Many molecular events are associated with small or large conformational changes occurring in the corresponding proteins. Modeling such changes is a challenge and requires significant amount of computing time. From point of view of electrostatics, these changes can be viewed as a reorganization of local charges and dipoles in response to the changes of the electrostatic field, if the cause is insertion or deletion of a charged amino acid. Here we report a large scale investigation of modeling the changes of the folding energy due to single mutations involving charged group. This allows the changes of the folding energy to be considered mostly electrostatics in origin and to be calculated with DelPhi assigning residue-specific value of the internal dielectric constant of protein. The predicted energy changes are benchmarked against experimentally measured changes of the folding energy on a set of 257 single mutations. The best fit between experimental values and predicted changes is used to find out the effective value of the internal dielectric constant for each type of amino acid. The predicted folding free energy changes with the optimal, amino acid specific, dielectric constants are within RMSD=0.86 kcal/mol from experimentally measured changes.
DelPhi; protein electrostatics; dielectric constant; Poisson-Boltzmann equation; protein flexibility; energy calculations; single point mutations
Here we report a web server, the DelPhi web server, which utilizes DelPhi program to calculate electrostatic energies and the corresponding electrostatic potential and ionic distributions, and dielectric map. The server provides extra services to fix structural defects, as missing atoms in the structural file and allows for generation of missing hydrogen atoms. The hydrogen placement and the corresponding DelPhi calculations can be done with user selected force field parameters being either Charmm22, Amber98 or OPLS. Upon completion of the calculations, the user is given option to download fixed and protonated structural file, together with the parameter and Delphi output files for further analysis. Utilizing Jmol viewer, the user can see the corresponding structural file, to manipulate it and to change the presentation. In addition, if the potential map is requested to be calculated, the potential can be mapped onto the molecule surface. The DelPhi web server is available from http://compbio.clemson.edu/delphi_webserver.
DelPhi; electrostatics; proteins; continuum models; electrostatic potential; Finite-Difference Poisson-Boltzmann solver
Experimental data shows that the binding of human prolactin (hPRL) to human prolactin receptor (hPRLr-ECD) is strongly pH-dependent, while the binding of the same receptor to human growth hormone (hGH) is pH-independent. Here we carry in silico analysis of the molecular effects causing such a difference and reveal the role of individual amino acids. It is shown that the computational modeling correctly predicts experimentally determined pKa’s of histidine residues in an unbound state in the majority of the cases and the pH-dependence of the binding free energy. Structural analysis carried in conjunction with calculated pH-dependence of the binding revealed that the main reason for pH-dependence of the binding of hPRL-hPRLr-ECD is a number of salt- bridges across the interface of the complex, while no salt-bridges are formed in the hGH-hPRlr-ECD. Specifically, most of the salt-bridges involve histidine residues and this is the reason for the pH-dependence across a physiological range of pH. The analysis not only revealed the molecular mechanism of the pH-dependence of the hPRL-hPRLr-ECD, but also provided critical insight into the underlying physic-chemical mechanism.
human prolactin; human prolactin receptor; human growth hormone; pKa calculations; pH-dependence; electrostatics
Motivation: Ions are essential component of the cell and frequently are found bound to various macromolecules, in particular to proteins. A binding of an ion to a protein greatly affects protein’s biophysical characteristics and needs to be taken into account in any modeling approach. However, ion’s bounded positions cannot be easily revealed experimentally, especially if they are loosely bound to macromolecular surface.
Results: Here, we report a web server, the BION web server, which addresses the demand for tools of predicting surface bound ions, for which specific interactions are not crucial; thus, they are difficult to predict. The BION is easy to use web server that requires only coordinate file to be inputted, and the user is provided with various, but easy to navigate, options. The coordinate file with predicted bound ions is displayed on the output and is available for download.
Supplementary data are available at Bioinformatics online.
