Key Protein-ligand Interactions Research Articles

Reliability of AlphaFold2 Models in Virtual Drug Screening: A Focus on Selected Class A GPCRs.

Protein three-dimensional (3D) structure prediction is one of the most challenging issues in the field of computational biochemistry, which has overwhelmed scientists for almost half a century. A significant breakthrough in structural biology has been established by developing the artificial intelligence (AI) system AlphaFold2 (AF2). The AF2 system provides a state-of-the-art prediction of protein structures from nearly all known protein sequences with high accuracy. This study examined the reliability of AF2 models compared to the experimental structures in drug discovery, focusing on one of the most common protein drug-targeted classes known as G protein-coupled receptors (GPCRs) class A. A total of 32 representative protein targets were selected, including experimental structures of X-ray crystallographic and Cryo-EM structures and their corresponding AF2 models. The quality of AF2 models was assessed using different structure validation tools, including the pLDDT score, RMSD value, MolProbity score, percentage of Ramachandran favored, QMEAN Z-score, and QMEANDisCo Global. The molecular docking was performed using the Genetic Optimization for Ligand Docking (GOLD) software. The AF2 models' reliability in virtual drug screening was determined by their ability to predict the ligand binding poses closest to the native binding pose by assessing the Root Mean Square Deviation (RMSD) metric and docking scoring function. The quality of the docking and scoring function was evaluated using the enrichment factor (EF). Furthermore, the capability of using AF2 models in molecular docking to identify hits with key protein-ligand interactions was analyzed. The posing power results showed that the AF2 models successfully predicted ligand binding poses (RMSD < 2 Å). However, they exhibited lower screening power, with average EF values of 2.24, 2.42, and 1.82 for X-ray, Cryo-EM, and AF2 structures, respectively. Moreover, our study revealed that molecular docking using AF2 models can identify competitive inhibitors. In conclusion, this study found that AF2 models provided docking results comparable to experimental structures, particularly for certain GPCR targets, and could potentially significantly impact drug discovery.

An Assessment of Dispersion-Corrected DFT Methods for Modeling Nonbonded Interactions in Protein Kinase Inhibitor Complexes.

Accurate modeling of nonbonded interactions between protein kinases and their small molecule inhibitors is essential for structure-based drug design. Quantum chemical methods such as density functional theory (DFT) hold significant promise for quantifying the strengths of these key protein-ligand interactions. However, the accuracy of DFT methods can vary substantially depending on the choice of exchange-correlation functionals and associated basis sets. In this study, a comprehensive benchmarking of nine widely used DFT methods was carried out to identify an optimal approach for quantitative modeling of nonbonded interactions, balancing both accuracy and computational efficiency. From a database of 2139 kinase-inhibitor crystal structures, a diverse library of 49 nonbonded interaction motifs was extracted, encompassing CH-π, π-π stacking, cation-π, hydrogen bonding, and salt bridge interactions. The strengths of nonbonded interaction energies for all 49 motifs were calculated at the advanced CCSD(T)/CBS level of theory, which serve as references for a systematic benchmarking of BLYP, TPSS, B97, ωB97X, B3LYP, M062X, PW6B95, B2PLYP, and PWPB95 functionals with D3BJ dispersion correction alongside def2-SVP, def2-TZVP, and def2-QZVP basis sets. The RI, RIJK, and RIJCOSX approximations were used for selected functionals. It was found that the B3LYP/def2-TZVP and RIJK RI-B2PLYP/def2-QZVP methods delivered the best combination of accuracy and computational efficiency, making them well-suited for efficient modeling of nonbonded interactions responsible for molecular recognition of protein kinase inhibitors in their targets.

Binding Modes of Xanthine-Derived Selective Allosteric Site Inhibitors of MTHFD2.

