THESIS
2022
1 online resource (xiii, 55 pages) : color illustrations
Abstract
With the development of state-of-the-art techniques in biological systems, many previous
inaccessible data now has become massive. To understanding this unprecedented data is
the key to discover new principles underlying the biological behaviour. The application
of statistical methods, which contains mathematical formulation and physical thinking,
could extract the hidden information from research data and shed light to the major research direction.
The evolution is essentially variation, namely mutation, in its genetic material, which
leads to the variation in the product, protein. Most of mutations at amino acid level come
from non-synonymous single nucleotide polymorphism (nsSNP). The functions of protein
could thus be altered. As a result, propagation of mutations and competitions...[
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With the development of state-of-the-art techniques in biological systems, many previous
inaccessible data now has become massive. To understanding this unprecedented data is
the key to discover new principles underlying the biological behaviour. The application
of statistical methods, which contains mathematical formulation and physical thinking,
could extract the hidden information from research data and shed light to the major research direction.
The evolution is essentially variation, namely mutation, in its genetic material, which
leads to the variation in the product, protein. Most of mutations at amino acid level come
from non-synonymous single nucleotide polymorphism (nsSNP). The functions of protein
could thus be altered. As a result, propagation of mutations and competitions between
different sub-types together, is regarded as evolution.
In this dissertation, two chapters are included under the title statistical methods for
protein function and evolution analysis. In the first chapter, a statistical method, deLemus,
is constructed for the time-resolved track of mutation trajectories, and the robustness
of deLemus is verified by mapping the SOI with sites with structural and functional
significance.In the second chapter, the mechanisms of CRISPR Cas9 were firstly introduced.
Then a model with consideration of confrontational changes is proposed to link the mutation and off-target effects.
During the ongoing CoVID-19 epidemic, the continuous genomic evolution of severe
acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has been generating new variants
with enhanced transmissibility and immune escape. Being one key target of antibodies,
mutations of the spike glycoprotein play a vital role in the trajectory of virus evasion.
Here, we present a time-resolved statistical method, dynamic expedition of leading mutations
(deLemus), to analyze the evolution dynamics of the spike protein. Together with
analysis on single amino-acid polymorphism (SAP), we proposed one L-index to quantify
the mutation strength of each amino acid for unravelling mutation pattern of spike glycoprotein.
The sites of interest (SOI) with high L-index hold great promise to detect potential
signal of emergent variants.
Found in approximately 50% of sequenced bacterial genomes and nearly 90% of sequenced
archaea,clustered regularly interspaced short palindromic repeats (CRISPR) CRISPR-associated
(Cas)systems employ the RNA–guided DNA endonuclease Cas9 to defend
against invading phages and conjugative plasmids by introducing site-specific double-stranded
breaks in target DNA.In this work, a domain motion dependent rate model is
constructed for a comprehensive understanding of CRISPR Cas9 dynamics, we believe
those differences in cleavage efficiencies and off-target rates are essentially due to altered
interaction inside Cas9:RNA:DNA complex with mutated residues. In order to find out
the driving force of these mutations, we applied Statistical Coupling Analysis (SCA) to
whole Cas9 protein sequences and domain-domain pair sequences to get sectors, which
represent a structural organization of proteins that reflects their evolutionary histories.
With all these analysis and 3D structures solved by experiments and MD simulations together,
we found out the role of mutations exhibiting different strategies in Bacteria-Phage
co-evolution by tuning the speed of conformational changes which sheds light on design
of Cas9 variant with higher specificity and efficiency.
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