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      pH-Dependent Conformational Changes in Proteins and Their Effect on Experimental pK as: The Case of Nitrophorin 4

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          Abstract

          The acid-base behavior of amino acids is an important subject of study due to their prominent role in enzyme catalysis, substrate binding and protein structure. Due to interactions with the protein environment, their pK as can be shifted from their solution values and, if a protein has two stable conformations, it is possible for a residue to have different “microscopic”, conformation-dependent pK a values. In those cases, interpretation of experimental measurements of the pK a is complicated by the coupling between pH, protonation state and protein conformation. We explored these issues using Nitrophorin 4 (NP4), a protein that releases NO in a pH sensitive manner. At pH 5.5 NP4 is in a closed conformation where NO is tightly bound, while at pH 7.5 Asp30 becomes deprotonated, causing the conformation to change to an open state from which NO can easily escape. Using constant pH molecular dynamics we found two distinct microscopic Asp30 pK as: 8.5 in the closed structure and 4.3 in the open structure. Using a four-state model, we then related the obtained microscopic values to the experimentally observed “apparent” pK a, obtaining a value of 6.5, in excellent agreement with experimental data. This value must be interpreted as the pH at which the closed to open population transition takes place. More generally, our results show that it is possible to relate microscopic structure dependent pKa values to experimentally observed ensemble dependent apparent pK as and that the insight gained in the relatively simple case of NP4 can be useful in several more complex cases involving a pH dependent transition, of great biochemical interest.

          Author Summary

          The interaction of an amino acid with its protein environment can result in an acid-base behavior that is very different from what would be observed in solution. This environment can be greatly altered when the protein changes conformation. As a result, the amino acid will have two different “microscopic” pK a values. Nitrophorin 4 is a good case study to explore this behavior, because it undergoes a pH-dependent conformational change that is well characterized experimentally. Using computer simulation tools, we found that the key titratable Aspartic acid 30, has two very different microscopic pK as: 4.3 and 8.5, which are significantly different to the observed transition pK a in solution. However, using a simple model, we were able to understand how this causes the conformational change to take place at pH∼6.5, as measured experimentally. The insight gained in this relatively simple case can be useful in other more complex cases where the apparent pK a is also a result of the interplay of different conformations where some amino acids experience very different environments.

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          Most cited references54

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          Constant pH molecular dynamics in generalized Born implicit solvent.

          A new method is proposed for constant pH molecular dynamics (MD), employing generalized Born (GB) electrostatics. Protonation states are modeled with different charge sets, and titrating residues sample a Boltzmann distribution of protonation states as the simulation progresses, using Monte Carlo sampling based on GB-derived energies. The method is applied to four different crystal structures of hen egg-white lysozyme (HEWL). pK(a) predictions derived from the simulations have root-mean-square (RMS) error of 0.82 relative to experimental values. Similarity of results between the four crystal structures shows the method to be independent of starting crystal structure; this is in contrast to most electrostatics-only models. A strong correlation between conformation and protonation state is noted and quantitatively analyzed, emphasizing the importance of sampling protonation states in conjunction with dynamics. (c) 2004 Wiley Periodicals, Inc.
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            Structural basis of perturbed pKa values of catalytic groups in enzyme active sites.

            In protein and RNA macromolecules, only a limited number of different side-chain chemical groups are available to function as catalysts. The myriad of enzyme-catalyzed reactions results from the ability of most of these groups to function either as nucleophilic, electrophilic, or general acid-base catalysts, and the key to their adapted chemical function lies in their states of protonation. Ionization is determined by the intrinsic pKa of the group and the microenvironment created around the group by the protein or RNA structure, which perturbs its intrinsic pKa to its functional or apparent pKa. These pKa shifts result from interactions of the catalytic group with other fully or partially charged groups as well as the polarity or dielectric of the medium that surrounds it. The electrostatic interactions between ionizable groups found on the surface of macromolecules are weak and cause only slight pKa perturbations ( 2 units) and are the subject of this review. The magnitudes of these pKa perturbations are analyzed with respect to the structural details of the active-site microenvironment and the energetics of the reactions that they catalyze.
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              Combining conformational flexibility and continuum electrostatics for calculating pK(a)s in proteins.

