A restraint molecular dynamics and simulated annealing approach for protein homology modeling utilizing mean angles

BACKGROUND: We have developed the program PERMOL for semi-automated homology modeling of proteins. It is based on restrained molecular dynamics using a simulated annealing protocol in torsion angle space. As main restraints defining the optimal local geometry of the structure weighted mean dihedral angles and their standard deviations are used which are calculated with an algorithm described earlier by Döker et al. (1999, BBRC, 257, 348–350). The overall long-range contacts are established via a small number of distance restraints between atoms involved in hydrogen bonds and backbone atoms of conserved residues. Employing the restraints generated by PERMOL three-dimensional structures are obtained using standard molecular dynamics programs such as DYANA or CNS. RESULTS: To test this modeling approach it has been used for predicting the structure of the histidine-containing phosphocarrier protein HPr from E. coli and the structure of the human peroxisome proliferator activated receptor γ (Ppar γ). The divergence between the modeled HPr and the previously determined X-ray structure was comparable to the divergence between the X-ray structure and the published NMR structure. The modeled structure of Ppar γ was also very close to the previously solved X-ray structure with an RMSD of 0.262 nm for the backbone atoms. CONCLUSION: In summary, we present a new method for homology modeling capable of producing high-quality structure models. An advantage of the method is that it can be used in combination with incomplete NMR data to obtain reasonable structure models in accordance with the experimental data

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)

Structure and dynamics of Pseudomonas aeruginosa ICP

Pseudomonas aeruginosa inhibitor of cysteine peptidases (PA-ICP) is a potent protein inhibitor of papain-like cysteine peptidases (CPs) identified in Pseudomonas aeruginosa, an opportunistic pathogenic bacteria that can cause severe infections in human. It belongs to the newly characterized natural CP inhibitors of the I42 family, designated the ICP family. The members of this family are present in some protozoa and bacterial pathogens. They can inhibit both parasite and mammalian CPs with high affinity and specificity. Whether the main biological function of the proteins in the pathogens is to regulate the hydrolytic activity of the organisms’ endogenous CPs or exogenous CPs so as to facilitate the pathogens’ invasion or survival is still under investigation. Although Pseudomonas aeruginosa contains a CP inhibitor, no CP genes are found in its genome, suggesting that the targets of PA-ICP may be exogenous. This hypothesis is supported by the presence of a putative secretion signal peptide at the N-terminus of PA-ICP which may be involved in exporting the protein to target exogenous CPs. In order to shed light on the biological function and inhibitory specificity of PA-ICP, the structure and backbone dynamics of this protein were characterised using NMR spectroscopy. In this project, the inhibitory activity of PA-ICP to a range of mammalian model CPs was also studied. Like its previously studied homologs, PA-ICP adopts an immunoglobulin fold comprised of seven β-strands. Three highly conserved sequence motifs located in mobile loop regions form the CP binding site. The inhibitor exhibits higher affinity toward the mammalian CP cathepsin L than cathepsins H and B. Homology modelling of the PA-ICP-cathspin L interaction based on the crystal structure of the chgasin-cathpsin L complex shows that PA-ICP may inhibit the peptidases by blocking the enzyme’s active site and that the interactions between chagasin and CPs may be conserved in PA-ICP-peptidase complexes. The specificity of the inhibitors may be determined by the relative flexibility of the loops bearing the binding site motifs and the electrostatic properties of certain residues near the binding sites

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

Creation, refinement, and evaluation of conformational ensembles of proteins using the Torsional Network Model

Máster Universitario en Bioinformática y Biología ComputacionalOne of the main limitations of structural bioinformatics lies in the difficulty of properly accounting for the dynamical aspects of proteins, which are often critical to their functional mechanisms. Among the tools developed to deal with this issue, the Torsional Network Model (TNM) relies on internal degrees of freedom (torsion angles of the protein backbone), and can give a description of the thermal fluctuations of a protein structure, as well as generate structural ensembles. However, the TNM is a coarse-grained model that cannot ensure that the newly created conformations are exempt from any structural defects. Therefore, the main hypothesis of this project is that TNM assembly process can be improved. The ability to generate high-quality structural ensembles describing the dynamical properties of a protein would indeed be highly valuable in various applications. In this thesis, we create, evaluate and refine TNM ensembles from a set of reference protein structures defined experimentally (Levin et al., 2007). An approximation used in Bastolla and Dehouck, 2019, is developed: the evaluation is performed by Molprobity analysis, and the refinement is done by SIDEpro. Furthermore, a new approach is taken when refining the ensembles by Energy Minimization (EM). The results show a potential improvement of the TNM ensembles when adjusting the target RMSD to the protein studied; point to a enhancement when using side-chain reconstructions , and to its combination with Energy Minimization as a way to optimize the structure quality. On the other hand, the pros and cons of the followed methodology are discussed, because the use of the available static-protein oriented measures and methods makes specially important to beware of their limitations when applied to the protein-dynamic oriented TNM. Exploring further target RMSD values, adjusting them to specific protein dynamic simulations or replicating the same pipe-line in different data-sets are some of the proposals for future work. Furthermore, taking into account variables like the temperature, the flexibility of the protein, and the estimated optimal RMSD would be interesting for the next studies

