6.2.2. Definition of Effective Properties 3064 6.3. Response Properties to Magnetic Fields 3066 6.3.1. Nuclear Shielding 3066 6.3.2. Indirect Spin−Spin Coupling 3067 6.3.3. EPR Parameters 3068 6.4. Properties of Chiral Systems 3069 6.4.1. Electronic Circular Dichroism (ECD) 3069 6.4.2. Optical Rotation (OR) 3069 6.4.3. VCD and VROA 3070 7. Continuum and Discrete Models 3071 7.1. Continuum Methods within MD and MC Simulations 3072

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Quantum mechanical continuum solvation models.

Continuum solvation models, such as Poisson–Boltzmann and Generalized Born methods, have become increasingly popular tools for investigating the influence of electrostatics on biomolecular structure, energetics and dynamics. However, the use of such methods requires accurate and complete structural data as well as force field parameters such as atomic charges and radii. Unfortunately, the limiting step in continuum electrostatics calculations is often the addition of missing atomic coordinates to molecular structures from the Protein Data Bank and the assignment of parameters to biomolecular structures. To address this problem, we have developed the PDB2PQR web service (http://agave.wustl.edu/pdb2pqr/). This server automates many of the common tasks of preparing structures for continuum electrostatics calculations, including adding a limited number of missing heavy atoms to biomolecular structures, estimating titration states and protonating biomolecules in a manner consistent with favorable hydrogen bonding, assigning charge and radius parameters from a variety of force fields, and finally generating ‘PQR’ output compatible with several popular computational biology packages. This service is intended to facilitate the setup and execution of electrostatics calculations for both experts and non-experts and thereby broaden the accessibility to the biological community of continuum electrostatics analyses of biomolecular systems.

/pdf/pdb2pqr-an-automated-pipeline-for-the-setup-of-poisson-53luup701x.pdf

PDB2PQR: an automated pipeline for the setup of Poisson–Boltzmann electrostatics calculations

A very fast empirical method is presented for structure-based protein pKa prediction and rationalization. The desolvation effects and intra-protein interactions, which cause variations in pKa values of protein ionizable groups, are empirically related to the positions and chemical nature of the groups proximate to the pKa sites. A computer program is written to automatically predict pKa values based on these empirical relationships within a couple of seconds. Unusual pKa values at buried active sites, which are among the most interesting protein pKa values, are predicted very well with the empirical method. A test on 233 carboxyl, 12 cysteine, 45 histidine, and 24 lysine pKa values in various proteins shows a root-mean-square deviation (RMSD) of 0.89 from experimental values. Removal of the 29 pKa values that are upper or lower limits results in an RMSD = 0.79 for the remaining 285 pKa values.

https://bioc.polytechnique.fr/biocomputing/papers/propka.pdf

Very fast empirical prediction and rationalization of protein pKa values

The docking field has come of age. The time is ripe to present the principles of docking, reviewing the current state of the field. Two reasons are largely responsible for the maturity of the computational docking area. First, the early optimism that the very presence of the "correct" native conformation within the list of predicted docked conformations signals a near solution to the docking problem, has been replaced by the stark realization of the extreme difficulty of the next scoring/ranking step. Second, in the last couple of years more realistic approaches to handling molecular flexibility in docking schemes have emerged. As in folding, these derive from concepts abstracted from statistical mechanics, namely, populations. Docking and folding are interrelated. From the purely physical standpoint, binding and folding are analogous processes, with similar underlying principles. Computationally, the tools developed for docking will be tremendously useful for folding. For large, multidomain proteins, domain docking is probably the only rational way, mimicking the hierarchical nature of protein folding. The complexity of the problem is huge. Here we divide the computational docking problem into its two separate components. As in folding, solving the docking problem involves efficient search (and matching) algorithms, which cover the relevant conformational space, and selective scoring functions, which are both efficient and effectively discriminate between native and non-native solutions. It is universally recognized that docking of drugs is immensely important. However, protein-protein docking is equally so, relating to recognition, cellular pathways, and macromolecular assemblies. Proteins function when they are bound to other molecules. Consequently, we present the review from both the computational and the biological points of view. Although large, it covers only partially the extensive body of literature, relating to small (drug) and to large protein-protein molecule docking, to rigid and to flexible. Unfortunately, when reviewing these, a major difficulty in assessing the results is the non-uniformity in the formats in which they are presented in the literature. Consequently, we further propose a way to rectify it here.

Principles of docking: An overview of search algorithms and a guide to scoring functions

The structure and function of macromolecules depend critically on the ionization (protonation) states of their acidic and basic groups. A number of existing practical methods predict protonation equilibrium pK constants of macromolecules based upon their atomic resolution Protein Data Bank (PDB) structures; the calculations are often performed within the framework of the continuum electrostatics model. Unfortunately, these methodologies are complex, involve multiple steps and require considerable investment of effort. Our web server http://biophysics.cs.vt.edu/H++ provides access to a tool that automates this process, allowing both experts and novices to quickly obtain estimates of pKs as well as other related characteristics of biomolecules such as isoelectric points, titration curves and energies of protonation microstates. Protons are added to the input structure according to the calculated ionization states of its titratable groups at the user-specified pH; the output is in the PQR (PDB + charges + radii) format. In addition, corresponding coordinate and topology files are generated in the format supported by the molecular modeling package AMBER. The server is intended for a broad community of biochemists, molecular modelers, structural biologists and drug designers; it can also be used as an educational tool in biochemistry courses.

/pdf/h-a-server-for-estimating-pkas-and-adding-missing-hydrogens-vejysze7pj.pdf

H++: a server for estimating pKas and adding missing hydrogens to macromolecules

Electrostatics and diffusion of molecules in solution: simulations with the University of Houston Brownian dynamics program

Prediction of pH-dependent properties of proteins.

Although validation studies show that theoretical models for predicting the pKas of ionizable groups in proteins are increasingly accurate, a number of important questions remain:  (1) What factors limit the accuracy of current models? (2) How can conformational flexibility of proteins best be accounted for? (3) Will use of solution structures in the calculations, rather than crystal structures, improve the accuracy of the computed pKas? and (4) Why does accurate prediction of protein pKas seem to require that a high dielectric constant be assigned to the protein interior? This paper addresses these and related issues. Among the conclusions are the following:  (1) computed pKas averaged over NMR structure sets are more accurate than those based upon single crystal structures; (2) use of atomic parameters optimized to reproduce hydration energies of small molecules improves agreement with experiment when a low protein dielectric constant is assumed; (3) despite use of NMR structures and optimized atomic pa...

The determinants of pKas in proteins.

A convenient computational approach for the calculation of the p Kas of ionizable groups in a protein is described. The method uses detailed models of the charges in both the neutral and ionized form of each ionizable group. A full derivation of the theoretical framework is presented, as are details of its implementation in the UHBD program. Application to four proteins whose crystal structures are known shows that the detailed charge model improves agreement with experimentally determined pKas when a low protein dielectric constant is assumed, relative to the results with a simpler single-site ionization model. It is also found that use of the detailed charge model increases the sensitivity of the computed pKas to the details of proton placement. © 1996 by John Wiley & Sons, Inc.

J. Antosiewicz

Papers

Electrostatics and diffusion of molecules in solution: simulations with the University of Houston Brownian dynamics program

Prediction of pH-dependent properties of proteins.

The determinants of pKas in proteins.

Computing ionization states of proteins with a detailed charge model

The determinants of pK(a)s in proteins