This review outlines the recent progress made in developing more accurate and efficient solutions to model electrostatics in systems comprised of bio-macromolecules and nano-objects, the last one referring to objects that do not have biological function themselves but nowadays are frequently used in biophysical and medical approaches in conjunction with bio-macromolecules. The problem of modeling macromolecular electrostatics is reviewed from two different angles: as a mathematical task provided the specific definition of the system to be modeled and as a physical problem aiming to better capture the phenomena occurring in the real experiments. In addition, specific attention is paid to methods to extend the capabilities of the existing solvers to model large systems toward applications of calculations of the electrostatic potential and energies in molecular motors, mitochondria complex, photosynthetic machinery and systems involving large nano-objects.
Continuum electrostatics; Poisson-Boltzmann equation; numerical techniques; dielectric constant; molecular surface
Chloride intracellular channel 2 (CLIC2) protein is a member of the glutathione transferase class of proteins. Its' only known function is the regulation of ryanodine receptor (RyR) intracellular Ca2+ release channels. These RyR proteins play a major role in the regulation of Ca2+ signaling in many cells. Utilizing exome capture and deep sequencing of genes on the X-chromosome, we have identified a mutation in CLIC2 (c.303C>G, p.H101Q) which is associated with X-linked intellectual disability (ID), atrial fibrillation, cardiomegaly, congestive heart failure (CHF), some somatic features and seizures. Functional studies of the H101Q variant indicated that it stimulated rather than inhibited the action of RyR channels, with channels remaining open for longer times and potentially amplifying Ca2+ signals dependent on RyR channel activity. The overly active RyRs in cardiac and skeletal muscle cells and neuronal cells would result in abnormal cardiac function and trigger post-synaptic pathways and neurotransmitter release. The presence of both cardiomegaly and CHF in the two affected males and atrial fibrillation in one are consistent with abnormal RyR2 channel function. Since the dysfunction of RyR2 channels in the brain via ‘leaky mutations’ can result in mild developmental delay and seizures, our data also suggest a vital role for the CLIC2 protein in maintaining normal cognitive function via its interaction with RyRs in the brain. Therefore, our patients appear to suffer from a new channelopathy comprised of ID, seizures and cardiac problems because of enhanced Ca2+ release through RyRs in neuronal cells and cardiac muscle cells.
The Gauss-Seidel method is a standard iterative numerical method widely used to solve a system of equations and, in general, is more efficient comparing to other iterative methods, such as the Jacobi method. However, standard implementation of the Gauss-Seidel method restricts its utilization in parallel computing due to its requirement of using updated neighboring values (i.e., in current iteration) as soon as they are available. Here we report an efficient and exact (not requiring assumptions) method to parallelize iterations and to reduce the computational time as a linear/nearly linear function of the number of CPUs. In contrast to other existing solutions, our method does not require any assumptions and is equally applicable for solving linear and nonlinear equations. This approach is implemented in the DelPhi program, which is a finite difference Poisson-Boltzmann equation solver to model electrostatics in molecular biology. This development makes the iterative procedure on obtaining the electrostatic potential distribution in the parallelized DelPhi several folds faster than that in the serial code. Further we demonstrate the advantages of the new parallelized DelPhi by computing the electrostatic potential and the corresponding energies of large supramolecular structures.
electrostatics; DelPhi; Poisson- Boltzmann equation; Gauss-Seidel iteration; parallel computing
Summary: A new edition of the DelPhi web server, DelPhi web server v2, is released to include atomic presentation of geometrical figures. These geometrical objects can be used to model nano-size objects together with real biological macromolecules. The position and size of the object can be manipulated by the user in real time until desired results are achieved. The server fixes structural defects, adds hydrogen atoms and calculates electrostatic energies and the corresponding electrostatic potential and ionic distributions.
Availability and implementation: The web server follows a client–server architecture built on PHP and HTML and utilizes DelPhi software. The computation is carried out on supercomputer cluster and results are given back to the user via http protocol, including the ability to visualize the structure and corresponding electrostatic potential via Jmol implementation. The DelPhi web server is available from http://compbio.clemson.edu/delphi_webserver.
Supplementary data are available at Bioinformatics online.
