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Abstract Background Integrase inhibitors (INI) form a new drug class in the treatment of HIV-1 patients. We developed a linear regression modeling approach to make a quantitative raltegravir (RAL) resistance phenotype prediction, as Fold Change in IC50 against a wild type virus, from mutations in the integrase genotype. Methods We developed a clonal genotype-phenotype database with 991 clones from 153 clinical isolates of INI naïve and RAL treated patients, and 28 site-directed mutants. We did the development of the RAL linear regression model in two stages, employing a genetic algorithm (GA) to select integrase mutations by consensus. First, we ran multiple GAs to generate first order linear regression models (GA models) that were stochastically optimized to reach a goal R2 accuracy, and consisted of a fixed-length subset of integrase mutations to estimate INI resistance. Secondly, we derived a consensus linear regression model in a forward stepwise regression procedure, considering integrase mutations or mutation pairs by descending prevalence in the GA models. Results The most frequently occurring mutations in the GA models were 92Q, 97A, 143R and 155H (all 100%), 143G (90%), 148H/R (89%), 148K (88%), 151I (81%), 121Y (75%), 143C (72%), and 74M (69%). The RAL second order model contained 30 single mutations and five mutation pairs (p 2 performance of this model on the clonal training data was 0.97, and 0.78 on an unseen population genotype-phenotype dataset of 171 clinical isolates from RAL treated and INI naïve patients. Conclusions We describe a systematic approach to derive a model for predicting INI resistance from a limited amount of clonal samples. Our RAL second order model is made available as an Additional file for calculating a resistance phenotype as the sum of integrase mutations and mutation pairs.

Abstract Background Linear regression models are used to quantitatively predict drug resistance, the phenotype, from the HIV-1 viral genotype. As new antiretroviral drugs become available, new resistance pathways emerge and the number of resistance associated mutations continues to increase. To accurately identify which drug options are left, the main goal of the modeling has been to maximize predictivity and not interpretability. However, we originally selected linear regression as the preferred method for its transparency as opposed to other techniques such as neural networks. Here, we apply a method to lower the complexity of these phenotype prediction models using a 3-fold cross-validated selection of mutations. Results Compared to standard stepwise regression we were able to reduce the number of mutations in the reverse transcriptase (RT) inhibitor models as well as the number of interaction terms accounting for synergistic and antagonistic effects. This reduction in complexity was most significant for the non-nucleoside reverse transcriptase inhibitor (NNRTI) models, while maintaining prediction accuracy and retaining virtually all known resistance associated mutations as first order terms in the models. Furthermore, for etravirine (ETR) a better performance was seen on two years of unseen data. By analyzing the phenotype prediction models we identified a list of forty novel NNRTI mutations, putatively associated with resistance. The resistance association of novel variants at known NNRTI resistance positions: 100, 101, 181, 190, 221 and of mutations at positions not previously linked with NNRTI resistance: 102, 139, 219, 241, 376 and 382 was confirmed by phenotyping site-directed mutants. Conclusions We successfully identified and validated novel NNRTI resistance associated mutations by developing parsimonious resistance prediction models in which repeated cross-validation within the stepwise regression was applied. Our model selection technique is computationally feasible for large data sets and provides an approach to the continued identification of resistance-causing mutations.

