A small molecule stabilises the disordered native state of the Alzheimer's Aβ peptide

The stabilisation of native states of proteins is a powerful drug discovery strategy. It is still unclear, however, whether this approach can be applied to intrinsically disordered proteins. Here we report a small molecule that stabilises the native state of the Aβ42 peptide, an intrinsically disordered protein fragment associated with Alzheimer's disease. We show that this stabilisation takes place by a dynamic binding mechanism, in which both the small molecule and the Aβ42 peptide remain disordered. This disordered binding mechanism involves enthalpically favourable local π-stacking interactions coupled with entropically advantageous global effects. These results indicate that small molecules can stabilise disordered proteins in their native states through transient non-specific interactions that provide enthalpic gain while simultaneously increasing the conformational entropy of the proteins.

786 Dose selection for DuoBody®-PD-L1×4-1BB (GEN1046) using a semimechanistic pharmacokinetics/pharmacodynamics model that leverages preclinical and clinical data

BackgroundDuoBody-PD-L1×4-1BB (GEN1046) is a class-defining bispecific antibody, designed to elicit an anti-tumor immune response by simultaneous and complementary blockade of PD-L1 on tumor cells and conditional stimulation of 4-1BB on T-cells and NK cells. Optimizing target engagement for a bispecific antibody is challenging, as it involves binding with two targets, and predicting trimer levels in tumors based on affinity of individual arms and target expression. Here we describe a semimechanistic, physiologically based pharmacokinetic/pharmacodynamic (PK/PD) model that predicts a dosing regimen for DuoBody-PD-L1×4-1BB, which results in the formation of maximum levels of a therapeutically active 4-1BB-bispecific antibody-PD-L1 trimolecular complex (trimer), and optimal PD-L1 receptor occupancy (RO).MethodsAn integrated semimechanistic PK/PD model that describes the distribution of DuoBody-PD-L1×4-1BB into central and peripheral compartments and partitioning into tumor/lymph nodes was developed. The model used PK/PD data and physiological parameters from the literature for parameterizations of PD-L1 and 4-1BB expression levels and T-cell trafficking. The model incorporates dynamic binding of DuoBody-PD-L1×4-1BB to its targets to predict trimer formation and RO for PD-L1 in tumors. Model parameters were calibrated to match in vitro PD studies, such as analyses of T-cell proliferation and cytokine release, as well as clinical PK data. Sensitivity to model assumptions were assessed by varying PK/PD parameters, and assessing their impact on trimer formation and PD-L1 RO. The model was subsequently used to explore in vivo trimer levels and PD-L1 RO in tumors at various dosing regimens.ResultsThe model was able to adequately describe the PK of DuoBody-PD-L1×4-1BB in the central compartment. Simulations showed a bell-shaped response for average trimer levels in tumors that peaked at 100 mg every 3 weeks (Q3W), with doses >100 mg resulting in reduced trimer formation. Average PD-L1 receptor occupancy at the 100 mg dose was predicted to be approximately 70% over 21 days and increased at higher doses. Based on these model predictions, and available safety, anti-tumor activity, and PD data from the ongoing GCT1046-01 trial (NCT03917381), 100 mg Q3W was chosen as the expansion dose for further evaluation in Part 2 of the study.ConclusionsThis semimechanistic PK/PD model provides a novel approach for dose selection of bispecific antibodies such as DuoBody-PD-L1×4-1BB, by using preclinical and clinical PK/PD data to predict formation of optimal trimer levels and PD-L1 receptor occupancy.AcknowledgementsThe authors thank Friederike Gieseke and Zuzana Jirakova at BioNTech SE; Kalyanasundaram Subramanian at Applied Biomath LLC for their valuable contributions.Trial RegistrationWritten informed consent, in accordance with principles that originated in the Declaration of Helsinki 2013, current ICH guidelines including ICH-GCP E6(R2), applicable regulatory requirements, and sponsor policy, was provided by the patients.

