# DelPhi: a comprehensive suite for DelPhi software and associated resources

- Lin Li
^{1}, - Chuan Li
^{1}, - Subhra Sarkar
^{1, 2}, - Jie Zhang
^{1, 2}, - Shawn Witham
^{1}, - Zhe Zhang
^{1}, - Lin Wang
^{1}, - Nicholas Smith
^{1}, - Marharyta Petukh
^{1}and - Emil Alexov
^{1}Email author

**5**:9

https://doi.org/10.1186/2046-1682-5-9

© Li et al.; licensee BioMed Central Ltd. 2012

**Received: **10 February 2012

**Accepted: **17 April 2012

**Published: **14 May 2012

## Abstract

### Background

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.

### Results

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.

### Conclusions

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

## Keywords

## Background

Electrostatic interactions play an important role in biological systems [1–5] because biomolecules are composed of atoms carrying partial charges. Since the typical distances between atoms inside a biomolecule are on the order of several angstroms, the resulting electrostatic energy could be very large and be the major component of total energy [6–8]. Even more, at large distances, the electrostatic energy is the dominant component of the energy because all other components vanish. Since electrostatic interactions are the dominant factors for both inner- and inter- molecular interactions, accurate calculations of electrostatic potential and energies are crucial to reveal the mechanisms of many different biological phenomena, such as protein folding [9], protein-protein and protein-DNA binding [10–13], pKa shifts in proteins [14–17] and RNAs [18], and many others [19, 20].

Biomolecules function in water, which makes the calculation of electrostatic potential a challenge due to the complexity of the water environment [21, 22]. Various models have been developed to calculate electrostatic energy of biomolecules in the presence of surrounding water. These models can be categorized into two types [23–28]: explicit [29, 30] and implicit [31–34] solvation models. Explicit solvation models treat the solvent as individual water molecules and are believed to be more accurate but time-consuming, and therefore, are usually suitable for systems involving small to medium size biomolecules. However, most biological systems are large and contain a huge amount of water molecules. To calculate electrostatics in such large systems, more computationally efficient algorithms are typically applied. These methods, called implicit solvation methods, treat the water phase as a continuum medium. The Poisson-Boltzmann Equation (PBE) is one of the most successful implicit solvation models and was implemented in many well-known programs, such as DelPhi [35, 36], APBS [37, 38], MEAD [39], ZAP [40], PBEQ [41], MIBPB [42], UHBD [43], ITPACT [44], and several others. Among the above mentioned software, DelPhi has been proven to be among the best performers due to its unique features, such as capabilities of handling systems with multiple dielectric constants, modeling systems with multivalent ions, rapidly constructing molecular surfaces, calculating charged geometric object systems, and deriving ion concentration and dielectric maps.

In addition to the DelPhi source code and executable files which are free for academic users, several other resources are provided [45]. DelPhi’s distribution is adapted for different operating systems and provides a series of examples along with the user manual. Parameter files for four of the most widely used force fields [46–51] are also provided on the DelPhi website, which gives the users more options to explore various scenarios and to port snap-shots from molecular dynamics simulations into DelPhi calculations. The DelPhi forum [52] is set up for users to exchange ideas, discuss and solve problems, post suggestions for further development, and other DelPhi related issues. Furthermore, the DelPhi web server is also developed [53], allowing non-experienced users to quickly perform calculations on their selected structures. The DelPhi suite offers various tools and plugins [54–56] developed by other researches which utilize DelPhi to address biological questions as well.

### Implementation

The PBE model treats solvent as a continuum medium with high dielectric constant. Biomolecules are considered as low dielectric cavities made of charged atoms. Ions in the water phase are modeled as non-interacting point charges and their distribution obeys the Boltzmann law. Utilizing the Gauss-Seidel method, DelPhi solves both linear and nonlinear PBE in a cube of N × N × N grid points [57, 58].