Methylenetetrahydrofolate dehydrogenase (MTHFD2) is a mitochondrial enzyme involved in 1 C metabolism that is upregulated in various cancer cells, but absent in normal proliferating cells. Xanthine derivatives are the first selective inhibitors of MTHFD2 which bind to its allosteric site. Xanthine derivatives (including the co-crystallized inhibitors) were herein interrogated by molecular/induced-fit docking, MM-GBSA binding free energy calculations and molecular dynamics simulations in both MTHFD2 and MTHFD1 (a close homolog expressed in healthy cells). The gained insights from our in silico protocol allowed us to study binding mode, key protein-ligand interactions and dynamic movement of the allosteric inhibitors, correlating with their experimental binding affinities, biological activities and selectivity for MTHFD2. The reported conformational changes with MTHFD2 upon binding of xanthine derivatives were furthermore evaluated and confirmed by RMSF analyses of the MD simulation trajectories. The results reported herein are expected to benefit in the rational design of selective MTHFD2 allosteric inhibitors.

Binding Analysis and Structure-Based Design of Tricyclic Coumarin-Derived MTHFD2 Inhibitors as Anticancer Agents: Insights from Computational Modeling.

Unfolded protein response (UPR)-dependent metabolic reprogramming diverts metabolites from glycolysis to mitochondrial 1C metabolism, highlighting pharmacological resistance to folate drugs and overexpression of certain enzymes. Methylenetetrahydrofolate dehydrogenase (MTHFD2) is a mitochondrial enzyme that plays a key role in 1C metabolism in purine and thymidine synthesis and is exclusively overexpressed in cancer cells but absent in most healthy adult human tissues. To the best of our knowledge, tricyclic coumarin-based compounds (substrate site binders) and xanthine derivatives (allosteric site binders) are the only selective inhibitors of MTHFD2 reported until the present date. The current study aims at the investigation of the available structural data of MTHFD2 in complex with potent and selective inhibitors that occupy the substrate binding site, further providing insights into binding mode, key protein-ligand interactions, and conformational dynamics, that correspond to the experimental binding affinities and biological activities. In addition, we carried out structure-based drug design on the substrate binding site of MTHFD2, by exploiting the cocrystal structure of MTHFD2 with the tricyclic coumarin-based inhibitor. The structure-based drug design campaign involves R-group enumeration, bioisostere replacement, molecular docking, ADME prediction, MM-GBSA binding free energy calculations, and molecular dynamics simulations, that led to a small library of new and potential compounds, capable of selectively inhibiting MTHFD2. The results reported herein are expected to benefit medicinal chemists working on the development of selective MTHFD2 inhibitors for cancer treatment, although experimental validation by biochemical and/or pharmacokinetic assays is required to substantiate the outcomes of the study.

Reconstruction of the unbinding pathways of noncovalent SARS-CoV and SARS-CoV-2 3CLpro inhibitors using unbiased molecular dynamics simulations.

The main protease (3CLpro) is one of the essential components of the SARS-CoVs viral life cycle, which makes it an interesting target for overpowering these viruses. Although many covalent and noncovalent inhibitors have been designed to inhibit this molecular target, none have gained FDA approval as a drug. Because of the high rate of COVID-19 pandemic development, in addition to laboratory research, we require in silico methods to accelerate rational drug design. The unbinding pathways of two SARS-CoV and SARS-CoV-2 3CLpro noncovalent inhibitors with the PDB IDs: 3V3M, 4MDS, 6W63, 5RF7 were explored from a comparative perspective using unbiased molecular dynamics (UMD) simulations. We uncovered common weak points for selected inhibitors that could not interact significantly with a binding pocket at specific residues by all their fragments. So water molecules entered the free binding S regions and weakened protein-inhibitor fundamental interactions gradually. N142, G143, and H163 are the essential residues, which cause key protein-ligand interactions in the binding pocket. We believe that these results will help design new potent inhibitors against SARS-CoV-2.

Protein-Ligand Interactions in the STING Binding Site Probed by Rationally Designed Single-Point Mutations: Experiment and Theory.

