Affinity-based Models Research Articles

Integration of Kinetic Data into Affinity-Based Models for Improved T Cell Specificity Prediction

T cell receptor (TCR) and peptide-major histocompatibility complex (pMHC) interactions that result in T cell activation are complex and have been distinguished by their equilibrium affinity and kinetic profiles. While prior affinity-based models can successfully predict meaningful TCR-pMHC interactions in many cases, they occasionally fail at identifying TCR-pMHC interactions with low binding affinity. This study analyzes TCR-pMHC systems for which empirical kinetic and affinity data exist and prior affinity-based predictions fail. We identify a criteria for TCR-pMHC systems with available kinetic information where the introduction of a correction factor improves energy-based model predictions. This kinetic correction factor offers a means to refine existing models with additional data and offers molecular insights to help reconcile previously conflicting reports concerning the influence of TCR-pMHC binding kinetics and affinity on T cell activation.

Contact map dependence of a T-cell receptor binding repertoire.

The adaptive immune system protects the host from pathogens by discriminating between host and foreign signatures. T cells of the adaptive immune system undergo training to avoid recognition of self-antigenic signatures displayed as peptides bound to the major histocompatibility complex (pMHC) by their T-cell receptors (TCRs). Similarly, T cell responses are directed toward TCRs recognizing non-self pMHC. Reliable prediction of relevant TCR-pMHC recognition is centrally important for understanding adaptive immunity predicting optimized infections and cancer, but currently prohibited by the theoretical diversity of antigens (∼1020) and TCR signatures. We study an affinity-based model for TCR-pMHC binding that uses a crystal structure to inform a distance-based contact map that weights the pairwise amino acid interactions between TCR and pMHC. We find that the TCR-pMHC binding energy distribution depends on the number of contacts and the topology of the contact map of choice; this impacts T-cell negative selection outcome, with higher T-cell recognition probability at higher variances. In addition, we quantify the degree to which neoantigens with mutations in sites with higher contacts are recognized at a higher rate.

Hemodialysis biocompatibility mathematical models to predict the inflammatory biomarkers released in dialysis patients based on hemodialysis membrane characteristics and clinical practices

Chronic kidney disease affects millions of people around the globe and many patients rely on hemodialysis (HD) to survive. HD is associated with undesired life-threatening side effects that are linked to membrane biocompatibility and clinical operating conditions. The present study develops a mathematical model to predict the inflammatory biomarkers released in HD patients based on membrane morphology, chemistry, and interaction affinity. Based on the morphological characteristics of two clinical-grade HD membrane modules (CTA and PAES-PVP) commonly used in Canadian hospitals, a molecular docking study, and the release of inflammatory cytokines during HD and in vitro incubation experiments, we develop five sets of equations that describe the concentration of eight biomarkers (serpin/antithrombin-III, properdin, C5a, 1L-1α, 1L-1β, C5b-9, IL6, vWF). The equations developed are functions of membrane properties (pore size, roughness, chemical composition, affinity to fibrinogen, and surface charge) and HD operating conditions (blood flow rate, Qb, and treatment time, t). We expand our model based on available clinical data and increase its range of applicability in terms of flow rate and treatment time. We also modify the original equations to expand their range of applicability in terms of membrane materials, allowing the prediction and validation of the inflammatory response of several clinical and synthesized membrane materials. Our affinity-based model solely relies on theoretical values of molecular docking, which can significantly reduce the experimental load related to the development of more biocompatible materials. Our model predictions agree with experimental clinical data and can guide the development of novel materials and support evidence-based membrane synthesis of HD membranes, reducing the need for trial-and-error approaches.

DeepLigand: accurate prediction of MHC class I ligands using peptide embedding.

MotivationThe computational modeling of peptide display by class I major histocompatibility complexes (MHCs) is essential for peptide-based therapeutics design. Existing computational methods for peptide-display focus on modeling the peptide-MHC-binding affinity. However, such models are not able to characterize the sequence features for the other cellular processes in the peptide display pathway that determines MHC ligand selection.ResultsWe introduce a semi-supervised model, DeepLigand that outperforms the state-of-the-art models in MHC Class I ligand prediction. DeepLigand combines a peptide language model and peptide binding affinity prediction to score MHC class I peptide presentation. The peptide language model characterizes sequence features that correspond to secondary factors in MHC ligand selection other than binding affinity. The peptide embedding is learned by pre-training on natural ligands, and can discriminate between ligands and non-ligands in the absence of binding affinity prediction. Although conventional affinity-based models fail to classify peptides with moderate affinities, DeepLigand discriminates ligands from non-ligands with consistently high accuracy.Availability and implementationWe make DeepLigand available at https://github.com/gifford-lab/DeepLigand.Supplementary information Supplementary data are available at Bioinformatics online.

