Growth of novel protein structural data

Levitt, Michael

doi:10.1073/pnas.0611678104

Cited by 145 publications

(98 citation statements)

References 44 publications

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“…Models within this threshold distance from the ith model are summed to give Neighbors i ; an initial estimate for k ¼ Σ i 1∕ ðNeighbors i þ 1Þ (45). If the initial estimate is too small, clusters would be merged, yielding less insightful interpretations.…”

Section: Methodsmentioning

confidence: 99%

Clustering to identify RNA conformations constrained by secondary structure

Sim

Levitt

2011

Proc. Natl. Acad. Sci. U.S.A.

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RNA often folds hierarchically, so that its sequence defines its secondary structure (helical base-paired regions connected by single-stranded junctions), which subsequently defines its tertiary fold. To preserve base-pairing and chain connectivity, the threedimensional conformations that RNA can explore are strongly confined compared to when secondary structure constraints are not enforced. Using three examples, we studied how secondary structure confines and dictates an RNA's preferred conformations. We made use of Macromolecular Conformations by SYMbolic programming (MC-Sym) fragment assembly to generate RNA conformations constrained by secondary structure. Then, to understand the correlations between different helix placements and orientations, we robustly clustered all RNA conformations by employing unique methods to remove outliers and estimate the best number of conformational clusters. We observed that the preferred conformation (as judged by largest cluster size) for each type of RNA junction molecule tested is consistent with its biological function. Further, the improved quality of models in our pruned datasets facilitates subsequent discrimination using scoring functions based either on statistical analysis (knowledge based) or experimental data.RNA structure prediction | RNA junctions | clustering | riboswitches | RNA folding R NA is an important molecule in gene regulation both in the presence and absence of proteins (1). The paradigm that "structure affects function," so commonly used on proteins, can also be applied to RNAs, which adopt intricate three-dimensional structures that depend strongly on their in vivo environment. The plethora of RNA types, ranging from small-interfering RNA to ribozymes and riboswitches, points to a need for a strong understanding of how RNA folds in order to better elucidate how RNA functions (2, 3).One test of our understanding of RNA folding is the extent to which we can predict RNA tertiary structure from sequence, a challenging problem that recent research has tackled in a variety of ways (4). Some of the approaches to predicting RNA tertiary structure include assembling RNA fragments from native-like RNA fragment libraries (5-7) or sampling RNA chains using discrete molecular dynamics (8). Such prediction methods are confined to small molecules or require coarse-graining of bases due to the difficulty associated with sufficiently complete all-atom conformational sampling of larger, more complex RNA systems.The sampling process can be simplified because most RNAs fold in a hierarchical fashion (9-11), in which the RNA sequence determines its secondary structure, or the base-pairing within the RNA (Fig. 1A). These base-pairing interactions are preserved during folding to a specific three-dimensional (3D) structure. Therefore, each RNA can generally be regarded as comprising of helical regions (simplified as uniform cylinders) connected by single-stranded junction regions in 3D (Fig. 1B). Hence every RNA molecule is made up of helix-junction-helix motifs.It is comm...

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Section: Methodsmentioning

confidence: 99%

Clustering to identify RNA conformations constrained by secondary structure

Sim

Levitt

2011

Proc. Natl. Acad. Sci. U.S.A.

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“…Homology or template based modeling has been the most successful method for protein structure prediction in the critical assessment of protein structure prediction (CASP) experiments (6,7). The power of this technique progressively increases as ever more structures are solved by world-wide structural genomics initiatives (8,9). Nevertheless, obtaining a model with the same accuracy as a crystal structure is still an unsolved problem: structure refinement of a rough model (within 1-3 Å rmsd) to bring it closer to the native structure remains a major challenge (6,10).…”

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confidence: 99%

Solvent dramatically affects protein structure refinement

Chopra

Summa

Levitt

2008

Proc. Natl. Acad. Sci. U.S.A.

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100

106

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One of the most challenging problems in protein structure prediction is improvement of homology models (structures within 1-3 Å C ␣ rmsd of the native structure), also known as the protein structure refinement problem. It has been shown that improvement could be achieved using in vacuo energy minimization with molecular mechanics and statistically derived continuously differentiable hybrid knowledge-based (KB) potential functions. Globular proteins, however, fold and function in aqueous solution in vivo and in vitro. In this work, we study the role of solvent in protein structure refinement. Molecular dynamics in explicit solvent and energy minimization in both explicit and implicit solvent were performed on a set of 75 native proteins to test the various energy potentials. A more stringent test for refinement was performed on 729 near-native decoys for each native protein. We use a powerfully convergent energy minimization method to show that implicit solvent (GBSA) provides greater improvement for some proteins than the KB potential: 24 of 75 proteins showing an average improvement of >20% in C ␣ rmsd from the native structure with GBSA, compared to just 7 proteins with KB. Molecular dynamics in explicit solvent moved the structures further away from their native conformation than the initial, unrefined decoys. Implicit solvent gives rise to a deep, smooth potential energy attractor basin that pulls toward the native structure.energy minimization ͉ implicit solvent ͉ knowledge-based ͉ molecular dynamics ͉ explicit solvent E xperimental determination of protein structures is very expensive, costing U.S. $250,000 in 2000 (1) and $66,000 today (2) and can be a notoriously difficult task, especially for membrane proteins. With the continuing exponential growth of genome sequence data, there is an increasing need for methods that accurately compute the high-resolution native structure of a protein, for use in biological applications that include virtual ligand screening (3), structure-based protein function prediction (4) and structure-based drug design (5). Homology or template based modeling has been the most successful method for protein structure prediction in the critical assessment of protein structure prediction (CASP) experiments (6, 7). The power of this technique progressively increases as ever more structures are solved by world-wide structural genomics initiatives (8, 9). Nevertheless, obtaining a model with the same accuracy as a crystal structure is still an unsolved problem: structure refinement of a rough model (within 1-3 Å rmsd) to bring it closer to the native structure remains a major challenge (6, 10). Work on structure refinement has been ongoing for many decades, starting from the first Molecular Mechanics (MM) energy minimization (11, 12) and continuing to a recent study with knowledgebased (KB) statistically derived potentials (13). During this period many different potentials and a variety of simulation methodologies such as energy minimization, molecular dynamics, and replica exchange Mon...

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“…Commercial software for homology modelling is widely available. Approximately 1,000 protein folds have been discovered so far; Levitt [15] predicts that the maximum number of folds is likely to be 1,613.…”

Section: Homology Methodsmentioning

confidence: 99%

Chemical genomics: a challenge for de novo drug design

Dean¹

2007

Mol Biotechnol

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De novo design provides an in silico toolkit for the design of novel small molecular structures to a set of specified structural constraints. With the avalanche of bioinformatics data, de novo design is ideally suited for exploring molecules that could be useful for chemical genomics. The design process involves manipulation of the input, modification of structural constraints, and further processing of the de novo generated molecules using various modular toolkits. The development of a theoretical framework for each of these stages will provide novel practical solutions to the problem of creating compounds with maximal chemical diversity. This short review describes the fundamental problems encountered in the application of novel chemical design technologies to chemical genomics by means of a formal representation. This notation helps to outline and clarify ideas and hypotheses that can then be explored using mathematical algorithms. It is only by developing this rigorous foundation that in silico design can progress in a rational way.

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Growth of novel protein structural data

Cited by 145 publications

References 44 publications

Clustering to identify RNA conformations constrained by secondary structure

Clustering to identify RNA conformations constrained by secondary structure

Solvent dramatically affects protein structure refinement

Chemical genomics: a challenge for de novo drug design

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