Structural biology

About: Structural biology is a research topic. Over the lifetime, 2206 publications have been published within this topic receiving 126070 citations.

Amino acid bulkiness defines the local conformations and dynamics of natively unfolded alpha-synuclein and tau.

Abstract: Natively unfolded proteins play key roles in normal and pathological biochemical processes. This category of proteins remains, however, beyond the reach of classical structural biology because of their inherent conformational heterogeneity. When confined in weakly aligning media, natively unfolded proteins such as α-synuclein, the major component of abnormal aggregates in the brain of patients with Parkinson's disease, display surprisingly variable NMR dipolar couplings as a function of position along the chain, suggesting the presence of residual secondary or tertiary structure. Here we show that the variation of NMR dipolar couplings and heteronuclear relaxation rates in α-synuclein closely follows the variations of the bulkiness of amino acids along the polypeptide chain. Our results demonstrate that the bulkiness of amino acids defines the local conformations and dynamics of α-synuclein and other natively unfolded proteins. Deviations from this random coil behavior can provide insight into residual se...

Protein structure networks

Abstract: The application of the field of network science to the scientific disciplines of structural biology and biochemistry, have yielded important new insights into the nature and determinants of protein structures, function, dynamics and the folding process. Advancements in further understanding protein relationships through network science have also reshaped the way we view the connectivity of proteins in the protein universe. The canonical hierarchical classification can now be visualized for example, as a protein fold continuum. This review will survey several key advances in the expanding area of research being conducted to study protein structures and folding using network approaches.

High-Throughput Computational and Experimental Techniques in Structural Genomics

Abstract: Structural genomics has as its goal the provision of structural information for all possible ORF sequences through a combination of experimental and computational approaches. The access to genome sequences and cloning resources from an ever-widening array of organisms is driving high-throughput structural studies by the New York Structural Genomics Research Consortium. In this report, we outline the progress of the Consortium in establishing its pipeline for structural genomics, and some of the experimental and bioinformatics efforts leading to structural annotation of proteins. The Consortium has established a pipeline for structural biology studies, automated modeling of ORF sequences using solved (template) structures, and a novel high-throughput approach (metallomics) to examining the metal binding to purified protein targets. The Consortium has so far produced 493 purified proteins from >1077 expression vectors. A total of 95 have resulted in crystal structures, and 81 are deposited in the Protein Data Bank (PDB). Comparative modeling of these structures has generated >40,000 structural models. We also initiated a high-throughput metal analysis of the purified proteins; this has determined that 10%-15% of the targets contain a stoichiometric structural or catalytic transition metal atom. The progress of the structural genomics centers in the U.S. and around the world suggests that the goal of providing useful structural information on most all ORF domains will be realized. This projected resource will provide structural biology information important to understanding the function of most proteins of the cell.

Predicting mostly disordered proteins by using structure-unknown protein data

Abstract: Predicting intrinsically disordered proteins is important in structural biology because they are thought to carry out various cellular functions even though they have no stable three-dimensional structure. We know the structures of far more ordered proteins than disordered proteins. The structural distribution of proteins in nature can therefore be inferred to differ from that of proteins whose structures have been determined experimentally. We know many more protein sequences than we do protein structures, and many of the known sequences can be expected to be those of disordered proteins. Thus it would be efficient to use the information of structure-unknown proteins in order to avoid training data sparseness. We propose a novel method for predicting which proteins are mostly disordered by using spectral graph transducer and training with a huge amount of structure-unknown sequences as well as structure-known sequences. When the proposed method was evaluated on data that included 82 disordered proteins and 526 ordered proteins, its sensitivity was 0.723 and its specificity was 0.977. It resulted in a Matthews correlation coefficient 0.202 points higher than that obtained using FoldIndex, 0.221 points higher than that obtained using the method based on plotting hydrophobicity against the number of contacts and 0.07 points higher than that obtained using support vector machines (SVMs). To examine robustness against training data sparseness, we investigated the correlation between two results obtained when the method was trained on different datasets and tested on the same dataset. The correlation coefficient for the proposed method is 0.14 higher than that for the method using SVMs. When the proposed SGT-based method was compared with four per-residue predictors (VL3, GlobPlot, DISOPRED2 and IUPred (long)), its sensitivity was 0.834 for disordered proteins, which is 0.052–0.523 higher than that of the per-residue predictors, and its specificity was 0.991 for ordered proteins, which is 0.036–0.153 higher than that of the per-residue predictors. The proposed method was also evaluated on data that included 417 partially disordered proteins. It predicted the frequency of disordered proteins to be 1.95% for the proteins with 5%–10% disordered sequences, 1.46% for the proteins with 10%–20% disordered sequences and 16.57% for proteins with 20%–40% disordered sequences. The proposed method, which utilizes the information of structure-unknown data, predicts disordered proteins more accurately than other methods and is less affected by training data sparseness.