Although the statistical thermodynamics of noncovalent binding has been considered in a number of theoretical papers, few methods of computing binding affinities are derived explicitly from this underlying theory. This has contributed to uncertainty and controversy in certain areas. This article therefore reviews and extends the connections of some important computational methods with the underlying statistical thermodynamics. A derivation of the standard free energy of binding forms the basis of this review. This derivation should be useful in formulating novel computational methods for predicting binding affinities. It also permits several important points to be established. For example, it is found that the double-annihilation method of computing binding energy does not yield the standard free energy of binding, but can be modified to yield this quantity. The derivation also makes it possible to define clearly the changes in translational, rotational, configurational, and solvent entropy upon binding. It is argued that molecular mass has a negligible effect upon the standard free energy of binding for biomolecular systems, and that the cratic entropy defined by Gurney is not a useful concept. In addition, the use of continuum models of the solvent in binding calculations is reviewed, and a formalism is presented for incorporating a limited number of solvent molecules explicitly.

BindingDB () is a publicly accessible database currently containing ∼20 000 experimentally determined binding affinities of protein–ligand complexes, for 110 protein targets including isoforms and mutational variants, and ∼11 000 small molecule ligands. The data are extracted from the scientific literature, data collection focusing on proteins that are drug-targets or candidate drug-targets and for which structural data are present in the Protein Data Bank. The BindingDB website supports a range of query types, including searches by chemical structure, substructure and similarity; protein sequence; ligand and protein names; affinity ranges and molecular weight. Data sets generated by BindingDB queries can be downloaded in the form of annotated SDfiles for further analysis, or used as the basis for virtual screening of a compound database uploaded by the user. The data in BindingDB are linked both to structural data in the PDB via PDB IDs and chemical and sequence searches, and to the literature in PubMed via PubMed IDs.

We present a numerical method for calculating the electrostatic potential of molecules in solution, using the linearized Poisson-Boltzmann equation. The emphasis in this work is on applications to biological macromolecules. The accuracy of the method is assessed by comparisons with analytic solutions for the case of a single charge in a dielectric sphere (Tanford-Kirkwood theory), which serves as a model for a macromolecule. We find that the solutions are generally accurate to within 5%. Larger errors occur close to the charge and the dielectric boundary, but the maximum error found at ion-bonding distance (3 A) from a charge close to the boundary (1 A deep) is only -15%. Several algorithmic improvements, described here, contribute to the accuracy of the method. The programs involved compose a coherent software package, called Del Phi, which goes from a Brookhaven Protein Data Bank format file to calculated electrostatic fields.

Accurate methods of computing the affinity of a small molecule with a protein are needed to speed the discovery of new medications and biological probes. This paper reviews physics-based models of binding, beginning with a summary of the changes in potential energy, solvation energy, and configurational entropy that influence affinity, and a theoretical overview to frame the discussion of specific computational approaches. Important advances are reported in modeling protein-ligand energetics, such as the incorporation of electronic polarization and the use of quantum mechanical methods. Recent calculations suggest that changes in configurational entropy strongly oppose binding and must be included if accurate affinities are to be obtained. The linear interaction energy (LIE) and molecular mechanics Poisson-Boltzmann surface area (MM-PBSA) methods are analyzed, as are free energy pathway methods, which show promise and may be ready for more extensive testing. Ultimately, major improvements in modeling accuracy will likely require advances on multiple fronts, as well as continued validation against experiment.

The molecular host cucurbit [7]uril forms an extremely stable inclusion complex with the dicationic ferrocene derivative bis(trimethylammoniomethyl)ferrocene in aqueous solution. The equilibrium association constant for this host-guest pair is 3 ؋ 10 15 M ؊1 (Kd ‫؍‬ 3 ؋ 10 ؊16 M), equivalent to that exhibited by the avidinbiotin pair. Although purely synthetic systems with larger association constants have been reported, the present one is unique because it does not rely on polyvalency. Instead, it achieves its extreme affinity by overcoming the compensatory enthalpyentropy relationship usually observed in supramolecular complexes. Its disproportionately low entropic cost is traced to extensive host desolvation and to the rigidity of both the host and the guest.cucurbituril ͉ entropy control ͉ ferrocene derivatives ͉ host-guest complexation ͉ thermodynamics

BindingDB, www.bindingdb.org, is a publicly accessible database of experimental protein-small molecule interaction data. Its collection of over a million data entries derives primarily from scientific articles and, increasingly, US patents. BindingDB provides many ways to browse and search for data of interest, including an advanced search tool, which can cross searches of multiple query types, including text, chemical structure, protein sequence and numerical affinities. The PDB and PubMed provide links to data in BindingDB, and vice versa; and BindingDB provides links to pathway information, the ZINC catalog of available compounds, and other resources. The BindingDB website offers specialized tools that take advantage of its large data collection, including ones to generate hypotheses for the protein targets bound by a bioactive compound, and for the compounds bound by a new protein of known sequence; and virtual compound screening by maximal chemical similarity, binary kernel discrimination, and support vector machine methods. Specialized data sets are also available, such as binding data for hundreds of congeneric series of ligands, drawn from BindingDB and organized for use in validating drug design methods. BindingDB offers several forms of programmatic access, and comes with extensive background material and documentation. Here, we provide the first update of BindingDB since 2007, focusing on new and unique features and highlighting directions of importance to the field as a whole.

A dicationic ferrocene derivative has previously been shown to bind cucurbit[7]uril (CB[7]) in water with ultra-high affinity (ΔGo= −21 kcal/mol). Here, we describe new compounds that bind aqueous CB[7] equally well, validating our prior suggestion that they, too, would be ultra-high affinity CB[7] guests. The present guests, which are based upon either a bicyclo[2.2.2]octane or adamantane core, have no metal atoms, so these results also confirm that the remarkably high affinities of the ferrocene-based guest need not be attributed to metal-specific interactions. Because we used the M2 method to compute the affinities of several of the new host-guest systems prior to synthesizing them, the present results also provide for the first blinded evaluation of this computational method. The blinded calculations agree reasonably well with experiment and successfully reproduce the observation that the new adamantane-based guests achieve extremely high affinities, despite the fact that they position a cationic substituent at only one electronegative portal of the CB[7] host. However, there are also significant deviations from experiment, and these lead to the correction of a procedural error and an instructive evaluation of the sensitivity of the calculations to physically reasonable variations in molecular energy parameters. The new experimental and computational results presented here bear on the physical mechanisms of molecular recognition, the accuracy of the M2 method, and the usefulness of host-guest systems as test-beds for computational methods.

In this report we describe an accurate numerical method for calculating the total electrostatic energy of molecules of arbitrary shape and charge distribution, accounting for both Coulombic and solvent polarization terms. In addition to the solvation energies of individual molecules, the method can be used to calculate the electrostatic energy associated with conformational changes in proteins as well as changes in solvation energy that accompany the binding of charged substrates. The validity of the method is examined by calculating the hydration energies of acetate, methyl ammonium, ammonium, and methanol. The method is then used to study the relationship between the depth of a charge within a protein and its interaction with the solvent. Calculations of the relative electrostatic energies of crystal and misfolded conformations of Themiste dyscritum hemerythrin and the VL domain of an antibody are also presented. The results indicate that electrostatic charge-solvent interactions strongly favor the crystal structures. More generally, it is found that charge-solvent interactions, which are frequently neglected in protein structure analysis, can make large contributions to the total energy of a macromolecular system.