Improving Structural Features Prediction in Protein Structure Modeling

Proteins play a vital role in the biological activities of all living species. In nature, a protein folds into a specific and energetically favorable three-dimensional structure which is critical to its biological function. Hence, there has been a great effort by researchers in both experimentally determining and computationally predicting the structures of proteins. The current experimental methods of protein structure determination are complicated, time-consuming, and expensive. On the other hand, the sequencing of proteins is fast, simple, and relatively less expensive. Thus, the gap between the number of known sequences and the determined structures is growing, and is expected to keep expanding. In contrast, computational approaches that can generate three-dimensional protein models with high resolution are attractive, due to their broad economic and scientific impacts. Accurately predicting protein structural features, such as secondary structures, disulfide bonds, and solvent accessibility is a critical intermediate step stone to obtain correct three-dimensional models ultimately. In this dissertation, we report a set of approaches for improving the accuracy of structural features prediction in protein structure modeling. First of all, we derive a statistical model to generate context-based scores characterizing the favorability of segments of residues in adopting certain structural features. Then, together with other information such as evolutionary and sequence information, we incorporate the context-based scores in machine learning approaches to predict secondary structures, disulfide bonds, and solvent accessibility. Furthermore, we take advantage of the emerging high performance computing architectures in GPU to accelerate the calculation of pairwise and high-order interactions in context-based scores. Finally, we make these prediction methods available to the public via web services and software packages

Structural investigation of the Bacillus subtilis morphogenic factor RodZ

A thesis to obtain a Master degree in Structural and Functional BiochemistryRodZ is a protein widely conserved in bacteria and a core component of the morphogenic apparatus of the cell. It is known to be required for assembly of the bacterial actin homologue, MreB, that controls cell wall synthesis and cell shape. The domain organization of RodZ consists of a well-conserved N-terminal (RodZn) with helix-turn-helix motif (HTH), a conserved transmembrane domain, and a conserved C-terminal domain (RodZc). RodZn, located in the cytoplasm, has been shown to interact with MreB actin-homologue by x-ray studies in T. maritima. However, the structure of RodZn from gram-positive B. subtilis showed low homology with the published one from gram-negative T. maritima. Here we present the solution structure of RodZn from B. subtilis determined for the first time, by NMR spectroscopy. Compared to previous structural data obtained from the crystallized RodZn from T. maritima and more recently from S. aureus, several differences could be observed, namely the length of the alpha-helices and the presence of an extended coil. Interaction studies were preformed between RodZn domain and MreB from which no significant results could be extrapolated. Since HTH motif is frequently associated with DNA interaction, the involvement of RodZn in DNA organization is being investigated. At the same time, RodZc domain, which structure has never been reported, was subject of study. Bioinformatic, biophysical and biochemical methodologies were employed to study this domain. A model based in a pseudo-ab initio methodology was built, revealing an Ig-like fold. The Ig superfamily is a large group of cell surface and soluble proteins that are involved in the recognition, binding, or adhesion processes of cells. Therefore, RodZ is thought to be a protein that establishes a link between the inner side of the cell membrane and the outer side, promoting spatiotemporal coordination between peptidoglycan synthesis and cell division

Mass & secondary structure propensity of amino acids explain their mutability and evolutionary replacements

Why is an amino acid replacement in a protein accepted during evolution? The answer given by bioinformatics relies on the frequency of change of each amino acid by another one and the propensity of each to remain unchanged. We propose that these replacement rules are recoverable from the secondary structural trends of amino acids. A distance measure between high-resolution Ramachandran distributions reveals that structurally similar residues coincide with those found in substitution matrices such as BLOSUM: Asn Asp, Phe Tyr, Lys Arg, Gln Glu, Ile Val, Met → Leu; with Ala, Cys, His, Gly, Ser, Pro, and Thr, as structurally idiosyncratic residues. We also found a high average correlation (\overline{R} R = 0.85) between thirty amino acid mutability scales and the mutational inertia (I X ), which measures the energetic cost weighted by the number of observations at the most probable amino acid conformation. These results indicate that amino acid substitutions follow two optimally-efficient principles: (a) amino acids interchangeability privileges their secondary structural similarity, and (b) the amino acid mutability depends directly on its biosynthetic energy cost, and inversely with its frequency. These two principles are the underlying rules governing the observed amino acid substitutions. © 2017 The Author(s)

