Protein model refinement using structural fragment tessellation

Computational protein structure refinement: Almost there, yet still so far to go

Protein structures are essential in modern biology yet experimental methods are far from being able to catch up with the rapid increase in available genomic data. Computational protein structure prediction methods aim to fill the gap while the role of protein structure refinement is to take approximate initial template-based models and bring them closer to the true native structure. Current methods for computational structure refinement rely on molecular dynamics simulations, related sampling methods, or iterative structure optimization protocols. The best methods are able to achieve moderate degrees of refinement but consistent refinement that can reach near-experimental accuracy remains elusive. Key issues revolve around the accuracy of the energy function, the inability to reliably rank multiple models, and the use of restraints that keep sampling close to the native state but also limit the degree of possible refinement. A different aspect is the question of what exactly the target of high-resolution refinement should be as experimental structures are affected by experimental conditions and different biological questions require varying levels of accuracy. While improvement of the global protein structure is a difficult problem, high-resolution refinement methods that improves local structural quality such as favorable stereochemistry and the avoidance of atomic clashes are much more successful.

Characterization of a Taura syndrome virus isolate originating from the 2004 Texas epizootic in cultured shrimp

A comprehensive investigation of the Taura syndrome virus (TSV) isolate that caused epizootics in shrimp farms in Texas in 2004 (Texas isolate) revealed that this virus was more virulent in laboratory bioassays than the TSV reference isolate, Hawaii 1994, causing severe symptom development and rapid mortality. Histopathology of moribund animals demonstrated epithelial necrosis within the stomach, appendages, general body cuticle and gills, and the surviving animals demonstrated moderate to numerous lymphoid organ spheroids. Purified virions showed icosahedral morphology, with a diameter of 31 nm. Comparative genome analysis showed that the Texas isolate is more closely related to TSV isolates from Thailand and China than to the Hawaii isolate. The predicted tertiary structures of the inhibition of apoptosis protein (IAP) and protease domains of the Texas isolate are very similar to those of the Hawaii isolate. However, the RNA-dependent RNA polymerase (RdRp) of the Texas isolate has significant structural differences from the Hawaii isolate due to point mutation(s) in the RdRp gene. Changes in the RdRp tertiary structure might contribute to the replication fidelity, virulence and ecological adaptability of the Texas isolate.

Functional site prediction selects correct protein models

BACKGROUND

The prediction of protein structure can be facilitated by the use of constraints based on a knowledge of functional sites. Without this information it is still possible to predict which residues are likely to be part of a functional site and this information can be used to select model structures from a variety of alternatives that would correspond to a functional protein.

RESULTS

Using a large collection of protein-like decoy models, a score was devised that selected those with predicted functional site residues that formed a cluster. When tested on a variety of small alpha/beta/alpha type proteins, including enzymes and non-enzymes, those that corresponded to the native fold were ranked highly. This performance held also for a selection of larger alpha/beta/alpha proteins that played no part in the development of the method.

CONCLUSION

The use of predicted site positions provides a useful filter to discriminate native-like protein models from non-native models. The method can be applied to any collection of models and should provide a useful aid to all modelling methods from ab initio to homology based approaches.

Sampling of near-native protein conformations during protein structure refinement using a coarse-grained model, normal modes, and molecular dynamics simulations

Protein structure refinement from comparative models with the goal of predicting structures at near-experimental accuracy remains an unsolved problem. Structure refinement might be achieved with an iterative protocol where the most native-like structure from a set of decoys generated from an initial model in one cycle is used as the starting structure for the next cycle. Conformational sampling based on the coarse-grained SICHO model, atomic level of detail molecular dynamics simulations, and normal-mode analysis is compared in the context of such a protocol. All of the sampling methods can achieve significant refinement close to experimental structures, although the distribution of structures and the ability to reach native-like structures differs greatly. Implications for the practical application of such sampling methods and the requirements for scoring functions in an iterative refinement protocol are analyzed in the context of theoretical predictions for the distribution of protein-like conformations with a random sampling protocol.