Prediction Of Protein-ligand Binding Affinities Research Articles

Binding Affinity Prediction for Protein–Ligand Complexes Based onβContacts and B Factor

Accurate determination of protein-ligand binding affinity is a fundamental problem in biochemistry useful for many applications including drug design and protein-ligand docking. A number of scoring functions have been proposed for the prediction of protein-ligand binding affinity. However, accurate prediction is still a challenging problem because poor performance is often seen in the evaluation under the leave-one-cluster-out cross-validation (LCOCV). We introduce a new scoring function named B2BScore to improve the prediction performance. B2BScore integrates two physicochemical properties for protein-ligand binding affinity prediction. One is the property of β contacts. A β contact between two atoms requires no other atoms to interrupt the atomic contact and assumes that the two atoms should have enough direct contact area. The other is the property of B factor to capture the atomic mobility in the dynamic protein-ligand binding process. Tested on the PDBBind2009 data set, B2BScore shows superior prediction performance to existing methods on independent test data as well as under the LCOCV evaluation framework. In particular, B2BScore achieves a significant LCOCV improvement across 26 protein clusters-a big increase of the averaged Pearson's correlation coefficients from 0.418 to 0.518 and a significant decrease of standard deviation of the coefficients from 0.352 to 0.196. We also identified several important and intuitive contact descriptors of protein-ligand binding through the random forest learning in B2BScore. Some of these descriptors are closely related to contacts between carbon atoms without covalent-bond oxygen/nitrogen, preferred contacts of metal ions, interfacial backbone atoms from proteins, or π rings. Some others are negative descriptors relating to those contacts with nitrogen atoms without covalent-bond hydrogens or nonpreferred contacts of metal ions. These descriptors can be directly used to guide protein-ligand docking.

A Comparative Assessment of Ranking Accuracies of Conventional and Machine-Learning-Based Scoring Functions for Protein-Ligand Binding Affinity Prediction

Accurately predicting the binding affinities of large sets of protein-ligand complexes efficiently is a key challenge in computational biomolecular science, with applications in drug discovery, chemical biology, and structural biology. Since a scoring function (SF) is used to score, rank, and identify drug leads, the fidelity with which it predicts the affinity of a ligand candidate for a protein's binding site has a significant bearing on the accuracy of virtual screening. Despite intense efforts in developing conventional SFs, which are either force-field based, knowledge-based, or empirical, their limited ranking accuracy has been a major roadblock toward cost-effective drug discovery. Therefore, in this work, we explore a range of novel SFs employing different machine-learning (ML) approaches in conjunction with a variety of physicochemical and geometrical features characterizing protein-ligand complexes. We assess the ranking accuracies of these new ML-based SFs as well as those of conventional SFs in the context of the 2007 and 2010 PDBbind benchmark data sets on both diverse and protein-family-specific test sets. We also investigate the influence of the size of the training data set and the type and number of features used on ranking accuracy. Within clusters of protein-ligand complexes with different ligands bound to the same target protein, we find that the best ML-based SF is able to rank the ligands correctly based on their experimentally determined binding affinities 62.5 percent of the time and identify the top binding ligand 78.1 percent of the time. For this SF, the Spearman correlation coefficient between ranks of ligands ordered by predicted and experimentally determined binding affinities is 0.771. Given the challenging nature of the ranking problem and that SFs are used to screen millions of ligands, this represents a significant improvement over the best conventional SF we studied, for which the corresponding ranking performance values are 57.8 percent, 73.4 percent, and 0.677.

Calculation of Host-Guest Binding Affinities Using a Quantum-Mechanical Energy Model.