Many studies have shown that missense mutations might play an important role in carcinogenesis. However, the extent to which cancer mutations might affect biomolecular interactions remains unclear. Here, we map glioblastoma missense mutations on the human protein interactome, model the structures of affected protein complexes and decipher the effect of mutations on protein-protein, protein-nucleic acid and protein-ion binding interfaces. Although some missense mutations over-stabilize protein complexes, we found that the overall effect of mutations is destabilizing, mostly affecting the electrostatic component of binding energy. We also showed that mutations on interfaces resulted in more drastic changes of amino acid physico-chemical properties than mutations occurring outside the interfaces. Analysis of glioblastoma mutations on interfaces allowed us to stratify cancer-related interactions, identify potential driver genes, and propose two dozen additional cancer biomarkers, including those specific to functions of the nervous system. Such an analysis also offered insight into the molecular mechanism of the phenotypic outcomes of mutations, including effects on complex stability, activity, binding and turnover rate. As a result of mutated protein and gene network analysis, we observed that interactions of proteins with mutations mapped on interfaces had higher bottleneck properties compared to interactions with mutations elsewhere on the protein or unaffected interactions. Such observations suggest that genes with mutations directly affecting protein binding properties are preferably located in central network positions and may influence critical nodes and edges in signal transduction networks.
In this review we discuss the role of protonation states in receptor-ligand interactions, providing experimental evidences and computational predictions that complex formation may involve titratable groups with unusual pKa’s and that protonation states frequently change from unbound to bound states. These protonation changes result in proton uptake/release, which in turn causes the pH-dependence of the binding. Indeed, experimental data strongly suggests that almost any binding is pH-dependent and to be correctly modeled, the protonation states must be properly assigned prior to and after the binding. One may accurately predict the protonation states when provided with the structures of the unbound proteins and their complex; however, the modeling becomes much more complicated if the bound state has to be predicted in a docking protocol or if the structures of either bound or unbound receptor-ligand are not available. The major challenges that arise in these situations are the coupling between binding and protonation states, and the conformational changes induced by the binding and ionization states of titratable groups. In addition, any assessment of the protonation state, either before or after binding, must refer to the pH of binding, which is frequently unknown. Thus, even if the pKa’s of ionizable groups can be correctly assigned for both unbound and bound state, without knowing the experimental pH one cannot assign the corresponding protonation states, and consequently one cannot calculate the resulting proton uptake/release. It is pointed out, that while experimental pH may not be the physiological pH and binding may involve proton uptake/release, there is a tendency that the native receptor-ligand complexes have evolved toward specific either subcellular or tissue characteristic pH at which the proton uptake/release is either minimal or absent.
protonation states; receptor-ligand interactions; pKa calculations; pH-dependence; electrostatics
The definition of a molecular surface which is physically sound and computationally efficient is a very interesting and long standing problem in the implicit solvent continuum modeling of biomolecular systems as well as in the molecular graphics field. In this work, two molecular surfaces are evaluated with respect to their suitability for electrostatic computation as alternatives to the widely used Connolly-Richards surface: the blobby surface, an implicit Gaussian atom centered surface, and the skin surface. As figures of merit, we considered surface differentiability and surface area continuity with respect to atom positions, and the agreement with explicit solvent simulations. Geometric analysis seems to privilege the skin to the blobby surface, and points to an unexpected relationship between the non connectedness of the surface, caused by interstices in the solute volume, and the surface area dependence on atomic centers. In order to assess the ability to reproduce explicit solvent results, specific software tools have been developed to enable the use of the skin surface in Poisson-Boltzmann calculations with the DelPhi solver. Results indicate that the skin and Connolly surfaces have a comparable performance from this last point of view.
Molecular Surface; Connolly Surface; Blobby Surface; Skin Surface; Poisson-Boltzmann
Motivation: The folding free energy is an important characteristic of proteins stability and is directly related to protein's wild-type function. The changes of protein's stability due to naturally occurring mutations, missense mutations, are typically causing diseases. Single point mutations made in vitro are frequently used to assess the contribution of given amino acid to the stability of the protein. In both cases, it is desirable to predict the change of the folding free energy upon single point mutations in order to either provide insights of the molecular mechanism of the change or to design new experimental studies.