Abstract Background G protein-coupled receptors (GPCRs) represent a family of well-characterized drug targets with significant therapeutic value. Phylogenetic classifications may help to understand the characteristics of individual GPCRs and their subtypes. Previous phylogenetic classifications were all based on the sequences of receptors, adding only minor information about the ligand binding properties of the receptors. In this work, we compare a sequence-based classification of receptors to a ligand-based classification of the same group of receptors, and evaluate the potential to use sequence relatedness as a predictor for ligand interactions thus aiding the quest for ligands of orphan receptors. Results We present a classification of GPCRs that is purely based on their ligands, complementing sequence-based phylogenetic classifications of these receptors. Targets were hierarchically classified into phylogenetic trees, for both sequence space and ligand (substructure) space. The overall organization of the sequence-based tree and substructure-based tree was similar; in particular, the adenosine receptors cluster together as well as most peptide receptor subtypes (e.g. opioid, somatostatin) and adrenoceptor subtypes. In ligand space, the prostanoid and cannabinoid receptors are more distant from the other targets, whereas the tachykinin receptors, the oxytocin receptor, and serotonin receptors are closer to the other targets, which is indicative for ligand promiscuity. In 93% of the receptors studied, de-orphanization of a simulated orphan receptor using the ligands of related receptors performed better than random (AUC > 0.5) and for 35% of receptors de-orphanization performance was good (AUC > 0.7). Conclusions We constructed a phylogenetic classification of GPCRs that is solely based on the ligands of these receptors. The similarities and differences with traditional sequence-based classifications were investigated: our ligand-based classification uncovers relationships among GPCRs that are not apparent from the sequence-based classification. This will shed light on potential cross-reactivity of GPCR ligands and will aid the design of new ligands with the desired activity profiles. In addition, we linked the ligand-based classification with a ligand-focused sequence-based classification described in literature and proved the potential of this method for de-orphanization of GPCRs.

Evaluation of kinetic parameters of drug-target binding, k on , k off , and residence time (RT), in addition to the traditional in vitro parameter of affinity is receiving increasing attention in the early stages of drug discovery. Target binding kinetics emerges as a meaningful concept for the evaluation of a ligand's duration of action and more generally drug efficacy and safety. We report the biological evaluation of a novel series of spirobenzo-oxazinepiperidinone derivatives as inhibitors of the human equilibrative nucleoside transporter 1 (hENT1, SLC29A1). The compounds were evaluated in radioligand binding experiments, i.e., displacement, competition association, and washout assays, to evaluate their affinity and binding kinetic parameters. We also linked these pharmacological parameters to the compounds' chemical characteristics, and learned that separate moieties of the molecules governed target affinity and binding kinetics. Among the 29 compounds tested, 28 stood out with high affinity and a long residence time of 87 min. These findings reveal the importance of supplementing affinity data with binding kinetics at transport proteins such as hENT1., (© 2023. The Author(s).)

The respiratory syncytial virus polymerase complex, consisting of the polymerase (L) and phosphoprotein (P), catalyzes nucleotide polymerization, cap addition, and cap methylation via the RNA dependent RNA polymerase, capping, and Methyltransferase domains on L. Several nucleoside and non-nucleoside inhibitors have been reported to inhibit this polymerase complex, but the structural details of the exact inhibitor-polymerase interactions have been lacking. Here, we report a non-nucleoside inhibitor JNJ-8003 with sub-nanomolar inhibition potency in both antiviral and polymerase assays. Our 2.9 Å resolution cryo-EM structure revealed that JNJ-8003 binds to an induced-fit pocket on the capping domain, with multiple interactions consistent with its tight binding and resistance mutation profile. The minigenome and gel-based de novo RNA synthesis and primer extension assays demonstrated that JNJ-8003 inhibited nucleotide polymerization at the early stages of RNA transcription and replication. Our results support that JNJ-8003 binding modulates a functional interplay between the capping and RdRp domains, and this molecular insight could accelerate the design of broad-spectrum antiviral drugs., (© 2023. Springer Nature Limited.)