The prediction of protein-ligand binding affinities is of central interest in computer-aided drug discovery, but it is still difficult to achieve a high degree of accuracy. Recent studies suggesting that available force fields may be a key source of error motivate the present study, which reports the first mining minima (M2) binding affinity calculations based on a quantum mechanical energy model, rather than an empirical force field. We apply a semi-empirical quantum-mechanical energy function, PM6-DH+, coupled with the COSMO solvation model, to 29 host-guest systems with a wide range of measured binding affinities. After correction for a systematic error, which appears to derive from the treatment of polar solvation, the computed absolute binding affinities agree well with experimental measurements, with a mean error 1.6 kcal/mol and a correlation coefficient of 0.91. These calculations also delineate the contributions of various energy components, including solute energy, configurational entropy, and solvation free energy, to the binding free energies of these host-guest complexes. Comparison with our previous calculations, which used empirical force fields, point to significant differences in both the energetic and entropic components of the binding free energy. The present study demonstrates successful combination of a quantum mechanical Hamiltonian with the M2 affinity method.

Blind prediction of host–guest binding affinities: a new SAMPL3 challenge

The computational prediction of protein-ligand binding affinities is of central interest in early-stage drug-discovery, and there is a widely recognized need for improved methods. Low molecular weight receptors and their ligands--i.e., host-guest systems--represent valuable test-beds for such affinity prediction methods, because their small size makes for fast calculations and relatively facile numerical convergence. The SAMPL3 community exercise included the first ever blind prediction challenge for host-guest binding affinities, through the incorporation of 11 new host-guest complexes. Ten participating research groups addressed this challenge with a variety of approaches. Statistical assessment indicates that, although most methods performed well at predicting some general trends in binding affinity, overall accuracy was not high, as all the methods suffered from either poor correlation or high RMS errors or both. There was no clear advantage in using explicit versus implicit solvent models, any particular force field, or any particular approach to conformational sampling. In a few cases, predictions using very similar energy models but different sampling and/or free-energy methods resulted in significantly different results. The protonation states of one host and some guest molecules emerged as key uncertainties beyond the choice of computational approach. The present results have implications for methods development and future blind prediction exercises.

Pharmaceutical Applications of the Polarizable Amoeba Potential, Including Protein-Ligand Binding Affinity and Drug Solubility, using the Force Field X Software

Accurate prediction of protein-ligand binding affinity is essential to computational drug discovery. Although virtual screening has been widely utilized, current approaches are seriously limited by the accuracy of the underlying potential energy model (i.e. force field) that describes atomic interactions. A more rigorous physical model combined with effective sampling of molecular configurations is critical for binding affinity prediction to chemical accuracy, which is defined as within one order of magnitude of the true equilibrium dissociation constant. We have demonstrated that electrostatic interactions, especially electronic polarization, are critical for protein-ligand recognition due to the significant change in electrostatic environments between bulk water and protein pockets and have achieved encouraging success in treating charged species using the polarizable Atomic Multipole Optimized Energetics for Biomolecular Applications (AMOEBA) force field. To maintain accuracy while also achieving efficiency, AMOEBA has been combined with the Orthogonal Space Random Walk enhanced alchemical free energy algorithm. Here we present applications of this strategy for the computation of protein-ligand binding affinities and, for the first time, drug solubility from alchemical simulations using the Force Field X software.View Large Image | View Hi-Res Image | Download PowerPoint Slide

Molecular simulation methods in drug discovery: a prospective outlook

Over the last decades, molecular simulations have spread through the drug discovery arena. This trend is expected to continue in the foreseeable future thanks to increased performance and the positive impact they can exert on productivity. In this article we highlight three aspects of molecular modelling for which we expect significant improvements over the next 25 years. Increased computational resources, faster algorithms and novel methods to sample rare events will provide a better handle on target flexibility and its relation with ligand binding. More accurate target druggability predictions will improve the success, but also broaden the scope of target-based drug discovery strategies. Finally, the use of higher levels of theory will increase the accuracy of protein-ligand binding affinity predictions, resulting in better hit identification success rates as well as more efficient lead optimization processes.

