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)

New Methods to Study Proline-Rich Disordered Regions and Their Structural Ensembles in Protein Signaling Pathways

Ph.DDOCTOR OF PHILOSOPH

Protein Structure

Since the dawn of recorded history, and probably even before, men and women have been grasping at the mechanisms by which they themselves exist. Only relatively recently, did this grasp yield anything of substance, and only within the last several decades did the proteins play a pivotal role in this existence. In this expose on the topic of protein structure some of the current issues in this scientific field are discussed. The aim is that a non-expert can gain some appreciation for the intricacies involved, and in the current state of affairs. The expert meanwhile, we hope, can gain a deeper understanding of the topic

Bionano-Interfaces through Peptide Design

The clinical success of restoring bone and tooth function through implants critically depends on the maintenance of an infection-free, integrated interface between the host tissue and the biomaterial surface. The surgical site infections, which are the infections within one year of surgery, occur in approximately 160,000-300,000 cases in the US annually. Antibiotics are the conventional treatment for the prevention of infections. They are becoming ineffective due to bacterial antibiotic-resistance from their wide-spread use. There is an urgent need both to combat bacterial drug resistance through new antimicrobial agents and to limit the spread of drug resistance by limiting their delivery to the implant site. This work aims to reduce surgical site infections from implants by designing of chimeric antimicrobial peptides to integrate a novel and effective delivery method. In recent years, antimicrobial peptides (AMPs) have attracted interest as natural sources for new antimicrobial agents. By being part of the immune system in all life forms, they are examples of antibacterial agents with successfully maintained efficacy across evolutionary time. Both natural and synthetic AMPs show significant promise for solving the antibiotic resistance problems. In this work, AMP1 and AMP2 was shown to be active against three different strains of pathogens in Chapter 4. In the literature, these peptides have been shown to be effective against multi-drug resistant bacteria. However, their effective delivery to the implantation site limits their clinical use. In recent years, different groups adapted covalent chemistry-based or non-specific physical adsorption methods for antimicrobial peptide coatings on implant surfaces. Many of these procedures use harsh chemical conditions requiring multiple reaction steps. Furthermore, none of these methods allow the orientation control of these molecules on the surfaces, which is an essential consideration for biomolecules. In the last few decades, solid binding peptides attracted high interest due to their material specificity and self-assembly properties. These peptides offer robust surface adsorption and assembly in diverse applications. In this work, a design method for chimeric antimicrobial peptides that can self-assemble and self-orient onto biomaterial surfaces was demonstrated. Three specific aims used to address this two-fold strategy of self-assembly and self-orientation are: 1) Develop classification and design methods using rough set theory and genetic algorithm search to customize antibacterial peptides; 2) Develop chimeric peptides by designing spacer sequences to improve the activity of antimicrobial peptides on titanium surfaces; 3) Verify the approach as an enabling technology by expanding the chimeric design approach to other biomaterials. In Aim 1, a peptide classification tool was developed because the selection of an antimicrobial peptide for an application was difficult among the thousands of peptide sequences available. A rule-based rough-set theory classification algorithm was developed to group antimicrobial peptides by chemical properties. This work is the first time that rough set theory has been applied to peptide activity analysis. The classification method on benchmark data sets resulted in low false discovery rates. The novel rough set theory method was combined with a novel genetic algorithm search, resulting in a method for customizing active antibacterial peptides using sequence-based relationships. Inspired by the fact that spacer sequences play critical roles between functional protein domains, in Aim 2, chimeric peptides were designed to combine solid binding functionality with antimicrobial functionality. To improve how these functions worked together in the same peptide sequence, new spacer sequences were engineered. The rough set theory method from Aim 1 was used to find structure-based relationships to discover new spacer sequences which improved the antimicrobial activity of the chimeric peptides. In Aim 3, the proposed approach is demonstrated as an enabling technology. In this work, calcium phosphate was tested and verified the modularity of the chimeric antimicrobial self-assembling peptide approach. Other chimeric peptides were designed for common biomaterials zirconia and urethane polymer. Finally, an antimicrobial peptide was engineered for a dental adhesive system toward applying spacer design concepts to optimize the antimicrobial activity