STING protein (stimulator of interferon genes) plays an important role in the innate immune system. A number of potent compounds regulating its activity have been reported, mostly derivatives of cyclic dinucleotides (CDNs), natural STING agonists. Here, we aim to provide complementary information to large-scale "ligand-profiling" studies by probing the importance of STING-CDN protein-ligand interactions on the protein side. We examined in detail six typical CDNs each in complex with 13 rationally devised mutations in STING: S162A, S162T, Y167F, G230A, R232K, R232H, A233L, A233I, R238K, T263A, T263S, R293Q, and G230A/R293Q. The mutations switch on and off various types of protein-ligand interactions: π-π stacking, hydrogen bonding, ionic pairing, and nonpolar contacts. We correlated experimental data obtained by differential scanning fluorimetry, X-ray crystallography, and isothermal titration calorimetry with theoretical calculations. This enabled us to provide a mechanistic interpretation of the differences in the binding of representative CDNs to STING. We observed that the G230A mutation increased the thermal stability of the protein-ligand complex, indicating an increased level of ligand binding, whereas R238K and Y167F led to a complete loss of stabilization (ligand binding). The effects of the other mutations depended on the type of ligand (CDN) and varied, to some extent. A very good correlation (R2 = 0.6) between the experimental binding affinities and interaction energies computed by quantum chemical methods enabled us to explain the effect of the studied mutations in detail and evaluate specific interactions quantitatively. Our work may inspire development of high-affinity ligands against the common STING haplotypes by targeting the key (sometimes non-intuitive) protein-ligand interactions.

Perspective on Acetylcholinesterase: A Potential Target for Alzheimer’s Disease Intervention

Background: Alzheimer's disease (AD) is an unexplained progressive degenerative brain disease; it accounts for 60-70% of dementia cases worldwide, has an estimated global incidence of 24.3 million, and is associated with deterioration of the central nervous system. Acetylcholinesterase (AChE) is an enzyme that catalyzes the breakdown of acetylcholine to acetate and choline. It is also the potential target of most of the clinically used drugs for the treatment of AD. The degeneration of cholinergic neurons and the loss of neural transmission are the main reasons for the decline of cognitive ability in AD patients. Objective: In recent years, the development of targeted drugs in AD has made significant achievements. The global impact of AD continues to grow, and as a result, the focus of disease-modifying therapies remains elusive. The role of treatment is not limited to pharmacology but involves many factors, such as the psychological, social, and economic aspects of the patient and family. AChE is an important target in AD to consider the use of AChE inhibitors in patients with mild to moderate AD, even though the costs are high and no other direct progress has been made. Although there are many AChE inhibitors, there is no selective potent inhibitor for AChE. There is still a need to address human AChE structure, provide key insights into key protein-ligand interactions, and provide the basis for molecular modeling and structure-based drug design. Conclusion: In this review, we briefly introduce the clinical characteristics of AD, the development of crystal structure, and the latest research on AD. In recent years, the development of different strategies for amyloid-β, BACE1, and type-1 interferon receptor alpha-1 has attracted great attention. There is an urgent need to further characterize available instrumental compounds to explore the use of new selectivity and key protein-ligand interactions in AD. In the past few decades, great progress has been made in the treatment of AD, but AD is still one of the world's problems, and the inability to cure AD, indicates that there is an urgent need for new treatments.

Abstract 4456: Discovery of E7766: A representative of a novel class of macrocycle-bridged STING agonists (MBSAs) with superior potency and pan-genotypic activity

