Dataset Information

Bayesian statistical method for detecting structural and topological diversity in polymorphic proteins.

ABSTRACT: Polymorphisms in immune-related proteins and viral spike proteins are high and complicate host-virus interactions. Therefore, diversity analysis of such protein structures is essential to understand the mechanism of the immune system. However, experimental methods, including X-ray crystallography, nuclear magnetic resonance, and cryo-electron microscopy, have several problems: (i) they are conducted under different conditions from the actual cellular environment, (ii) they are laborious, time-consuming, and expensive, and (iii) they do not provide information on the thermodynamic behaviors. In this paper, we propose a computational method to solve these problems by using MD simulations, persistent homology, and a Bayesian statistical model. We apply our method to eight types of HLA-DR complexes to evaluate the structural diversity. The results show that our method can correctly discriminate the intrinsic structural variations caused by amino acid mutations from the random fluctuations caused by thermal vibrations. In the end, we discuss the applicability of our method in combination with existing deep learning-based methods for protein structure analysis.

SUBMITTER: Hayashi S

PROVIDER: S-EPMC9706128 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature

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Bayesian statistical method for detecting structural and topological diversity in polymorphic proteins.

Hayashi Shuto S Koseki Jun J Shimamura Teppei T

Computational and structural biotechnology journal 20221121

Polymorphisms in immune-related proteins and viral spike proteins are high and complicate host-virus interactions. Therefore, diversity analysis of such protein structures is essential to understand the mechanism of the immune system. However, experimental methods, including X-ray crystallography, nuclear magnetic resonance, and cryo-electron microscopy, have several problems: (i) they are conducted under different conditions from the actual cellular environment, (ii) they are laborious, time-co ...[more]

PMID: 36467576

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Project description:BackgroundOne method of identifying cis regulatory differences is to analyze allele-specific expression (ASE) and identify cases of allelic imbalance (AI). RNA-seq is the most common way to measure ASE and a binomial test is often applied to determine statistical significance of AI. This implicitly assumes that there is no bias in estimation of AI. However, bias has been found to result from multiple factors including: genome ambiguity, reference quality, the mapping algorithm, and biases in the sequencing process. Two alternative approaches have been developed to handle bias: adjusting for bias using a statistical model and filtering regions of the genome suspected of harboring bias. Existing statistical models which account for bias rely on information from DNA controls, which can be cost prohibitive for large intraspecific studies. In contrast, data filtering is inexpensive and straightforward, but necessarily involves sacrificing a portion of the data.ResultsHere we propose a flexible Bayesian model for analysis of AI, which accounts for bias and can be implemented without DNA controls. In lieu of DNA controls, this Poisson-Gamma (PG) model uses an estimate of bias from simulations. The proposed model always has a lower type I error rate compared to the binomial test. Consistent with prior studies, bias dramatically affects the type I error rate. All of the tested models are sensitive to misspecification of bias. The closer the estimate of bias is to the true underlying bias, the lower the type I error rate. Correct estimates of bias result in a level alpha test.ConclusionsTo improve the assessment of AI, some forms of systematic error (e.g., map bias) can be identified using simulation. The resulting estimates of bias can be used to correct for bias in the PG model, without data filtering. Other sources of bias (e.g., unidentified variant calls) can be easily captured by DNA controls, but are missed by common filtering approaches. Consequently, as variant identification improves, the need for DNA controls will be reduced. Filtering does not significantly improve performance and is not recommended, as information is sacrificed without a measurable gain. The PG model developed here performs well when bias is known, or slightly misspecified. The model is flexible and can accommodate differences in experimental design and bias estimation.

Project description:BACKGROUND: Identification of the structural domains of proteins is important for our understanding of the organizational principles and mechanisms of protein folding, and for insights into protein function and evolution. Algorithmic methods of dissecting protein of known structure into domains developed so far are based on an examination of multiple geometrical, physical and topological features. Successful as many of these approaches are, they employ a lot of heuristics, and it is not clear whether they illuminate any deep underlying principles of protein domain organization. Other well-performing domain dissection methods rely on comparative sequence analysis. These methods are applicable to sequences with known and unknown structure alike, and their success highlights a fundamental principle of protein modularity, but this does not directly improve our understanding of protein spatial structure. RESULTS: We present a novel graph-theoretical algorithm for the identification of domains in proteins with known three-dimensional structure. We represent the protein structure as an undirected, unweighted and unlabeled graph whose nodes correspond to the secondary structure elements and edges represent physical proximity of at least one pair of alpha carbon atoms from two elements. Domains are identified as constrained partitions of the graph, corresponding to sets of vertices obtained by the maximization of the cycle distributions found in the graph. When a partition is found, the algorithm is iteratively applied to each of the resulting subgraphs. The decision to accept or reject a tentative cut position is based on a specific classifier. The algorithm is applied iteratively to each of the resulting subgraphs and terminates automatically if partitions are no longer accepted. The distribution of cycles is the only type of information on which the decision about protein dissection is based. Despite the barebone simplicity of the approach, our algorithm approaches the best heuristic algorithms in accuracy. CONCLUSION: Our graph-theoretical algorithm uses only topological information present in the protein structure itself to find the domains and does not rely on any geometrical or physical information about protein molecule. Perhaps unexpectedly, these drastic constraints on resources, which result in a seemingly approximate description of protein structures and leave only a handful of parameters available for analysis, do not lead to any significant deterioration of algorithm accuracy. It appears that protein structures can be rigorously treated as topological rather than geometrical objects and that the majority of information about protein domains can be inferred from the coarse-grained measure of pairwise proximity between elements of secondary structure elements.

Dataset Information

Bayesian statistical method for detecting structural and topological diversity in polymorphic proteins.

Publications

Bayesian statistical method for detecting structural and topological diversity in polymorphic proteins.

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