Browsing by Subject "Structural bioinformatics"
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Item Open Access Analysis and Error Correction in Structures of Macromolecular Interiors and Interfaces(2009) Headd, Jeffrey JohnAs of late 2009, the Protein Data Bank (PDB) has grown to contain over 70,000 models. This recent increase in the amount of structural data allows for more extensive explication of the governing principles of macromolecular folding and association to complement traditional studies focused on a single molecule or complex. PDB-wide characterization of structural features yields insights that are useful in prediction and validation of the 3D structure of macromolecules and their complexes. Here, these insights lead to a deeper understanding of protein--protein interfaces, full-atom critical assessment of increasingly more accurate structure predictions, a better defined library of RNA backbone conformers for validation and building 3D models, and knowledge-based automatic correction of errors in protein sidechain rotamers.
My study of protein--protein interfaces identifies amino acid pairing preferences in a set of 146 transient interfaces. Using a geometric interface surface definition devoid of arbitrary cutoffs common to previous studies of interface composition, I calculate inter- and intrachain amino acid pairing preferences. As expected, salt-bridges and hydrophobic patches are prevalent, but likelihood correction of observed pairing frequencies reveals some surprising pairing preferences, such as Cys-His interchain pairs and Met-Met intrachain pairs. To complement my statistical observations, I introduce a 2D visualization of the 3D interface surface that can display a variety of interface characteristics, including residue type, atomic distance and backbone/sidechain composition.
My study of protein interiors finds that 3D structure prediction from sequence (as part of the CASP experiment) is very close to full-atom accuracy. Validation of structure prediction should therefore consider all atom positions instead of the traditional Calpha-only evaluation. I introduce six new full-model quality criteria to assess the accuracy of CASP predictions, which demonstrate that groups who use structural knowledge culled from the PDB to inform their prediction protocols produce the most accurate results.
My study of RNA backbone introduces a set of rotamer-like "suite" conformers. Initially hand-identified by the Richardson laboratory, these 7D conformers represent backbone segments that are found to be genuine and favorable. X-ray crystallographers can use backbone conformers for model building in often poor backbone density and in validation after refinement. Increasing amounts of high quality RNA data allow for improved conformer identification, but also complicate hand-curation. I demonstrate that affinity propagation successfully differentiates between two related but distinct suite conformers, and is a useful tool for automated conformer clustering.
My study of protein sidechain rotamers in X-ray structures identifies a class of systematic errors that results in sidechains misfit by approximately 180 degrees. I introduce Autofix, a method for automated detection and correction of such errors. Autofix corrects over 40% of errors for Leu, Thr, and Val residues, and a significant number of Arg residues. On average, Autofix made four corrections per PDB file in 945 X-ray structures. Autofix will be implemented into MolProbity and PHENIX for easy integration into X-ray crystallography workflows.
Item Open Access NMR Structure Improvement: A Structural Bioinformatics & Visualization Approach(2010) Block, JeremyThe overall goal of this project is to enhance the physical accuracy of individual models in macromolecular NMR (Nuclear Magnetic Resonance) structures and the realism of variation within NMR ensembles of models, while improving agreement with the experimental data. A secondary overall goal is to combine synergistically the best aspects of NMR and crystallographic methodologies to better illuminate the underlying joint molecular reality. This is accomplished by using the powerful method of all-atom contact analysis (describing detailed sterics between atoms, including hydrogens); new graphical representations and interactive tools in 3D and virtual reality; and structural bioinformatics approaches to the expanded and enhanced data now available.
The resulting better descriptions of macromolecular structure and its dynamic variation enhances the effectiveness of the many biomedical applications that depend on detailed molecular structure, such as mutational analysis, homology modeling, molecular simulations, protein design, and drug design.
Item Open Access Rare Sidechain Conformations in Proteins and DNA(2015) Hintze, Bradley JoelMedical advances often come as a result of understanding the underlying mechanisms of life. Life, in this sense, happens at various scales. A very complex and interesting one is the molecular scale. Understanding life’s mechanistic details at this level will provide the most promising therapies to modern ailments. Because of structure and function’s close relationship, knowledge of macromolecular structure provides invaluable insight into molecular mechanism.
A major tool used to get structural information at the molecular scale is X-ray crystallography. Such experiments result in an electron density map from which a model is built. Building such a model is a difficult task, especially at low resolu- tion where detailed features in the electron density deteriorate making it difficult to interpret. However, many advances in the field have greatly eased the model build- ing task, in fact, at high resolutions it has become automated. However, human inspection is still required to get a correct solution.
The largest boon to model building has been the application of structural knowl- edge. A prominent example is bond and dihedral angles. We often know what is absolutely not allowed and often convince ourselves we know everything that is al- lowed. This work focuses on the fuzzy border between allowed and disallowed. The hypothesis is that rare structural conformations exist but one needs to take great care in modeling them.
This work has two major components – rotamers (protein sidechain conformation)
and Hoogsteen base pairing in DNA. I first describe methods used to gain empirical knowledge about rotamers and how that knowledge is used in model validation. Part of this knowledge is rotamer-dependent bond angle deviations. I describe how the observation and quantitation of these deviations is used in a novel set of restraints in protein structure refinement. To provide structural context to rare rotamers, I describe where and why some occur.
My DNA work has focused on Hoogsteen base pairing. I describe a collaborative survey of existing Hoogsteen base pairs in the PDB. Lessons learned during the survey led to the other DNA topic, the detection and correction of mismodeled purines. I identified Hoogsteens in the PDB mismodeled as Watson-Crick base pairs. This work underscores that Hoogsteens are extremely rare but nonetheless do occur.
The fuzzy borderland between allowed and disallowed is a strange place filled with the most interesting structural features. My work here has focused on this area, bringing into view many rare conformations. Going forward we need to ensure that conformational frequency is taken into account during model building, refinement, and validation.