[PDF] Computational Design Of Protein Ligand Interfaces Using Rosettaligand eBook

Author : Barry L. Stoddard
Publisher :
Page : 375 pages
File Size : 40,44 MB
Release : 2016
Category : Carrier proteins
ISBN : 9781493935697

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This volume provides a collection of protocols and approaches for the creation of novel ligand binding proteins, compiled and described by many of today's leaders in the field of protein engineering. Chapters focus on modeling protein ligand binding sites, accurate modeling of protein-ligand conformational sampling, scoring of individual docked solutions, structure-based design program such as ROSETTA, protein engineering, and additional methodological approaches. Examples of applications include the design of metal-binding proteins and light-induced ligand binding proteins, the creation of binding proteins that also display catalytic activity, and the binding of larger peptide, protein, DNA and RNA ligands. Written in the highly successful Methods in Molecular Biology series format, chapters include introductions to their respective topics, lists of the necessary materials and reagents, step-by-step, readily reproducible laboratory protocols, and tips on troubleshooting and avoiding known pitfalls.

Author : Shahir Samir Rizk
Publisher :
Page : 105 pages
File Size : 32,23 MB
Release : 2006
Category : Protein engineering
ISBN : 9780549088127

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This work describes the application of engineering protein-ligand interactions to the design of biosensors and multisensors. Structure-based computational design was used to engineer a zinc binding site in the enzyme ATPase. As a result, zinc acts as an allosteric regulator of the enzymatic activity. Computational design was further applied to the redesign of the binding specificity of glucose- and ribose binding proteins to bind pinacolymethylphosphonic acid (PMPA), a degradation product of the nerve agent soman. The computationally redesigned binding proteins were labeled with a thiolreactive fluorophore at a unique cysteine position and as a result, a change in fluorescence is exhibited by the protein-fluorophore conjugate in response to ligand binding. The results demonstrate that the engineered proteins act as reagentless fluorescent biosensors for PMPA and exhibit a range of affinities between 0.045 and 10 muM. Protein engineering techniques were used to extent the ability of a single biosensor element to distinguish between several similar target ligands by incorporating many sensor elements in a multisensor system. The protein PhnD, a periplasmic binding protein that binds many phosphonates, was characterized, and variants were constructed by introducing point mutations in its binding pocket. The PhnD variants exhibit differential binding affinities to several similar molecules and were used as sensor elements in a fluorescent multisensor system. The multisensor can be used to determine the concentrations of many analytes in a solution and can detect the presence of an interferent for which it has not been characterized by taking advantage of the non-linear nature of the fluorescent response to ligand binding.

Author : Irena Roterman-Konieczna
Publisher : Springer Science & Business Media
Page : 173 pages
File Size : 31,63 MB
Release : 2012-10-19
Category : Medical
ISBN : 9400752857

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This volume presents a review of the latest numerical techniques used to identify ligand binding and protein complexation sites. It should be noted that there are many other theoretical studies devoted to predicting the activity of specific proteins and that useful protein data can be found in numerous databases. The aim of advanced computational techniques is to identify the active sites in specific proteins and moreover to suggest a generalized mechanism by which such protein-ligand (or protein-protein) interactions can be effected. Developing such tools is not an easy task – it requires extensive expertise in the area of molecular biology as well as a firm grasp of numerical modeling methods. Thus, it is often viewed as a prime candidate for interdisciplinary research.

Author : Robert Aron Broom
Publisher :
Page : 251 pages
File Size : 36,41 MB
Release : 2017
Category : Ligand binding (Biochemistry)
ISBN :

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Proteins perform a tremendous array of finely-tuned functions which are not only critical in living organisms, but can be used for industrial and medical purposes. The ability to rationally design these molecular machines could provide a wealth of opportunities, for example to improve human health and to expand the range and reduce cost of many industrial chemical processes. The modularity of a protein sequence combined with many degrees of structural freedom yield a problem that can frequently be best tackled using computational methods. These computational methods, which include the use of: bioinformatics analysis, molecular dynamics, empirical forcefields, statistical potentials, and machine learning approaches, amongst others, are collectively known as Computational Protein Design (CPD). Here CPD is examined from the perspective of four different goals: successful design of an intended structure, the prediction of folding and unfolding kinetics from structure (kinetic stability in particular), engineering of improved stability, and prediction of binding sites and energetics. A considerable proportion of protein folds, and the majority of the most common folds ("superfolds"), are internally symmetric, suggesting emergence from an ancient repetition event. CPD, an increasingly popular and successful method for generating de novo folded sequences and topologies, suffers from exponential scaling of complexity with protein size. Thus, the overwhelming majority of successful designs are of relatively small proteins ( 100 amino acids). Designing proteins comprised of repeated modular elements allows the design space to be partitioned into more manageable portions. Here, a bioinformatics analysis of a "superfold", the beta-trefoil, demonstrated that formation of a globular fold via repetition was not only an ancient event, but an ongoing means of generating diverse and functional sequences. Modular repetition also promotes rapid evolution for binding multivalent targets in the "evolutionary arms race" between host and pathogen. Finally, modular repetition was used to successfully design, on the first attempt, a well-folded and functional beta-trefoil, called ThreeFoil. Improving protein design requires understanding the outcomes of design and not simply the 3D structure. To this end, I undertook an extensive biophysical characterization of ThreeFoil, with the key finding that its unfolding is extraordinarily slow, with a half-life of almost a decade. This kinetic stability grants ThreeFoil near-immunity to common denaturants as well as high resistance to proteolysis. A large scale analysis of hundreds of proteins, and coarse-grained modelling of ThreeFoil and other beta-trefoils, indicates that high kinetic stability results from a folded structure rich in contacts between residues distant in sequence (long-range contacts). Furthermore, an analysis of unrelated proteins known to have similar protease resistance, demonstrates that the topological complexity resulting from these long-range contacts may be a general mechanism by which proteins remain folded in harsh environments. Despite the wonderful kinetic stability of ThreeFoil, it has only moderate thermodynamic stability. I sought to improve this in order to provide a stability buffer for future functional engineering and mutagenesis. Numerous computational tools which predict stability change upon point mutation were used, and 10 mutations made based on their recommendations. Despite claims of 80% accuracy for these predictions, only 2 of the 10 mutations were stabilizing. An in-depth analysis of more than 20 such tools shows that, to a large extent, while they are capable of recognizing highly destabilizing mutations, they are unable to distinguish between moderately destabilizing and stabilizing mutations. Designing protein structure tests our understanding of the determinants of protein folding, but useful function is often the final goal of protein engineering. I explored protein-ligand binding using molecular dynamics for several protein-ligand systems involving both flexible ligand binding to deep pockets and more rigid ligand binding to shallow grooves. I also used various levels of simulation complexity, from gas-phase, to implicit solvent, to fully explicit solvent, as well as simple equilibrium simulations to interrogate known interactions to more complex energetically biased simulations to explore diverse configurations and gain novel information.