Hybrid Maintainability Prediction using Soft Computing Techniques

Effective software maintenance is a crucial factor to measure that can be achieved with the help of software metrics. In this paper, authors derived a new approach for measuring the maintainability of software based on hybrid metrics that takes advantages of both i.e. static metrics and dynamic metrics in an object-oriented environment whereas, dynamic metrics capture the run time features of object-oriented languages i.e. run time polymorphism, dynamic binding etc. which is not covered by static metrics. To achieve this, the authors proposed a model based on static and hybrid metrics to measure maintainability factor by using soft computing techniques and it is found that the proposed neuro-fuzzy model was trained well and predict adequate results with MAE 0.003 and RMSE 0.009 based on hybrid metrics. Additionally, the proposed model was validated on two test datasets and it is concluded that the proposed model performed well, based on hybrid metrics.

Elucidation of Agonist and Antagonist Dynamic Binding Patterns in ER-α by Integration of Molecular Docking, Molecular Dynamics Simulations and Quantum Mechanical Calculations

Estrogen receptor alpha (ERα) is a ligand-dependent transcriptional factor in the nuclear receptor superfamily. Many structures of ERα bound with agonists and antagonists have been determined. However, the dynamic binding patterns of agonists and antagonists in the binding site of ERα remains unclear. Therefore, we performed molecular docking, molecular dynamics (MD) simulations, and quantum mechanical calculations to elucidate agonist and antagonist dynamic binding patterns in ERα. 17β-estradiol (E2) and 4-hydroxytamoxifen (OHT) were docked in the ligand binding pockets of the agonist and antagonist bound ERα. The best complex conformations from molecular docking were subjected to 100 nanosecond MD simulations. Hierarchical clustering was conducted to group the structures in the trajectory from MD simulations. The representative structure from each cluster was selected to calculate the binding interaction energy value for elucidation of the dynamic binding patterns of agonists and antagonists in the binding site of ERα. The binding interaction energy analysis revealed that OHT binds ERα more tightly in the antagonist conformer, while E2 prefers the agonist conformer. The results may help identify ERα antagonists as drug candidates and facilitate risk assessment of chemicals through ER-mediated responses.

Dynamic and Facilitated Binding of Topoisomerase Accelerates Topological Relaxation

How type 2 Topoisomerase (TopoII) proteins relax and simplify the topology of DNA molecules is one of the most intriguing open questions in biophysics. Most of the existing models neglect the dynamics of TopoII which is characteristics for proteins searching their targets via facilitated diffusion. Here, we show that dynamic binding of TopoII speeds up the topological relaxation of knotted substrates by enhancing the search of the knotted arc. Intriguingly, this in turn implies that the timescale of topological relaxation is virtually independent of the substrate length. We then discover that considering binding biases due to facilitated diffusion on looped substrates steers the sampling of the topological space closer to the boundaries between different topoisomers yielding an optimally fast topological relaxation. We discuss our findings in the context of topological simplification in vitro and in vivo.

Iminodiacetic Acid (IDA) Cation-Exchange Nonwoven Membranes for Efficient Capture of Antibodies and Antibody Fragments

There is strong need to reduce the manufacturing costs and increase the downstream purification efficiency of high-value therapeutic monoclonal antibodies (mAbs). This paper explores the performance of a weak cation-exchange membrane based on the coupling of IDA to poly(butylene terephthalate) (PBT) nonwoven fabrics. Uniform and conformal layers of poly(glycidyl methacrylate) (GMA) were first grafted to the surface of the nonwovens. Then IDA was coupled to the polyGMA layers under optimized conditions, resulting in membranes with very high permeability and binding capacity. This resulted in IgG dynamic binding capacities at very short residence times (0.1–2.0 min) that are much higher than those achieved by the best cation-exchange resins. Similar results were obtained in the purification of a single-chain (scFv) antibody fragment. As is customary with membrane systems, the dynamic binding capacities did not change significantly over a wide range of residence times. Finally, the excellent separation efficiency and potential reusability of the membrane were confirmed by five consecutive cycles of mAb capture from its cell culture harvest. The present work provides significant evidence that this weak cation-exchange nonwoven fabric platform might be a suitable alternative to packed resin chromatography for low-cost, higher productivity manufacturing of therapeutic mAbs and antibody fragments.