The notion that data should be Findable, Accessible, Interoperable and Reusable, according to the FAIR Principles, has become a global norm for good data stewardship and a prerequisite for reproducibility. Nowadays, FAIR guides data policy actions and professional practices in the public and private sectors. Despite such global endorsements, however, the FAIR Principles are aspirational, remaining elusive at best, and intimidating at worst. To address the lack of practical guidance, and help with capability gaps, we developed the FAIR Cookbook, an open, online resource of hands-on recipes for "FAIR doers" in the Life Sciences. Created by researchers and data managers professionals in academia, (bio)pharmaceutical companies and information service industries, the FAIR Cookbook covers the key steps in a FAIRification journey, the levels and indicators of FAIRness, the maturity model, the technologies, the tools and the standards available, as well as the skills required, and the challenges to achieve and improve data FAIRness. Part of the ELIXIR ecosystem, and recommended by funders, the FAIR Cookbook is open to contributions of new recipes., (© 2023. The Author(s).)

Optimization of binding affinities for compounds to their target protein is a primary objective in drug discovery. Herein we report on a collaborative study that evaluates a set of compounds binding to ROS1 kinase. We use ESMACS (enhanced sampling of molecular dynamics with approximation of continuum solvent) and TIES (thermodynamic integration with enhanced sampling) protocols to rank the binding free energies. The predicted binding free energies from ESMACS simulations show good correlations with experimental data for subsets of the compounds. Consistent binding free energy differences are generated for TIES and ESMACS. Although an unexplained overestimation exists, we obtain excellent statistical rankings across the set of compounds from the TIES protocol, with a Pearson correlation coefficient of 0.90 between calculated and experimental activities., (© 2022. The Author(s).)

Ibrutinib is the first covalent inhibitor of Bruton's tyrosine kinase (BTK) to be used in the treatment of B-cell cancers. Understanding the mechanism of covalent inhibition will aid in the design of safer and more selective covalent inhibitors that target BTK. The mechanism of covalent inhibition in BTK has been uncertain because there is no appropriate residue nearby that can act as a base to deprotonate the cysteine thiol prior to covalent bond formation. We investigate several mechanisms of covalent modification of C481 in BTK by ibrutinib using combined quantum mechanics/molecular mechanics (QM/MM) molecular dynamics reaction simulations. The lowest energy pathway involves direct proton transfer from C481 to the acrylamide warhead in ibrutinib, followed by covalent bond formation to form an enol intermediate. There is a subsequent rate-limiting keto-enol tautomerisation step (Δ G ‡ = 10.5 kcal mol -1 ) to reach the inactivated BTK/ibrutinib complex. Our results represent the first mechanistic study of BTK inactivation by ibrutinib to consider multiple mechanistic pathways. These findings should aid in the design of covalent drugs that target BTK and other similar targets., Competing Interests: There are no conflicts to declare., (This journal is © The Royal Society of Chemistry.)

Ligand binding affinity calculations based on molecular dynamics (MD) simulations and non-physical (alchemical) thermodynamic cycles have shown great promise for structure-based drug design. However, their broad uptake and impact is held back by the notoriously complex setup of the calculations. Only a few tools other than the free energy perturbation approach by Schrödinger Inc. (referred to as FEP+) currently enable end-to-end application. Here, we present for the first time an approach based on the open-source software pmx that allows to easily set up and run alchemical calculations for diverse sets of small molecules using the GROMACS MD engine. The method relies on theoretically rigorous non-equilibrium thermodynamic integration (TI) foundations, and its flexibility allows calculations with multiple force fields. In this study, results from the Amber and Charmm force fields were combined to yield a consensus outcome performing on par with the commercial FEP+ approach. A large dataset of 482 perturbations from 13 different protein-ligand datasets led to an average unsigned error (AUE) of 3.64 ± 0.14 kJ mol -1 , equivalent to Schrödinger's FEP+ AUE of 3.66 ± 0.14 kJ mol -1 . For the first time, a setup is presented for overall high precision and high accuracy relative protein-ligand alchemical free energy calculations based on open-source software., Competing Interests: There are no conflicts to declare., (This journal is © The Royal Society of Chemistry.)

