Sortase as a Tool in Biotechnology and Medicine

We have harnessed two reactions catalyzed by the enzyme sortase A and applied them to generate new methods for the purification and site-selective modification of recombinant protein therapeutics.

We utilized native peptide ligation —a well-known function of sortase A— to attach a small molecule drug specifically to the carboxy-terminus of a recombinant protein. By combining this reaction with the unique phase behavior of elastin-like polypeptides, we developed a protocol that produces homogenously-labeled protein-small molecule conjugates using only centrifugation. The same reaction can be used to produce unmodified therapeutic proteins simply by substituting a single reactant. The isolated proteins or protein-small molecule conjugates do not have any exogenous purification tags, eliminating the potential influence of these tags on bioactivity. Because both unmodified and modified proteins are produced by a general process that is the same for any protein of interest and does not require any chromatography, the time, effort, and cost associated with protein purification and modification is greatly reduced.

We also developed an innovative and unique method that attaches a tunable number of drug molecules to any recombinant protein of interest in a site-specific manner. Although the ability of sortase A to carry out native peptide ligation is widely used, we demonstrated that Sortase A is also capable of attaching small molecules to proteins through an isopeptide bond at lysine side chains within a unique amino acid sequence. This reaction —isopeptide ligation— is a new site-specific conjugation method that is orthogonal to all available protein-small conjugation technologies and is the first site-specific conjugation method that attaches the payload to lysine residues. We show that isopeptide ligation can be applied broadly to peptides, proteins, and antibodies using a variety of small molecule cargoes to efficiently generate stable conjugates. We thoroughly assessed the site-selectivity of this reaction using a variety of analytical methods and showed that in many cases the reaction is site-specific for lysines in flexible, disordered regions of the substrate proteins. Finally, we showed that isopeptide ligation can be used to create clinically-relevant antibody-drug conjugates that have potent cytotoxicity towards cancerous cells

New Advancements of Scalable Statistical Methods for Learning Latent Structures in Big Data

Constant technology advances have caused data explosion in recent years. Accord- ingly modern statistical and machine learning methods must be adapted to deal with complex and heterogeneous data types. This phenomenon is particularly true for an- alyzing biological data. For example DNA sequence data can be viewed as categorical variables with each nucleotide taking four different categories. The gene expression data, depending on the quantitative technology, could be continuous numbers or counts. With the advancement of high-throughput technology, the abundance of such data becomes unprecedentedly rich. Therefore efficient statistical approaches are crucial in this big data era.

Previous statistical methods for big data often aim to find low dimensional struc- tures in the observed data. For example in a factor analysis model a latent Gaussian distributed multivariate vector is assumed. With this assumption a factor model produces a low rank estimation of the covariance of the observed variables. Another example is the latent Dirichlet allocation model for documents. The mixture pro- portions of topics, represented by a Dirichlet distributed variable, is assumed. This dissertation proposes several novel extensions to the previous statistical methods that are developed to address challenges in big data. Those novel methods are applied in multiple real world applications including construction of condition specific gene co-expression networks, estimating shared topics among newsgroups, analysis of pro- moter sequences, analysis of political-economics risk data and estimating population structure from genotype data.