Browsing by Subject "Protein design"

Now showing 1 - 4 of 4
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    Computational Ways to Enhance Protein Inhibitor Design
    (Frontiers Media, 2021-02-03) Jernigan, Robert L.; Sankar, Kannan; Jia, Kejue; Faraggi, Eshel; Kloczkowski, Andrzej; Physics, School of Science
    Two new computational approaches are described to aid in the design of new peptide-based drugs by evaluating ensembles of protein structures from their dynamics and through the assessing of structures using empirical contact potential. These approaches build on the concept that conformational variability can aid in the binding process and, for disordered proteins, can even facilitate the binding of more diverse ligands. This latter consideration indicates that such a design process should be less restrictive so that multiple inhibitors might be effective. The example chosen here focuses on proteins/peptides that bind to hemagglutinin (HA) to block the large-scale conformational change for activation. Variability in the conformations is considered from sets of experimental structures, or as an alternative, from their simple computed dynamics; the set of designe peptides/small proteins from the David Baker lab designed to bind to hemagglutinin, is the large set considered and is assessed with the new empirical contact potentials.
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    Direct prediction of profiles of sequences compatible to a protein structure by neural networks with fragment-based local and energy-based nonlocal profiles
    (Wiley Online Library, 2014-10) Li, Zhixiu; Yang, Yuedong; Faraggi, Eshel; Zhou, Jian; Zhou, Yaoqi; Department of BioHealth Informatics, IU School of Informatics and Computing
    Locating sequences compatible with a protein structural fold is the well-known inverse protein-folding problem. While significant progress has been made, the success rate of protein design remains low. As a result, a library of designed sequences or profile of sequences is currently employed for guiding experimental screening or directed evolution. Sequence profiles can be computationally predicted by iterative mutations of a random sequence to produce energy-optimized sequences, or by combining sequences of structurally similar fragments in a template library. The latter approach is computationally more efficient but yields less accurate profiles than the former because of lacking tertiary structural information. Here we present a method called SPIN that predicts Sequence Profiles by Integrated Neural network based on fragment-derived sequence profiles and structure-derived energy profiles. SPIN improves over the fragment-derived profile by 6.7% (from 23.6 to 30.3%) in sequence identity between predicted and wild-type sequences. The method also reduces the number of residues in low complex regions by 15.7% and has a significantly better balance of hydrophilic and hydrophobic residues at protein surface. The accuracy of sequence profiles obtained is comparable to those generated from the protein design program RosettaDesign 3.5. This highly efficient method for predicting sequence profiles from structures will be useful as a single-body scoring term for improving scoring functions used in protein design and fold recognition. It also complements protein design programs in guiding experimental design of the sequence library for screening and directed evolution of designed sequences. The SPIN server is available at http://sparks-lab.org.
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    New Horizons: Next-Generation Insulin Analogues: Structural Principles and Clinical Goals
    (The Endocrine Society, 2022) Jarosinski, Mark A.; Chen, Yen-Shan; Varas, Nicolás; Dhayalan, Balamurugan; Chatterjee, Deepak; Weiss, Michael A.; Biochemistry and Molecular Biology, School of Medicine
    Design of “first-generation” insulin analogues over the past 3 decades has provided pharmaceutical formulations with tailored pharmacokinetic (PK) and pharmacodynamic (PD) properties. Application of a molecular tool kit—integrating protein sequence, chemical modification, and formulation—has thus led to improved prandial and basal formulations for the treatment of diabetes mellitus. Although PK/PD changes were modest in relation to prior formulations of human and animal insulins, significant clinical advantages in efficacy (mean glycemia) and safety (rates of hypoglycemia) were obtained. Continuing innovation is providing further improvements to achieve ultrarapid and ultrabasal analogue formulations in an effort to reduce glycemic variability and optimize time in range. Beyond such PK/PD metrics, next-generation insulin analogues seek to exploit therapeutic mechanisms: glucose-responsive (“smart”) analogues, pathway-specific (“biased”) analogues, and organ-targeted analogues. Smart insulin analogues and delivery systems promise to mitigate hypoglycemic risk, a critical barrier to glycemic control, whereas biased and organ-targeted insulin analogues may better recapitulate physiologic hormonal regulation. In each therapeutic class considerations of cost and stability will affect use and global distribution. This review highlights structural principles underlying next-generation design efforts, their respective biological rationale, and potential clinical applications.
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    Structure-based stabilization of insulin as a therapeutic protein assembly via enhanced aromatic-aromatic interactions
    (American Society for Biochemistry and Molecular Biology, 2018-07-13) Rege, Nischay K.; Wickramasinghe, Nalinda P.; Tustan, Alisar N.; Phillips, Nelson F. B.; Yee, Vivien C.; Ismail-Beigi, Faramarz; Weiss, Michael A.; Biochemistry & Molecular Biology, IU School of Medicine
    Key contributions to protein structure and stability are provided by weakly polar interactions, which arise from asymmetric electronic distributions within amino acids and peptide bonds. Of particular interest are aromatic side chains whose directional π-systems commonly stabilize protein interiors and interfaces. Here, we consider aromatic-aromatic interactions within a model protein assembly: the dimer interface of insulin. Semi-classical simulations of aromatic-aromatic interactions at this interface suggested that substitution of residue TyrB26 by Trp would preserve native structure while enhancing dimerization (and hence hexamer stability). The crystal structure of a [TrpB26]insulin analog (determined as a T3Rf3 zinc hexamer at a resolution of 2.25 Å) was observed to be essentially identical to that of WT insulin. Remarkably and yet in general accordance with theoretical expectations, spectroscopic studies demonstrated a 150-fold increase in the in vitro lifetime of the variant hexamer, a critical pharmacokinetic parameter influencing design of long-acting formulations. Functional studies in diabetic rats indeed revealed prolonged action following subcutaneous injection. The potency of the TrpB26-modified analog was equal to or greater than an unmodified control. Thus, exploiting a general quantum-chemical feature of protein structure and stability, our results exemplify a mechanism-based approach to the optimization of a therapeutic protein assembly.