Knowledge of the three-dimensional structure of a protein often gives a basis for understanding its function. The gap between numbers of known protein sequences and structures has been dramatically increasing as a huge number of sequences have being generated by new sequencing technologies. One important way to bridge this gap is computational prediction. Compared to experimental approaches, the computational prediction approach is cost-effective and efficient, and has become more and more important in studying protein structures.
We provide a comprehensive platform, MUFOLD, for efficient and consistently accurate protein tertiary structure prediction. The long-term objective of MUFOLD is to help experimental biologists understand structures and functions of the proteins of their interest thereby facilitating hypotheses for experimental design. Currently, in MUFOLD platform we already provided pdbLight, a web-based database which integrates protein sequence and structure data from multiple sources for protein structure prediction and analysis, MUFOLD 3D structure prediction, a web-server which provides the structure predictions from the sequences that users submitted, and MUFOLD_CL, a fast tool for protein structural model clustering, visualization and quality assessment.