Herman W. T. van Vlijmen

Affinity enhancement of an in vivo matured therapeutic antibody using structure-based computational design

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 ∼7 nM) and the moderate resolution (2.8 Å) 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 Å. 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.

Proteochemometric modeling as a tool to design selective compounds and for extrapolating to novel targets

‘Proteochemometric modeling’ is a bioactivity modeling technique founded on the description of both small molecules (the ligands), and proteins (the targets). By combining those two elements of a ligand – target interaction proteochemometrics techniques model the interaction complex or the full ligand – target interaction space, and they are able to quantify the similarity between both ligands and targets simultaneously. Consequently, proteochemometric models or complex based models, can be considered an extension of QSAR models, which are ligand based. As proteochemometric models are able to incorporate target information they outperform conventional QSAR models when extrapolating from the activities of known ligands on known targets to novel targets. Vice versa, proteochemometrics can be used to virtually screen for selective compounds that are solely active on a single member of a subfamily of targets, as well as to select compounds with a desired bioactivity profile – a topic particularly relevant with concepts such as ‘ligand polypharmacology’ in mind. Here we illustrate the concept of proteochemometrics and provide a review of relevant methodological publications in the field. We give an overview of the target families proteochemometrics modeling has previously been applied to, and introduce some novel application areas of the modeling technique. We conclude that proteochemometrics is a promising technique in preclinical drug research that allows merging data sets that were previously considered separately, with the potential to extrapolate more reliably both in ligand as well as target space.

Trends in Antibody Sequence Changes during the Somatic Hypermutation Process

Probable germline gene sequences from thousands of aligned mature Ab sequences are inferred using simple computational matching to known V(D)J genes. Comparison of the germline to mature sequences in a structural region-dependent fashion allows insights into the methods that nature uses to mature Abs during the somatic hypermutation process. Four factors determine the residue type mutation patterns: biases in the germline, accessibility from single base permutations, location of mutation hotspots, and functional pressures during selection. Germline repertoires at positions that commonly contact the Ag are biased with tyrosine, serine, and tryptophan. These residue types have a high tendency to be present in mutation hotspot motifs, and their abundance is decreased during maturation by a net conversion to other types. The heavy use of tyrosines on mature Ab interfaces is thus a reflection of the germline composition rather than being due to selection during maturation. Potentially stabilizing changes such as increased proline usage and a small number of double cysteine mutations capable of forming disulfide bonds are ascribed to somatic hypermutation. Histidine is the only residue type for which usage increases in each of the interface, core, and surface regions. The net overall effect is a conversion from residue types that could provide nonspecific initial binding into a diversity of types that improve affinity and stability. Average mutation probabilities are ∼4% for core residues, ∼5% for surface residues, and ∼12% for residues in common Ag-contacting positions, excepting the those coded by the D gene.

Binding of a potent small-molecule inhibitor of six-helix bundle formation requires interactions with both heptad-repeats of the RSV fusion protein

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.

Which Compound to Select in Lead Optimization? Prospectively Validated Proteochemometric Models Guide Preclinical Development

In quite a few diseases, drug resistance due to target variability poses a serious problem in pharmacotherapy. This is certainly true for HIV, and hence, it is often unknown which drug is best to use or to develop against an individual HIV strain. In this work we applied ‘proteochemometric’ modeling of HIV Non-Nucleoside Reverse Transcriptase (NNRTI) inhibitors to support preclinical development by predicting compound performance on multiple mutants in the lead selection stage. Proteochemometric models are based on both small molecule and target properties and can thus capture multi-target activity relationships simultaneously, the targets in this case being a set of 14 HIV Reverse Transcriptase (RT) mutants. We validated our model by experimentally confirming model predictions for 317 untested compound – mutant pairs, with a prediction error comparable with assay variability (RMSE 0.62). Furthermore, dependent on the similarity of a new mutant to the training set, we could predict with high accuracy which compound will be most effective on a sequence with a previously unknown genotype. Hence, our models allow the evaluation of compound performance on untested sequences and the selection of the most promising leads for further preclinical research. The modeling concept is likely to be applicable also to other target families with genetic variability like other viruses or bacteria, or with similar orthologs like GPCRs.