Intrinsically Disordered Proteins and Chronic Diseases

This book is an embodiment of a series of articles that were published as part of a Special Issue of Biomolecules. It is dedicated to exploring the role of intrinsically disordered proteins (IDPs) in various chronic diseases. The main goal of the articles is to describe recent progress in elucidating the mechanisms by which IDPs cause various human diseases, such as cancer, cardiovascular disease, amyloidosis, neurodegenerative diseases, diabetes, and genetic diseases, to name a few. Contributed by leading investigators in the field, this compendium serves as a valuable resource for researchers, clinicians as well as postdoctoral fellows and graduate student

Extending the Boundaries of the Usage of NMR Chemical Shifts in Deciphering Biomolecular Structure and Dynamics

NMR chemical shifts have an extremely high information content on the behaviour of macromolecules, owing to their non-trivial dependence on myriads of structural and environmental factors. Although such complex dependence creates an initial barrier for their use for the characterisation of the structures of protein and nucleic acids, recent developments in prediction methodologies and their successful implementation in resolving the structures of these molecules have clearly demonstrated that such barrier can be crossed. Furthermore, the significance of chemical shifts as useful observables in their own right has been substantially increased since the development of the NMR techniques to study low populated "excited" states of biomolecules. This work is aimed at increasing our understanding of the multiple factors that affect chemical shifts in proteins and nucleic acids, and at developing high-quality chemical shift predictors for atom types that so far have largely escaped the attention in chemical shift restrained molecular dynamics simulations. A general approach is developed to optimise the models for structure-based chemical shift prediction, which is then used to construct CH3Shift and ArShift chemical shift predictors for the nuclei of protein side-chain methyl and aromatic moieties. These results have the potential of making a significant impact in structural biology, in particular when taking into account the advent of recent techniques for specific isotope labelling of protein side-chain atoms, which make large biomolecules accessible to NMR techniques. Through their incorporation as restraints in molecular dynamics simulations, the chemical shifts predicted by the approach described in this work create the opportunity of studying the structure and dynamics of proteins in a wide range of native and non-native states in order to characterise the mechanisms underlying the function and dysfunction of these molecules.This work was supported by Herchel Smith Fun

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

Program and Proceedings: The Nebraska Academy of Sciences 1880-2012

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Sequence Determinants of the Individual and Collective Behaviour of Intrinsically Disordered Proteins

Intrinsically disordered proteins and protein regions (IDPs) represent around thirty percent of the eukaryotic proteome. IDPs do not fold into a set three dimensional structure, but instead exist in an ensemble of inter-converting states. Despite being disordered, IDPs are decidedly not random; well-defined - albeit transient - local and long-range interactions give rise to an ensemble with distinct statistical biases over many length-scales. Among a variety of cellular roles, IDPs drive and modulate the formation of phase separated intracellular condensates, non-stoichiometric assemblies of protein and nucleic acid that serve many functions. In this work, we have explored how the amino acid sequence of IDPs determines their conformational behaviour, and how sequence and single chain behaviour influence their collective behaviour in the context of phase separation. In part I, in a series of studies, we used simulation, theory, and statistical analysis coupled with a wide range of experimental approaches to uncover novel rules that further explore how primary sequence and local structure influence the global and local behaviour of disordered proteins, with direct implications for protein function and evolution. We found that amino acid sidechains counteract the intrinsic collapse of the peptide backbone, priming the backbone for interaction and providing a fully reconciliatory explanation for the mechanism of action associated with the denaturants urea and GdmCl. We discovered that proline can engender a conformational buffering effect in IDPs to counteract standard electrostatic effects, and that the patterning those proline residues can be a crucial determinant of the conformational ensemble. We developed a series of tools for analysing primary sequences on a proteome wide scale and used them to discover that different organisms can have substantially different average sequence properties. Finally, we determined that for the normally folded protein NTL9, the unfolded state under folding conditions is relatively expanded but has well defined native and non-native structural preferences. In part II, we identified a novel mode of phase separation in biology, and explored how this could be tuned through sequence design. We discovered that phase separated liquids can be many orders of magnitude more dilute than simple mean-field theories would predict, and developed an analytic framework to explain and understand this phenomenon. Finally, we designed, developed and implemented a novel lattice-based simulation engine (PIMMS) to provide sequence-specific insight into the determinants of conformational behaviour and phase separation. PIMMS allows us to accurately and rapidly generate sequence-specific conformational ensembles and run simulations of hundreds of polymers with the goal of allowing us to systematically elucidate the link between primary sequence of phase separation