抗原变异可使得多种致病微生物易于逃避宿主免疫应答。表达在感染红细胞表面的恶性疟原虫红细胞表面蛋白1（PfPMP1）与感染红细胞、内皮细胞、树突状细胞以及胎盘的单个或多个受体作用，在黏附及免疫逃避中起关键的作用。每个单倍体基因组var基因家族编码约60种成员，通过启动转录不同的var基因变异体为抗原变异提供了分子基础。

疟原虫var基因转换速率变化导致抗原变异[英]／Paul H, Robert P, Christodoulou Z, et al//Proc Natl Acad Sci U S A

Molecular recognition, which is the process of biological macromolecules interacting with each other or various small molecules with a high specificity and affinity to form a specific complex, constitutes the basis of all processes in living organisms. Proteins, an important class of biological macromolecules, realize their functions through binding to themselves or other molecules. A detailed understanding of the protein–ligand interactions is therefore central to understanding biology at the molecular level. Moreover, knowledge of the mechanisms responsible for the protein-ligand recognition and binding will also facilitate the discovery, design, and development of drugs. In the present review, first, the physicochemical mechanisms underlying protein–ligand binding, including the binding kinetics, thermodynamic concepts and relationships, and binding driving forces, are introduced and rationalized. Next, three currently existing protein-ligand binding models—the “lock-and-key”, “induced fit”, and “conformational selection”—are described and their underlying thermodynamic mechanisms are discussed. Finally, the methods available for investigating protein–ligand binding affinity, including experimental and theoretical/computational approaches, are introduced, and their advantages, disadvantages, and challenges are discussed.

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Insights into Protein-Ligand Interactions: Mechanisms, Models, and Methods

As one of the most popular computational approaches in modern structure-based drug design, molecular docking can be used not only to identify the correct conformation of a ligand within the target binding pocket but also to estimate the strength of the interaction between a target and a ligand. Nowadays, as a variety of docking programs are available for the scientific community, a comprehensive understanding of the advantages and limitations of each docking program is fundamentally important to conduct more reasonable docking studies and docking-based virtual screening. In the present study, based on an extensive dataset of 2002 protein–ligand complexes from the PDBbind database (version 2014), the performance of ten docking programs, including five commercial programs (LigandFit, Glide, GOLD, MOE Dock, and Surflex-Dock) and five academic programs (AutoDock, AutoDock Vina, LeDock, rDock, and UCSF DOCK), was systematically evaluated by examining the accuracies of binding pose prediction (sampling power) and binding affinity estimation (scoring power). Our results showed that GOLD and LeDock had the best sampling power (GOLD: 59.8% accuracy for the top scored poses; LeDock: 80.8% accuracy for the best poses) and AutoDock Vina had the best scoring power (rp/rs of 0.564/0.580 and 0.569/0.584 for the top scored poses and best poses), suggesting that the commercial programs did not show the expected better performance than the academic ones. Overall, the ligand binding poses could be identified in most cases by the evaluated docking programs but the ranks of the binding affinities for the entire dataset could not be well predicted by most docking programs. However, for some types of protein families, relatively high linear correlations between docking scores and experimental binding affinities could be achieved. To our knowledge, this study has been the most extensive evaluation of popular molecular docking programs in the last five years. It is expected that our work can offer useful information for the successful application of these docking tools to different requirements and targets.

Comprehensive evaluation of ten docking programs on a diverse set of protein-ligand complexes: the prediction accuracy of sampling power and scoring power.

In the drug discovery process, accurate methods of computing the affinity of small molecules with a biological target are strongly needed. This is particularly true for molecular docking and virtual screening methods, which use approximated scoring functions and struggle in estimating binding energies in correlation with experimental values. Among the various methods, MM-PBSA and MM-GBSA are emerging as useful and effective approaches. Although these methods are typically applied to large collections of equilibrated structures of protein-ligand complexes sampled during molecular dynamics in water, the possibility to reliably estimate ligand affinity using a single energy-minimized structure and implicit solvation models has not been explored in sufficient detail. Herein, we thoroughly investigate this hypothesis by comparing different methods for the generation of protein-ligand complexes and diverse methods for free energy prediction for their ability to correlate with experimental values. The methods were tested on a series of structurally diverse inhibitors of Plasmodium falciparum DHFR with known binding mode and measured affinities. The results showed that correlations between MM-PBSA or MM-GBSA binding free energies with experimental affinities were in most cases excellent. Importantly, we found that correlations obtained with the use of a single protein-ligand minimized structure and with implicit solvation models were similar to those obtained after averaging over multiple MD snapshots with explicit water molecules, with consequent save of computing time without loss of accuracy. When applied to a virtual screening experiment, such an approach proved to discriminate between true binders and decoy molecules and yielded significantly better enrichment curves.