Affinity aware scheduling model of cluster nodes in private clouds

Running applications on a cloud environment without checking and meeting their allocation and performance requirements may lead to unexpected application slowdown and infrastructure under-utilization. In addition, competition for same shared resources may cause performance degradation when applications with similar resource usage profiles are scheduled concurrently. This paper presents an affinity-based model for scheduling virtual machines that host and run scientific applications on a private cloud environment. The main contributions of the proposed model are: i) an approach that exploits the concept of affinity relations among competitive applications; ii) a set of experiments using consolidated HPC benchmarks and their analysis to assess the performance of two concurrent applications; and iii) a novel scheduling algorithm based on an affinity relation among competitive applications.

Magnesite Step Growth Rates as a Function of the Aqueous Magnesium:Carbonate Ratio

Step velocities of monolayer-height steps on the (1014) magnesite surface have been measured as functions of the aqueous magnesium:carbonate ratio and saturation index (SI) using a hydrothermal atomic force microscope. At SI ≤ 1.9 and 80–90 °C, step velocities were found to be invariant with changes in the magnesium:carbonate ratio, an observation in contrast with standard models for growth and dissolution of ionically bonded, multicomponent crystals. However, at high saturation indices (SI = 2.15), step velocities displayed a ratio dependence, maximized at magnesium:carbonate ratios slightly greater than 1:1. Traditional affinity-based models could not describe growth rates at the higher saturation index. Step velocities also could not be modeled solely through nucleation of kink sites, in contrast to other minerals whose bonding between constituent ions is also dominantly ionic in nature, such as calcite and barite. Instead, they could be described only by a model that incorporates both kink nucleation...

Next generation models of carbonate mineral growth and dissolution

The long-term success of carbon sequestration lies in part on the ability to trap carbon dioxide as a carbonate mineral phase. As such, the ability to predict the extent of carbonate mineral precipitation over the lifetime of a proposed geologic sequestration site will be necessary. In this review, different methods of predicting the growth of carbonate minerals, particularly calcite, and their disadvantages and advantages are summarized. Starting from a simple description of the solution saturation state, more advanced affinity-based models are described that comprise the status quo. In these, the reaction rate is measured by the difference in concentration from an equilibrium value or the Gibbs Free Energy of reaction. It is shown that these models fail to capture some important aspects of carbonate mineral growth rates. Next-generation models in development are those that reflect the processes that occur on a mineral surface while it is growing, not just the concentration of dissolved species. While incomplete, these process-based models are already addressing some long-standing questions in geochemistry and are enhancing the accuracy and robustness of the predictive ability for calcite precipitation. Lastly, the importance of the step density, analogous to the reactive site density in a natural sample, is shown. The factors that may influence the step density are described and the potentially complex relationship between step density and solution conditions is presented. While still in development, these models suggest that many of the historical problems in quantitative prediction of mineral growth and dissolution reactions can be resolved.

A review of bioactive release from nerve conduits as a neurotherapeutic strategy for neuronal growth in peripheral nerve injury.

Peripheral nerve regeneration strategies employ the use of polymeric engineered nerve conduits encompassed with components of a delivery system. This allows for the controlled and sustained release of neurotrophic growth factors for the enhancement of the innate regenerative capacity of the injured nerves. This review article focuses on the delivery of neurotrophic factors (NTFs) and the importance of the parameters that control release kinetics in the delivery of optimal quantities of NTFs for improved therapeutic effect and prevention of dose dumping. Studies utilizing various controlled-release strategies, in attempt to obtain ideal release kinetics, have been reviewed in this paper. Release strategies discussed include affinity-based models, crosslinking techniques, and layer-by-layer technologies. Currently available synthetic hollow nerve conduits, an alternative to the nerve autografts, have proven to be successful in the bridging and regeneration of primarily the short transected nerve gaps in several patient cases. However, current research emphasizes on the development of more advanced nerve conduits able to simulate the effectiveness of the autograft which includes, in particular, the ability to deliver growth factors.