Structure-Based Site of Metabolism Prediction for Cytochrome P450 2D6

Realistic representation of protein flexibility in biomolecular simulations remains an unsolved fundamental problem and is an active area of research. The high flexibility of the cytochrome P450 2D6 (CYP2D6) active site represents a challenge for accurate prediction of the preferred binding mode and site of metabolism (SOM) for compounds metabolized by this important enzyme. To account for this flexibility, we generated a large ensemble of unbiased CYP2D6 conformations, to which small molecule substrates were docked to predict their experimentally observed SOM. SOM predictivity was investigated as a function of the number of protein structures, the scoring function, the SOM-heme cutoff distance used to distinguish metabolic sites, and intrinsic reactivity. Good SOM predictions for CYP2D6 require information from the protein. A critical parameter is the distance between the heme iron and the candidate site of metabolism. The best predictions were achieved with cutoff distances consistent with the chemistry relevant to CYP2D6 metabolism. Combination of the new ensemble-based docking method with estimated intrinsic reactivities of substrate sites considerably improved the predictivity of the model. Testing on an independent set of substrates yielded area under curve values as high as 0.93, validating our new approach.

A novel chemogenomics analysis of G protein-coupled receptors (GPCRs) and their ligands: a potential strategy for receptor de-orphanization

BackgroundG 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.ResultsWe 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).ConclusionsWe 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.

Identifying novel adenosine receptor ligands by simultaneous proteochemometric modeling of rat and human bioactivity data.

The four subtypes of adenosine receptors form relevant drug targets in the treatment of, e.g., diabetes and Parkinsons disease. In the present study, we aimed at finding novel small molecule ligands for these receptors using virtual screening approaches based on proteochemometric (PCM) modeling. We combined bioactivity data from all human and rat receptors in order to widen available chemical space. After training and validating a proteochemometric model on this combined data set (Q(2) of 0.73, RMSE of 0.61), we virtually screened a vendor database of 100910 compounds. Of 54 compounds purchased, six novel high affinity adenosine receptor ligands were confirmed experimentally, one of which displayed an affinity of 7 nM on the human adenosine A(1) receptor. We conclude that the combination of rat and human data performs better than human data only. Furthermore, we conclude that proteochemometric modeling is an efficient method to quickly screen for novel bioactive compounds.

Selecting an optimal number of binding site waters to improve virtual screening enrichments against the adenosine A2A receptor.

A major challenge in structure-based virtual screening (VS) involves the treatment of explicit water molecules during docking in order to improve the enrichment of active compounds over decoys. Here we have investigated this in the context of the adenosine A2A receptor, where water molecules have previously been shown to be important for achieving high enrichment rates with docking, and where the positions of some binding site waters are known from a high-resolution crystal structure. The effect of these waters (both their presence and orientations) on VS enrichment was assessed using a carefully curated set of 299 high affinity A2A antagonists and 17,337 decoys. We show that including certain crystal waters greatly improves VS enrichment and that optimization of water hydrogen positions is needed in order to achieve the best results. We also show that waters derived from a molecular dynamics simulation - without any knowledge of crystallographic waters - can improve enrichments to a similar degree as the crystallographic waters, which makes this strategy applicable to structures without experimental knowledge of water positions. Finally, we used decision trees to select an ensemble of structures with different water molecule positions and orientations that outperforms any single structure with water molecules. The approach presented here is validated against independent test sets of A2A receptor antagonists and decoys from the literature. In general, this water optimization strategy could be applied to any target with waters-mediated protein-ligand interactions.

Significantly improved HIV inhibitor efficacy prediction employing proteochemometric models generated from antivirogram data.

Infection with HIV cannot currently be cured; however it can be controlled by combination treatment with multiple anti-retroviral drugs. Given different viral genotypes for virtually each individual patient, the question now arises which drug combination to use to achieve effective treatment. With the availability of viral genotypic data and clinical phenotypic data, it has become possible to create computational models able to predict an optimal treatment regimen for an individual patient. Current models are based only on sequence data derived from viral genotyping; chemical similarity of drugs is not considered. To explore the added value of chemical similarity inclusion we applied proteochemometric models, combining chemical and protein target properties in a single bioactivity model. Our dataset was a large scale clinical database of genotypic and phenotypic information (in total ca. 300,000 drug-mutant bioactivity data points, 4 (NNRTI), 8 (NRTI) or 9 (PI) drugs, and 10,700 (NNRTI) 10,500 (NRTI) or 27,000 (PI) mutants). Our models achieved a prediction error below 0.5 Log Fold Change. Moreover, when directly compared with previously published sequence data, derived models PCM performed better in resistance classification and prediction of Log Fold Change (0.76 log units versus 0.91). Furthermore, we were able to successfully confirm both known and identify previously unpublished, resistance-conferring mutations of HIV Reverse Transcriptase (e.g. K102Y, T216M) and HIV Protease (e.g. Q18N, N88G) from our dataset. Finally, we applied our models prospectively to the public HIV resistance database from Stanford University obtaining a correct resistance prediction rate of 84% on the full set (compared to 80% in previous work on a high quality subset). We conclude that proteochemometric models are able to accurately predict the phenotypic resistance based on genotypic data even for novel mutants and mixtures. Furthermore, we add an applicability domain to the prediction, informing the user about the reliability of predictions.