Results: We report an approach that predicts the free energy change upon single point mutation by utilizing the 3D structure of the wild-type protein. It is based on variation of the molecular mechanics Generalized Born (MMGB) method, scaled with optimized parameters (sMMGB) and utilizing specific model of unfolded state. The corresponding mutations are built in silico and the predictions are tested against large dataset of 1109 mutations with experimentally measured changes of the folding free energy. Benchmarking resulted in root mean square deviation = 1.78 kcal/mol and slope of the linear regression fit between the experimental data and the calculations was 1.04. The sMMGB is compared with other leading methods of predicting folding free energy changes upon single mutations and results discussed with respect to various parameters.
Availability: All the pdb files we used in this article can be downloaded from http://compbio.clemson.edu/downloadDir/mentaldisorders/sMMGB_pdb.rar
Supplementary data are available at Bioinformatics online.
Spermine synthase (SMS) is an enzyme which function is to convert spermidine into spermine. It was shown that gene defects resulting in amino acid changes of the wild type SMS cause Snyder-Robinson syndrome, which is a mild-to-moderate mental disability associated with osteoporosis, facial asymmetry, thin habitus, hypotonia, and a nonspecific movement disorder. These disease-causing missense mutations were demonstrated, both in silico and in vitro, to affect the wild type function of SMS by either destabilizing the SMS dimer/monomer or directly affecting the hydrogen bond network of the active site of SMS. In contrast to these studies, here we report an artificial engineering of a more efficient SMS variant by transferring sequence information from another organism. It is confirmed experimentally that the variant, bearing four amino acid substitutions, is catalytically more active than the wild type. The increased functionality is attributed to enhanced monomer stability, lowering the pKa of proton donor catalytic residue, optimized spatial distribution of the electrostatic potential around the SMS with respect to substrates, and increase of the frequency of mechanical vibration of the clefts presumed to be the gates toward the active sites. The study demonstrates that wild type SMS is not particularly evolutionarily optimized with respect to the reaction spermidine → spermine. Having in mind that currently there are no variations (non-synonymous single nucleotide polymorphism, nsSNP) detected in healthy individuals, it can be speculated that the human SMS function is precisely tuned toward its wild type and any deviation is unwanted and disease-causing.
Proteins are constantly subjected to evolutionary pressure to assure the organism's survival and reproduction. At the same time, the proteins' amino acid sequence undergoes mutations, some of which may cause diseases while others may be reflecting natural differences within the population (non-synonymous single nucleotide polymorphism, nsSNP). In this study we examine the human spermine synthase (HsSMS), for which currently there are no nsSNPs, while rare disease mutations are known to cause Snyder-Robinson syndrome. What is so special with this protein? Maybe the HsSMS is so well optimized for its function that any change of the wild type sequence should be degrading its performance. To check such a possibility, we engineered a mutant of HsSMS with enhanced stability, electrostatic and mechanical properties. The mutant was confirmed experimentally to be a better enzyme than the wild type. Thus, the HsSMS is not evolutionally optimized with respect to its enzymatic reaction, its amino acid sequence differs only in sick individuals and so far its sequence was found to be identical in all healthy individuals. Therefore, it can be speculated that the HsSMS function is precisely tuned toward the wild type characteristics such so any deviation is unwanted and is disease-causing.
With the progress of nanotechnology, one frequently has to model biological macromolecules simultaneously with nano-objects. However, the atomic structures of the nano objects are typically not available or they are solid state entities. Because of that, the researchers have to investigate such nano systems by generating models of the nano objects in a manner that the existing software be able to carry the simulations. In addition, it should allow generating composite objects with complex shape by combining basic geometrical figures and embedding biological macromolecules within the system.
Here we report the Protein Nano-Object Integrator (ProNOI) which allows for generating atomic-style geometrical objects with user desired shape and dimensions. Unlimited number of objects can be created and combined with biological macromolecules in Protein Data Bank (PDB) format file. Once the objects are generated, the users can use sliders to manipulate their shape, dimension and absolute position. In addition, the software offers the option to charge the objects with either specified surface or volumetric charge density and to model them with user-desired dielectric constants. According to the user preference, the biological macromolecule atoms can be assigned charges and radii according to four different force fields: Amber, Charmm, OPLS and PARSE. The biological macromolecules and the atomic-style objects are exported as a position, charge and radius (PQR) file, or if a default dielectric constant distribution is not selected, it is exported as a position, charge, radius and epsilon (PQRE) file. As illustration of the capabilities of the ProNOI, we created a composite object in a shape of a robot, aptly named the Clemson Robot, whose parts are charged with various volumetric charge densities and holds the barnase-barstar protein complex in its hand.