9. Glucocorticoid receptor gene BclI polymorphism associates with immunomodulatory response to stress hormone in human PBMC

The functional consequences of signaling receptor endocytosis are determined by the endosomal sorting of receptors between degradation and recycling pathways. How receptors recycle efficiently, in a sequence-dependent manner that is distinct from bulk membrane recycling, is not known. Here, in live cells, we visualize the sorting of a prototypical sequence-dependent recycling receptor, the beta-2 adrenergic receptor, from bulk recycling proteins and the degrading delta-opioid receptor. Our results reveal a remarkable diversity in recycling routes at the level of individual endosomes, and indicate that sequence-dependent recycling is an active process mediated by distinct endosomal subdomains distinct from those mediating bulk recycling. We identify a specialized subset of tubular microdomains on endosomes, stabilized by a highly localized but dynamic actin machinery, that mediate this sorting, and provide evidence that these actin-stabilized domains provide the physical basis for a two-step kinetic and affinity-based model for protein sorting into the sequence-dependent recycling pathway.

Sequence-Dependent Sorting of Recycling Proteins by Actin-Stabilized Endosomal Microdomains

The functional consequences of signaling receptor endocytosis are determined by the endosomal sorting of receptors between degradation and recycling pathways. How receptors recycle efficiently, in a sequence-dependent manner that is distinct from bulk membrane recycling, is not known. Here, in live cells, we visualize the sorting of a prototypical sequence-dependent recycling receptor, the beta-2 adrenergic receptor, from bulk recycling proteins and the degrading delta-opioid receptor. Our results reveal a remarkable diversity in recycling routes at the level of individual endosomes, and indicate that sequence-dependent recycling is an active process mediated by distinct endosomal subdomains distinct from those mediating bulk recycling. We identify a specialized subset of tubular microdomains on endosomes, stabilized by a highly localized but dynamic actin machinery, that mediate this sorting, and provide evidence that these actin-stabilized domains provide the physical basis for a two-step kinetic and affinity-based model for protein sorting into the sequence-dependent recycling pathway.

Kinetics of near-equilibrium calcite precipitation at 100°C: An evaluation of elementary reaction-based and affinity-based rate laws

Three affinity-based rate models based upon physical growth mechanisms were used to fit surface-controlled precipitation rate data for calcite using a continuously stirred tank reactor in NaOHCaCl 2CO 2H 2O solutions at 100°C and 100 bars total pressure between pH 6.38 and 6.98. At higher stirring speeds, when a H 2CO 3 ∗ was smaller than 2.33 × 10 −3, rate showed a parabolic dependence upon exp( Δ G RT ) for exp( Δ G RT ) < 1.72. However, the rate increased exponentially for exp( Δ DG RT ) > 1.72 and followed a rate law based upon the assumption that surface nucleation is rate-limiting. When α H 2CO 3 ∗ was greater than 5.07 × 10 −3, the rate showed a linear dependence upon exp( Δ G RT ), suggesting growth by a simple surface adsorption mechanism. The rate of these three mechanisms at 100°C can be expressed by the following equations: ( spiral growth) R ppt = 10 −9.00±0.15 exp ΔG RT − 1 1.93±0.14 , ( adsorption) R ppt = 10 −8.64±0.07 exp ΔG RT − 1 1.09±0.10 , ( surface nucleation) R ppt = 10 −7.28±0.49 exp − 2.36±0.21 ΔG/RT . The mechanistic model of Plummer et al. (1978) given by R net = k 1a H + + k 2a H2 CO3 ∗ + k 3a H2O − k 4a Ca 2+a HCO − 3 also describes the precipitation rate when growth followed the spiral growth equation. The rate constant for precipitation, k 4, ranges between 7.08 × 10 −4 to 1.01 × 10 −3 moles cm −2 s −1 in the a H 2CO 3 ∗ range studied. This work shows that precipitation at 100°C in the spiral growth regime is well fit by both the mechanistic model of Plummer et al. (1978), based on multiple elementary reactions, and by a model derived for growth at screw dislocations. Outside of the regime of spiral growth, however, the model of Plummer et al. (1978) fails, suggesting that different elementary reactions control growth in the adsorption or two-dimensional nucleation regimes. However, the model of Plummer et al. (1978), based upon individual elementary reactions, accurately predicts both dissolution and precipitation of calcite under certain conditions; tests of the affinity based models reveal that none of these models accurately predict dissolution. Therefore, although affinity-based models may yield insights concerning the physical mechanism of growth, they may not be as useful in modelling dissolution and growth over the full range of Δ G.