In addition to the routines described above, DelPhi also has some unique functionalities, such as handling multiple dielectric constants and mixed multivalent ions, rapid constructing molecular surface, generating geometric objects and performing calculations. The multiple dielectric constant method divides the bio-molecular system into different parts, and assigns each part a specific dielectric constant allowing the difference in conformational flexibility to be modeled by different dielectric constants as illustrated in Ref. [59]. DelPhi can also model solvents with mixed ions, which may have different concentrations and valences. In order to speed up the process of generating a molecular surface, a rapid surface construction method has been developed in DelPhi, which implements the marching cube algorithm to construct the surface quickly and accurately. These features are described in detail in [35]. Four types of basic objects are now available in DelPhi package: sphere, cylinder, cone and box. Using the object functions, together with the multiple dielectric constants option, users can create complex geometric structures with different dielectric constants and shapes [35]. The DelPhi package is written in the FORTRAN and C language and can be compiled as single or double precision according to the practical usage. DelPhi version 5.1 is now available on different operating systems, including Windows, Linux and Mac. Although many useful functions and options have been implemented, DelPhi is still user friendly and compatible. Several other groups have developed third-party plugins and tools to utilize DelPhi on other software, such as UCSF Chimera [56], DelEnsembleElec (GUI and a plugin for VMD [55]), Biskit [54], and others [60].

## Results and discussion

There are several important characteristics used to classify methods and software packages: accuracy, rate of convergence and speed of calculations. In the next several subsections, we performed several tests on DelPhi with respect to these features.

### Accuracy test

Accuracy is one major concern of any numerical solver. In order to measure the solver’s accuracy and demonstrate that it solves the exact problem, the solver is usually tested on simple examples for which analytical solutions exist. In this subsection, three simple examples with regular geometry were selected. We compared their analytical solutions with the numerical ones obtained by DelPhi. Since the computational algorithm does not distinguish simple geometrical objects from real biological macromolecules with more complex shape, it indicates that DelPhi solves the PB equation and produces close numerical approximations to the real solutions. The following constants were fixed in all examples: elementary charge $e=1.602176565\times {10}^{-19}C$_{,} vacuum permittivity ${\epsilon}_{0}=8.8541878176\times {10}^{-12}F/m,$ Boltzmann constant $k=1.38\times {10}^{-23}J/K$ and temperature $T=297.33K$_{.}

### A sphere in water

*ϵ*

_{int}immersed in a continuum media with a higher dielectric constant

*ϵ*

_{ ext }. In this example, the electrostatic component of solvation energy Δ

*G*

^{ sol }can be obtained by the Born formula and is explicitly given by

Setting *ε*_{
int
} = 4.0, *ε*_{
ext
} = 80.0 and *Q* = 10.*e*, values of Δ*G*^{
sol
} obtained by Equation (1) are −6673.71kT, -3336.86kT and −2224.57kT (rounded to two decimals) for radii *r* = 1 Å, 2 Å and 3 Å, respectively. These values were compared to those obtained by DelPhi at various scales (points/Å) and the results are shown in Figure 2B. It is clear that no visual difference can be observed when the scale is greater than 0.5 points/Å.

### Two charges in a protein

*b*locating inside a media without any ions. Dielectric constants in the interior and exterior of the protein are denoted by

*ε*

_{int}and

*ε*

_{ ext }again. Two atoms with radii

*r*

_{ i }and

*r*

_{ j }are centered at points with polar coordinates $({R}_{i},{\theta}_{i})$ and $({R}_{j},{\theta}_{j})$. These two atoms are placed inside the protein and are assigned charges

*Q*

_{ i }and

*Q*

_{ j }, respectively. This example has been studied by Barry Honig and co-workers [1]. The analytical solution of the electrostatic component of solvation energy Δ

*G*is composed of four terms:

*i*

*j*$\Delta {G}_{\mathit{ij}}^{\mathit{pol}}$ is the pairwise polarization interaction energy, $\Delta {G}_{\mathit{ii}}^{\mathit{self}}$ is the total self-energy of atom

*i*and $\Delta {G}_{\mathit{jj}}^{\mathit{self}}$ is the total self-energy of atom

*j*. Energies on the right-hand side of Equation (2) can be calculated by

Substituting ${Q}_{i}={Q}_{j}=10\cdot e$, ${R}_{i}={R}_{j}=5\sqrt{2}$ Å,${\theta}_{i}=\pi /4$, ${\theta}_{j}=3\pi /4$, $b=10$ Å, $r=1$ Å, ${\epsilon}_{int}=2.0$ and ${\epsilon}_{\mathit{ext}}=80.0$ into Equations (2) – (3) yields $\Delta G=-5083.19$ kT after rounding to two decimals. Numerical calculations were performed at grid size = 85, 125, and 165 and various scales. The numerical results, together with the value of Δ*G*, were compared and shown in Figure 3B. One can see that the numerical solutions converge to the real solution quickly as scale increases for all three tested grid sizes.