Abstract Introduction STING (stimulator of interferon genes) is an emerging target for cancer immunotherapy. 2’,3'-cGAMP, a natural cyclic dinucleotide (CDN) STING agonist, and its phosphorothioate analogs, have drawn broad attention as lead molecules for STING targeted drug discovery. These CDNs, however, lack efficacy in some common STING genotypes disproportionally represented in non-Caucasians. Moreover, such CDNs have not fully addressed liability in chemical/metabolic stability. Here we report our chemistry approach to control STING agonist conformation to enhance binding affinity across all common STING genotypes and broaden the therapeutic potential of such compounds. Methods Our SBDD approach started with analysis of the binding pocket and key protein-ligand interactions to prioritize a focused set of analogs for chemical synthesis. Systematic SAR was built upon in vitro assays for STING binding affinity and activation of STING genotypes. X-ray single crystal structures were established for STING and diverse analogs, in free and bound states, to provide structural insight for rational analog design. Results Structural modeling was refined to evaluate different binding modes and dynamic conformational changes in the STING-ligand interface. We observed that STING-bound CDNs had the two ancillary nucleobases specifically oriented in close proximity with parallel pi-pi stacking and discovered that covalently linking the nucleobases advantageously pre-organize the bioactive constrained conformation for enhanced STING affinity. Our discovery established a novel class of macrocycle-bridged STING agonists (MBSAs). E7766, a representative of Eisai MBSA platform, shows superior in vitro activity against all the major human STING genotypes over reference CDNs, most distinctly in STINGREF. E7766 co-crystal structures with STINGWT and STINGREF provide structural basis for the added benefit of the topological novelty. The macrocyclic linker bridging the top of nucleobases perturbs the STING lid loop conformation and create new and specific interactions with both genotypes. In twelve subcutaneous tumor models in immune competent mice, single intra-tumoral injections achieved either complete regression or significant tumor growth delay with no serious adverse effect. E7766 also shows excellent chemical and metabolic stability, presumably conferred by conformational rigidity of the unique macrocycle bridge. More biological characterization of E7766 can be found in abstract #. Conclusion Eisai successfully discovered E7766, a representative of a novel class of macrocycle-bridged STING agonist topologically distinct from conventional STING agonists. E7766 demonstrated pan-genotypic STING activation, potent anti-cancer activities and excellent chemical and metabolic stability for further development. Citation Format: Atsushi ENDO, Dae-Shik Kim, Kuan-Chun Huang, Ming-Hong Hao, Steven Mathieu, Hyeong-wook Choi, Utpal Majumder, Xiaojie Zhu, Yongchun Shen, Kristen Sanders, Thomas Noland, Dinesh Chandra, Yu Chen, Karen TenDyke, Kara Loiacono, Donna Kolber-Simonds, Rongrong Jiang, Vaishali Dixit, Janna Hutz, John Wang, Xingfeng Bao, Francis Fang, Nadeem Sarwar. Discovery of E7766: A representative of a novel class of macrocycle-bridged STING agonists (MBSAs) with superior potency and pan-genotypic activity [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr 4456.

QSAR and Molecular docking studies of 4-anilinoquinoline-triazine hybrids as pf-DHFR inhibitors

&lt;p&gt;A quantitative structure-activity relationship (QSAR) investigation was performed towards 41 hybrids of 4-anilinoquinoline-triazines as potential antimalarial agents. The study was carried out using descendant multiple linear regression analyses (MLR), and artificial neural networks (ANN). Quantum chemical descriptors were calculated using DFT-B3LYP method, with the basis set 6-31G. The values obtained for the correlation coefficient of 0.87 and 0.92 by MLR and ANN, respectively, show a good predictive quality of the established model. In addition, the predicted model has been confirmed by several validation methods such as leave-one-out (LOO) cross-validation, Y-randomization, and external validation. The observed activity and the structural features of the studied molecules were further highlighted by molecular docking study on both wild and quadruple mutant type of &lt;em&gt;pf-DHFR&lt;/em&gt; protein. Furthermore, the present work deals to study the binding modes and the key protein-ligand interactions. This methodology will be used to design new antimalarial drugs.&lt;/p&gt;

QSAR and Molecular docking studies of 4-anilinoquinoline-triazine hybrids as pf-DHFR inhibitors

A quantitative structure-activity relationship (QSAR) investigation was performed towards 41 hybrids of 4-anilinoquinoline-triazines as potential antimalarial agents. The study was carried out using descendant multiple linear regression analyses (MLR), and artificial neural networks (ANN). Quantum chemical descriptors were calculated using DFT-B3LYP method, with the basis set 6-31G. The values obtained for the correlation coefficient of 0.87 and 0.92 by MLR and ANN, respectively, show a good predictive quality of the established model. In addition, the predicted model has been confirmed by several validation methods such as leave-one-out (LOO) cross-validation, Y-randomization, and external validation. The observed activity and the structural features of the studied molecules were further highlighted by molecular docking study on both wild and quadruple mutant type of pf-DHFR protein. Furthermore, the present work deals to study the binding modes and the key protein-ligand interactions. This methodology will be used to design new antimalarial drugs.

