Mendelian tumour syndromes are caused by rare mutations, which usually lead to protein inactivation. Few studies have determined whether or not the same genes harbour other, more common variants, which might have a lower penetrance and/or cause mild disease, perhaps indistinguishable from sporadic disease and accounting for a considerable proportion of the unexplained inherited risk of tumours in the general population. Germline variants at the APC locus are excellent candidates for explaining why some individuals are predisposed to colorectal adenomas, but do not have the florid phenotype of familial adenomatous polyposis. We have screened 164 unrelated patients with 'multiple' (3-100) colorectal adenomas for germline variants throughout the APC gene, including promoter mutations. In addition to three Ashkenazi patients with I1307K, we found seven patients with the E1317Q variant. E1317Q is significantly associated with multiple colorectal adenomas (OR = 11. 17, 95% CI = 2.30-54.3, p < 0.001), accounting for approximately 4% of all patients with multiple colorectal adenomas. In addition, four patients with truncating APC variants in exon 9 or in the 3' part of the gene were identified. Germline APC variants account for approximately 10% of patients with multiple adenomas. Unidentified predisposition genes almost certainly exist. We argue that it is worthwhile to screen multiple adenoma patients for a restricted number of germline APC variants, namely the missense changes E1317Q and I1307K (if of Ashkenazi descent), and, if there is a family history of colorectal tumours, for truncating mutations 5' to exon 5, in exon 9 and 3' to codon 1580.

Summary We have developed a suite of protein redesign algorithms that improves realistic in silico modeling of proteins. These algorithms are based on three characteristics that make them unique: (1) improved flexibility of the protein backbone, protein side chains, and ligand to accurately capture the conformational changes that are induced by mutations to the protein sequence; (2) modeling of proteins and ligands as ensembles of low-energy structures to better approximate binding affinity; and (3) a globally-optimal protein design search, guaranteeing that the computational predictions are optimal with respect to the input model. Here, we illustrate the importance of these three characteristics. We then describe OSPREY, a protein redesign suite that implements our protein design algorithms. OSPREY has been used prospectively, with experimental validation, in several biomedically-relevant settings. We show in detail how OSPREY has been used to predict resistance mutations and explain why improved flexibility, ensembles, and provability are essential for this application.

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To maintain normal physiology, cells must properly process diverse signals arising from changes in temperature, pH, nutrient concentrations, and other factors. Many physiological processes are controlled by temporal aspects of oscillating signals; that is, these signals can encode information in the frequency domain. By modeling simple gene circuits, we analyze the impact of cellular noise on the fidelity and speed of frequency-signal transmission. We find that transmission of frequency signals is "all-or-none", limited by a critical frequency (f(c)). Signals with frequencies f(c) are severely corrupted or completely lost in transmission. We argue that f(c) is an intrinsic property of a gene circuit and it varies with circuit parameters and additional feedback or feedforward regulation. Our results may have implications for understanding signal processing in natural biological networks and for engineering synthetic gene circuits.

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