Effective Methods for Estimation of Binding Energies in Computer-Aided Drug Design

Experimental and Computational Approaches to Improve Binding Affinity in Chemical Biology and Drug Discovery

Drug discovery is one of the most complicated processes and establishment of a single drug may require multidisciplinary attempts to design efficient and commercially viable drugs. The main purpose of drug design is to identify a chemical compound or inhibitor that can bind to an active site of a specific cavity on a target protein. The traditional drug design methods involved various experimental based approaches including random screening of chemicals found in nature or can be synthesized directly in chemical laboratories. Except for the long cycle design and time, high cost is also the major issue of concern. Modernized computer-based algorithm including structure-based drug design has accelerated the drug design and discovery process adequately. Surprisingly from the past decade remarkable progress has been made concerned with all area of drug design and discovery. CADD (Computer Aided Drug Designing) based tools shorten the conventional cycle size and also generate chemically more stable and worthy compounds and hence reduce the drug discovery cost. This special edition of editorial comprises the combination of seven research and review articles set emphasis especially on the computational approaches along with the experimental approaches using a chemical synthesizing for the binding affinity in chemical biology and discovery as a salient used in de-novo drug designing. This set of articles exfoliates the role that systems biology and the evaluation of ligand affinity in drug design and discovery for the future.

Uncertainty Quantification in Alchemical Free Energy Methods

Alchemical free energy methods have gained much importance recently from several reports of improved ligand–protein binding affinity predictions based on their implementation using molecular dynamics simulations. A large number of variants of such methods implementing different accelerated sampling techniques and free energy estimators are available, each claimed to be better than the others in its own way. However, the key features of reproducibility and quantification of associated uncertainties in such methods have barely been discussed. Here, we apply a systematic protocol for uncertainty quantification to a number of popular alchemical free energy methods, covering both absolute and relative free energy predictions. We show that a reliable measure of error estimation is provided by ensemble simulation—an ensemble of independent MD simulations—which applies irrespective of the free energy method. The need to use ensemble methods is fundamental and holds regardless of the duration of time of the molecular dynamics simulations performed.

BAR-based optimum adaptive sampling regime for variance minimization in alchemical transformation: the nonequilibrium stratification

Following the previously proposed equilibrate-state sampling based adaptive sampling regime Optimum Bennett Acceptance Ratio (OBAR), we introduce its nonequilibrium extension, the Optimum Crooks' Equation (OCE) in the current work. The efficiency of the NonEquilibrium Work (NEW) stratification is improved by adaptively manipulating the significance of each nonequilibrium realization followed by importance sampling. As is exhibited in the equilibrium case, the nonequilibrium extension outperforms the simple equal time rule used in nonequilibrium stratification in the sense of minimizing the total variance of the free energy estimate. The speedup of this non-equal time rule is more than 1-fold. The Time Derivative of total Variance (TDV) proposed for the OBAR criterion is extended to determine the importance of each nonequilibrium transformation, which is linearly dependent on the variance. The TDV in the nonequilibrium case gives a totally different importance rank from the standard errors of the free energy differences and OBAR TDV due to the duration of nonequilibrium pulling being added into the OCE equation. The performance of the OCE workflow is demonstrated in the solvation of several small molecules with a series of lambda increments and relaxation times between successive perturbations. To the best of our knowledge, such a nonequilibrium adaptive sampling regime in alchemical transformation is unprecedented.

Extensive Assessment of Various Computational Methods for Aspartate's pKa Shift

A series of computational methods for pK_a shift prediction are extensively tested on a set of benchmark protein systems, aiming at identifying pitfalls and evaluating their performance on high variants. Including 19 ASP residues in 10 protein systems, the benchmark set consists of both residues with highly shifted pK_a values as well as those varying little from the reference value, with an experimental RMS free energy differences of 2.49 kcal/mol with respect to blocked amino acid, namely the RMS pK_a shift being 1.82 pK_a units. The constant pH molecular dynamics (MD), alchemical methods, PROPKA3.1, and multiconformation continuum electrostatics give RMSDs of 1.52, 2.58, 1.37, and 3.52 pK_a units, respectively, on the benchmark set. The empirical scoring method is the most accurate one with extremely low computational cost, and the pH-dependent model is also able to provide accurate results, while the accuracy of MD sampling incorporating alchemical free energy simulation is prohibited by convergence achievement and the performance of conformational search incorporating multiconformation continuum electrostatics is bad. Former research works did not define statistical uncertainty with care and yielded the questionable conclusion that alchemical methods perform well in most benchmarks. In this work the traditional alchemical methods are thoroughly tested for high variants. We also performed the first application of nonequilibrium alchemical methods to the pK_a cases.