### A sphere in semi-infinite dielectric region

*ϵ*

_{1}and that in the right region is

*ε*

_{2}(${\epsilon}_{2}>{\epsilon}_{1}$). A sphere with radius

*r*and dielectric constant

*ϵ*

_{1}is initially positioned in the right region. The distance between the center of the sphere and the boundary of two regions is denoted by

*d*. Let the sphere move towards the boundary and eventually get into the left region. We consider

*d*> 0 when the center of the sphere is still in the right region and

*d*< 0 when it is in the left region. The sign of

*d*indicates the position of the sphere. During the moving process of the sphere, except the moment when the sphere intersects both regions (i.e., $\left|d\right|\le r$), the electrostatic component of the solvation energy Δ

*G*can be analytically expressed as a function of distance

*d*

The blue curve in Figure 4B represents the function Δ*G*(*d*)(Equation 4), here we set ${\epsilon}_{2}=80.0$, ${\epsilon}_{1}=2.0$, $r=2$ Å, $Q=1\cdot e$. Numerical results obtained by running DelPhi at a series of discrete *d* values are shown by red circles in Figure 4B and fit the curve very well. Our tests in this example indicate that DelPhi delivers accurate numerical approximations to the real solution.

### Rate of convergence

The rate of convergence is another major concern from the numerical point of view. DelPhi utilizes the Gauss-Seidel iteration method, along with the optimized Successive Over-Relaxation method [58], to solve PBE in a cube. The solution is more accurate when the cube is discretized into finer grids. In order to determine the minimal requirement of computational time cost for DelPhi to achieve results within a desired accuracy, a series of tests were designed and implemented on a typical protein of medium size, namely the bovine alpha-chymotrypsin-eglin C complex [PDB:1ACB], to demonstrate the performance of DelPhi.

*perfil*= 70%, was fixed in all tests. The resulting electrostatic component of solvation energy Δ

*G*as a function of scale is shown in Figure 5.

The energy calculations on the structure of 1ACB show that the approximate scale threshold is 1 points/Å. At scale larger than the threshold, the calculated electrostatic component of solvation energy is almost scale-independent and reaches steady value of −28089 kT. Achieving such steady value at scale 1 – 2 points/Å demonstrates the robustness of the algorithm that calculates the electrostatic component of solvation energy, so termed the corrected reaction field energy method [35].

### Speed of calculations

DelPhi utilizes various algorithms and modules to calculate electrostatic potential and energy. The basic modules include generating molecular surface, calculating electrostatic potential distribution and obtaining the corresponding electrostatic energy. The speed of calculations for each of these modules depends on various factors, such as scale, number of atoms/charges, shape/net charge of the molecule. In order to reveal their impact on the performance of DelPhi from the users’ point of view, we first tested DelPhi on a particular protein complex with fixed filling of the cube and increasing scale, and next, tested DelPhi on multiple proteins with fixed scale. All calculations were performed on the same type of CPU, Intel Xeon E5410 (2.33 G Hz), on the Palmetto cluster [61] at Clemson University. Each run was repeated 5 times and the average is reported here in order to reduce unexpected fluctuations caused by system workload at run time. The resulting CPU time against scale and protein size, are reported as follows.

#### Speed of calculations as a function of scale

*barstar*[PDB:1A19] using DelPhi, with scale values increasing from 0.5 to 10 points/Å at step size 0.1 points/Å. The

*perfil*value was set to 70% regardless of the changing scales to keep the filling of the cube fixed. The resulting CPU time, plotted as a function of scale, is shown in Figure 6. It can be seen that the computational time rapidly increases with the scale, because the corresponding grid size increases as well. However, at scale of 2–4 points/Å. DelPhi is still very fast, resulting in runs of about a second to several seconds.

