Dataset Information

Anti-HK antibody reveals critical roles of a 20-residue HK region for Aβ-induced plasma contact system activation.

ABSTRACT: Alzheimer's disease (AD) is a neurodegenerative disorder and the leading cause of dementia. Vascular abnormalities and neuroinflammation play roles in AD pathogenesis. Plasma contact activation, which leads to fibrin clot formation and bradykinin release, is elevated in many AD patients, likely due to the ability of AD's pathogenic peptide β-amyloid (Aβ) to induce its activation. Since overactivation of this system may be deleterious to AD patients, the development of inhibitors could be beneficial. Here, we show that 3E8, an antibody against a 20-amino acid region in domain 6 of high molecular weight kininogen (HK), inhibits Aβ-induced intrinsic coagulation. Mechanistically, 3E8 inhibits contact system activation by blocking the binding of prekallikrein (PK) and factor XI (FXI) to HK, thereby preventing their activation and the continued activation of factor XII (FXII). The 3E8 antibody can also disassemble HK/PK and HK/FXI complexes in normal human plasma in the absence of a contact system activator due to its strong binding affinity for HK, indicating its prophylactic ability. Furthermore, the binding of Aβ to both FXII and HK is critical for Aβ-mediated contact system activation. These results suggest that a 20-amino acid region in domain 6 of HK plays a critical role in Aβ-induced contact system activation, and this region may provide an effective strategy to inhibit or prevent contact system activation in related disorders.

SUBMITTER: Chen ZL

PROVIDER: S-EPMC9131899 | biostudies-literature | 2022 May

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Anti-HK antibody reveals critical roles of a 20-residue HK region for Aβ-induced plasma contact system activation.

Chen Zu-Lin ZL Singh Pradeep Kumar PK Horn Katharina K Strickland Sidney S Norris Erin H EH

Blood advances 20220501 10

Alzheimer's disease (AD) is a neurodegenerative disorder and the leading cause of dementia. Vascular abnormalities and neuroinflammation play roles in AD pathogenesis. Plasma contact activation, which leads to fibrin clot formation and bradykinin release, is elevated in many AD patients, likely due to the ability of AD's pathogenic peptide β-amyloid (Aβ) to induce its activation. Since overactivation of this system may be deleterious to AD patients, the development of inhibitors could be benefic ...[more]

PMID: 35147669

Similar Datasets

Project description:BackgroundFor over 30 years potentials of mean force have been used to evaluate the relative energy of protein structures. The most commonly used potentials define the energy of residue-residue interactions and are derived from the empirical analysis of the known protein structures. However, single-body residue 'environment' potentials, although widely used in protein structure analysis, have not been rigorously compared to these classical two-body residue-residue interaction potentials. Here we do not try to combine the two different types of residue interaction potential, but rather to assess their independent contribution to scoring protein structures.ResultsA data set of nearly three thousand monomers was used to compare pairwise residue-residue 'contact-type' propensities to single-body residue 'contact-count' propensities. Using a large and standard set of protein decoys we performed an in-depth comparison of these two types of residue interaction propensities. The scores derived from the contact-type and contact-count propensities were assessed using two different performance metrics and were compared using 90 different definitions of residue-residue contact. Our findings show that both types of score perform equally well on the task of discriminating between near-native protein decoys. However, in a statistical sense, the contact-count based scores were found to carry more information than the contact-type based scores.ConclusionOur analysis has shown that the performance of either type of score is very similar on a range of different decoys. This similarity suggests a common underlying biophysical principle for both types of residue interaction propensity. However, several features of the contact-count based propensity suggests that it should be used in preference to the contact-type based propensity. Specifically, it has been shown that contact-counts can be predicted from sequence information alone. In addition, the use of a single-body term allows for efficient alignment strategies using dynamic programming, which is useful for fold recognition, for example. These facts, combined with the relative simplicity of the contact-count propensity, suggests that contact-counts should be studied in more detail in the future.

Project description:Sequence-based residue contact prediction plays a crucial role in protein structure reconstruction. In recent years, the combination of evolutionary coupling analysis (ECA) and deep learning (DL) techniques has made tremendous progress for residue contact prediction, thus a comprehensive assessment of current methods based on a large-scale benchmark data set is very needed. In this study, we evaluate 18 contact predictors on 610 non-redundant proteins and 32 CASP13 targets according to a wide range of perspectives. The results show that different methods have different application scenarios: (1) DL methods based on multi-categories of inputs and large training sets are the best choices for low-contact-density proteins such as the intrinsically disordered ones and proteins with shallow multi-sequence alignments (MSAs). (2) With at least 5L (L is sequence length) effective sequences in the MSA, all the methods show the best performance, and methods that rely only on MSA as input can reach comparable achievements as methods that adopt multi-source inputs. (3) For top L/5 and L/2 predictions, DL methods can predict more hydrophobic interactions while ECA methods predict more salt bridges and disulfide bonds. (4) ECA methods can detect more secondary structure interactions, while DL methods can accurately excavate more contact patterns and prune isolated false positives. In general, multi-input DL methods with large training sets dominate current approaches with the best overall performance. Despite the great success of current DL methods must be stated the fact that there is still much room left for further improvement: (1) With shallow MSAs, the performance will be greatly affected. (2) Current methods show lower precisions for inter-domain compared with intra-domain contact predictions, as well as very high imbalances in precisions between intra-domains. (3) Strong prediction similarities between DL methods indicating more feature types and diversified models need to be developed. (4) The runtime of most methods can be further optimized.

Project description:Protein contact prediction helps reconstruct the tertiary structure that greatly determines a protein's function; therefore, contact prediction from the sequence is an important problem. Recently there has been exciting progress on this problem, but many of the existing methods are still low quality of prediction accuracy. In this paper, we present a new mixed integer linear programming (MILP)-based consensus method: a Consensus scheme based On a Mixed integer linear opTimization method for prOtein contact Prediction (COMTOP). The MILP-based consensus method combines the strengths of seven selected protein contact prediction methods, including CCMpred, EVfold, DeepCov, NNcon, PconsC4, plmDCA, and PSICOV, by optimizing the number of correctly predicted contacts and achieving a better prediction accuracy. The proposed hybrid protein residue-residue contact prediction scheme was tested in four independent test sets. For 239 highly non-redundant proteins, the method showed a prediction accuracy of 59.68%, 70.79%, 78.86%, 89.04%, 94.51%, and 97.35% for top-5L, top-3L, top-2L, top-L, top-L/2, and top-L/5 contacts, respectively. When tested on the CASP13 and CASP14 test sets, the proposed method obtained accuracies of 75.91% and 77.49% for top-L/5 predictions, respectively. COMTOP was further tested on 57 non-redundant ɑ-helical transmembrane proteins and achieved prediction accuracies of 64.34% and 73.91% for top-L/2 and top-L/5 predictions, respectively. For all test datasets, the improvement of COMTOP in accuracy over the seven individual methods increased with the increasing number of predicted contacts. For example, COMTOP performed much better for large number of contact predictions (such as top-5L and top-3L) than for small number of contact predictions such as top-L/2 and top-L/5. The results and analysis demonstrate that COMTOP can significantly improve the performance of the individual methods; therefore, COMTOP is more robust against different types of test sets. COMTOP also showed better/comparable predictions when compared with the state-of-the-art predictors.

Dataset Information

Anti-HK antibody reveals critical roles of a 20-residue HK region for Aβ-induced plasma contact system activation.

Publications

Anti-HK antibody reveals critical roles of a 20-residue HK region for Aβ-induced plasma contact system activation.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets