Amino acid interactions that facilitate enzyme catalysis

Coulther, Timothy A.; Ko, Jaeju; Ondrechen, Mary Jo

doi:10.1063/5.0041156

Cited by 12 publications

(30 citation statements)

References 32 publications

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“…Likewise, catalytic lysines tend to be coupled to other lysines, or to anionforming residues with high intrinsic pK a s, such as tyrosines and cysteines. 13 In the examples studied here, the couplings that impart catalytic proficiency to aspartates, glutamates, and lysines, and special properties that enable nucleophilicity, are reported.…”

Section: Resultsmentioning

confidence: 97%

“…Recently, 13 we reported expressions that define certain types of pairwise interactions between amino acids that help to promote catalysis. The proton transfer equilibria of polyprotic acids 14 depend on the energy of interaction ε between the pairs of functional groups and on the p K a difference between them.…”

Section: Resultsmentioning

confidence: 99%

“…For two like‐charge functional groups, an expanded buffer range is achieved if the difference in the intrinsic p K a s of the two residues is approximately within 1 pH unit, as: 13,15

goodbreak- 1 \overset{<}{true} normalp K_{a ((), intrinsic) 1} goodbreak- normalp K_{a ((), intrinsic) 2} \overset{<}{true} goodbreak+ 1

For a cation‐forming residue coupled to an anion‐forming residue, an elongated buffer range occurs when the intrinsic p K a of the anion‐forming (acid) residue is higher than the intrinsic p K a of the (conjugate acid of the) cation‐forming (base) residue and depends on the energy of interaction ε 13,15 . Expressing ε in units of −(ln10)RT, noting that the units are defined so that ε is positive, the optimum difference in intrinsic p K a s is within approximately 1 pH unit of ε , as: 13

ε goodbreak- 1 \overset{<}{true} normalp K_{a ((), intrinsic) acid} goodbreak- normalp K_{a ((), intrinsic) base} \overset{<}{true} ε goodbreak+ 1

Thus, Equations () and () suggest that catalytic aspartates and glutamates tend to be coupled to other aspartates and glutamates, or to histidine residues, if the protein environment can bring the intrinsic p K a of the histidine below that of the acid. Likewise, catalytic lysines tend to be coupled to other lysines, or to anion‐forming residues with high intrinsic p K a s, such as tyrosines and cysteines 13 .…”

Section: Resultsmentioning

confidence: 99%

“…For a cation-forming residue coupled to an anionforming residue, an elongated buffer range occurs when the intrinsic pK a of the anion-forming (acid) residue is higher than the intrinsic pK a of the (conjugate acid of the) cation-forming (base) residue and depends on the energy of interaction ε. 13,15 Expressing ε in units of À(ln10)RT, noting that the units are defined so that ε is positive, the optimum difference in intrinsic pK a s is within approximately 1 pH unit of ε, as: 13…”

Section: Resultsmentioning

confidence: 99%

“…This is consistent with available experimental evidence. Søndergaard and co-workers reported fitted titration curves of individual residues obtained from experimental C 13 NMR data for Bacillus circulans xylanase (Uniprot P09850; EC 3.2.1.8; PDB 1XNB). 25 This 20.4 kDa protein has only two glutamates: E78, the nucleophile and E172, the proton donor.…”

Section: Glycoside Hydrolasesmentioning

confidence: 99%

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Electrostatic fingerprints of catalytically active amino acids in enzymes

Iyengar

Barnsley

et al. 2022

Protein Science

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The computed electrostatic and proton transfer properties are studied for 20 enzymes that represent all six major enzyme commission classes and a variety of different folds. The properties of aspartate, glutamate, and lysine residues that have been previously experimentally determined to be catalytically active are reported. The catalytic aspartate and glutamate residues studied here are strongly coupled to at least one other aspartate or glutamate residue and often to multiple other carboxylate residues with intrinsic pK a differences less than 1 pH unit. Sometimes these catalytic acidic residues are also coupled to a histidine residue, such that the intrinsic pK a of the acidic residue is higher than that of the histidine. All catalytic lysine residues studied here are strongly coupled to tyrosine or cysteine residues, wherein the intrinsic pK a of the anion-forming residue is higher than that of the lysine. Some catalytic lysines are also coupled to other lysines with intrinsic pK a differences within 1 pH unit. Some evidence of the possible types of interactions that facilitate nucleophilicity is discussed. The interactions reported here provide important clues about how side chain functional groups that are weak Brønsted acids or bases for the free amino acid in solution can achieve catalytic potency and become strong acids, bases or nucleophiles in the enzymatic environment.

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Section: Resultsmentioning

confidence: 97%

Section: Resultsmentioning

confidence: 99%

“…For two like‐charge functional groups, an expanded buffer range is achieved if the difference in the intrinsic p K a s of the two residues is approximately within 1 pH unit, as: 13,15

goodbreak- 1 \overset{<}{true} normalp K_{a ((), intrinsic) 1} goodbreak- normalp K_{a ((), intrinsic) 2} \overset{<}{true} goodbreak+ 1

ε goodbreak- 1 \overset{<}{true} normalp K_{a ((), intrinsic) acid} goodbreak- normalp K_{a ((), intrinsic) base} \overset{<}{true} ε goodbreak+ 1

Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Glycoside Hydrolasesmentioning

confidence: 99%

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Electrostatic fingerprints of catalytically active amino acids in enzymes

Iyengar

Barnsley

et al. 2022

Protein Science

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MAHOMES II: A webserver for predicting if a metal binding site is enzymatic

et al. 2023

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Recent advances have enabled high‐quality computationally generated structures for proteins with no solved crystal structures. However, protein function data remains largely limited to experimental methods and homology mapping. Since structure determines function, it is natural that methods capable of using computationally generated structures for functional annotations need to be advanced. Our laboratory recently developed a method to distinguish between metalloenzyme and nonenzyme sites. Here we report improvements to this method by upgrading our physicochemical features to alleviate the need for structures with sub‐angstrom precision and using machine learning to reduce training data labeling error. Our improved classifier identifies protein bound metal sites as enzymatic or nonenzymatic with 94% precision and 92% recall. We demonstrate that both adjustments increased predictive performance and reliability on sites with sub‐angstrom variations. We constructed a set of predicted metalloprotein structures with no solved crystal structures and no detectable homology to our training data. Our model had an accuracy of 90%–97.5% depending on the quality of the predicted structures included in our test. Finally, we found the physicochemical trends that drove this model's successful performance were local protein density, second shell ionizable residue burial, and the pocket's accessibility to the site. We anticipate that our model's ability to correctly identify catalytic metal sites could enable identification of new enzymatic mechanisms and improve de novo metalloenzyme design success rates.

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Mathematical Description of the Enzymatic Activity of Proteins with Ionizable Groups Exhibiting Deviations from the Henderson-Hasselbalch Equation

Franco

Filho

2021

Appl Biochem Biotechnol

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Amino acid interactions that facilitate enzyme catalysis

Cited by 12 publications

References 32 publications

Electrostatic fingerprints of catalytically active amino acids in enzymes

Electrostatic fingerprints of catalytically active amino acids in enzymes

MAHOMES II: A webserver for predicting if a metal binding site is enzymatic

Mathematical Description of the Enzymatic Activity of Proteins with Ionizable Groups Exhibiting Deviations from the Henderson-Hasselbalch Equation

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