#### Speed of calculations as a function of protein size

*perfil*was 70%, which are reasonable and common values for calculations on real biomolecules. The resulting calculation time is plotted as a function of protein size in Figure 7A. One can see that the resulting computational time, in general, increases with the size of the protein. However, the energy of the largest protein in the dataset, composed of more than 16,000 atoms, was calculated in less than 120 s, almost the same time needed for small proteins made of about 8,000 atoms. This indicates that other factors, such as the number of charged groups, may play important roles as well. To test such a possibility, the number of charged residues for each protein was obtained and the calculation time is plotted against it (Figure 7B). The resulting plot is not much different from Figure 7A and the calculation time for the protein with the largest number of charged groups is not necessarily the longest one. This illustrates that the computation time is a complex function depending on the combination of protein size, number of charges, shape and many others.

Electrostatic energy calculations on large biomolecules usually cost more CPU time primarily due to two factors: Firstly, large biomolecules need a large cube and consequently more grids to be represented. Secondly, larger biomolecules contain more charged atoms and require more time to calculate the energy terms. However, the curve of the 200 proteins is not smooth, because there are several other factors which influence the calculation time. The size of the modeling cube depends not only on the atom number, but also on the molecule’s shape. A narrow and long molecule may need a larger cube than a spherical molecule even if their atom numbers are the same. The irregularity of molecular surface also affects the iteration time. A molecule with an irregular surface requires more iterations to converge than a molecule with a regular, smooth surface. Finally, a molecule with a higher charge needs more iterations than a molecule with a lower charge. Due to above reasons, larger molecules usually (but not necessarily) cost more time than smaller molecules to calculate the corresponding potential and energies.

### Effect of force field parameters (Charmm, Amber, OPLS and Parse)

*perfil*was set to be 70%, probe radius was 1.4 Å, the dielectric constants were set as 4.0 inside the protein and as 80.0 in the water, and the scales varied from 0.5 to 6.0 points/Å.

Results of the calculated electrostatic energies on 1HVC are shown in Figure 8, using AMBER, CHARMM, OPLS, and PARSE. The results reaffirm the previously made observations that calculations at very small scales are not accurate. However, once the scale is larger than 1 point/Å, DelPhi achieves convergence quickly and results are almost scale independent. There is a slight tendency that CHARMM and PARSE converge faster than other force fields, but the difference is small. At the same time, the electrostatic energies calculated using different force fields are quite different. When scale reaches 6 points/Å, the calculated energies are: -25109.58, -21474.99, -20471.78, -19234.74 kT for AMBER, CHARMM, OPLS, and PARSE, respectively. The largest difference in calculated energy is obtained by AMBER force field parameters versus others. Such a large difference should not be surprising since the force field parameters are developed with respect to the total energy, not just the electrostatic component. However, several studies [16, 67, 68] indicate that the energy difference remains even in the calculations of total energy, although the differences are smaller compared to the differences in the electrostatic component. The same is valid for calculations involving the difference of energies, as for example the electrostatic component of the binding energy [66]. It was shown [66] that the difference could be larger than 50 kcal/mol. These observations and the results presented in this work indicate the sensitivity of calculations with respect to the force field parameters and suggest that the outcome of the modeling should be tested with this regard.

## Conclusions

In this work, we described the DelPhi package and associated resources. DelPhi is a comprehensive suite including DelPhi website, web server, forum, DelPhi software and other tools. Several tests were performed on DelPhi in this work to demonstrate DelPhi’s capabilities in terms of accuracy, rate of convergence and speed of calculations. It was shown that DelPhi is a robust solver and capable of solving various biological applications. The benchmarks confirmed that DelPhi delivers energies that are almost grid independent, reaches convergence at scales equal to or larger than 1–2 grids/Å, and the speed of calculations is impressively fast. Finally, as shown in comparison with analytical solutions, the algorithm is, most importantly, capable of providing accurate energy calculations.

## Availability and requirements

Project name: DelPhi

Project home page: e.g. http://compbio.clemson.edu/delphi.php

Operating system(s): Linux, Mac, Windows

Programming language: Fortran and C

Other requirements: no

License: free of charge license is required

Any restrictions to use by non-academics: Commercial users should contact Accelrys Inc.

## Declarations

### Acknowledgements

The work was supported by a grant from the Institute of General Medical Sciences, National Institutes of Health, award number 1R01GM093937.