The Protein Nano-Object Integrator (ProNOI) is a convenient tool for generating atomic-style nano shapes in conjunction with biological macromolecule(s). Charges and radii on the macromolecule atoms and the atoms in the shapes are assigned according to the user’s preferences allowing various scenarios of modeling. The default output file is in PQR (PQRE) format which is readable by almost any software available in biophysical field. It can be downloaded from: http://compbio.clemson.edu/downloadDir/ProNO_integrator.tar.gz
Biological macromolecules; Electrostatic calculations; Molecular modeling; Nano technology; DelPhi; Poisson-Boltzmann equation
Accurate predictions of pKa values of titratable groups require taking into account all relevant processes associated with the ionization/deionization. Frequently, however, the ionization does not involve significant structural changes and the dominating effects are purely electrostatic in origin allowing accurate predictions to be made based on the electrostatic energy difference between ionized and neutral forms alone using a static structure. On another hand, if the change of the charge state is accompanied by a structural reorganization of the target protein, then the relevant conformational changes have to be taken into account in the pKa calculations. Here we report a hybrid approach that first predicts the titratable groups, which ionization is expected to cause conformational changes, termed “problematic” residues, then applies a special protocol on them, while the rest of the pKa’s are predicted with rigid backbone approach as implemented in multi-conformation continuum electrostatics (MCCE) method. The backbone representative conformations for “problematic” groups are generated with either molecular dynamics simulations with charged and uncharged amino acid or with ab-initio local segment modeling. The corresponding ensembles are then used to calculate the pKa of the “problematic” residues and then the results are averaged.
pKa calculations; conformational changes; MD simulations; homology modeling; electrostatics
The pKa-cooperative aims to provide a forum for experimental and theoretical researchers interested in protein pKa values and protein electrostatics in general. The first round of the pKa-cooperative, which challenged computational labs to carry out blind predictions against pKas experimentally determined in the laboratory of Bertrand Garcia-Moreno, was completed and results discussed at the Telluride meeting (July 6–10, 2009). This paper serves as an introduction to the reports submitted by the blind prediction participants that will be published in a special issue of PROTEINS: Structure, Function and Bioinformatics. Here we briefly outline existing approaches for pKa calculations, emphasizing methods that were used by the participants in calculating the blind pKa values in the first round of the cooperative. We then point out some of the difficulties encountered by the participating groups in making their blind predictions, and finally try to provide some insights for future developments aimed at improving the accuracy of pKa calculations.
pKa; protein electrostatics; pH dependent properties of proteins; predicting pKa values in proteins
Large-scale next generation resequencing of X chromosome genes identified a missense mutation in the CLIC2 gene on Xq28 in a male with X-linked intellectual disability (XLID) and not found in healthy individuals. At the same time, numerous nsSNPs (nonsynonomous SNP) have been reported in the CLIC2 gene in healthy individuals indicating that the CLIC2 protein can tolerate amino acid substitutions and be fully functional. To test the possibility that p.H101Q is a disease-causing mutation, we performed in silico simulations to calculate the effects of the p.H101Q mutation on CLIC2 stability, dynamics and ionization states while comparing the effects obtained for presumably harmless nsSNPs. It was found that p.H101Q, in contrast with other nsSNPs, (a) lessens the flexibility of the joint loop which is important for the normal function of CLIC2, (b) makes the overall 3D structure of CLIC2 more stable and thus reduces the possibility of the large conformational change expected to occur when CLIC2 moves from a soluble to membrane form and (c) removes the positively charged residue, H101, which may be important for the membrane association of CLIC2. The results of in silico modeling, in conjunction with the polymorphism analysis, suggest that p.H101Q may be a disease-causing mutation, the first one suggested in the CLIC family.