Fast and accurate predictions of binding free energies using MM‐PBSA and MM‐GBSA

By using different evaluation strategies, we systemically evaluated the performance of Molecular Mechanics/Generalized Born Surface Area (MM/GBSA) and Molecular Mechanics/Poisson–Boltzmann Surface Area (MM/PBSA) methodologies based on more than 1800 protein–ligand crystal structures in the PDBbind database. The results can be summarized as follows: (1) for the one-protein-family/one-binding-ligand case which represents the unbiased protein–ligand complex sampling, both MM/GBSA and MM/PBSA methodologies achieve approximately equal accuracies at the interior dielectric constant of 4 (with rp = 0.408 ± 0.006 of MM/GBSA and rp = 0.388 ± 0.006 of MM/PBSA based on the minimized structures); while for the total dataset (1864 crystal structures), the overall best Pearson correlation coefficient (rp = 0.579 ± 0.002) based on MM/GBSA is better than that of MM/PBSA (rp = 0.491 ± 0.003), indicating that biased sampling may significantly affect the accuracy of the predicted result (some protein families contain too many instances and can bias the overall predicted accuracy). Therefore, family based classification is needed to evaluate the two methodologies; (2) the prediction accuracies of MM/GBSA and MM/PBSA for different protein families are quite different with rp ranging from 0 to 0.9, whereas the correlation and ranking scores (an averaged rp/rs over a list of protein folds and also representing the unbiased sampling) given by MM/PBSA (rp-score = 0.506 ± 0.050 and rs-score = 0.481 ± 0.052) are comparable to those given by MM/GBSA (rp-score = 0.516 ± 0.047 and rs-score = 0.463 ± 0.047) at the fold family level; (3) for the overall prediction accuracies, molecular dynamics (MD) simulation may not be quite necessary for MM/GBSA (rp-minimized = 0.579 ± 0.002 and rp-1ns = 0.564 ± 0.002), but is needed for MM/PBSA (rp-minimized = 0.412 ± 0.003 and rp-1ns = 0.491 ± 0.003). However, for the individual systems, whether to use MD simulation is depended. (4) both MM/GBSA and MM/PBSA may be unable to give successful predictions for the ligands with high formal charges, with the Pearson correlation coefficient ranging from 0.621 ± 0.003 (neutral ligands) to 0.125 ± 0.142 (ligands with a formal charge of 5). Therefore, it can be summarized that, although MM/GBSA and MM/PBSA perform similarly in the unbiased dataset, for the currently available crystal structures in the PDBbind database, compared with MM/GBSA, which may be used in multi-target comparisons, MM/PBSA is more sensitive to the investigated systems, and may be more suitable for individual-target-level binding free energy ranking. This study may provide useful guidance for the post-processing of docking based studies.

Assessing the performance of MM/PBSA and MM/GBSA methods. 4. Accuracies of MM/PBSA and MM/GBSA methodologies evaluated by various simulation protocols using PDBbind data set

In the last two decades, isothermal titration calorimetry (ITC) has become the preferred technique to determine the binding energetics of biological processes, including protein-ligand binding, protein-protein binding, DNA-protein binding, protein-carbohydrate binding, protein-lipid binding, and antigen-antibody binding. In this unit several protocols are presented, ranging from the basic ones that are aimed at characterizing binding of moderate affinity to advanced protocols that are aimed at determining very high or very low affinity binding processes. Also, alternate protocols for special cases (homodimeric proteins and unstable proteins) and additional information accessible by ITC (heat capacity and protonation/deprotonation processes coupled to binding) are presented.

Isothermal Titration Calorimetry

A common strategy to improve the potency of drug candidates is to introduce chemical functionalities, like hydrogen bond donors or acceptors, at positions where they are able to establish strong interactions with the target. However, it is often observed that the added functionalities do not necessarily improve potency even if they form strong hydrogen bonds. Here, we explore the thermodynamic and structural basis for those observations. KNI-10033 is a potent experimental HIV-1 protease inhibitor with picomolar affinity against the wild-type enzyme (Kd = 13 pm). The potency of the inhibitor is the result of favorable enthalpic (?H = -8.2 kcal/mol) and entropic (-T?S = -6.7 kcal/mol) interactions. The replacement of the thioether group in KNI-10033 by a sulfonyl group (KNI-10075) results in a strong hydrogen bond with the amide of Asp 30B of the HIV-1 protease. This additional hydrogen bond improves the binding enthalpy by 3.9 kcal/mol; however, the enthalpy gain is completely compensated by an entropy loss, resulting in no affinity change. Crystallographic and thermodynamic analysis of the inhibitor/protease complexes indicates that the entropy losses are due to a combination of conformational and solvation effects. These results provide a set of practical guidelines aimed at overcoming enthalpy/entropy compensation andmore » improve binding potency.« less

Compensating enthalpic and entropic changes hinder binding affinity optimization.