Physics-Based Scoring of Protein–Ligand Interactions: Explicit Polarizability, Quantum Mechanics and Free Energies

The ability to accurately predict the interaction of a ligand with its receptor is a key limitation in computer-aided drug design approaches such as virtual screening and de novo design. In this article, we examine current strategies for a physics-based approach to scoring of protein-ligand affinity, as well as outlining recent developments in force fields and quantum chemical techniques. We also consider advances in the development and application of simulation-based free energy methods to study protein-ligand interactions. Fuelled by recent advances in computational algorithms and hardware, there is the opportunity for increased integration of physics-based scoring approaches at earlier stages in computationally guided drug discovery. Specifically, we envisage increased use of implicit solvent models and simulation-based scoring methods as tools for computing the affinities of large virtual ligand libraries. Approaches based on end point simulations and reference potentials allow the application of more advanced potential energy functions to prediction of protein-ligand binding affinities. Comprehensive evaluation of polarizable force fields and quantum mechanical (QM)/molecular mechanical and QM methods in scoring of protein-ligand interactions is required, particularly in their ability to address challenging targets such as metalloproteins and other proteins that make highly polar interactions. Finally, we anticipate increasingly quantitative free energy perturbation and thermodynamic integration methods that are practical for optimization of hits obtained from screened ligand libraries.

A knowledge-guided strategy for improving the accuracy of scoring functions in binding affinity prediction

BackgroundCurrent scoring functions are not very successful in protein-ligand binding affinity prediction albeit their popularity in structure-based drug designs. Here, we propose a general knowledge-guided scoring (KGS) strategy to tackle this problem. Our KGS strategy computes the binding constant of a given protein-ligand complex based on the known binding constant of an appropriate reference complex. A good training set that includes a sufficient number of protein-ligand complexes with known binding data needs to be supplied for finding the reference complex. The reference complex is required to share a similar pattern of key protein-ligand interactions to that of the complex of interest. Thus, some uncertain factors in protein-ligand binding may cancel out, resulting in a more accurate prediction of absolute binding constants.ResultsIn our study, an automatic algorithm was developed for summarizing key protein-ligand interactions as a pharmacophore model and identifying the reference complex with a maximal similarity to the query complex. Our KGS strategy was evaluated in combination with two scoring functions (X-Score and PLP) on three test sets, containing 112 HIV protease complexes, 44 carbonic anhydrase complexes, and 73 trypsin complexes, respectively. Our results obtained on crystal structures as well as computer-generated docking poses indicated that application of the KGS strategy produced more accurate predictions especially when X-Score or PLP alone did not perform well.ConclusionsCompared to other targeted scoring functions, our KGS strategy does not require any re-parameterization or modification on current scoring methods, and its application is not tied to certain systems. The effectiveness of our KGS strategy is in theory proportional to the ever-increasing knowledge of experimental protein-ligand binding data. Our KGS strategy may serve as a more practical remedy for current scoring functions to improve their accuracy in binding affinity prediction.

Assessment of Nonequilibrium Free Energy Methods

One of the factors preventing the general application of free energy methods in rational drug design remains the lack of sufficient computational resources. Many nonequilibrium (NE) free energy methods, however, are easily made embarrassingly parallel in comparison to equilibrium methods and may be conveniently run on desktop computers using distributed computing software. In recent years, there has been a proliferation of NE methods, but the general applicability of these approaches has not been determined. In this study, a subset including only those NE methods which are easily parallelised were considered for examination, with a view to their application to the prediction of protein-ligand binding affinities. A number of test systems were examined, including harmonic oscillator (HO) systems and the calculation of relative free energies of hydration of water-methane. The latter system uses identical potentials to the protein ligand case and is therefore an appropriate model system on which methods may be tested. As well as investigating existing protocols, a replica exchange NE approach was developed, which was found to offer advantages over conventional methods. It was found that Rosenbluth-based approaches to optimizing the NE work values used in NE free energy estimates were not consistent in the improvements in accuracy achieved and that, given their computational cost, the simple approach of taking each work value in an unbiased way is to be preferred. Of the two free energy estimators examined, Bennett's acceptance ratio was the most consistent and is, therefore, to be preferred over the Jarzynski estimator. The recommended protocols may be run very efficiently within a distributed computing environment and are of similar accuracy and precision to equilibrium free energy methods.