구형성과 뒤틀림각에 기반한 단백질 구조 방법론 개발

학위논문 (박사)-- 서울대학교 대학원 : 협동과정 생물정보학전공, 2013. 2. 손현석.The structure of protein has intimate relationship with the function of protein. The structure of protein is experimentally determined through X-ray crystallography and NMR methods. However, X-ray crystallography is hard to obtain mobile protein structure and crystallization often causes practical problems. NMR structure is impossible in the observation of membranous or large proteins. Thus, theoretical methods for the determination of protein structures are highly concerned to circumvent practical problems. Homology, threading and ab initio modeling are the three typical approaches in protein structure modeling. ab initio modeling is often called as protein folding problem. The natural stable state of protein structure is believed to be the minimal energy state. The critical problem of protein folding research is the impossibility of the exhaustive search of possible conformations. Globularity of the protein structure was assessed in the pursuit of the universal structural constraint while approximated measurement name Gb-index was developed. Strong perfect globe-like character and the relationship between small size and the loss of globular structure was found among 7131 proteins which implies that living organisms have mechanisms to aid folding into the globular structure to reduce irreversible aggregation. This also implies the possible mechanisms of diseases caused by protein aggregation, including some forms of trinucleotide repeat expansion-mediated diseases. Torsion angle constraint mimics natural process of conformational change of proteins which lacks significant movement along covalent bonds and change in bond angles. This torsion angle system was applied to structure alignment to prove the validity as a structural representation. It was more effective to accurately anticipate homology among 1891 pairs of proteins of 62 different proteases and among 1770 pairs of 60 proteins of kinases and proteases with the string of φ and ψ dihedral angle array than famous 3D structural alignment tool TM-align. Secondary structure database and structure alignment web server was constructed from PDB and SCOP entries based on the simple classification scheme according to the backbone torsion angles. The database introduced here offers functions of secondary database searching, secondary structure calculation, and pair-wise protein structure comparison. Visualization during the process of the protein folding simulation is quite interesting regarding the fast apprehension of the states while previous algorithms such as molecular dynamics offers very few options of interference. Computational application named ProtTorter which visualizes three-dimensional conformation, calculates the potential energy, and supplies the user interface for backbone torsion angle manipulation was developed. Using this application, simple folding algorithm was newly investigated. Cotranslational and torsional folding path was utilized in the context of Levinthal paradox. The validity of the folding method was investigated using the test sets of small peptides. Positive result for the possibility of this method was obtained as the stable negative energy minimal structures and fast convergence. Application of torsional system of which validity was proved in the structure alignment assays and globular constraints which might infer solvent interactions by minimizing solvent accessible surface area might be worth for further studies based on the folding algorithm using ProtTorter application.1 Introduction 1 1.1 Background of Protein Research 1 1.1.1 The Function and Structure of Protein 2 1.1.2 Protein Secondary Structure 3 1.1.3 Torsion Angle 4 1.1.4 Hydrophobic Effect 5 1.2 Experimental Structure Determination Methods 6 1.2.1 X-ray Crystallography 6 1.2.2 NMR Spectroscopy 6 1.2.3 Limitations of Experimental Methods 7 1.3 Protein Structure Prediction Methods 8 1.3.1 Homology or Comparative Modeling Method 9 1.3.2 Threading Method 10 1.3.3 ab initio Method 12 1.3.3.1 Molecular Dynamics Simulation Method 13 1.3.3.2 Levinthal Paradox 15 1.3.3.3 Lattice Model 15 1.3.3.4 Monte Carlo Method 17 1.3.4 Competition of Protein Structure Prediction Methods: CASP 19 1.4 Studies and Concerns of the Protein Folding Research 20 2 Analysis of Globular Nature of Proteins 24 2.1 Introduction 24 2.2 Materials and Methods 26 2.2.1 Data Sets 26 2.2.2 Globularity Measurement 27 2.3 Results and Discussion 28 2.4 Conclusion 32 3 Validity of Protein Structure Alignment Based on Backbone Torsion Angles 39 3.1 Introduction 39 3.2 Materials and Methods 43 3.2.1 Definition of φ and ψ Angles 43 3.2.2 Ramachandran Plot RMSD (RamRMSD) 44 3.2.3 Statistical Similarity Measurement with Weight Imposition 45 3.2.4 Alignment Algorithm 46 3.2.5 Parameter Settings for Alignments and Clustering 47 3.2.6 Performance-evaluating Quantities 48 3.2.7 Test Set Preparation 49 3.3 Results and Discussion 50 3.3.1 Sequence and Structure Trees of Different Groups of Proteases 50 3.3.2 Comparison of Backbone Torsion Angle-based Method and TM-align 52 3.3.3 Clustring Trees and Accuracy Analysis with Delineation Set of 30 Kinases and 30 Proteases 55 3.3.4 Computational Time and Complexity 58 3.4 Conclusion 59 4 Secondary Structure Information Repository from Backbone Torsion Angle 67 4.1 Introduction 67 4.2 Materials and Methods 72 4.3 Results 72 4.3.1 User Interface and Architecture 72 4.3.2 Computational Mechanisms 75 4.4 Discussion 79 5 Computational Application for Protein Folding Modeling Based on Backbone Torsion Angle and for Protein Structure Viewing 86 5.1 Introduction 86 5.2 Materials and Methods 90 5.2.1 Computational Framework 90 5.2.2 Model Energy Calculation 90 5.3 Results 93 5.3.1 User Interface 93 5.3.2 Protein Structure File Import 96 5.3.3 Protein Structure File Export 96 5.3.4 Parsing and Initialization of Structure File 96 5.3.5 Structural Representation 98 5.3.6 Modifying Graphical Representation of Structure 99 5.3.7 Protein Model Building 101 5.3.8 Model Modification 103 5.3.9 Model Energy Calculation 104 5.3.10 Local Energy Minima Calculation and Cotranslational Folding 107 5.4 Discussion 107 6 Protein Folding of Cotranslational Initial Structure with Torsional Levinthal Path 114 6.1 Introduction 114 6.2 Materials and Methods 120 6.2.1 Dataset 120 6.2.2 Cotranslational Folding of Initial Structure 121 6.2.3 Iterative Optimization of Initial Structure Following Torsional Folding Path 122 6.3 Results and Discussion 123 6.4 Conclusion 128 7 Summary 137Docto

