Complete Configuration Space Analysis for Structure Determination of Symmetric Homo-oligomers by NMR

Symmetric homo-oligomers (protein complexes with similar subunits arranged symmetrically) play pivotal roles in complex biological processes such as ion transport and cellular regulation. Structure determination of these complexes is necessary in order to gain valuable insights into their mechanisms. Nuclear Magnetic Resonance (NMR) spectroscopy is an experimental technique used for structural studies of such complexes. The data available for structure determination of symmetric homo-oligomers by NMR is often sparse and ambiguous in nature, raising concerns about existing heuristic approaches for structure determination. We have developed an approach that is complete in that it identifies all consistent conformations, data-driven in that it separately evaluates the consistency of structures to data and biophysical constraints and efficient in that it avoids explicit consideration of each of the possible structures separately. By being complete, we ensure that native conformations are not missed. By being data-driven, we are able to separately quantify the information content in the data alone versus data and biophysical modeling. We take a configuration space (degree-of-freedom) approach that provides a compact representation of the conformation space and enables us to efficiently explore the space of possible conformations. This thesis demonstrates that the configuration space-based method is robust to sparsity and ambiguity in the data and enables complete, data-driven and efficient structure determination of symmetric homo-oligomers

Geometrical and probabilistic methods for determining association models and structures of protein complexes

Protein complexes play vital roles in cellular processes within living organisms. They are formed by interactions between either different proteins (hetero-oligomers) or identical proteins (homo-oligomers). In order to understand the functions of the complexes, it is important to know the manner in which they are assembled from the component subunits and their three dimensional structure. This thesis addresses both of these questions by developing geometrical and probabilistic methods for analyzing data from two complementary experiment types: Small Angle Scattering (SAS) and Nuclear Magnetic Resonance (NMR) spectroscopy. Data from an SAS experiment is a set of scattering intensities that can give the interatomic probability distributions. NMR experimental data used in this thesis is set of atom pairs and the maximum distance between them. From SAS data, this thesis determines the association model of the complex and intensities through an approach that is robust to noise and contaminants in solution. Using NMR data, this thesis computes the complex structure by using probabilistic inference and geometry of convex shapes. The structure determination methods are complete, that is they identify all consistent conformations and are data driven wherein the structures are evaluated separately for consistency to data and biophysical energy

The RCSB Protein Data Bank: views of structural biology for basic and applied research and education.

The RCSB Protein Data Bank (RCSB PDB, http://www.rcsb.org) provides access to 3D structures of biological macromolecules and is one of the leading resources in biology and biomedicine worldwide. Our efforts over the past 2 years focused on enabling a deeper understanding of structural biology and providing new structural views of biology that support both basic and applied research and education. Herein, we describe recently introduced data annotations including integration with external biological resources, such as gene and drug databases, new visualization tools and improved support for the mobile web. We also describe access to data files, web services and open access software components to enable software developers to more effectively mine the PDB archive and related annotations. Our efforts are aimed at expanding the role of 3D structure in understanding biology and medicine

BioSuper: A web tool for the superimposition of biomolecules and assemblies with rotational symmetry

Background Most of the proteins in the Protein Data Bank (PDB) are oligomeric complexes consisting of two or more subunits that associate by rotational or helical symmetries. Despite the myriad of superimposition tools in the literature, we could not find any able to account for rotational symmetry and display the graphical results in the web browser. Results BioSuper is a free web server that superimposes and calculates the root mean square deviation (RMSD) of protein complexes displaying rotational symmetry. To the best of our knowledge, BioSuper is the first tool of its kind that provides immediate interactive visualization of the graphical results in the browser, biomolecule generator capabilities, different levels of atom selection, sequence-dependent and structure-based superimposition types, and is the only web tool that takes into account the equivalence of atoms in side chains displaying symmetry ambiguity. BioSuper uses ICM program functionality as a core for the superimpositions and displays the results as text, HTML tables and 3D interactive molecular objects that can be visualized in the browser or in Android and iOS platforms with a free plugin. Conclusions BioSuper is a fast and functional tool that allows for pairwise superimposition of proteins and assemblies displaying rotational symmetry. The web server was created after our own frustration when attempting to superimpose flexible oligomers. We strongly believe that its user-friendly and functional design will be of great interest for structural and computational biologists who need to superimpose oligomeric proteins (or any protein). BioSuper web server is freely available to all users at http://ablab.ucsd.edu/BioSuper webcite

Navigating the Extremes of Biological Datasets for Reliable Structural Inference and Design

Structural biologists currently confront serious challenges in the effective interpretation of experimental data due to two contradictory situations: a severe lack of structural data for certain classes of proteins, and an incredible abundance of data for other classes. The challenge with small data sets is how to extract sufficient information to draw meaningful conclusions, while the challenge with large data sets is how to curate, categorize, and search the data to allow for its meaningful interpretation and application to scientific problems. Here, we develop computational strategies to address both sparse and abundant data sets. In the category of sparse data sets, we focus our attention on the problem of transmembrane (TM) protein structure determination. As X-ray crystallography and NMR data is notoriously difficult to obtain for TM proteins, we develop a novel algorithm which uses low-resolution data from protein cross-linking or scanning mutagenesis studies to produce models of TM helix oligomers and show that our method produces models with an accuracy on par with X-ray crystallography or NMR for a test set of known TM proteins. Turning to instances of data abundance, we examine how to mine the vast stores of protein structural data in the Protein Data Bank (PDB) to aid in the design of proteins with novel binding properties. We show how the identification of an anion binding motif in an antibody structure allowed us to develop a phosphate binding module that can be used to produce novel antibodies to phosphorylated peptides - creating antibodies to 7 novel phospho-peptides to illustrate the utility of our approach. We then describe a general strategy for designing binders to a target protein epitope based upon recapitulating protein interaction geometries which are over-represented in the PDB. We follow this by using data describing the transition probabilities of amino acids to develop a novel set of degenerate codons to create more efficient gene libraries. We conclude by describing a novel, real-time, all-atom structural search engine, giving researchers the ability to quickly search known protein structures for a motif of interest and providing a new interactive paradigm of protein design