As the application of computational methods in drug discovery pipelines becomes more widespread it is increasingly important to understand how reproducible their results are and how sensitive they are to choices made in simulation setup and analysis. Here we use ensemble simulation protocols, termed ESMACS (enhanced sampling of molecular dynamics with approximation of continuum solvent), to investigate the sensitivity of the popular molecular mechanics Poisson-Boltzmann surface area (MMPBSA) methodology. Using the bromodomain-containing protein 4 (BRD4) system bound to a diverse set of ligands as our target, we show that robust rankings can be produced only through combining ensemble sampling with multiple trajectories and enhanced solvation via an explicit ligand hydration shell.

A congeneric series of 21 phosphodiesterase 2 (PDE2) inhibitors are reported. Crystal structures show how the molecules can occupy a 'top-pocket' of the active site. Molecules with small substituents do not enter the pocket, a critical leucine (Leu770) is closed and water molecules are present. Large substituents enter the pocket, opening the Leu770 conformation and displacing the waters. We also report an X-ray structure revealing a new conformation of the PDE2 active site domain. The relative binding affinities of these compounds were studied with free energy perturbation (FEP) methods and it represents an attractive real-world test case. In general, the calculations could predict the energy of small-to-small, or large-to-large molecule perturbations. However, accurately capturing the transition from small-to-large proved challenging. Only when using alternative protein conformations did results improve. The new X-ray structure, along with a modelled dimer, conferred stability to the catalytic domain during the FEP molecular dynamics (MD) simulations, increasing the convergence and thereby improving the prediction of ΔΔG of binding for some small-to-large transitions. In summary, we found the most significant improvement in results when using different protein structures, and this data set is useful for future free energy validation studies.

Computer-aided drug discovery activities at Janssen are carried out by scientists in the Computational Chemistry group of the Discovery Sciences organization. This perspective gives an overview of the organizational and operational structure, the science, internal and external collaborations, and the impact of the group on Drug Discovery at Janssen.

Background: Next generation sequencing enables studying heterogeneous populations of viral infections. When the sequencing is done at high coverage depth ("deep sequencing"), low frequency variants can be detected. Here we present QQ-SNV (http://sourceforge.net/projects/qqsnv), a logistic regression classifier model developed for the Illumina sequencing platforms that uses the quantiles of the quality scores, to distinguish true single nucleotide variants from sequencing errors based on the estimated SNV probability. To train the model, we created a dataset of an in silico mixture of five HIV-1 plasmids. Testing of our method in comparison to the existing methods LoFreq, ShoRAH, and V-Phaser 2 was performed on two HIV and four HCV plasmid mixture datasets and one influenza H1N1 clinical dataset., Results: For default application of QQ-SNV, variants were called using a SNV probability cutoff of 0.5 (QQ-SNV(D)). To improve the sensitivity we used a SNV probability cutoff of 0.0001 (QQ-SNV(HS)). To also increase specificity, SNVs called were overruled when their frequency was below the 80(th) percentile calculated on the distribution of error frequencies (QQ-SNV(HS-P80)). When comparing QQ-SNV versus the other methods on the plasmid mixture test sets, QQ-SNV(D) performed similarly to the existing approaches. QQ-SNV(HS) was more sensitive on all test sets but with more false positives. QQ-SNV(HS-P80) was found to be the most accurate method over all test sets by balancing sensitivity and specificity. When applied to a paired-end HCV sequencing study, with lowest spiked-in true frequency of 0.5%, QQ-SNV(HS-P80) revealed a sensitivity of 100% (vs. 40-60% for the existing methods) and a specificity of 100% (vs. 98.0-99.7% for the existing methods). In addition, QQ-SNV required the least overall computation time to process the test sets. Finally, when testing on a clinical sample, four putative true variants with frequency below 0.5% were consistently detected by QQ-SNV(HS-P80) from different generations of Illumina sequencers., Conclusions: We developed and successfully evaluated a novel method, called QQ-SNV, for highly efficient single nucleotide variant calling on Illumina deep sequencing virology data.