## Authors’ Affiliations

## References

- Gilson MK, Rashin A, Fine R, Honig B: On the calculation of electrostatic interactions in proteins. J Mol Biol. 1985, 184: 503-516. 10.1016/0022-2836(85)90297-9.View ArticleGoogle Scholar
- Honig B, Nicholls A: Classical electrostatics in biology and chemistry. Science. 1995, 268: 1144-10.1126/science.7761829.View ArticleADSGoogle Scholar
- Russell S, Warshel A: Calculations of electrostatic energies in proteins* 1: The energetics of ionized groups in bovine pancreatic trypsin inhibitor. J Mol Biol. 1985, 185: 389-404. 10.1016/0022-2836(85)90411-5.View ArticleGoogle Scholar
- Zhang Z, Witham S, Alexov E: On the role of electrostatics in protein–protein interactions. Phys Biol. 2011, 8: 035001-10.1088/1478-3975/8/3/035001.View ArticleADSGoogle Scholar
- Sharp KA, Honig B: Electrostatic interactions in macromolecules: theory and applications. Annu Rev Biophys Biophys Chem. 1990, 19: 301-332. 10.1146/annurev.bb.19.060190.001505.View ArticleGoogle Scholar
- Guest WC, Cashman NR, Plotkin SS: Electrostatics in the stability and misfolding of the prion protein: salt bridges, self energy, and solvation. Biochem Cell Biol. 2010, 88: 371-381. 10.1139/O09-180.View ArticleGoogle Scholar
- Laederach A, Shcherbakova I, Jonikas MA, Altman RB, Brenowitz M: Distinct contribution of electrostatics, initial conformational ensemble, and macromolecular stability in RNA folding. Proc Natl Acad Sci U S A. 2007, 104: 7045-7050. 10.1073/pnas.0608765104.View ArticleADSGoogle Scholar
- Avbelj F, Fele L: Role of main-chain electrostatics, hydrophobic effect and side-chain conformational entropy in determining the secondary structure of proteins. J Mol Biol. 1998, 279: 665-684. 10.1006/jmbi.1998.1792.View ArticleGoogle Scholar
- Yang AS, Honig B: On the pH dependence of protein stability. J Mol Biol. 1993, 231: 459-474. 10.1006/jmbi.1993.1294.View ArticleGoogle Scholar
- Bertonati C, Honig B, Alexov E: Poisson-Boltzmann calculations of nonspecific salt effects on protein-protein binding free energies. Biophys J. 2007, 92: 1891-1899. 10.1529/biophysj.106.092122.View ArticleGoogle Scholar
- Jensen JH: Calculating pH and salt dependence of protein-protein binding. Curr Pharm Biotechnol. 2008, 9: 96-102. 10.2174/138920108783955146.View ArticleGoogle Scholar
- Spencer DS, Xu K, Logan TM, Zhou HX: Effects of pH, salt, and macromolecular crowding on the stability of FK506-binding protein: an integrated experimental and theoretical study. J Mol Biol. 2005, 351: 219-232. 10.1016/j.jmb.2005.05.029.View ArticleGoogle Scholar
- Talley K, Kundrotas P, Alexov E: Modeling salt dependence of protein-protein association: Linear vs non-linear Poisson-Boltzmann equation. Commun Comput Phys. 2008, 3: 1071-1086.Google Scholar
- Yang AS, Gunner MR, Sampogna R, Sharp K, Honig B: On the calculation of pKas in proteins. Proteins. 1993, 15: 252-265. 10.1002/prot.340150304.View ArticleGoogle Scholar
- Georgescu RE, Alexov EG, Gunner MR: Combining conformational flexibility and continuum electrostatics for calculating pK(a)s in proteins. Biophys J. 2002, 83: 1731-1748. 10.1016/S0006-3495(02)73940-4.View ArticleGoogle Scholar
- Zhang Z, Teng S, Wang L, Schwartz CE, Alexov E: Computational analysis of missense mutations causing Snyder‒Robinson syndrome. Hum Mutat. 2010, 31: 1043-1049. 10.1002/humu.21310.View ArticleGoogle Scholar
