Acknowledgements

This work has been partially supported by NIH grants 5RO1GM067823, 5RO1AI055649, R24 DK 064403, AI055338, and R01 AR050688. We also gratefully acknowledge the support from the University of Cincinnati College of Medicine and Cincinnati Children's Hospital Medical Center.

The following persons contributed to the development of our web resources and related research projects: Alexey Porollo, Rafal Adamczak, Baoqiang Cao, Amit Sinha, Mukta Phatak, Karthikeyan Swaminathan, Mirek Kordos, Michael Wagner, Bradley Slaven, George Smulian, Melanie Cushion, and Jarek Meller. We would like to thank Prakash Velayutham for his help in overcoming numerous technical issues during the implementation of our projects. And we also would like to thank all the users for their feedback.

We are very grateful to the authors of all software that is utilized by our servers for making it available to the community and graciously agreeing to incorporate it into public domain servers.

• W.L. DeLano's PyMol from DeLano Scientific, San Carlos, CA, USA and
  Roger Sayle's RasMol (v 2.7.3) from Biomolecular Structures Group, Hertfordshire, UK
  They are being used to render 3D images of macromolecular structures.
• Jmol java applet
  It is being used for the quick protein structure lookups, and for setting up specific parameters.
• The DSSP program by W. Kabsch and C. Sander available at the Centre for Molecular and Biomolecular Informatics, University of Nijmegen, Netherlands
  It is being used for calculation of the protein secondary structure and relative solvent accessibility.
• The BLAST program from National Center for Biotechnology Information (NCBI), USA
  It is being used to perform sequence homology search and obtain multiple sequence alignment.
• The CASTp server from L. Liang lab at University of Illinois at Chicago, USA
  It is being used for finding pockets within macromolecular structure.
• The Pfam database from Wellcome Trust Sanger Institute, UK
  It is being used to find and map known protein domains to the submitted structure.
• The PDBTM database from the Institute of Enzymology, Budapest, Hungary
  It is being used for mapping the protein regions putatively spanning membrane to the submitted structure.
• The SNNS - Stuttgart Neural Network Simulator from University of Stuttgart, Germany
  It was used to build and train neural networks as part of the systems for prediction of protein secondary structure, relative solvent (or lipid) accessibility, trans-membrane domains, and protein interaction sites.
• The LibSVM - a Library for Support Vector Machines by C.-C. Chang and C.-J. Lin from National Taiwan University, Taipei, Taiwan
  It was used to build and train SVR-based models for predicting relative solvent accessibility.