164 research outputs found
SPRINT: side-chain prediction inference toolbox for multistate protein design
No full textAbstract
Summary: 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
Contact: [email protected]
Supplementary information: Supplementary data are available at Bioinformatics online.</jats:p
Accurate prediction for atomic‐level protein design and its application in diversifying the near‐optimal sequence space
No full textA computational framework to empower probabilistic protein design
No full textABSTRACT Motivation: The task of engineering a protein to perform a target biological function is known as protein design. A commonly used paradigm casts this functional design problem as a structural one, assuming a fixed backbone. In probabilistic protein design, positional amino acid probabilities are used to create a random library of sequences to be simultaneously screened for biological activity. Clearly, certain choices of probability distributions will be more successful in yielding functional sequences. However, since the number of sequences is exponential in protein length, computational optimization of the distribution is difficult. Results: In this paper, we develop a computational framework for probabilistic protein design following the structural paradigm. We formulate the distribution of sequences for a structure using the Boltzmann distribution over their free energies. The corresponding probabilistic graphical model is constructed, and we apply belief propagation (BP) to calculate marginal amino acid probabilities. We test this method on a large structural dataset and demonstrate the superiority of BP over previous methods. Nevertheless, since the results obtained by BP are far from optimal, we thoroughly assess the paradigm using high-quality experimental data. We demonstrate that, for small scale sub-problems, BP attains identical results to those produced by exact inference on the paradigmatic model. However, quantitative analysis shows that the distributions predicted significantly differ from the experimental data. These findings, along with the excellent performance we observed using BP on the smaller problems, suggest potential shortcomings of the paradigm. We conclude with a discussion of how it may be improved in the future
Exposing the co-adaptive potential of protein–protein interfaces through computational sequence design
No full textAbstract
Motivation: In nature, protein–protein interactions are constantly evolving under various selective pressures. Nonetheless, it is expected that crucial interactions are maintained through compensatory mutations between interacting proteins. Thus, many studies have used evolutionary sequence data to extract such occurrences of correlated mutation. However, this research is confounded by other evolutionary pressures that contribute to sequence covariance, such as common ancestry.
Results: Here, we focus exclusively on the compensatory mutations deriving from physical protein interactions, by performing large-scale computational mutagenesis experiments for &gt;260 protein–protein interfaces. We investigate the potential for co-adaptability present in protein pairs that are always found together in nature (obligate) and those that are occasionally in complex (transient). By modeling each complex both in bound and unbound forms, we find that naturally transient complexes possess greater relative capacity for correlated mutation than obligate complexes, even when differences in interface size are taken into account.
Contact: [email protected]
Supplementary information: Supplementary data are available at Bioinformatics online.</jats:p
- …