Fast Learning in Complex Domains

Bayesian formulations of learning imply that whenever the evidence for a correlation between events in an animal’s habitat is sufficient, the correlation is learned. This implies that regularities can be learnt rapidly, from small numbers of learning examples. This speed of learning gives maximum possible fitness, and no faster learning is possible. There is evidence in many domains that animals and people can learn at nearly Bayesian optimal speeds. These domains include associative conditioning, and the more complex domains of navigation and language. There are computational models of learning which learn at near-Bayesian speeds in complex domains, and which can scale well – to learn thousands of pieces of knowledge (i.e., relations and associations). These are not neural net models. They can be defined in computational terms, as algorithms and data structures at David Marr’s [1] Level Two. Their key data structures are composite feature structures, which are graphs of multiple linked nodes. This leads to the hypothesis that animal learning results not from deep neural nets (which typically require thousands of training exam-ples), but from neural implementations of the Level Two models of fast learning; and that neu-rons provide the facilities needed to implement those models at Marr’s Level Three. The required facilities include feature structures, dynamic binding, one-shot memory for many feature struc-tures, pattern-based associative retrieval, unification and generalization of feature structures. These may be supported by multiplexing of data and metadata in the same neural fibres.

Focused peptide library screening as a route to a superior affinity ligand for antibody purification

Affinity chromatography is the linchpin of antibody downstream processing and typically relies on bacterial immunoglobulin (Ig)-binding proteins, epitomized by staphylococcal protein A-based ligands. However, such affinity ligands are fairly costly and suffer from chemical instability, leading to ligand denaturation and leaching from chromatographic support. Innovations in this area are aimed at developing robust and highly selective antibody ligands capable of withstanding harsh column sanitization conditions. We report the development and first-stage characterization of a selective short linear peptide ligand of the IgG Fc region capable of capturing all four IgG subclasses. The ligand was discovered through in vitro directed evolution. A focused phage-display library based on a previously identified peptide lead was subjected to a single-round screen against a pool of human IgG. The hits were identified with next-generation sequencing and ranked according to the enrichment ratio relative to their frequency in the pre-screened library. The top enriched peptide GSYWYNVWF displaying highest affinity for IgG was coupled to bromohydrin-activated agarose beads via a branched linker. The resulting affinity matrix was characterized with a dynamic binding capacity of approx. 43 mg/mL, on par with commercially employed protein A-based resin.

Prevalent and Dynamic Binding of the Cell Cycle Checkpoint Kinase Rad53 to Gene Promoters

Replication of the genome must be coordinated with gene transcription and cellular metabolism. These processes are controlled in part by the Rad53 (CHEK2 in mammals) checkpoint kinase and the Mrc1 replisome component, especially following replication stress in the presence of limiting deoxyribonucleotides. We examined cell cycle regulated, genome-wide binding of Rad53 to chromatin. The kinase bound to sites of active DNA replication initiation and fork progression, but unexpectedly to the promoters of numerous genes (>20% of all genes) involved in many cellular functions. At some genes, Rad53 promoter binding correlated with changes in gene expression. Rad53 promoter binding to certain genes is influenced by sequence-specific transcription factors and less by checkpoint signaling. In checkpoint mutants, untimely activation of late-replicating origins reduces the transcription of nearby genes, with concomitant localization of Rad53 to their gene bodies. We suggest that the Rad53 checkpoint kinase coordinates genome-wide replication and transcription under stress conditions.