Structure-Based Identification of Potent Natural Product Chemotypes as Cannabinoid Receptor 1 Inverse Agonists.

Natural products are an abundant source of potential drugs, and their diversity makes them a rich and viable prospective source of bioactive cannabinoid ligands. Cannabinoid receptor 1 (CB1) antagonists are clinically established and well documented as potential therapeutics for treating obesity, obesity-related cardiometabolic disorders, pain, and drug/substance abuse, but their associated CNS-mediated adverse effects hinder the development of potential new drugs and no such drug is currently on the market. This limitation amplifies the need for new agents with reduced or no CNS-mediated side effects. We are interested in the discovery of new natural product chemotypes as CB1 antagonists, which may serve as good starting points for further optimization towards the development of CB1 therapeutics. In search of new chemotypes as CB1 antagonists, we screened the in silico purchasable natural products subset of the ZINC12 database against our reported CB1 receptor model using the structure-based virtual screening (SBVS) approach. A total of 18 out of 192 top-scoring virtual hits, selected based on structural diversity and key protein–ligand interactions, were purchased and subjected to in vitro screening in competitive radioligand binding assays. The in vitro screening yielded seven compounds exhibiting >50% displacement at 10 μM concentration, and further binding affinity (Ki and IC50) and functional data revealed compound 16 as a potent and selective CB1 inverse agonist (Ki = 121 nM and EC50 = 128 nM) while three other compounds—2, 12, and 18—were potent but nonselective CB1 ligands with low micromolar binding affinity (Ki). In order to explore the structure–activity relationship for compound 16, we further purchased compounds with >80% similarity to compound 16, screened them for CB1 and CB2 activities, and found two potent compounds with sub-micromolar activities. Most importantly, these bioactive compounds represent structurally new natural product chemotypes in the area of cannabinoid research and could be considered for further structural optimization as CB1 ligands.

Discovery, optimization and biological evaluation for novel c-Met kinase inhibitors

The c-Met kinase has emerged as an attractive target for developing antitumor agents because of its close relationship with the development of many human cancers, poor clinical outcomes and even drug resistance. A series of novel c-Met kinase inhibitors have been identified with multiple workflow in this work, including virtual screening, X-ray crystallography, biological evaluation and structural optimization. The experimentally determined crystal structure of the best hit compound HL-11 in c-Met kinase domain was highly consistent with the computational prediction. Comparison of the hit compounds with different c-Met kinase inhibitory activity by molecular dynamics simulations suggested the key protein-ligand interactions for structural optimization. Based on these, structural optimization produced compound 11e with better c-Met kinase inhibitory activity and improved anti-proliferative activity. These experimental findings proved the reliability and efficiency of our in silico methods. This strategy will facilitate further lead discovery and optimization for novel c-Met kinase inhibitors.

The Binding Mode of the Sonic Hedgehog Inhibitor Robotnikinin, a Combined Docking and QM/MM MD Study

Erroneous activation of the Hedgehog pathway has been linked to a great amount of cancerous diseases and therefore a large number of studies aiming at its inhibition have been carried out. One leverage point for novel therapeutic strategies targeting the proteins involved, is the prevention of complex formation between the extracellular signaling protein Sonic Hedgehog and the transmembrane protein Patched 1. In 2009 robotnikinin, a small molecule capable of binding to and inhibiting the activity of Sonic Hedgehog has been identified, however in the absence of X-ray structures of the Sonic Hedgehog-robotnikinin complex, the binding mode of this inhibitor remains unknown. In order to aid with the identification of novel Sonic Hedgehog inhibitors, the presented investigation elucidates the binding mode of robotnikinin by performing an extensive docking study, including subsequent molecular mechanical as well as quantum mechanical/molecular mechanical molecular dynamics simulations. The attained configurations enabled the identification of a number of key protein-ligand interactions, aiding complex formation and providing stabilizing contributions to the binding of the ligand. The predicted structure of the Sonic Hedgehog-robotnikinin complex is provided via a PDB file as Supplementary Material and can be used for further reference.

