SPRINT: side-chain prediction inference toolbox for multistate protein design
نویسندگان
چکیده
UNLABELLED SPRINT is a software package that performs computational multistate protein design using state-of-the-art inference on probabilistic graphical models. The input to SPRINT is a list of protein structures, the rotamers modeled for each structure and the pre-calculated rotamer energies. Probabilistic inference is performed using the belief propagation or A* algorithms, and dead-end elimination can be applied as pre-processing. The output can either be a list of amino acid sequences simultaneously compatible with these structures, or probabilistic amino acid profiles compatible with the structures. In addition, higher order (e.g. pairwise) amino acid probabilities can also be predicted. Finally, SPRINT also has a module for protein side-chain prediction and single-state design. AVAILABILITY The full C++ source code for SPRINT can be freely downloaded from http://www.protonet.cs.huji.ac.il/sprint.
منابع مشابه
An efficient algorithm for multistate protein design based on FASTER
Most of the methods that have been developed for computational protein design involve the selection of side-chain conformations in the context of a single, fixed main-chain structure. In contrast, multistate design (MSD) methods allow sequence selection to be driven by the energetic contributions of multiple structural or chemical states simultaneously. This methodology is expected to be useful...
متن کاملMethyl side-chain dynamics prediction based on protein structure
MOTIVATION Protein dynamics is believed to influence protein function through a variety of mechanisms, some of which are not fully understood. Thus, prediction of protein flexibility from sequence or structural characteristics would assist in comprehension of the ways dynamics is linked to function, and would be important in protein modeling and design. In particular, quantitative description o...
متن کاملBayesian Methods in Biological Sequence Analysis
Hidden Markov models, the expectation–maximization algorithm, and the Gibbs sampler were introduced for biological sequence analysis in early 1990s. Since then the use of formal statistical models and inference procedures has revolutionized the field of computational biology. This chapter reviews the hidden Markov and related models, as well as their Bayesian inference procedures and algorithms...
متن کاملApproximate Inference and Side-chain Prediction
Side-chain prediction is an important subtask in the protein-folding problem. We show that finding a minimal energy side-chain configuration is equivalent to performing inference in an undirected graphical model. The graphical model is relatively sparse yet has many cycles. We used this equivalence to assess the performance of approximate inference algorithms in a real-world setting. Specifical...
متن کاملThyroid disorder diagnosis based on Mamdani fuzzy inference system classifier
Introduction: Classification and prediction are two most important applications of statistical methods in the field of medicine. According to this note that the classical classification are provided due to the clinical symptom and do not involve the use of specialized information and knowledge. Therefore, using a classifier that can combine all this information, is necessary. The aim of this s...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
عنوان ژورنال:
- Bioinformatics
دوره 26 19 شماره
صفحات -
تاریخ انتشار 2010