- Witham S, Talley K, Wang L, Zhang Z, Sarkar S, Gao D, Yang W, Alexov E: Developing hybrid approaches to predict pKa values of ionizable groups. Proteins: Structure, Function, and Bioinformatics. 2011, 79: 3260-3275. 10.1002/prot.23189.View ArticleGoogle Scholar
- Tang CL, Alexov E, Pyle AM, Honig B: Calculation of pK(a)s in RNA: On the structural origins and functional roles of protonated nucleotides. J Mol Biol. 2007, 366: 1475-1496. 10.1016/j.jmb.2006.12.001.View ArticleGoogle Scholar
- Mitra RC, Zhang Z, Alexov E: In silico modeling of pH-optimum of protein-protein binding. Proteins-Structure Function and Bioinformatics. 2011, 79: 925-936. 10.1002/prot.22931.View ArticleGoogle Scholar
- Alexov E: Numerical calculations of the pH of maximal protein stability. The effect of the sequence composition and three-dimensional structure. Eur J Biochem. 2004, 271: 173-185.View ArticleGoogle Scholar
- Harvey SC: Treatment of electrostatic effects in macromolecular modeling. Proteins. 1989, 5: 78-92. 10.1002/prot.340050109.View ArticleGoogle Scholar
- Lebard DN, Matyushov DV: Protein-water electrostatics and principles of bioenergetics. Phys Chem Chem Phys. 2010, 12: 15335-15348.View ArticleGoogle Scholar
- Ma B, Nussinov R: Explicit and implicit water simulations of a beta-hairpin peptide. Proteins. 1999, 37: 73-87.View ArticleGoogle Scholar
- Zhou R: Free energy landscape of protein folding in water: explicit vs. implicit solvent. Proteins. 2003, 53: 148-161. 10.1002/prot.10483.View ArticleGoogle Scholar
- Spaeth JR, Kevrekidis IG, Panagiotopoulos AZ: A comparison of implicit- and explicit-solvent simulations of self-assembly in block copolymer and solute systems. J Chem Phys. 2011, 134: 164902-10.1063/1.3580293.View ArticleADSGoogle Scholar
- Tan C, Yang L, Luo R: How well does Poisson-Boltzmann implicit solvent agree with explicit solvent? A quantitative analysis. Journal of Physical Chemistry B. 2006, 110: 18680-18687. 10.1021/jp063479b.View ArticleGoogle Scholar
- Rod TH, Rydberg P, Ryde U: Implicit versus explicit solvent in free energy calculations of enzyme catalysis: Methyl transfer catalyzed by catechol O-methyltransferase. J Chem Phys. 2006, 124: 174503-10.1063/1.2186635.View ArticleADSGoogle Scholar
- Pham TT, Schiller UD, Prakash JR, Dunweg B: Implicit and explicit solvent models for the simulation of a single polymer chain in solution: Lattice Boltzmann versus Brownian dynamics. J Chem Phys. 2009, 131: 164114-10.1063/1.3251771.View ArticleADSGoogle Scholar
- Druchok M, Vlachy V, Dill KA: Explicit-water molecular dynamics study of a short-chain 3,3 ionene in solutions with sodium halides. J Chem Phys. 2009, 130: 134903-10.1063/1.3078268.View ArticleADSGoogle Scholar
- Kony DB, Damm W, Stoll S, van Gunsteren WF, Hunenberger PH: Explicit-solvent molecular dynamics simulations of the polysaccharide schizophyllan in water. Biophys J. 2007, 93: 442-455. 10.1529/biophysj.106.086116.View ArticleGoogle Scholar
- Baker NA: Poisson-Boltzmann methods for biomolecular electrostatics. Methods Enzymol. 2004, 383: 94-118.View ArticleGoogle Scholar
- Gilson MK, Honig B: Calculation of the total electrostatic energy of a macromolecular system: solvation energies, binding energies, and conformational analysis. Proteins. 1988, 4: 7-18. 10.1002/prot.340040104.View ArticleGoogle Scholar
- Lee MC, Yang R, Duan Y: Comparison between Generalized-Born and Poisson-Boltzmann methods in physics-based scoring functions for protein structure prediction. J Mol Model. 2005, 12: 101-110. 10.1007/s00894-005-0013-y.View ArticleGoogle Scholar