Homology modeling in the time of collective and artificial intelligence

Homology modeling is a method for building protein 3D structures using protein primary sequence and utilizing prior knowledge gained from structural similarities with other proteins. The homology modeling process is done in sequential steps where sequence/structure alignment is optimized, then a backbone is built and later, side-chains are added. Once the low-homology loops are modeled, the whole 3D structure is optimized and validated. In the past three decades, a few collective and collaborative initiatives allowed for continuous progress in both homology and ab initio modeling. Critical Assessment of protein Structure Prediction (CASP) is a worldwide community experiment that has historically recorded the progress in this field. Folding@Home and Rosetta@Home are examples of crowd-sourcing initiatives where the community is sharing computational resources, whereas RosettaCommons is an example of an initiative where a community is sharing a codebase for the development of computational algorithms. Foldit is another initiative where participants compete with each other in a protein folding video game to predict 3D structure. In the past few years, contact maps deep machine learning was introduced to the 3D structure prediction process, adding more information and increasing the accuracy of models significantly. In this review, we will take the reader in a journey of exploration from the beginnings to the most recent turnabouts, which have revolutionized the field of homology modeling. Moreover, we discuss the new trends emerging in this rapidly growing field.O

The Solution Structure, Binding Properties, and Dynamics of the Bacterial Siderophore-binding Protein FepB

The periplasmic binding protein (PBP) FepB plays a key role in transporting the catecholate siderophore ferric enterobactin from the outer to the inner membrane in Gram-negative bacteria. The solution structures of the 34-kDa apo- and holo-FepB from Escherichia coli, solved by NMR, represent the first solution structures determined for the type III class of PBPs. Unlike type I and II PBPs, which undergo large "Venus flytrap" conformational changes upon ligand binding, both forms of FepB maintain similar overall folds; however, binding of the ligand is accompanied by significant loop movements. Reverse methyl cross-saturation experiments corroborated chemical shift perturbation results and uniquely defined the binding pocket for gallium enterobactin (GaEnt). NMR relaxation experiments indicated that a flexible loop (residues 225-250) adopted a more rigid and extended conformation upon ligand binding, which positioned residues for optimal interactions with the ligand and the cytoplasmic membrane ABC transporter (FepCD), respectively. In conclusion, this work highlights the pivotal role that structural dynamics plays in ligand binding and transporter interactions in type III PBPs

Size does not matter: a molecular insight into the biological activity of chemical fragments utilizing computational approaches.

Masters Degree. University of KwaZulu-Natal, Durban.Insight into the functional and physiological state of a drug target is of essential importance in the drug discovery process, with the lack of emerging (3D) drug targets we propose the integration of homology modeling which may aid in the accurate yet efficient construction of 3D protein structures. In this study we present the applications of homology modeling in drug discovery, a conclusive route map and detailed technical guideline that can be utilised to obtain the most accurate model. Even with the presence of available drug targets and substantial advancements being made in the field of drug discovery, the prevalence of incurable diseases still remains at an all-time high. In this study we explore the biological activity of chemically derived fragments from natural products utilising a range of computational approaches and implement its use in a new route towards innovative drug discovery. A potential avenue referred to as the reduce to maximum concept recently proposed by organic chemists, entails reducing the size of a chemical compound to obtain a structural analogs with retained or enhanced biological activity, better synthetic approachability and reduced toxicity. Displaying that size may not in fact matter. Molecular dynamic simulations along with toxicity profiling were comparatively performed, on natural compound Anguinomycin D and its derived analog SB 640 each in complex with the CRM1 protein which plays an avid role in cancer pathogenesis. Each system was post-dynamically studied to comprehend structural dynamics adopted by the parent compound to that exhibited by the analog. Although being reduced by 60% the analog SB 640 displayed an overall exhibition of attractive pharmacophore properties which include minimal reduction in binding affinity, enhanced synthetic approachability and reduced toxicity in comparison to the parent compound. Potent inhibitor of CRM1, Leptomycin B (LMB) displayed substantial inhibition of the CRM1 export protein by binding to four of the PKIαNES residues (ϕ0, ϕ1, ϕ2, ϕ3, and ϕ4) present within the hydrophobic binding groove of CRM1. Although being drastically reduced in size and lacking the presence of the polyketide chain present in the parent compound Anguinomycin D and LMB the analog SB 640 displaced three of these essential NES residues. The potential therapeutic activity of the structural analog remains undeniable, however the application of this approach in drug design still remains ambiguous as to which chemical fragments must be retained or truncated to ensure retention or enhanced pharmacophore properties. In this study we aimed to the use of thermodynamic calculations, which was accomplished by incorporating a MM/GBSA per-residue energy contribution footprint from molecular dynamics simulation. The proposed approach was generated for each system. Anguinomycin D and analog SB 640 each in complex with CRM1 protein, each system formed interactions with the conserved active site residues Leu 536, Thr 575, Val 576 and Lys 579. These residues were highlighted as the most energetically favourable amino acid residues contributing substantially to the total binding free energy. Thus implying a conserved selectivity and binding mode adopted by both compounds despite the omission of the prominent polyketide chain in the analog SB 640, present in the parent compound. A strategic computational approach presented in this study could serve as a beneficial tool to enhance novel drug discovery. This entire work provides an invaluable contribution to the understanding of the phenomena underlying the reduction in the size of a chemical compound to obtain the most beneficial pharmacokinetic properties and could largely contribute to the design of potent analog inhibitors for a range of drug targets implicated in the orchestration of diseases