Developing a computational approach to investigate the impacts of disease-causing mutations on protein function

This project uses bioinformatics protocols to explore the impacts of non-synonymous mutations (nsSNPs) in proteins associated with diseases, including germline, rare diseases and somatic diseases such as cancer. New approaches were explored for determining the impacts of disease-associated mutations on protein structure and function. Whilst this work has mainly concentrated on the analysis of cancer mutations, the methods developed are generic and could be applied to analysing other types of disease mutations. Different types of disease-causing mutations have been studied including germline diseases, somatic cancer mutations in oncogenes and tumour-suppressors, along with known activating and inactivating mutations in kinases. The proximity of disease-associated mutations has been analysed with respect to known functional sites reported by CSA, IBIS, along with predicted functional sites derived from the CATH classification of domain structure superfamilies. The latter are called FunSites, and are highly conserved residues within a CATH functional family (FunFam) – which is a functionally coherent subset of a CATH superfamily. Such sites include key catalytic residues as well as specificity determining residues and interface residues. Clear differences were found between oncogenes, tumour suppressor and germ-line mutations with oncogene mutations more likely to locate close to FunSites. Functional families that are highly enriched in disease mutations were identified and exploited structural data to identify clusters within proteins in these families that are enriched in mutations (using our MutClust program). We examined the tendencies of these clusters to lie close to the functional sites discussed above. For selected genes, the stability effects of disease mutations in cancer have also been investigated with a particular focus on activating mutations in FGFR3. These studies, which were supported by experimental validation, showed that activating mutations implicated in cancer tend to cause stabilisation of the active FGFR3 form, leading to its abnormal activity and oncogenesis. Mutationally enriched CATH FunFams were also used in the identification of cancer driver genes, which were then subjected to pathway and GO biological process analysis

Parallelization of dynamic programming recurrences in computational biology

The rapid growth of biosequence databases over the last decade has led to a performance bottleneck in the applications analyzing them. In particular, over the last five years DNA sequencing capacity of next-generation sequencers has been doubling every six months as costs have plummeted. The data produced by these sequencers is overwhelming traditional compute systems. We believe that in the future compute performance, not sequencing, will become the bottleneck in advancing genome science. In this work, we investigate novel computing platforms to accelerate dynamic programming algorithms, which are popular in bioinformatics workloads. We study algorithm-specific hardware architectures that exploit fine-grained parallelism in dynamic programming kernels using field-programmable gate arrays: FPGAs). We advocate a high-level synthesis approach, using the recurrence equation abstraction to represent dynamic programming and polyhedral analysis to exploit parallelism. We suggest a novel technique within the polyhedral model to optimize for throughput by pipelining independent computations on an array. This design technique improves on the state of the art, which builds latency-optimal arrays. We also suggest a method to dynamically switch between a family of designs using FPGA reconfiguration to achieve a significant performance boost. We have used polyhedral methods to parallelize the Nussinov RNA folding algorithm to build a family of accelerators that can trade resources for parallelism and are between 15-130x faster than a modern dual core CPU implementation. A Zuker RNA folding accelerator we built on a single workstation with four Xilinx Virtex 4 FPGAs outperforms 198 3 GHz Intel Core 2 Duo processors. Furthermore, our design running on a single FPGA is an order of magnitude faster than competing implementations on similar-generation FPGAs and graphics processors. Our work is a step toward the goal of automated synthesis of hardware accelerators for dynamic programming algorithms

On interaction motif inference from biomolecular interactions: Riding the growth of the high throughput sequential and structural data

Ph.DDOCTOR OF PHILOSOPH

Spatial statistics from hyperplexed immunofluorescence images: to elucidate tumor microenvironment, to characterize intratumor heterogeneity, and to predict metastatic potential

The composition of the tumor microenvironment (TME)–the malignant, immune, and stromal cells implicated in tumor biology as well as the extracellular matrix and noncellular elements–and the spatial relationships between its constituents are important diagnostic biomarkers for cancer progression, proliferation, and therapeutic response. In this thesis, we develop methods to quantify spatial intratumor heterogeneity (ITH). We apply a novel pattern recognition framework to phenotype cells, encode spatial information, and calculate pairwise association statistics between cell phenotypes in the tumor using pointwise mutual information. These association statistics are summarized in a heterogeneity map, used to compare and contrast cancer subtypes and identify interaction motifs that may underlie signaling pathways and functional heterogeneity. Additionally, we test the prognostic power of spatial protein expression and association profiles for predicting clinical cancer staging and recurrence, using multivariate modeling techniques. By demonstrating the relationship between spatial ITH and outcome, we advocate this method as a novel source of information for cancer diagnostics. To this end, we have released an open-source analysis and visualization platform, THRIVE (Tumor Heterogeneity Research Image Visualization Environment), to segment and quantify multiplexed imaging samples, and assess underlying heterogeneity of those samples. The quantification of spatial ITH will uncover key spatial interactions, which contribute to disease proliferation and progression, and may confer metastatic potential in the primary neoplasm

Structural and functional analysis of DDR1 autoinhibition

Discoidin domain receptor 1 (DDR1) is a collagen activated receptor tyrosine kinase (RTK) which controls cellular proliferation and migration. DDR1 plays important roles in organogenesis and wound healing. Furthermore, aberrant DDR1 signalling is implicated in the progression and poor prognosis of several diseases, including organ fibroses and cancers. DDR1 is therefore an attractive target for pharmacological intervention. However, unlike in many other RTKs, the regulatory mechanisms underpinning DDR1 signalling are poorly understood. This project investigated the regulatory function of the long intracellular juxtamembrane (JM) region of DDR1. The kinase proximal JM segment, termed JM4, is shown to be an important regulator of DDR1 kinase activity. A 2.58 Å resolution crystal structure revealed that the JM4 segment forms a hairpin which enters the kinase active site and reinforces activation loop autoinhibition. Enzymological analysis of purified DDR1 constructs demonstrated that this autoinhibition is relieved in an ordered process which begins with the rapid, in cis, phosphorylation of the JM4 segment (Tyr569 and Tyr586), followed by slow, in trans, phosphorylation of the activation loop (Tyr796). Both successive phosphorylation events are shown to have drastic activating effects on the kinase catalytic rate. Analysis of cell expressed DDR1 also revealed that JM4 Tyr mutation (DDR1-Y569F/Y586F) abolishes collagen induced receptor activation. A secondary positive role for the JM4 region in DDR1 activation is also identified through cell-based analysis. This role could be the recruitment of Src, a non-receptor tyrosine kinase, which is shown to be an activator of DDR1, but not DDR1-Y569F/Y586F, signalling. The identification of the DDR1 JM4 region as a regulator of receptor signalling provides an interesting avenue for the development of DDR1-specific kinase inhibitors.Open Acces

Protein-Ligand Interactions: Target Identification and Drug Discovery

Bioactive compounds and drugs are designed and screened on the basis of specific molecular targets as well as via the identification of active ingredients from traditional medicine or by serendipitous discovery. The development of novel therapeutic strategies not only requires a deep knowledge of the molecular processes and the cellular pathways involved in each pathological condition and disease, but also the specific protein targets and the effects of drug binding on protein conformation and activity. Understanding of how drugs can modify and modulate specific cellular pathways and functions will be helpful during the process of drug development and clinical trials