              Protein stability and function relies on residues being in their appropriate ionization states at physiological pH. In situ residue pK(a)s also provides a sensitive measure of the local protein environment. Multiconformation continuum electrostatics (MCCE) combines continuum electrostatics and molecular mechanics force fields in Monte Carlo sampling to simultaneously calculate side chain ionization and conformation. The response of protein to charges is incorporated both in the protein dielectric constant (epsilon(prot)) of four and by explicit conformational changes. The pK(a) of 166 residues in 12 proteins was determined. The root mean square error is 0.83 pH units, and >90% have errors of 2 pH units. Similar results are found with crystal and solution structures, showing that the method's explicit conformational sampling reduces sensitivity to the initial structure. The outcome also changes little with protein dielectric constant (epsilon(prot) 4-20). Multiconformation continuum electrostatics titrations show coupling of conformational flexibility and changes in ionization state. Examples are provided where ionizable side chain position (protein G), Asn orientation (lysozyme), His tautomer distribution (RNase A), and phosphate ion binding (RNase A and H) change with pH. Disallowing these motions changes the calculated pK(a).
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                Author and article information

                Contributors
                Role: Editor
                Journal
                PLoS Comput Biol
                PLoS Comput. Biol
                plos
                ploscomp
                PLoS Computational Biology
                Public Library of Science (San Francisco, USA )
                1553-734X
                1553-7358
                November 2012
                November 2012
                1 November 2012
                : 8
                : 11
                : e1002761
                Affiliations
                [1 ]Quantum Theory Project and Department of Chemistry, University of Florida, Gainesville, Florida, United States of America
                [2 ]Departamento de Química Inorgánica, Analítica y Química Física/INQUIMAE-CONICET, Facultad de Ciencias Exactas y Naturales, Universidad de Buenos Aires, Ciudad Universitaria, Buenos Aires, Argentina
                [3 ]Departamento de Química Biológica, Facultad de Ciencias Exactas y Naturales, Universidad de Buenos Aires, Ciudad Universitaria, Buenos Aires, Argentina
                University of Houston, United States of America
                Author notes

                The authors have declared that no competing interests exist.

                Conceived and designed the experiments: DAE MAM AER. Performed the experiments: NVDR. Analyzed the data: NVDR MAM AER. Contributed reagents/materials/analysis tools: DAE MAM AER. Wrote the paper: NVDR DAE MAM AER.

                Article
                PCOMPBIOL-D-12-01033
                10.1371/journal.pcbi.1002761
                3486867
                23133364
                d111de2e-8d25-4f93-a21a-a4b1d0d7e6be
                Copyright @ 2012

                This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

                History
                : 22 June 2012
                : 13 September 2012
                Page count
                Pages: 9
                Funding
                This work is supported by the National Institute of Health ( http://www.nih.gov/) under contract 1R01AI073674. This work was partially supported by grants PICT-2010-0416 ( http://www.agencia.gov.ar/), UBACYT 2010-2012 ( http://www.uba.ar/) and a grant from the Bunge & Born Foundation ( http://www.fundacionbyb.org/) to MAM. Computer resources and support were provided by the Large Allocations Resource Committee through grant TG-MCA05S010 and the University of Florida High-Performance Computing Center. Publication of this article was funded in part by the University of Florida Open Access Publishing Fund. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
                Categories
                Research Article
                Biology
                Biochemistry
                Proteins
                Hemoproteins
                Protein Chemistry
                Protein Structure
                Biochemistry Simulations
                Computational Biology
                Biochemical Simulations
                Chemistry
                Computational Chemistry
                Molecular Dynamics

                Quantitative & Systems biology
                Quantitative & Systems biology

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