Structural basis for the ligand-binding specificity of fatty acid-binding proteins (pFABP4 and pFABP5) in gentoo penguin

Fatty acid-binding proteins (FABPs) are involved in transporting hydrophobic fatty acids between various aqueous compartments of the cell by directly binding ligands inside their β-barrel cavities. Here, we report the crystal structures of ligand-unbound pFABP4, linoleate-bound pFABP4, and palmitate-bound pFABP5, obtained from gentoo penguin (Pygoscelis papua), at a resolution of 2.1 Å, 2.2 Å, and 2.3 Å, respectively. The pFABP4 and pFABP5 proteins have a canonical β-barrel structure with two short α-helices that form a cap region and fatty acid ligand binding sites in the hydrophobic cavity within the β-barrel structure. Linoleate-bound pFABP4 and palmitate-bound pFABP5 possess different ligand-binding modes and a unique ligand-binding pocket due to several sequence dissimilarities (A76/L78, T30/M32, underlining indicates pFABP4 residues) between the two proteins. Structural comparison revealed significantly different conformational changes in the β3–β4 loop region (residues 57–62) as well as the flipped Phe60 residue of pFABP5 than that in pFABP4 (the corresponding residue is Phe58). A ligand-binding study using fluorophore displacement assays shows that pFABP4 has a relatively strong affinity for linoleate as compared to pFABP5. In contrast, pFABP5 exhibits higher affinity for palmitate than that for pFABP4. In conclusion, our high-resolution structures and ligand-binding studies provide useful insights into the ligand-binding preferences of pFABPs based on key protein–ligand interactions.

Including Explicit Water Molecules as Part of the Protein Structure in MM/PBSA Calculations

Water is the natural medium of molecules in the cell and plays an important role in protein structure, function and interaction with small molecule ligands. However, the widely used molecular mechanics Poisson-Boltzmann surface area (MM/PBSA) method for binding energy calculation does not explicitly take account of water molecules that mediate key protein-ligand interactions. We have developed a protocol to include water molecules that mediate ligand-protein interactions as part of the protein structure in calculation of MM/PBSA binding energies (a method we refer to as water-MM/PBSA) for a series of JNK3 kinase inhibitors. Improved correlation between water-MM/PBSA binding energies and experimental IC50 values was obtained compared to that obtained from classical MM/PBSA binding energy. This improved correlation was further validated using sets of neuraminidase and avidin inhibitors. The observed improvement, however, appears to be limited to systems in which there are water-mediated ligand-protein hydrogen bond interactions. We conclude that the water-MM/PBSA method performs better than classical MM/PBSA in predicting binding affinities when water molecules play a direct role in mediating ligand-protein hydrogen bond interactions.

Biocatalytic Reductions of Baylis–Hillman Adducts

Baylis–Hillman adducts are highly useful synthetic intermediates; to enhance their value further, we sought enantiocomplementary alkene reductases to introduce chirality. Two solutions emerged: (1) a wild-type protein from Pichia stipitis (OYE 2.6), whose performance significantly outstrips that of the standard enzyme (Saccharomyces pastorianus OYE1), and (2) a series of OYE1 mutants at position 116 (Trp in the wild-type enzyme). To understand how mutations could lead to inverted enantioselectivity, we solved the X-ray crystal structure of the Trp116Ile OYE1 variant complexed with a cyclopentenone substrate. This revealed key protein–ligand interactions that control the orientation of substrate binding above the FMN cofactor.