- Grochowski P, Trylska J: Continuum molecular electrostatics, salt effects, and counterion binding–a review of the Poisson-Boltzmann theory and its modifications. Biopolymers. 2008, 89: 93-113. 10.1002/bip.20877.View ArticleGoogle Scholar
- Rocchia W, Sridharan S, Nicholls A, Alexov E, Chiabrera A, Honig B: Rapid grid-based construction of the molecular surface and the use of induced surface charge to calculate reaction field energies: Applications to the molecular systems and geometric objects. J Comput Chem. 2002, 23: 128-137. 10.1002/jcc.1161.View ArticleGoogle Scholar
- Rocchia W, Alexov E, Honig B: Extending the applicability of the nonlinear Poisson-Boltzmann equation: Multiple dielectric constants and multivalent ions. Journal of Physical Chemistry B. 2001, 105: 6507-6514. 10.1021/jp010454y.View ArticleGoogle Scholar
- Holst M, Baker N, Wang F: Adaptive multilevel finite element solution of the Poisson–Boltzmann equation I. Algorithms and examples. J Comput Chem. 2000, 21: 1319-1342. 10.1002/1096-987X(20001130)21:15<1319::AID-JCC1>3.0.CO;2-8.View ArticleGoogle Scholar
- Baker N, Holst M, Wang F: Adaptive multilevel finite element solution of the Poisson–Boltzmann equation II. Refinement at solvent‒accessible surfaces in biomolecular systems. J Comput Chem. 2000, 21: 1343-1352. 10.1002/1096-987X(20001130)21:15<1343::AID-JCC2>3.0.CO;2-K.View ArticleGoogle Scholar
- Bashford D: An object-oriented programming suite for electrostatic effects in biological molecules An experience report on the MEAD project. Springer. 1997, 233: 240-Google Scholar
- Grant JA, Pickup BT, Nicholls A: A smooth permittivity function for Poisson–Boltzmann solvation methods. J Comput Chem. 2001, 22: 608-640. 10.1002/jcc.1032.View ArticleGoogle Scholar
- Banavali NK, Roux B: Atomic radii for continuum electrostatics calculations on nucleic acids. The Journal of Physical Chemistry B. 2002, 106: 11026-11035. 10.1021/jp025852v.View ArticleGoogle Scholar
- Zhou Y, Feig M, Wei G: Highly accurate biomolecular electrostatics in continuum dielectric environments. J Comput Chem. 2008, 29: 87-97. 10.1002/jcc.20769.View ArticleGoogle Scholar
- Davis ME, McCammon JA: Solving the finite difference linearized Poisson‒Boltzmann equation: A comparison of relaxation and conjugate gradient methods. J Comput Chem. 1989, 10: 386-391. 10.1002/jcc.540100313.View ArticleGoogle Scholar
- Cortis CM, Friesner RA: Numerical solution of the Poisson-Boltzmann equation using tetrahedral finite-element meshes. J Comput Chem. 1997, 18: 1591-1608. 10.1002/(SICI)1096-987X(199710)18:13<1591::AID-JCC3>3.0.CO;2-M.View ArticleGoogle Scholar
- DelPhi Website: http://compbio.clemson.edu/delphi.php,
- Ponder JW, Case DA: Force fields for protein simulations. Advances in protein chemistry. 2003, 66: 27-85.View ArticleGoogle Scholar
- Brooks BR, Brooks C, Mackerell A, Nilsson L, Petrella R, Roux B, Won Y, Archontis G, Bartels C, Boresch S: CHARMM: the biomolecular simulation program. J Comput Chem. 2009, 30: 1545-1614. 10.1002/jcc.21287.View ArticleGoogle Scholar
- Kahn K, Bruice TC: Parameterization of OPLS–AA force field for the conformational analysis of macrocyclic polyketides. J Comput Chem. 2002, 23: 977-996. 10.1002/jcc.10051.View ArticleGoogle Scholar
- Kony D, Damm W, Stoll S, Van Gunsteren W: An improved OPLS–AA force field for carbohydrates. J Comput Chem. 2002, 23: 1416-1429. 10.1002/jcc.10139.View ArticleGoogle Scholar
