Knowing the coordination number and relative solvent accessibility of all the residues in a protein is crucial for deriving constraints useful in modeling protein folding and protein structure and in scoring remote homology searches. We develop ensembles of bidirectional recurrent neural network architectures to improve the state of the art in both contact and accessibility prediction, leveraging a large corpus of curated data together with evolutionary information. The ensembles are used to discriminate between two different states of residue contacts or relative solvent accessibility, higher or lower than a threshold determined by the average value of the residue distribution or the accessibility cutoff. For coordination numbers, the ensemble achieves performances ranging within 70.6–73.9% depending on the radius adopted to discriminate contacts (6Å–12Å). These performances represent gains of 16–20% over the baseline statistical predictor, always assigning an amino acid to the largest class, and are 4–7% better than any previous method. A combination of different radius predictors further improves performance. For accessibility thresholds in the relevant 15–30% range, the ensemble consistently achieves a performance above 77%, which is 10–16% above the baseline prediction and better than other existing predictors, by up to several percentage points. For both problems, we quantify the improvement due to evolutionary information in the form of PSI-BLAST-generated profiles over BLAST profiles. The prediction programs are implemented in the form of two web servers, CONpro and ACCpro, available at http://promoter.ics.uci.edu/BRNN-PRED/. Proteins 2002;47:142–153. © 2002 Wiley-Liss, Inc.