Human dipeptidyl peptidase III: insights into ligand binding from a combined experimental and computational approach

Human dipeptidyl peptidase III (DPP III) is a zinc-exopeptidase with implied roles in protein catabolism, pain modulation, and defense against oxidative stress. To understand the mode of ligand binding into its active site, we performed molecular modeling, site-directed mutagenesis, and biochemical analyses. Using the recently determined crystal structure of the human DPP III we built complexes between both, the wild-type (WT) protein and its mutant H568N with the preferred substrate Arg-Arg-2-naphthylamide (RRNA) and a competitive inhibitor Tyr-Phe-hydroxamate (Tyr-Phe-NHOH). The mutation of the conserved His568, structurally equivalent to catalytically important His231 in thermolysin, to Asn, resulted in a 1300-fold decrease of k(cat) for RRNA hydrolysis and in significantly lowered affinity for the inhibitor. Molecular dynamics simulations revealed the key protein-ligand interactions as well as the ligand-induced reorganization of the binding site and its partial closure. Simultaneously, the non-catalytic domain was observed to stretch and the opening at the wide side of the inter-domain cleft became enhanced. The driving force for these changes was the formation of the hydrogen bond between Asp372 and the bound ligand. The structural and dynamical differences, found for the ligand binding to the WT enzyme and the H568N mutant, and the calculated binding free energies, agree well with the measured affinities. On the basis of the obtained results we suggest a possible reaction mechanism. In addition, this work provides a foundation for further site-directed mutagenesis experiments, as well as for modeling the reaction itself.

Biophysical Mapping of the Adenosine A2A Receptor

A new approach to generating information on ligand receptor interactions within the binding pocket of G protein-coupled receptors has been developed, called Biophysical Mapping (BPM). Starting from a stabilized receptor (StaR), minimally engineered for thermostability, additional single mutations are then added at positions that could be involved in small molecule interactions. The StaR and a panel of binding site mutants are captured onto Biacore chips to enable characterization of the binding of small molecule ligands using surface plasmon resonance (SPR) measurement. A matrix of binding data for a set of ligands versus each active site mutation is then generated, providing specific affinity and kinetic information (KD, kon, and koff) of receptor–ligand interactions. This data set, in combination with molecular modeling and docking, is used to map the small molecule binding site for each class of compounds. Taken together, the many constraints provided by these data identify key protein–ligand interactions and allow the shape of the site to be refined to produce a high quality three-dimensional picture of ligand binding, thereby facilitating structure based drug design. Results of biophysical mapping of the adenosine A2A receptor are presented.

Potentially increasing the metabolic stability of drug candidates via computational site of metabolism prediction by CYP2C9: The utility of incorporating protein flexibility via an ensemble of structures

Cytochrome P450 enzymes are responsible for metabolizing many endogenous and xenobiotic molecules encountered by the human body. It has been estimated that 75% of all drugs are metabolized by cytochrome P450 enzymes. Thus, predicting a compound’s potential sites of metabolism (SOM) is highly advantageous early in the drug development process. We have combined molecular dynamics, AutoDock Vina docking, the neighboring atom type (NAT) reactivity model, and a solvent-accessible surface-area term to form a reactivity–accessibility model capable of predicting SOM for cytochrome P450 2C9 substrates. To investigate the importance of protein flexibility during the ligand-binding process, the results of SOM prediction using a static protein structure for docking were compared to SOM prediction using multiple protein structures in ensemble docking. The results reported here indicate that ensemble docking increases the number of ligands that can be docked in a bioactive conformation (ensemble: 96%, static: 85%) but only leads to a slight improvement (49% vs. 44%) in predicting an experimentally known SOM in the top-1 position for a ligand library of 75 CYP2C9 substrates. Using ensemble docking, the reactivity–accessibility model accurately predicts SOM in the top-1 ranked position for 49% of the ligand library and considering the top-3 predicted sites increases the prediction success rate to approximately 70% of the ligand library. Further classifying the substrate library according to K m values leads to an improvement in SOM prediction for substrates with low K m values (57% at top-1). While the current predictive power of the reactivity–accessibility model still leaves significant room for improvement, the results illustrate the usefulness of this method to identify key protein–ligand interactions and guide structural modifications of the ligand to increase its metabolic stability.

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.