Background: Different high-dimensional regression methodologies exist for the selection of variables to predict a continuous variable. To improve the variable selection in case clustered observations are present in the training data, an extension towards mixed-effects modeling (MM) is requested, but may not always be straightforward to implement.In this article, we developed such a MM extension (GA-MM-MMI) for the automated variable selection by a linear regression based genetic algorithm (GA) using multi-model inference (MMI). We exemplify our approach by training a linear regression model for prediction of resistance to the integrase inhibitor Raltegravir (RAL) on a genotype-phenotype database, with many integrase mutations as candidate covariates. The genotype-phenotype pairs in this database were derived from a limited number of subjects, with presence of multiple data points from the same subject, and with an intra-class correlation of 0.92., Results: In generation of the RAL model, we took computational efficiency into account by optimizing the GA parameters one by one, and by using tournament selection. To derive the main GA parameters we used 3 times 5-fold cross-validation. The number of integrase mutations to be used as covariates in the mixed effects models was 25 (chrom.size). A GA solution was found when R2MM > 0.95 (goal.fitness). We tested three different MMI approaches to combine the results of 100 GA solutions into one GA-MM-MMI model. When evaluating the GA-MM-MMI performance on two unseen data sets, a more parsimonious and interpretable model was found (GA-MM-MMI TOP18: mixed-effects model containing the 18 most prevalent mutations in the GA solutions, refitted on the training data) with better predictive accuracy (R2) in comparison to GA-ordinary least squares (GA-OLS) and Least Absolute Shrinkage and Selection Operator (LASSO)., Conclusions: We have demonstrated improved performance when using GA-MM-MMI for selection of mutations on a genotype-phenotype data set. As we largely automated setting the GA parameters, the method should be applicable on similar datasets with clustered observations.

Six-helix bundle (6HB) formation is an essential step for many viruses that rely on a class I fusion protein to enter a target cell and initiate replication. Because the binding modes of small molecule inhibitors of 6HB formation are largely unknown, precisely how they disrupt 6HB formation remains unclear, and structure-based design of improved inhibitors is thus seriously hampered. Here we present the high resolution crystal structure of TMC353121, a potent inhibitor of respiratory syncytial virus (RSV), bound at a hydrophobic pocket of the 6HB formed by amino acid residues from both HR1 and HR2 heptad-repeats. Binding of TMC353121 stabilizes the interaction of HR1 and HR2 in an alternate conformation of the 6HB, in which direct binding interactions are formed between TMC353121 and both HR1 and HR2. Rather than completely preventing 6HB formation, our data indicate that TMC353121 inhibits fusion by causing a local disturbance of the natural 6HB conformation.

Monocyte chemoattractant proteins (MCPs) are cytokines that direct immune cells bearing appropriate receptors to sites of inflammation or injury and are therefore attractive therapeutic targets for inhibitory molecules. 11K2 is a blocking mouse monoclonal antibody active against several human and murine MCPs. A 2.5 A structure of the Fab fragment of this antibody in complex with human MCP-1 has been solved. The Fab blocks CCR2 receptor binding to MCP-1 through an adjacent but distinct binding site. The orientation of the Fab indicates that a single MCP-1 dimer will bind two 11K2 antibodies. Several key residues on the antibody and on human MCPs were predicted to be involved in antibody selectivity. Mutational analysis of these residues confirms their involvement in the antibody-chemokine interaction. In addition to mutations that decreased or disrupted binding, one antibody mutation resulted in a 70-fold increase in affinity for human MCP-2. A key residue missing in human MCP-3, a chemokine not recognized by the antibody, was identified and engineering the preferred residue into the chemokine conferred binding to the antibody.

