Protein location and function can change dynamically depending on many factors, including environmental stress, disease state, age, developmental stage, and cell type. Here, we describe an integrative computational framework, called the conditional function predictor (CoFP; http://nbm.ajou.ac.kr/cofp/), for predicting changes in subcellular location and function on a proteome-wide scale. The essence of the CoFP approach is to cross-reference general knowledge about a protein and its known network of physical interactions, which typically pool measurements from diverse environments, against gene expression profiles that have been measured under specific conditions of interest. Using CoFP, we predict condition-specific subcellular locations, biological processes, and molecular functions of the yeast proteome under 18 specified conditions. In addition to highly accurate retrieval of previously known gold standard protein locations and functions, CoFP predicts previously unidentified condition-dependent locations and functions for nearly all yeast proteins. Many of these predictions can be confirmed using high-resolution cellular imaging. We show that, under DNA-damaging conditions, Tsr1, Caf120, Dip5, Skg6, Lte1, and Nnf2 change subcellular location and RNA polymerase I subunit A43, Ino2, and Ids2 show changes in DNA binding. Beyond specific predictions, this work reveals a global landscape of changing protein location and function, highlighting a surprising number of proteins that translocate from the mitochondria to the nucleus or from endoplasmic reticulum to Golgi apparatus under stress.dynamic function prediction | protein translocation | DTT and MMS | systems biology | bioinformatics A cellular response can induce striking changes in the subcellular location and function of proteins. As a recent example, the activating transcription factor-2 (ATF2) plays an oncogenic role in the nucleus, whereas genotoxic stress-induced localization within the mitochondria gives ATF2 the ability to play tumor suppressor, resulting in promotion of cell death (1). Changes in protein location are typically identified using a variety of experimental methods [e.g., protein tagging (2), immunolabeling (3), or cellular subfractionation of target organelles followed by mass spectrometry (4)]. Although highly successful, such measurements can be laborious and time-consuming, even for a single protein (all methods except mass spectrometry) and condition (all methods).For these reasons and others, computational prediction of protein location and function has been a very active area of bioinformatic research. Early methods attempted to infer protein function based mainly on individual protein features, such as sequence similarity or structural homology (3,(5)(6)(7)(8)(9)(10)(11)(12)(13)(14)(15)(16)(17). These methods range from simple sequence-sequence comparisons to profile-or pattern-based supervised learning methods. Other methods predicted protein function using gene expression data (18, 19) based on the observation that proteins wit...