The appearance of viral strains that are resistant to protease inhibitors is one of the most serious problems in the chemotherapy of HIV-1/AIDS. The most pervasive drug-resistant mutants are those that affect all inhibitors in clinical use. In this paper, we have characterized a multiple-drug-resistant mutant of the HIV-1 protease that affects indinavir, nelfinavir, saquinavir, ritonavir, amprenavir, and lopinavir. This mutant (MDR-HM) contains six amino acid mutations (L10I/M46I/I54V/V82A/I84V/L90M) located within and outside the active site of the enzyme. Microcalorimetric and enzyme kinetic measurements indicate that this mutant lowers the affinity of all inhibitors by 2−3 orders of magnitude. By comparison, the multiiple-drug-resistant mutant only increased the Km of the substrate by a factor of 2, indicating that the substrate is able to adapt to the changes caused by the mutations and maintain its binding affinity. To understand the origin of resistance, three submutants containing mutations in spec...

Multidrug resistance to HIV-1 protease inhibition requires cooperative coupling between distal mutations.

A significant obstacle to the efficacy of drugs directed against viral targets is the presence of amino acid polymorphisms in the targeted molecules. Amino acid polymorphisms may occur naturally due to the existence of variations within and between viral strains or as the result of mutations associated with drug resistance. An ideal drug will be one that is extremely effective against a primary target and maintains its effectiveness against the most important variations of the target molecule. A drug that simultaneously inhibits different variants of the target will lead to a faster suppression of the virus, retard the appearance of drug-resistant mutants and provide more efficacious and, in the long range, more affordable therapies. Drug molecules with the ability to inhibit several variants of a target with high affinity have been termed adaptive drugs (Nat. Biotechnol. 20 (2002) 15; Biochemistry 42 (2003) 8459; J. Cell. Biochem. S37 (2001) 82). Current drug design paradigms are predicated upon the lock-and-key hypothesis, which emphasizes shape complementarity as a way to attain specificity and improved binding affinity. Shape complementarity is accomplished by the introduction of conformational constraints in the drug molecule. While highly constrained molecules do well against a unique target, they lack the ability to adapt to target variations like those originating from naturally occurring polymorphisms or drug-resistant mutations. Targeting an array of closely related targets rather than a single one while still maintaining selectivity, requires a different approach. A plausible strategy for designing high affinity adaptive inhibitors is to engineer their most critical interactions (for affinity and specificity) with conserved regions of the target while allowing for adaptability through the introduction of flexible asymmetric functionalities in places facing variable regions of the target. The fundamental thermodynamics and structural principles associated with this approach are discussed in this chapter.

Adaptive inhibitors of the HIV-1 protease

Amprenavir is one of six protease inhibitors presently approved for clinical use in the therapeutic treatment of AIDS. Biochemical and clinical studies have shown that, unlike other inhibitors, Amprenavir is severely affected by the protease mutation I50V, located in the flap region of the enzyme. TMC-126 is a second-generation inhibitor, chemically related to Amprenavir, with a reported extremely low susceptibility to existing resistant mutations including I50V. In this paper, we have studied the thermodynamic and molecular origin of the response of these two inhibitors to the I50V mutation and the double active-site mutation V82F/I84V that affects all existing clinical inhibitors. Amprenavir binds to the wild-type HIV-1 protease with high affinity (5.0 × 109 M−1 or 200 pM) in a process equally favored by enthalpic and entropic contributions. The mutations I50V and V82F/I84V lower the binding affinity of Amprenavir by a factor of 147 and 104, respectively. TMC-126, on the other hand, binds to the wild-type protease with extremely high binding affinity (2.6 × 1011 M−1 or 3.9 pM) in a process in which enthalpic contributions overpower entropic contributions by almost a factor of 4. The mutations I50V and V82F/I84V lower the binding affinity of TMC-126 by only a factor of 16 and 11, respectively, indicating that the binding affinity of TMC-126 to the drug-resistant mutants is still higher than the affinity of Amprenavir to the wild-type protease. Analysis of the data for TMC-126 and KNI-764, another second-generation inhibitor, indicates that their low susceptibility to mutations is caused by their ability to compensate for the loss of interactions with the mutated target by a more favorable entropy of binding.

Overcoming drug resistance in HIV-1 chemotherapy: the binding thermodynamics of Amprenavir and TMC-126 to wild-type and drug-resistant mutants of the HIV-1 protease.

Hiroyasu Ohtaka

Papers

Isothermal Titration Calorimetry

Compensating enthalpic and entropic changes hinder binding affinity optimization.

Multidrug resistance to HIV-1 protease inhibition requires cooperative coupling between distal mutations.

Adaptive inhibitors of the HIV-1 protease

Overcoming drug resistance in HIV-1 chemotherapy: the binding thermodynamics of Amprenavir and TMC-126 to wild-type and drug-resistant mutants of the HIV-1 protease.