Machine learning-based modulation of Ca2+-binding affinity in EF-hand proteins and comparative structural insights into site-specific cooperative binding

Mazumder, Mohit; Kumar, Sanjeev; Kumar, Devbrat; Bhattacharya, Alok; Gourinath, S.

doi:10.1016/j.ijbiomac.2023.125866

Cited by 4 publications

(3 citation statements)

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“…Mazumder et al employed Support Vector Machines (SVM) to classify binding sites into EF and non-EF categories and predict the affinity and design based on their sequence pattern [43][44] .…”

Section: Introductionmentioning

confidence: 99%

“…The FEATURE program-based algorithm from Altman’s group, requiring physiochemical descriptors, has also been leveraged for calcium-binding site predictions 38–42 . Mazumder et al employed Support Vector Machines (SVM) to classify binding sites into EF and non-EF categories and predict the affinity and design based on their sequence pattern 43-44 . For in-depth information on these methods and their applications, readers are encouraged to refer to related review articles for a comprehensive overview of the evolving field of metal-binding site prediction in proteins 45–50 .…”

Section: Introductionmentioning

confidence: 99%

“…employed Support Vector Machines (SVM) to classify binding sites into EF and non-EF categories and predict the affinity and design based on their sequence pattern [43][44] . For in-depth information on these methods and their applications, readers are encouraged to refer to related review articles for a comprehensive overview of the evolving field of metal-binding site prediction in proteins [45][46][47][48][49][50] .…”

Section: Introductionmentioning

confidence: 99%

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Prediction of Ca²⁺binding site in proteins with a fast and accurate method based on statistical mechanics and analysis of crystal structures

Basit,

Choudhury,

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2024

Preprint

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Predicting the precise locations of metal binding sites within metalloproteins is a crucial challenge in biophysics. It is challenging for both experimental and computational approaches. In the current work, we have predicted the location of Ca2+ ions in calcium-binding proteins using a physics-based method with an all-atom description of the proteins, which is substantially faster than the molecular dynamics simulation-based methods with accuracy as good as data-driven approaches. Our methodology uses the three-dimensional reference interaction site model (3D-RISM), a statistical mechanical theory, to calculate Ca2+ ion density around protein structures. We have taken previously used datasets to assess the efficacy of our method as compared to previous works. Our accuracy is found to be 88%, comparable with the FEATURE program, one of the well-known data-driven methods. Moreover, our method being physical, the reasons for failures can be ascertained in most cases. We have thoroughly examined the failed cases using different structural and crystallographic measures, such as B-factor, R-factor, electron density map, and geometry at the binding site. It has been found that X-ray structures have issues in many of the failed cases. Our algorithm, along with the checks for structural accuracy, is a major step in predicting calcium ion positions in metalloproteins.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Prediction of Ca²⁺binding site in proteins with a fast and accurate method based on statistical mechanics and analysis of crystal structures

Basit,

Choudhury,

Bandyopadhyay

2024

Preprint

View full text Add to dashboard Cite

show abstract

Prediction of Ca²⁺ Binding Site in Proteins With a Fast and Accurate Method Based on Statistical Mechanics and Analysis of Crystal Structures

Basit,

Choudhury,

Bandyopadhyay

2024

Proteins

View full text Add to dashboard Cite

Predicting the precise locations of metal binding sites within metalloproteins is a crucial challenge in biophysics. A fast, accurate, and interpretable computational prediction method can complement the experimental studies. In the current work, we have developed a method to predict the location of Ca2+ ions in calcium‐binding proteins using a physics‐based method with an all‐atom description of the proteins, which is substantially faster than the molecular dynamics simulation‐based methods with accuracy as good as data‐driven approaches. Our methodology uses the three‐dimensional reference interaction site model (3D‐RISM), a statistical mechanical theory, to calculate Ca2+ ion density around protein structures, and the locations of the Ca2+ ions are obtained from the density. We have taken previously used datasets to assess the efficacy of our method as compared to previous works. Our accuracy is 88%, comparable with the FEATURE program, one of the well‐known data‐driven methods. Moreover, our method is physical, and the reasons for failures can be ascertained in most cases. We have thoroughly examined the failed cases using different structural and crystallographic measures, such as B‐factor, R‐factor, electron density map, and geometry at the binding site. It has been found that x‐ray structures have issues in many of the failed cases, such as geometric irregularities and dubious assignment of ion positions. Our algorithm, along with the checks for structural accuracy, is a major step in predicting calcium ion positions in metalloproteins.

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Interaction amongst calcium salts, trypsin, and hide proteins and its application for high-quality leather bating process

Chen,

Liu,

Lei

et al. 2024

Process Biochemistry

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Machine learning-based modulation of Ca2+-binding affinity in EF-hand proteins and comparative structural insights into site-specific cooperative binding

Cited by 4 publications

References 69 publications

Prediction of Ca²⁺binding site in proteins with a fast and accurate method based on statistical mechanics and analysis of crystal structures

Prediction of Ca²⁺binding site in proteins with a fast and accurate method based on statistical mechanics and analysis of crystal structures

Prediction of Ca²⁺ Binding Site in Proteins With a Fast and Accurate Method Based on Statistical Mechanics and Analysis of Crystal Structures

Interaction amongst calcium salts, trypsin, and hide proteins and its application for high-quality leather bating process

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Machine learning-based modulation of Ca2+-binding affinity in EF-hand proteins and comparative structural insights into site-specific cooperative binding

Cited by 4 publications

References 69 publications

Prediction of Ca2+binding site in proteins with a fast and accurate method based on statistical mechanics and analysis of crystal structures

Prediction of Ca2+binding site in proteins with a fast and accurate method based on statistical mechanics and analysis of crystal structures

Prediction of Ca2+ Binding Site in Proteins With a Fast and Accurate Method Based on Statistical Mechanics and Analysis of Crystal Structures

Interaction amongst calcium salts, trypsin, and hide proteins and its application for high-quality leather bating process

Contact Info

Product

Resources

About

Prediction of Ca²⁺binding site in proteins with a fast and accurate method based on statistical mechanics and analysis of crystal structures

Prediction of Ca²⁺binding site in proteins with a fast and accurate method based on statistical mechanics and analysis of crystal structures

Prediction of Ca²⁺ Binding Site in Proteins With a Fast and Accurate Method Based on Statistical Mechanics and Analysis of Crystal Structures