Interaction Model Based on Local Protein Substructures Generalizes to the Entire Structural Enzyme-Ligand Space

Chemogenomics is a new strategy in in silico drug discovery, where the ultimate goal is to understand molecular recognition for all molecules interacting with all proteins in the proteome. To study such cross interactions, methods that can generalize over proteins that vary greatly in sequence, structure, and function are needed. We present a general quantitative approach to protein-ligand binding affinity prediction that spans the entire structural enzyme-ligand space. The model was trained on a data set composed of all available enzymes cocrystallized with druglike ligands, taken from four publicly available interaction databases, for which a crystal structure is available. Each enzyme was characterized by a set of local descriptors of protein structure that describe the binding site of the cocrystallized ligand. The ligands in the training set were described by traditional QSAR descriptors. To evaluate the model, a comprehensive test set consisting of enzyme structures and ligands was manually curated. The test set contained enzyme-ligand complexes for which no crystal structures were available, and thus the binding modes were unknown. The test set enzymes were therefore characterized by matching their entire structures to the local descriptor library constructed from the training set. Both the training and the test set contained enzyme-ligand complexes from all major enzyme classes, and the enzymes spanned a large range of sequences and folds. The experimental binding affinities (p K i) ranged from 0.5 to 11.9 (0.7-11.0 in the test set). The induced model predicted the binding affinities of the external test set enzyme-ligand complexes with an r (2) of 0.53 and an RMSEP of 1.5. This demonstrates that the use of local descriptors makes it possible to create rough predictive models that can generalize over a wide range of protein targets.

Protein-Ligand Binding Affinity Predictions by Implicit Solvent Simulations: A Tool for Lead Optimization?

Continuum electrostatics is combined with rigorous free-energy calculations in an effort to deliver a reliable and efficient method for in silico lead optimization. The methodology is tested by calculation of the relative binding free energies of a set of inhibitors of neuraminidase, cyclooxygenase2, and cyclin-dependent kinase 2. The calculated free energies are compared to the results obtained with explicit solvent simulations and empirical scoring functions. For cyclooxygenase2, deficiencies in the continuum electrostatics theory are identified and corrected with a modified simulation protocol. For neuraminidase, it is shown that a continuum representation of the solvent leads to markedly different protein-ligand interactions compared to the explicit solvent simulations, and a reconciliation of the two protocols is problematic. Cyclin-dependent kinase 2 proves more challenging, and none of the methods employed in this study yield high quality predictions. Despite the differences observed, for these systems, the use of an implicit solvent framework to predict the ranking of congeneric inhibitors to a protein is shown to be faster, as accurate or more accurate than the explicit solvent protocol, and superior to empirical scoring schemes.

A new concept for multidimensional selection of ligand conformations (MultiSelect) and multidimensional scoring (MultiScore) of protein-ligand binding affinities.

In this work, eight different scoring functions have been combined with the aim of improving the prediction of protein-ligand binding conformations and affinities. The obtained scores were analyzed using multivariate statistical methods to generate expressions, with the ability (1) to select the best candidate between different docked conformations of an inhibitor (MultiSelect) and (2) to quantify the protein-ligand binding affinity (MultiScore). By use of the docking program GOLD, 40 different inhibitors were docked into the active site of three matrix metalloproteinases (MMP's), yielding a total of 120 enzyme-inhibitor complexes. For each complex, a single conformation of the inhibitor was selected using principal component analysis (PCA) for the scores obtained by the eight functions SCORE, LUDI, GRID, PMF_Score, D_Score, G_Score, ChemScore, and F_Score. Binding affinities were estimated based on partial least-squares projections onto latent structures (PLS) on the eight scores of each selected inhibitor conformation. By use of this procedure, R(2) = 0.78 and Q(2) = 0.78 were obtained when comparing experimental and calculated binding affinities. MultiSelect was evaluated by applying the same method for selecting docked conformations for 18 different protein-ligand complexes of known three-dimensional structure. In all cases, the selected ligand conformations were found to be very similar to the experimentally determined ligand conformations. A more general evaluation of MultiScore was performed using a set of 120 different protein-ligand complexes for which both the three-dimensional structures and the binding affinities were known. This approach allowed an evaluation of MultiScore independently of MultiSelect. The generality of the method was verified by obtaining R(2) = 0.68 and Q(2) = 0.67, when comparing calculated and experimental binding affinities for the 120 X-ray structures. In all cases, LUDI, SCORE, GRID, and F_Score were included as important functions, whereas the fifth function was PMF_Score and ChemScore for the MMP and X-ray models, respectively.

