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Mapping polymerization and allostery of hemoglobin S using point mutations.

ABSTRACT: Hemoglobin is a complex system that undergoes conformational changes in response to oxygen, allosteric effectors, mutations, and environmental changes. Here, we study allostery and polymerization of hemoglobin and its variants by application of two previously described methods: (i) AllosMod for simulating allostery dynamics given two allosterically related input structures and (ii) a machine-learning method for dynamics- and structure-based prediction of the mutation impact on allostery (Weinkam et al. J. Mol. Biol. 2013, 425, 647-661), now applicable to systems with multiple coupled binding sites, such as hemoglobin. First, we predict the relative stabilities of substates and microstates of hemoglobin, which are determined primarily by entropy within our model. Next, we predict the impact of 866 annotated mutations on hemoglobin's oxygen binding equilibrium. We then discuss a subset of 30 mutations that occur in the presence of the sickle cell mutation and whose effects on polymerization have been measured. Seven of these HbS mutations occur in three predicted druggable binding pockets that might be exploited to directly inhibit polymerization; one of these binding pockets is not apparent in the crystal structure, but only in structures generated by AllosMod. For the 30 mutations, we predict that mutation-induced conformational changes within a single tetramer tend not to significantly impact polymerization; instead, these mutations more likely impact polymerization by directly perturbing a polymerization interface. Finally, our analysis of allostery allows us to hypothesize why hemoglobin evolved to have multiple subunits and a persistent low frequency sickle cell mutation.

SUBMITTER: Weinkam P

PROVIDER: S-EPMC3973026 | biostudies-literature | 2013 Oct

REPOSITORIES: biostudies-literature

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Mapping polymerization and allostery of hemoglobin S using point mutations.

Weinkam Patrick P Sali Andrej A

The journal of physical chemistry. B 20130909 42

Hemoglobin is a complex system that undergoes conformational changes in response to oxygen, allosteric effectors, mutations, and environmental changes. Here, we study allostery and polymerization of hemoglobin and its variants by application of two previously described methods: (i) AllosMod for simulating allostery dynamics given two allosterically related input structures and (ii) a machine-learning method for dynamics- and structure-based prediction of the mutation impact on allostery (Weinkam ...[more]

PMID: 23957820

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Project description:Protein point mutations are an essential component of the evolutionary and experimental analysis of protein structure and function. While many manually curated databases attempt to index point mutations, most experimentally generated point mutations and the biological impacts of the changes are described in the peer-reviewed published literature. We describe an application, Mutation GraB (Graph Bigram), that identifies, extracts, and verifies point mutations from biomedical literature. The principal problem of point mutation extraction is to link the point mutation with its associated protein and organism of origin. Our algorithm uses a graph-based bigram traversal to identify these relevant associations and exploits the Swiss-Prot protein database to verify this information. The graph bigram method is different from other models for point mutation extraction in that it incorporates frequency and positional data of all terms in an article to drive the point mutation-protein association. Our method was tested on 589 articles describing point mutations from the G protein-coupled receptor (GPCR), tyrosine kinase, and ion channel protein families. We evaluated our graph bigram metric against a word-proximity metric for term association on datasets of full-text literature in these three different protein families. Our testing shows that the graph bigram metric achieves a higher F-measure for the GPCRs (0.79 versus 0.76), protein tyrosine kinases (0.72 versus 0.69), and ion channel transporters (0.76 versus 0.74). Importantly, in situations where more than one protein can be assigned to a point mutation and disambiguation is required, the graph bigram metric achieves a precision of 0.84 compared with the word distance metric precision of 0.73. We believe the graph bigram search metric to be a significant improvement over previous search metrics for point mutation extraction and to be applicable to text-mining application requiring the association of words.

Project description:Nearly 7% of the world's population live with a hemoglobin variant. Hemoglobins S, C, and E are the most common and significant hemoglobin variants worldwide. Sickle cell disease, caused by hemoglobin S, is highly prevalent in sub-Saharan Africa and in tribal populations of Central India. Hemoglobin C is common in West Africa, and hemoglobin E is common in Southeast Asia. Screening for significant hemoglobin disorders is not currently feasible in many low-income countries with the high disease burden. Lack of early diagnosis leads to preventable high morbidity and mortality in children born with hemoglobin variants in low-resource settings. Here, we describe HemeChip, the first miniaturized, paper-based, microchip electrophoresis platform for identifying the most common hemoglobin variants easily and affordably at the point-of-care in low-resource settings. HemeChip test works with a drop of blood. HemeChip system guides the user step-by-step through the test procedure with animated on-screen instructions. Hemoglobin identification and quantification is automatically performed, and hemoglobin types and percentages are displayed in an easily understandable, objective way. We show the feasibility and high accuracy of HemeChip via testing 768 subjects by clinical sites in the United States, Central India, sub-Saharan Africa, and Southeast Asia. Validation studies include hemoglobin E testing in Bangkok, Thailand, and hemoglobin S testing in Chhattisgarh, India, and in Kano, Nigeria, where the sickle cell disease burden is the highest in the world. Tests were performed by local users, including healthcare workers and clinical laboratory personnel. Study design, methods, and results are presented according to the Standards for Reporting Diagnostic Accuracy (STARD). HemeChip correctly identified all subjects with hemoglobin S, C, and E variants with 100% sensitivity, and displayed an overall diagnostic accuracy of 98.4% in comparison to reference standard methods. HemeChip is a versatile, mass-producible microchip electrophoresis platform that addresses a major unmet need of decentralized hemoglobin analysis in resource-limited settings.

Dataset Information

Mapping polymerization and allostery of hemoglobin S using point mutations.

Publications

Mapping polymerization and allostery of hemoglobin S using point mutations.

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