present M3, a machine learning method for pointwise inference of diffusive coefficients, anomalous exponents, and states along noisy heterogenous protein trajectories. M3 makes use of long short-term memory cells to achieve small mean absolute errors for the diffusion coefficient and anomalous exponent alongside high state accuracies (>90%). Subsequently, we implement changepoint detection to determine timepoints at which protein behaviour changes. M3 removes the need for expert fine-tuning required in most conventional statistical methods while being computationally inexpensive to train. The model finished in the Top 5 of the Anomalous Diffusive Challenge 2024, with small improvements made since challenge closure.https://doi.org/10.1088/2515-7647/adede9anomalous diffusionpointwise inferencediffusionAnDi2 challengemachine learningLSTM |
| spellingShingle | Rasched Haidari Achillefs N Kapanidis Pointwise prediction of protein diffusive properties using machine learning JPhys Photonics anomalous diffusion pointwise inference diffusion AnDi2 challenge machine learning LSTM |
| title | Pointwise prediction of protein diffusive properties using machine learning |
| title_full | Pointwise prediction of protein diffusive properties using machine learning |
| title_fullStr | Pointwise prediction of protein diffusive properties using machine learning |
| title_full_unstemmed | Pointwise prediction of protein diffusive properties using machine learning |
| title_short | Pointwise prediction of protein diffusive properties using machine learning |
| title_sort | pointwise prediction of protein diffusive properties using machine learning |
| topic | anomalous diffusion pointwise inference diffusion AnDi2 challenge machine learning LSTM |
| url | https://doi.org/10.1088/2515-7647/adede9 |
| work_keys_str_mv | AT raschedhaidari pointwisepredictionofproteindiffusivepropertiesusingmachinelearning AT achillefsnkapanidis pointwisepredictionofproteindiffusivepropertiesusingmachinelearning |