Use of polylysine-coated slides in preparation of cell samples for diagnostic cytology with special reference to urine sample.

<h3>Abstract</h3> The prediction of acid dissociation constants (p<i>K</i>_a) is a prerequisite for predicting many other properties of a small molecule, such as its protein-ligand binding affinity, distribution coefficient (log <i>D</i>), membrane permeability, and solubility. The prediction of each of these properties requires knowledge of the relevant protonation states and solution free energy penalties of each state. The SAMPL6 p<i>K</i>_a Challenge was the first time that a separate challenge was conducted for evaluating p<i>K</i>_a predictions as part of the Statistical Assessment of Modeling of Proteins and Ligands (SAMPL) exercises. This challenge was motivated by significant inaccuracies observed in prior physical property prediction challenges, such as the SAMPL5 log <i>D</i> Challenge, caused by protonation state and p<i>K</i>_a prediction issues. The goal of the p<i>K</i>_a challenge was to assess the performance of contemporary p<i>K</i>_a prediction methods for drug-like molecules. The challenge set was composed of 24 small molecules that resembled fragments of kinase inhibitors, a number of which were multiprotic. Eleven research groups contributed blind predictions for a total of 37 p<i>K</i>_a distinct prediction methods. In addition to blinded submissions, four widely used p<i>K</i>_a prediction methods were included in the analysis as reference methods. Collecting both microscopic and macroscopic p<i>K</i>_a predictions allowed in-depth evaluation of p<i>K</i>_a prediction performance. This article highlights deficiencies of typical p<i>K</i>_a prediction evaluation approaches when the distinction between microscopic and macroscopic p<i>K</i>_as is ignored; in particular, we suggest more stringent evaluation criteria for microscopic and macroscopic p<i>K</i>_a predictions guided by the available experimental data. Top-performing submissions for macroscopic p<i>K</i>_a predictions achieved RMSE of 0.7-1.0 p<i>K</i>_a units and included both quantum chemical and empirical approaches, where the total number of extra or missing macroscopic p<i>K</i>_as predicted by these submissions were fewer than 8 for 24 molecules. A large number of submissions had RMSE spanning 1-3 p<i>K</i>_a units. Molecules with sulfur-containing heterocycles or iodo and bromo groups were less accurately predicted on average considering all methods evaluated. For a subset of molecules, we utilized experimentally-determined microstates based on NMR to evaluate the dominant tautomer predictions for each macroscopic state. Prediction of dominant tautomers was a major source of error for microscopic p<i>K</i>_a predictions, especially errors in charged tautomers. The degree of inaccuracy in p<i>K</i>_a predictions observed in this challenge is detrimental to the protein-ligand binding affinity predictions due to errors in dominant protonation state predictions and the calculation of free energy corrections for multiple protonation states. Underestimation of ligand p<i>K</i>_a by 1 unit can lead to errors in binding free energy errors up to 1.2 kcal/mol. The SAMPL6 p<i>K</i>_a Challenge demonstrated the need for improving p<i>K</i>_a prediction methods for drug-like molecules, especially for challenging moieties and multiprotic molecules.