pH-Dependent Conformational Changes Lead to a Highly Shifted p<i>K</i><sub>a</sub> for a Buried Glutamic Acid Mutant of SNase

Sarkar, Ankita; Roitberg, Adrián E.

doi:10.1021/acs.jpcb.0c07136

Cited by 8 publications

(10 citation statements)

References 63 publications

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“…In some cases, proteins may be trapped in nonrepresentative conformations 72 or largely coupled between conformational and protonation states. 73 It is also important to note the variability in pKa measurements of titratable residues in proteins. The pKa of Cys residue is strongly influenced by protein microenvironment 74 -nearby basic residues decrease Cys pKa by stabilizing thiolate state.…”

Section: Resultsmentioning

confidence: 99%

Benchmarking in silico Tools for Cysteine pKa Prediction

Awoonor-Williams

Golosov

Horn̆ák

2023

Preprint

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Accurate estimation of the pKas of cysteine residues in proteins could inform targeted approaches in hit discovery. The pKa of a targetable cysteine residue in a disease-related protein is an important physiochemical parameter in covalent drug dis- covery, as it influences the fraction of nucleophilic thiolate amenable to chemical protein modification. Traditional structure-based in silico tools are limited in their predictive accuracy of cysteine pKas relative to other titratable residues. Additionally, there are limited comprehensive benchmark assessments for cysteine pKa predictive tools. This raises the need for extensive assessment and evaluation of methods for cysteine pKa prediction. Here, we report the performance of several computational pKa methods, including single structure and ensemble-based approaches, on a diverse test set of experimental cysteine pKas retrieved from the PKAD data- base. The dataset consisted of 16 wildtype and 10 mutant proteins with experimentally measured cysteine pKa values. Our results highlight that these methods are varied in their overall predictive accuracies. Among the test set of wildtype proteins evaluated, the best method yielded a mean absolute error of 2.3 pK units highlighting the need for improvement of existing pKa methods for accurate cysteine pKa estimation. Given the limited accuracy of these methods, further development is needed before these approaches can be routinely employed to drive design decisions in early drug discovery efforts.

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Section: Resultsmentioning

confidence: 99%

Benchmarking in silico Tools for Cysteine pKa Prediction

Awoonor-Williams

Golosov

Horn̆ák

2023

Preprint

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“…A major limitation of traditional single-structure-based p K a methods is that they are unable to capture dynamic changes in the local environment of ionizable residues. In some cases, proteins may be trapped in nonrepresentative conformations or largely coupled between conformational and protonation states . It is also important to note the variability in p K a measurements of titratable residues in proteins.…”

Section: Resultsmentioning

confidence: 99%

Benchmarking In Silico Tools for Cysteine pK_a Prediction

Awoonor-Williams¹,

Golosov²,

Horn̆ák³

2023

J. Chem. Inf. Model.

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Accurate estimation of the pK a’s of cysteine residues in proteins could inform targeted approaches in hit discovery. The pK a of a targetable cysteine residue in a disease-related protein is an important physiochemical parameter in covalent drug discovery, as it influences the fraction of nucleophilic thiolate amenable to chemical protein modification. Traditional structure-based in silico tools are limited in their predictive accuracy of cysteine pK a’s relative to other titratable residues. Additionally, there are limited comprehensive benchmark assessments for cysteine pK a predictive tools. This raises the need for extensive assessment and evaluation of methods for cysteine pK a prediction. Here, we report the performance of several computational pK a methods, including single-structure and ensemble-based approaches, on a diverse test set of experimental cysteine pK a’s retrieved from the PKAD database. The dataset consisted of 16 wildtype and 10 mutant proteins with experimentally measured cysteine pK a values. Our results highlight that these methods are varied in their overall predictive accuracies. Among the test set of wildtype proteins evaluated, the best method (MOE) yielded a mean absolute error of 2.3 pK units, highlighting the need for improvement of existing pK a methods for accurate cysteine pK a estimation. Given the limited accuracy of these methods, further development is needed before these approaches can be routinely employed to drive design decisions in early drug discovery efforts.

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“…Conformational changes in turn can influence the p K a values, making the determination of p K a values even more challenging. This coupling between protonation/deprotonation and conformational changes is often exploited for function in many biological processes. − Engineered internal ionizable residues in variants of Staphylococcal nuclease (SNase) have been studied in the past two decades to catalogue both structures and p K a values. − For such residues, conformational rearrangements have been shown to be important for their p K a values. ,− In addition, these residues were used as targets in the first blind p K a prediction contest, p K a Cooperative, and found to be challenging for many methodologies. , …”

Section: Introductionmentioning

confidence: 99%

Protein pK_a Prediction by Tree-Based Machine Learning

Chen

Lee

Damjanović

et al. 2022

J. Chem. Theory Comput.

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Protonation states of ionizable protein residues modulate many essential biological processes. For correct modeling and understanding of these processes, it is crucial to accurately determine their pK a values. Here, we present four tree-based machine learning models for protein pK a prediction. The four models, Random Forest, Extra Trees, eXtreme Gradient Boosting (XGBoost), and Light Gradient Boosting Machine (LightGBM), were trained on three experimental PDB and pK a datasets, two of which included a notable portion of internal residues. We observed similar performance among the four machine learning algorithms. The best model trained on the largest dataset performs 37% better than the widely used empirical pK a prediction tool PROPKA and 15% better than the published result from the pK a prediction method DelPhiPKa. The overall root-mean-square error (RMSE) for this model is 0.69, with surface and buried RMSE values being 0.56 and 0.78, respectively, considering six residue types (Asp, Glu, His, Lys, Cys, and Tyr), and 0.63 when considering Asp, Glu, His, and Lys only. We provide pK a predictions for proteins in human proteome from the AlphaFold Protein Structure Database and observed that 1% of Asp/Glu/Lys residues have highly shifted pK a values close to the physiological pH.

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pH-Dependent Conformational Changes Lead to a Highly Shifted pK_a for a Buried Glutamic Acid Mutant of SNase

Cited by 8 publications

References 63 publications

Benchmarking in silico Tools for Cysteine pKa Prediction

Benchmarking in silico Tools for Cysteine pKa Prediction

Benchmarking In Silico Tools for Cysteine pK_a Prediction

Protein pK_a Prediction by Tree-Based Machine Learning

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pH-Dependent Conformational Changes Lead to a Highly Shifted pKa for a Buried Glutamic Acid Mutant of SNase

Cited by 8 publications

References 63 publications

Benchmarking in silico Tools for Cysteine pKa Prediction

Benchmarking in silico Tools for Cysteine pKa Prediction

Benchmarking In Silico Tools for Cysteine pKa Prediction

Protein pKa Prediction by Tree-Based Machine Learning

Contact Info

Product

Resources

About

pH-Dependent Conformational Changes Lead to a Highly Shifted pK_a for a Buried Glutamic Acid Mutant of SNase

Benchmarking In Silico Tools for Cysteine pK_a Prediction

Protein pK_a Prediction by Tree-Based Machine Learning