CLIC2; missense mutations; mental disorder; energy calculations; pKa calculations; electrostatics; molecular dynamics simulations
The role of electrostatics on protein-protein interactions and binding is reviewed in this article. A brief outline of the computational modeling, in the framework of continuum electrostatics, is presented and basic electrostatic effects occurring upon the formation of the complex are discussed. The role of the salt concentration and pH of the water phase on protein-protein binding free energy is demonstrated and indicates that the increase of the salt concentration tends to weaken the binding, an observation that is attributed to the optimization of the charge-charge interactions across the interface. It is pointed out that the pH-optimum (pH of optimal binding affinity) varies among the protein-protein complexes, and perhaps is a result of their adaptation to particular subcellular compartment. At the end, the similarities and differences between hetero- and homo-complexes are outlined and discussed with respect to the binding mode and charge complementarity.
electrostatics; protein-protein interactions; pH; salt; nsSNPs; missense mutations
Accurate modeling of electrostatic potential and corresponding energies becomes increasingly important for understanding properties of biological macromolecules and their complexes. However, this is not an easy task due to the irregular shape of biological entities and the presence of water and mobile ions.
Here we report a comprehensive suite for the well-known Poisson-Boltzmann solver, DelPhi, enriched with additional features to facilitate DelPhi usage. The suite allows for easy download of both DelPhi executable files and source code along with a makefile for local installations. The users can obtain the DelPhi manual and parameter files required for the corresponding investigation. Non-experienced researchers can download examples containing all necessary data to carry out DelPhi runs on a set of selected examples illustrating various DelPhi features and demonstrating DelPhi’s accuracy against analytical solutions.
DelPhi suite offers not only the DelPhi executable and sources files, examples and parameter files, but also provides links to third party developed resources either utilizing DelPhi or providing plugins for DelPhi. In addition, the users and developers are offered a forum to share ideas, resolve issues, report bugs and seek help with respect to the DelPhi package. The resource is available free of charge for academic users from URL: http://compbio.clemson.edu/DelPhi.php.
DelPhi; Poisson-Boltzmann equation; Implicit solvation model; Electrostatics; Biological macromolecules; Software
Single-point mutation in genome, for example, single-nucleotide polymorphism (SNP) or rare genetic mutation, is the change of a single nucleotide for another in the genome sequence. Some of them will produce an amino acid substitution in the corresponding protein sequence (missense mutations); others will not. This paper focuses on genetic mutations resulting in a change in the amino acid sequence of the corresponding protein and how to assess their effects on protein wild-type characteristics. The existing methods and approaches for predicting the effects of mutation on protein stability, structure, and dynamics are outlined and discussed with respect to their underlying principles. Available resources, either as stand-alone applications or webservers, are pointed out as well. It is emphasized that understanding the molecular mechanisms behind these effects due to these missense mutations is of critical importance for detecting disease-causing mutations. The paper provides several examples of the application of 3D structure-based methods to model the effects of protein stability and protein-protein interactions caused by missense mutations as well.
A structure-based approach is described for predicting the effects of amino acid substitutions on protein function. Structures were predicted using a homology modelling method. Folding and binding energy differences between wild-type and mutant structures were computed to quantitatively assess the effects of amino acid substitutions on protein stability and protein–protein interaction, respectively. We demonstrated that pathogenic mutations at the interaction interface could affect binding energy and destabilise protein complex, whereas mutations at the non-interface might reduce folding energy and destabilise monomer structure. The results suggest that the structure-based analysis can provide useful information for understanding the molecular mechanisms of diseases.
Amino acid substitutions; homology modeling; folding energy; binding energy; protein stability; protein-protein interaction
Protein-protein association is a pH-dependent process and thus the binding affinity depends on the local pH. In vivo the association occurs in a particular cellular compartment, where the individual monomers are supposed to meet and form a complex. Since the monomers and the complex exist in the same micro environment, it is plausible that they coevolved toward its properties, in particular, toward the characteristic subcellular pH. Here we show that the pH at which the monomers are most stable (pH-optimum) or the pH at which stability is almost pH-independent (pH-flat) of monomers are correlated with the pH-optimum of maximal affinity (pH-optimum of binding) or pH interval at which affinity is almost pH-independent (pH-flat of binding) of the complexes made of the corresponding monomers. The analysis of interfacial properties of protein complexes demonstrates that pH-dependent properties can be roughly estimated using the interface charge alone. In addition, we introduce a parameter beta, proportional to the square root of the absolute product of the net charges of monomers, and show that protein complexes characterized with small or very large beta tend to have neutral pH-optimum. Further more, protein complexes made of monomers carrying the same polarity net charge at neutral pH have either very low or very high pH-optimum of binding. These findings are used to propose empirical rule for predicting pH-optimum of binding provided that the amino acid compositions of the corresponding monomers are available.