Improving the affinity of a high-affinity protein-protein interaction is a challenging problem that has practical applications in the development of therapeutic biomolecules. We used a combination of structure-based computational methods to optimize the binding affinity of an antibody fragment to the I-domain of the integrin VLA1. Despite the already high affinity of the antibody (Kd approximately 7 nM) and the moderate resolution (2.8 A) of the starting crystal structure, the affinity was increased by an order of magnitude primarily through a decrease in the dissociation rate. We determined the crystal structure of a high-affinity quadruple mutant complex at 2.2 A. The structure shows that the design makes the predicted contacts. Structural evidence and mutagenesis experiments that probe a hydrogen bond network illustrate the importance of satisfying hydrogen bonding requirements while seeking higher-affinity mutations. The large and diverse set of interface mutations allowed refinement of the mutant binding affinity prediction protocol and improvement of the single-mutant success rate. Our results indicate that structure-based computational design can be successfully applied to further improve the binding of high-affinity antibodies.

Anti-Müllerian hormone (AMH), a TGF-beta family member, determines whether an individual develops a uterus and Fallopian tubes. Mutations in the AMH gene lead to persistent Müllerian duct syndrome in males. The wild-type human AMH protein is synthesized as a disulfide-linked dimer of two identical 70-kDa polypeptides, which undergoes proteolytic processing to generate a 110-kDa N-terminal dimer and a bioactive 25-kDa TGF-beta-like C-terminal dimer. We have studied the biosynthesis and secretion of wild-type AMH and of seven persistent Müllerian duct syndrome proteins, containing mutations in either the N- or C-terminal domain. Mutant proteins lacking the C-terminal domain are secreted more rapidly than full-length AMH, whereas single amino acid changes in both domains can have profound effects on protein stability and folding. The addition of a cysteine in an N-terminal domain mutant, R194C, prevents proper folding, whereas the elimination of the cysteine involved in forming the interchain disulfide bond, in a C-terminal domain mutant, C525Y, leads to a truncation at the C terminus. A molecular model of the AMH C-terminal domain provides insights into how some mutations could affect biosynthesis and function.

Tumor necrosis factor (TNF) ligand and receptor superfamily members play critical roles in diverse developmental and pathological settings. In search for novel TNF superfamily members, we identified a murine chromosomal locus that contains three new TNF receptor-related genes. Sequence alignments suggest that the ligand binding regions of these murine TNF receptor homologues, mTNFRH1, -2 and -3, are most homologous to those of the tumor necrosis factor-related apoptosis-inducing ligand (TRAIL) receptors. By using a number of in vitro ligand-receptor binding assays, we demonstrate that mTNFRH1 and -2, but not mTNFRH3, bind murine TRAIL, suggesting that they are indeed TRAIL receptors. This notion is further supported by our demonstration that both mTNFRH1:Fc and mTNFRH2:Fc fusion proteins inhibited mTRAIL-induced apoptosis of Jurkat cells. Unlike the only other known murine TRAIL receptor mTRAILR2, however, neither mTNFRH2 nor mTNFRH3 has a cytoplasmic region containing the well characterized death domain motif. Coupled with our observation that overexpression of mTNFRH1 and -2 in 293T cells neither induces apoptosis nor triggers NFkappaB activation, we propose that the mTnfrh1 and mTnfrh2 genes encode the first described murine decoy receptors for TRAIL, and we renamed them mDcTrailr1 and -r2, respectively. Interestingly, the overall sequence structures of mDcTRAILR1 and -R2 are quite distinct from those of the known human decoy TRAIL receptors, suggesting that the presence of TRAIL decoy receptors represents a more recent evolutionary event.