- Xu Z, Luo HH, Tieleman DP: Modifying the OPLS‒AA force field to improve hydration free energies for several amino acid side chains using new atomic charges and an off‒plane charge model for aromatic residues. J Comput Chem. 2007, 28: 689-697. 10.1002/jcc.20560.View ArticleGoogle Scholar
- Sitkoff D, Lockhart DJ, Sharp KA, Honig B: Calculation of electrostatic effects at the amino terminus of an alpha helix. Biophys J. 1994, 67: 2251-2260. 10.1016/S0006-3495(94)80709-X.View ArticleGoogle Scholar
- DelPhi Forum: http://compbio.clemson.edu/forum/index.php,
- DelPhi Web Server: [http://compbio.clemson.edu/sapp/delphi_webserver/]
- Grünberg R, Nilges M, Leckner J: Biskit—a software platform for structural bioinformatics. Bioinformatics. 2007, 23: 769-10.1093/bioinformatics/btl655.View ArticleGoogle Scholar
- Humphrey W, Dalke A, Schulten K: VMD: visual molecular dynamics. J Mol Graph. 1996, 14: 33-38. 10.1016/0263-7855(96)00018-5.View ArticleGoogle Scholar
- Pettersen EF, Goddard TD, Huang CC, Couch GS, Greenblatt DM, Meng EC, Ferrin TE: UCSF Chimera—a visualization system for exploratory research and analysis. J Comput Chem. 2004, 25: 1605-1612. 10.1002/jcc.20084.View ArticleGoogle Scholar
- Klapper I, Hagstrom R, Fine R, Sharp K, Honig B: Focusing of electric fields in the active site of Cu-Zn superoxide dismutase: Effects of ionic strength and amino-acid modification. Proteins: Structure, Function, and Bioinformatics. 1986, 1: 47-59. 10.1002/prot.340010109.View ArticleGoogle Scholar
- Nicholls A, Honig B: A rapid finite difference algorithm, utilizing successive over‒relaxation to solve the Poisson–Boltzmann equation. J Comput Chem. 1991, 12: 435-445. 10.1002/jcc.540120405.View ArticleGoogle Scholar
- Wang L, Zhang Z, Rocchia W, Alexov E: Using DelPhi Capabilities to Mimic Protein’s Conformational Reorganization with Amino Acid Specific Dielectric Constants. Comm Comp Phys. 2012, in pressGoogle Scholar
- DelPhi Tools: http://compbio.clemson.edu/delphi_tools.php,
- Palmettol Cluster: [http://citi.clemson.edu/training_palm]
- Zhang Y, Skolnick J: TM-align: a protein structure alignment algorithm based on the TM-score. Nucleic Acids Res. 2005, 33: 2302-2309. 10.1093/nar/gki524.View ArticleGoogle Scholar
- Patapati KK, Glykos NM: Three Force Fields' Views of the 310 Helix. Biophys J. 2011, 101: 1766-1771. 10.1016/j.bpj.2011.08.044.View ArticleGoogle Scholar
- Yoda T, Sugita Y, Okamoto Y: Comparisons of force fields for proteins by generalized-ensemble simulations. Chem Phys Lett. 2004, 386: 460-467. 10.1016/j.cplett.2004.01.078.View ArticleADSGoogle Scholar
- Matthes D, De Groot BL: Secondary structure propensities in peptide folding simulations: a systematic comparison of molecular mechanics interaction schemes. Biophys J. 2009, 97: 599-608. 10.1016/j.bpj.2009.04.061.View ArticleGoogle Scholar
- Talley K, Ng C, Shoppell M, Kundrotas P, Alexov E: On the electrostatic component of protein-protein binding free energy. PMC Biophys. 2008, 1: 2-View ArticleGoogle Scholar
- Zhang Z, Norris J, Schwartz C, Alexov E: In silico and in vitro investigations of the mutability of disease-causing missense mutation sites in spermine synthase. PLoS One. 2011, 6: e20373-10.1371/journal.pone.0020373.View ArticleADSGoogle Scholar
- Witham S, Takano K, Schwartz C, Alexov E: A missense mutation in CLIC2 associated with intellectual disability is predicted by in silico modeling to affect protein stability and dynamics. Proteins: Structure, Function, and Bioinformatics. 2011, 79: 2444-2454. 10.1002/prot.23065.View ArticleGoogle Scholar

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