pH-optimum; protein binding; pKa's; electrostatics; pH-dependent effects
The Snyder-Robinson syndrome is caused by missense mutations in the spermine sythase gene that encodes a protein (SMS) of 529 amino acids. Here we investigate, in silico, the molecular effect of three missense mutations, c.267G>A (p.G56S), c.496T>G (p.V132G) and c.550T>C (p.I150T) in SMS that were clinically identified to cause the disease. Single-point energy calculations, molecular dynamics simulations and pKa calculations revealed the effects of these mutations on SMS's stability, flexibility and interactions. It was predicted that the catalytic residue, Asp276, should be protonated prior binding the substrates. The pKa calculations indicated the p.I150T mutation causes pKa changes with respect to the wild type SMS which involve titratable residues interacting with the S-methyl-5'-thioadenosine (MTA) substrate. The p.I150T missense mutation was also found to decrease the stability of the C-terminal domain and to induce structural changes in the vicinity of the MTA binding site. The other two missense mutations, p.G56S and p.V132G, are away from active site and do not perturb its wild type properties, but affect the stability of both the monomers and the dimer. Specifically, the p.G56S mutation is predicted to greatly reduce the affinity of monomers to form a dimer and therefore should have a dramatic effect on SMS function since dimerization is essential for SMS activity.
spermine synthase; SMS; Snyder-Robinson syndrome; mental retardation; protein stability
Biological macromolecules evolved to perform their function in specific cellular environment (subcellular compartments or tissues); therefore, they should be adapted to the biophysical characteristics of the corresponding environment, one of them being the characteristic pH. Many macromolecular properties are pH dependent, such as activity and stability. However, only activity is biologically important, while stability may not be crucial for the corresponding reaction. Here we show that the pH-optimum of activity (the pH of maximal activity) is correlated with the pH-optimum of stability (the pH of maximal stability) on a set of 310 proteins with available experimental data. We speculate that such a correlation is needed to allow the corresponding macromolecules to tolerate small pH fluctuations that are inevitable with cellular function. Our findings rationalize the efforts of correlating the pH of maximal stability and the characteristic pH of subcellular compartments, since only pH of activity is subject of evolutionary pressure. In addition, our analysis confirmed the previous observation that pH-optimum of activity and stability are not correlated with the isoelectric point, pI, or with the optimal temperature.
pH-optimum; activity; stability; subcellular compartments; pH-dependent effects; BRENDA database
Spermine synthase (SMS) is a key enzyme controlling the concentration of spermidine and spermine in the cell. The importance of SMS is manifested by the fact that single missense mutations were found to cause Snyder-Robinson Syndrome (SRS). At the same time, currently there are no non-synonymous single nucleoside polymorphisms, nsSNPs (harmless mutations), found in SMS, which may imply that the SMS does not tolerate amino acid substitutions, i.e. is not mutable.
To investigate the mutability of the SMS, we carried out in silico analysis and in vitro experiments of the effects of amino acid substitutions at the missense mutation sites (G56, V132 and I150) that have been shown to cause SRS. Our investigation showed that the mutation sites have different degree of mutability depending on their structural micro-environment and involvement in the function and structural integrity of the SMS. It was found that the I150 site does not tolerate any mutation, while V132, despite its key position at the interface of SMS dimer, is quite mutable. The G56 site is in the middle of the spectra, but still quite sensitive to charge residue replacement.
The performed analysis showed that mutability depends on the detail of the structural and functional factors and cannot be predicted based on conservation of wild type properties alone. Also, harmless nsSNPs can be expected to occur even at sites at which missense mutations were found to cause diseases.