As we move into the new century, the human genome and those of many other organisms, comprising billions of basepairs, have been sequenced. The number of known structures of protein domains, which provide the primary way to interpret gene sequences as functioning molecules, has risen to more than 25,000. And a variety of functional genomics experiments, such as cDNA microarrays, are providing large amounts of standardized data on each gene in the genome. Remarkably, with the simultaneous advance of computer technology, all this information fits easily onto a laptop. Interpreting it, however, will require new approaches.

Broadly, the goal of my laboratory is to make sense of this data deluge, by carrying out integrative surveys and systematic data mining on the expanding amount of biological information. Specifically, we are focused on computational proteomics: understanding the structure, function, and evolution of proteins through analyzing populations of them in the databases and in whole-genome experiments. Through such work we believe we can address two central post- genomic challenges: understanding genes in detail and interpreting the regions between genes. With regard to the first challenge, we are trying to predict protein function on a genomic scale and trying to understand how we get a range of structural and functional diversity from a limited repertoire of protein folds. With regard to the second challenge, we are analyzing protein fossils (pseudogenes) in intergenic regions.

The ongoing research program in the lab extends and expands previous work as described below, with work broadly falling into three areas: (i) analysis of structures, focused on understanding protein motions in terms of packing; (ii) analysis of sequences, focused on surveying the occurrence of folds and families in genomes (both for genes and pseudogenes); (iii) analysis of functional genomics data, focused on trying to predict protein function on a genomic scale.

Our work is fundamentally data-driven and different in conception from previous computational work related to proteins, which often concentrated on describing the physical process of folding or predicting 3D structure given an amino-acid sequence. It is closely coupled with experiment and involves a number of detailed collaborations with experimentalists. Finally, our work, which falls into the new discipline of bioinformatics, is fundamentally interdisciplinary in character, combining questions drawn from biology with quantitative approaches from computer science and physics.

While we found that the specific most common folds often differed between genomes, in all cases the occurrence of folds (and many other aspects of genomic biology) tends to follow power-law statistics, with a few common ones and many rare ones. We have proposed a simple evolutionary model that naturally gives rise to these statistics [92, Fig. 5]. Finally, we found that there were many global, statistical differences between folds from different phylogenetic groups -- e.g. with longer and more numerous all-beta proteins from eukaryotes than prokaryotes and more salt-bridge pairs in thermophiles [42,65,87].

Our pseudogene work forms a valuable backdrop to experimental work aimed at accurately identifying genes and annotating genomes as well as probing the sequence characteristics of intergenic regions [94,101].

One often overlooked type of information that is critical to integrate into biological databases is journal articles, which contain the overwhelming bulk of what is known about function, albeit in unstructured form. Connecting databases and journals is a vast challenge, and in this area we have identified issues, advocated approaches and developed standards [44,46,49,50,59,74,88,90,102]. We envision a future, where there will be less distinction between databases and journals. One will be able to both find understandable prose in database entries and do computation directly on specially constructed parts of journal articles. Such a scenario will help overcome many of the problems now facing biological databases, including quality control, attribution of credit, and error correction.

There are number of future directions that we are pursuing. These are all direct extensions of our current program. In terms of structure, we would like to develop "rich templates" for the most common folds (e.g. helical bundles and TIM-barrels) that highlight their flexibility about around a common framework. We envision that these will be useful in protein design, in simplifying complex conformational changes, and in providing annotation to the newly solved structures that do not have novel folds (e.g. from structural genomics). In terms of genome analysis, we plan to scale up in two ways. On the one hand, we would like to enlarge our surveys of pseudogenes to the entire human genome, and, on the other, we would like to focus on comparing the human proteome against those of pathogens, trying to identify unique pathogen proteins as antibiotic targets. Of particular interest here are membrane proteins, which are often exposed on the surface. In terms of function, we plan to extend our Bayesian system for localization to the prediction of protein- protein interactions. We believe that integrating the new data from protein-chip experiments and from many of the many recent protein interaction sets creates for the first time a large enough database to be able to rigorously tackle this problem.

** NOTE: This document is closely coupled to my publication list in the following fashion: Almost each publication between 1/97 and early 2002 is referred to once and only once here, using its bibliography number. My top publications and reviews are highlighted, and the sentence before the citation, which explains their significance, is italicized. Thus, from glancing at the highlighting, one should be able to get a quick bullet-point summary of the main papers. To keep things simple, no attempt has been made to refer to the scientific literature generally, and this document should not be construed as a review of the field.