Refinement of modelled structures by knowledge-based energy profiles and secondary structure prediction: application to the human procarboxypeptidase A2

Splitting statistical potentials into meaningful scoring functions: testing the prediction of near-native structures from decoy conformations

Background

Recent advances on high-throughput technologies have produced a vast amount of protein sequences, while the number of high-resolution structures has seen a limited increase. This has impelled the production of many strategies to built protein structures from its sequence, generating a considerable amount of alternative models. The selection of the closest model to the native conformation has thus become crucial for structure prediction. Several methods have been developed to score protein models by energies, knowledge-based potentials and combination of both.

Results

Here, we present and demonstrate a theory to split the knowledge-based potentials in scoring terms biologically meaningful and to combine them in new scores to predict near-native structures. Our strategy allows circumventing the problem of defining the reference state. In this approach we give the proof for a simple and linear application that can be further improved by optimizing the combination of Zscores. Using the simplest composite score () we obtained predictions similar to state-of-the-art methods. Besides, our approach has the advantage of identifying the most relevant terms involved in the stability of the protein structure. Finally, we also use the composite Zscores to assess the conformation of models and to detect local errors.

Conclusion

We have introduced a method to split knowledge-based potentials and to solve the problem of defining a reference state. The new scores have detected near-native structures as accurately as state-of-art methods and have been successful to identify wrongly modeled regions of many near-native conformations.

Comparative modelling of protein structure and its impact on microbial cell factories

Comparative modeling is becoming an increasingly helpful technique in microbial cell factories as the knowledge of the three-dimensional structure of a protein would be an invaluable aid to solve problems on protein production. For this reason, an introduction to comparative modeling is presented, with special emphasis on the basic concepts, opportunities and challenges of protein structure prediction. This review is intended to serve as a guide for the biologist who has no special expertise and who is not involved in the determination of protein structure. Selected applications of comparative modeling in microbial cell factories are outlined, and the role of microbial cell factories in the structural genomics initiative is discussed.

The crystal structure of the inhibitor-complexed carboxypeptidase D domain II and the modeling of regulatory carboxypeptidases

The three-dimensional crystal structure of duck carboxypeptidase D domain II has been solved in a complex with the peptidomimetic inhibitor, guanidinoethylmercaptosuccinic acid, occupying the specificity pocket. This structure allows a clear definition of the substrate binding sites and the substrate funnel-like access. The structure of domain II is the only one available from the regulatory carboxypeptidase family and can be used as a general template for its members. Here, it has been used to model the structures of domains I and III from the former protein and of human carboxypeptidase E. The models obtained show that the overall topology is similar in all cases, the main differences being local and because of insertions in non-regular loops. In both carboxypeptidase D domain I and carboxypeptidase E slightly different shapes of the access to the active site are predicted, implying some kind of structural selection of protein or peptide substrates. Furthermore, emplacement of the inhibitor structure in the active site of the constructed models showed that the inhibitor fits very well in all of them and that the relevant interactions observed with domain II are conserved in domain I and carboxypeptidase E but not in the non-active domain III because of the absence of catalytically indispensable residues in the latter protein. However, in domain III some of the residues potentially involved in substrate binding are well preserved, together with others of unknown roles, which also are highly conserved among all carboxypeptidases. These observations, taken together with others, suggest that domain III might play a role in the binding and presentation of proteins or peptide substrates, such as the pre-S domain of the large envelope protein of duck hepatitis B virus.

Comparative protein structure modeling of genes and genomes

Comparative modeling predicts the three-dimensional structure of a given protein sequence (target) based primarily on its alignment to one or more proteins of known structure (templates). The prediction process consists of fold assignment, target-template alignment, model building, and model evaluation. The number of protein sequences that can be modeled and the accuracy of the predictions are increasing steadily because of the growth in the number of known protein structures and because of the improvements in the modeling software. Further advances are necessary in recognizing weak sequence-structure similarities, aligning sequences with structures, modeling of rigid body shifts, distortions, loops and side chains, as well as detecting errors in a model. Despite these problems, it is currently possible to model with useful accuracy significant parts of approximately one third of all known protein sequences. The use of individual comparative models in biology is already rewarding and increasingly widespread. A major new challenge for comparative modeling is the integration of it with the torrents of data from genome sequencing projects as well as from functional and structural genomics. In particular, there is a need to develop an automated, rapid, robust, sensitive, and accurate comparative modeling pipeline applicable to whole genomes. Such large-scale modeling is likely to encourage new kinds of applications for the many resulting models, based on their large number and completeness at the level of the family, organism, or functional network.