BAR-based optimum adaptive sampling regime for variance minimization in alchemical transformation

The efficiency of alchemical free energy simulations with a staging strategy is improved by adaptively manipulating the significance of each ensemble followed by importance sampling.

Evaluating the Accuracy of the Quasiharmonic Approximation

The quasiharmonic approximation (QH) allows the configurational entropy of a molecule to be estimated on the basis of a molecular dynamics simulation, through construction of a Gaussian probability distribution of conformations with variances equal to those provided by the simulation. At its introduction in 1981, the QH method was successfully applied to simple molecular systems with only one highly occupied energy well, and fluctuations were analyzed in a system of internal bond-angle-torsion coordinates. However, more recent studies have applied the QH method to complex biomolecular systems and have relied upon Cartesian coordinates. The present study evaluates the accuracy of the QH method through comparisons with more detailed methods. The chief findings are that the QH method can markedly overestimate the configurational entropy for systems with multiple occupied energy wells and that such errors tend to be magnified by the use of Cartesian coordinates instead of bond-angle-torsion coordinates.

Electrostatic effects in macromolecules: fundamental concepts and practical modeling

The past few years have seen an exponential growth in the calculations of electrostatic energies of macromolecules and an increased recognition of the crucial role of electrostatic effects. This review considers the current state of the field. Focus is placed on calculations of pKas, redox potentials and binding energies in macromolecules and clarification of the fact that the value of the dielectric 'constant' of a protein depends on its definition and that small dielectric constants should not be used in describing charge-charge interactions by current continuum models.

A new class of models for computing receptor-ligand binding affinities

Models for predicting the binding affinities of molecules in solution are either very detailed, making them computationally intensive and hard to test, or very simple, and thus less informative than one might wish. A new class of models that focus on the predominant states of the binding molecules promise to capture the essential physics of binding at modest computational cost.

Electrostatic control of GTP and GDP binding in the oncoprotein p21ras

BACKGROUND

p21ras is one of the GTP-binding proteins that act as intercellular molecular switches. The GTP-bound form of p21ras sends a growth-promoting signal that is terminated once the protein is cycled back into its GDP-bound form. The interaction of guanine-nucleotide-exchange factors (GEFs) with p21ras leads to activation of the protein by promoting GDP --> GTP exchange. Oncogenic mutations of p21ras trap the protein in its biological active GTP-bound form. Other mutations interfere with the activity of GEF. Thus, it is important to explore the structural basis for the action of different mutations.

RESULTS

The crystal structures of p21ras are correlated with the binding affinities of GTP and GDP by calculating the relevant electrostatic energies. It is demonstrated that such calculations can provide a road map to the location of 'hot' residues whose mutations are likely to change functional properties of the protein. Furthermore, calculations of the effect of specific mutations on GTP and GDP binding are consistent with those observed. This helps to analyze and locate functionally important parts of the protein.

CONCLUSIONS

Our calculations indicate that the protein main chain provides a major contribution to the binding energies of nucleotides and probably plays a key role in relaying the effect of GEF action. Analysis of p21ras mutations in residues that are important for the proper function of GEFs suggests that the region comprising residues 62-67 in p21ras is the major GEF-binding site. This analysis and our computer simulations indicate that the effect of GEF is probably propagated to the P-loop (residues 10-17) through interaction between Gly60 and Gly12. This then reduces the interaction between the main-chain dipoles of the P-loop and the nucleotide. Finally, the results also suggest a possible relationship between the GTP --> GDP structural transition and the catalytic effect of the GTPase-activating protein.

Molecular docking towards drug discovery

Fueled by advances in molecular structure determination, tools for structure-based drug design are proliferating rapidly. Lead discovery through searching of ligand databases with molecular docking techniques represents an attractive alternative to high-throughout random screening. The size of commercial databases imposes severe computational constraints on molecular docking, compromising the level of calculational detail permitted for each putative ligand. We describe alternative philosophies for docking which effectively address this challenge. With respect to the dynamic aspects of molecular recognition, these strategies lie along a spectrum of models bounded by the Lock-and-Key and Induced-Fit theories for ligand binding. We explore the potential of a rigid model in exploiting species specificity and of a tolerant model in predicting absolute ligand binding affinity. Current molecular docking methods are limited primarily by their ability to rank docked complexes; we therefore place particular emphasis on this aspect of the problem throughout our validation of docking strategies.