주형 기반 도킹과 Ab Initio 도킹을 이용한 단백질 복합체 구조 예측

학위논문(박사) -- 서울대학교대학원 : 자연과학대학 화학부, 2021.8. 석차옥.Protein-protein interactions play crucial roles in diverse biological processes, including various disease progressions. Atomistic structural details of protein-protein interactions that can be obtained from protein complex structures may provide vital information for the design of therapeutic agents. However, a large portion of protein complex structures is hard to be experimentally captured due to their weak and transient protein-protein interactions. Indeed, a limited fraction of protein-protein interactions happening in the human body has been experimentally determined. Computational protein complex structure prediction methods have been spotlighted for their roles in providing insights into protein-protein interactions in the absence of complete structural information by experiment. In this dissertation, three protein complex structure prediction methods are explained: GalaxyTongDock, GalaxyHeteromer, and GalaxyHomomer2. GalaxyTongDock performs ab initio docking for structure prediction of hetero- and homo-oligomers. GalaxyHeteromer and GalaxyHomomer2 predict heterodimer and homo-oligomer structures, respectively, by template-based docking and ab initio docking depending on the template's availability. Lastly, examples of how these methods were utilized to predict protein complex structures in CASP and CAPRI, community-wide prediction experiments, are presented.단백질 사이의 상호작용은 세포분열, 항상성 유지, 면역반응, 질병의 발생 등 많은 생물학적 과정에서 핵심적인 역할을 한다. 단백질 복합체 구조로부터 얻을 수 있는 단백질 상호작용에 대한 구조적 이해는 효과적인 항체 신약, 단백질 상호작용 저해제 등의 약물 설계를 위해 필수적인 요소이다. 그러나 단백질 복합체는 대체로 약한 상호작용에 의해 일시적으로 형성되어 실험을 통해 결정하기가 어렵다. 실제로 우리 몸에서 일어나는 수많은 단백질 상호작용 중 극히 일부에 대해서만 복합체 구조가 알려져 있다. 컴퓨터를 이용한 단백질 복합체 구조 예측 방법은 실험에 의해 결정된 단백질 복합체 구조가 없는 경우에 단백질 상호작용에 대한 정보를 제공하는 중요한 역할을 해왔다. 이 논문에서는 단백질 복합체 구조 예측 방법인 GalaxyTongDock과 GalaxyHomomer2, GalaxyHeteromer에 대해서 소개한다. GalaxyTongDock은 ab initio 도킹을 통해 동종 올리고머 단백질과 이종 올리고머 단백질의 구조를 예측한다. GalaxyHomomer2와 GalaxyHeteromer는 각각 동종 올리고머 단백질과 이종 올리고머 단백질의 구조를 주형 기반 도킹과 ab initio 도킹을 모두 이용하여 예측한다. 마지막으로, 이 방법들이 국제 단백질 구조 및 복합체 구조 예측 대회인 CASP과 CAPRI에서 단백질 복합체 구조를 예측하기 위해 어떻게 활용되었는지 몇 가지 예시를 통해 소개한다.1. Introduction 1 2. GalaxyTongDock 4 2.1. Methods 4 2.2. Performance of GalaxyTongDock 21 3. GalaxyHeteromer 27 3.1. Methods 27 3.2. Performance of GalaxyHeteromer 34 4. GalaxyHomomer2 40 4.1. Methods 41 4.2. Performance of GalaxyHomomer2 47 5. CASP and CAPRI 54 5.1. CASP13 54 5.2. CASP14 57 5.3. CAPRI 64 6. Conclusion 65 7. References 67 국문초록 71 감사의 글 73박

Unique opportunities for NMR methods in structural genomics

This Perspective, arising from a workshop held in July 2008 in Buffalo NY, provides an overview of the role NMR has played in the United States Protein Structure Initiative (PSI), and a vision of how NMR will contribute to the forthcoming PSI-Biology program. NMR has contributed in key ways to structure production by the PSI, and new methods have been developed which are impacting the broader protein NMR community

Unique opportunities for NMR methods in structural genomics

This Perspective, arising from a workshop held in July 2008 in Buffalo NY, provides an overview of the role NMR has played in the United States Protein Structure Initiative (PSI), and a vision of how NMR will contribute to the forthcoming PSI-Biology program. NMR has contributed in key ways to structure production by the PSI, and new methods have been developed which are impacting the broader protein NMR community

Analysis of biostructural changes, dynamics, and interactions - Small-angle X-ray scattering to the rescue

AbstractSolution small angle X-ray scattering from biological macromolecules (BioSAXS) plays an increasingly important role in biostructural research. The analysis of complex protein mixtures, dynamic equilibriums, intrinsic disorder and evolving structural processes is facilitated by SAXS data, either in stand-alone applications, or with SAXS taking a prominent role in hybrid biostructural analysis. This is not the least due to the significant advances in both hardware and software that have taken place in particular at the large-scale facilities. Here, recent developments and the future potential of BioSAXS are reviewed, exemplified by numerous examples of elegant applications to challenging systems