The integrin VLA-4 (alpha4,beta1) is involved in the migration of white blood cells to sites of inflammation, and is implicated in the pathology of a variety of diseases including asthma and multiple sclerosis. We report the structure-activity relationships of a series of VLA-4 antagonists that were based upon the integrin-binding sequence of the connecting segment peptide of fibronectin (Leu-Asp-Val), and of VCAM-1 (Ile-Asp-Ser), both natural ligands of VLA-4. We explore variation in the ligand derived peptide portion of these antagonists and also in the novel N-terminal cap, which have discovered through chemical optimization, and which confers high affinity and selectivity. Using the X-ray derived conformation of the Ile-Asp-Ser region of VCAM-1, we rationalize the structure-activity relationships of these antagonists using 3D QSAR (COMFA). The COMFA model was found to be highly predictive with a cross-validated R2CV of 0.7 and a PRESS of 0.49. The robustness of the model was confirmed by testing the influence of various parameters, including grid size, column filtering, as well as the role of orientation of the aligned molecules. Our results suggest that the VCAM-1 structure is useful in generating highly predictive models of our VLA-4 antagonists. The COMFA model coupled with the knowledge that the peptide amides are tolerant to methylation should prove useful in future peptidomimetic design studies.

CD40 Ligand (CD40L) is transiently expressed on the surface of T-cells and binds to CD40, which is expressed on the surface of B-cells. This binding event leads to the differentiation, proliferation, and isotype switching of the B-cells. The physiological importance of CD40L has been demonstrated by the fact that expression of defective CD40L protein causes an immunodeficiency state characterized by high IgM and low IgG serum levels, indicating faulty T-cell dependent B-cell activation. To understand the structural basis for CD40L/CD40 association, we have used a combination of molecular modeling, mutagenesis, and X-ray crystallography. The structure of the extracellular region of CD40L was determined by protein crystallography, while the CD40 receptor was built using homology modeling based upon a novel alignment of the TNF receptor superfamily, and using the X-ray structure of the TNF receptor as a template. The model shows that the interface of the complex is composed of charged residues, with CD40L presenting basic side chains (K143, R203, R207), and CD40 presenting acidic side chains (D84, E114, E117). These residues were studied experimentally through site-directed mutagenesis, and also theoretically using electrostatic calculations with the program Delphi. The mutagenesis data explored the role of the charged residues in both CD40L and CD40 by switching to Ala (K143A, R203A, R207A of CD40L, and E74A, D84A, E114A, E117A of CD40), charge reversal (K143E, R203E, R207E of CD40L, and D84R, E114R, E117R of CD40), mutation to a polar residue (K143N, R207N, R207Q of CD40L, and D84N, E117N of CD40), and for the basic side chains in CD40L, isosteric substitution to a hydrophobic side chain (R203M, R207M). All the charge-reversal mutants and the majority of the Met and Ala substitutions led to loss of binding, suggesting that charged interactions stabilize the complex. This was supported by the Delphi calculations which confirmed that the CD40/CD40L residue pairs E74-R203, D84-R207, and E117-R207 had a net stabilizing effect on the complex. However, the substitution of hydrophilic side chains at several of the positions was tolerated, which suggests that although charged interactions stabilize the complex, charge per se is not crucial at all positions. Finally, we compared the electrostatic surface of TNF/TNFR with CD40L/CD40 and have identified a set of polar interactions surrounded by a wall of hydrophobic residues that appear to be similar but inverted between the two complexes.

The recent elucidation of the primary structure of the cell membrane-bound beta-adrenoceptor has prompted us to explore putative ligand binding sites on this physiologically important receptor. By minimizing the energies of the 'prototype' ligand propranolol, (part of) the receptor and the proposed ligand-receptor complex with the aid of force field and quantum chemical calculations, we identified amino acid residue Trp313 as a highly probable candidate for interaction with the aromatic moiety of propranolol. The charge distribution on the indole nucleus of another beta-blocker, pindolol, with higher affinity for the beta-adrenoceptor, enables an even stronger interaction with the tryptophan residue. The carboxylic amino acid residue Glu306, located near the extracellular space of the cell membrane, interacts favorably with the positively charged nitrogen atom in the aliphatic side chain of the ligands. Finally, this putative model is discussed in the light of recent findings in mutagenesis studies, and compared to other ideas with respect to ligand-receptor interactions.