The aqueous environment as an active participant in the protein folding process

Existing computational models applied in the protein structure prediction process do not sufficiently account for the presence of the aqueous solvent. The solvent is usually represented by a predetermined number of H2O molecules in the bounding box which contains the target chain. The fuzzy oil drop (FOD) model, presented in this paper, follows an alternative approach, with the solvent assuming the form of a continuous external hydrophobic force field, with a Gaussian distribution. The effect of this force field is to guide hydrophobic residues towards the center of the protein body, while promoting exposure of hydrophilic residues on its surface. This work focuses on the following sample proteins: Engrailed homeodomain (RCSB: 1 enh), Chicken villin subdomain hp-35, n68h (RCSB: 1yrf), Chicken villin sub-domain hp-35, k65(nle), n68h, k70(nle) (RCSB: 2f4k), Thermostable subdomain from chicken villin headpiece (RCSB: 1vii), de novo designed single chain three-helix bundle (a3d) (RCSB: 2a3d), albumin-binding domain (RCSB: 1prb) and lambda repressor-operator complex (RCSB: 1lmb). (C) 2018 The Authors. Published by Elsevier Inc

The aqueous environment as an active participant in the protein folding process

© 2018 The Authors Existing computational models applied in the protein structure prediction process do not sufficiently account for the presence of the aqueous solvent. The solvent is usually represented by a predetermined number of H2O molecules in the bounding box which contains the target chain. The fuzzy oil drop (FOD) model, presented in this paper, follows an alternative approach, with the solvent assuming the form of a continuous external hydrophobic force field, with a Gaussian distribution. The effect of this force field is to guide hydrophobic residues towards the center of the protein body, while promoting exposure of hydrophilic residues on its surface. This work focuses on the following sample proteins: Engrailed homeodomain (RCSB: 1enh), Chicken villin subdomain hp-35, n68h (RCSB: 1yrf), Chicken villin subdomain hp-35, k65(nle), n68h, k70(nle) (RCSB: 2f4k), Thermostable subdomain from chicken villin headpiece (RCSB: 1vii), de novo designed single chain three-helix bundle (a3d) (RCSB: 2a3d), albumin-binding domain (RCSB: 1prb) and lambda repressor-operator complex (RCSB: 1lmb)

In Silico Optimisation Of Domain Antibodies Against HSP16.3 From Mycobacterium Tuberculosis

Heat shock protein 16.3 (HSP16.3) from Mycobacterium tuberculosis (Mtb) is critical for its survival during latent infection in human, thus making it an attractive target for developing diagnostic and therapeutic strategies. The predicted structure of HSP16.3 was docked against a known HSP hydrophobic probe, namely 4,4′-dianilino-1,1′-binaphthyl-5,5′-disulfonic acid (bisANS) and to the comparative models of HSP16.3 specific single domain antibodies (sdAbs), clone E3 and F1. The binding interactions were further elucidated by free energy calculations. The non-polar interactions were identified as the main force for antigen-antibody association

Methods for the refinement of protein structure 3D models

The refinement of predicted 3D protein models is crucial in bringing them closer towards experimental accuracy for further computational studies. Refinement approaches can be divided into two main stages: The sampling and scoring stages. Sampling strategies, such as the popular Molecular Dynamics (MD)-based protocols, aim to generate improved 3D models. However, generating 3D models that are closer to the native structure than the initial model remains challenging, as structural deviations from the native basin can be encountered due to force-field inaccuracies. Therefore, different restraint strategies have been applied in order to avoid deviations away from the native structure. For example, the accurate prediction of local errors and/or contacts in the initial models can be used to guide restraints. MD-based protocols, using physics-based force fields and smart restraints, have made significant progress towards a more consistent refinement of 3D models. The scoring stage, including energy functions and Model Quality Assessment Programs (MQAPs) are also used to discriminate near-native conformations from non-native conformations. Nevertheless, there are often very small differences among generated 3D models in refinement pipelines, which makes model discrimination and selection problematic. For this reason, the identification of the most native-like conformations remains a major challenge

Protein Structure Prediction

Práce popisuje prostorovou strukturu molekul bílkovin a databází uchovávajících representace této struktury, či její hierarchické klasifikace. Je poskytnut přehled současných metod výpočetní predikce struktury bílkovin, přičemž největší pozornost je soustředěna na komparativní modelování. Tato metoda je rovněž v základní podobě implementována a na závěr její implementace analyzována.This work describes the three dimensional structure of protein molecules and biological databases used to store information about this structure or its hierarchical classification. Current methods of computational structure prediction are overviewed with an emphasis on comparative modeling. This particular method is also implemented in a proof-of-concept program and finally, the implementation is analysed.