A physical model for PDZ-domain/peptide interactions

The PDZ domain is an interaction motif that recognizes and binds the C-terminal peptides of target proteins. PDZ domains are ubiquitous in nature and help assemble multiprotein complexes that control cellular organization and signaling cascades. We present an optimized energy function to predict the binding free energy (ΔΔG) of PDZ domain/peptide interactions computationally. Geometry-optimized models of PDZ domain/peptide interfaces were built using Rosetta, and protein and peptide side chain and backbone degrees of freedom are minimized simultaneously. Using leave-one-out cross-validation, Rosetta’s energy function is adjusted to reproduce experimentally determined ΔΔG values with a correlation coefficient of 0.66 and a standard deviation of 0.79 kcal mol−1. The energy function places an increased weight on hydrogen bonding interactions when compared to a previously developed method to analyze protein/protein interactions. Binding free enthalpies (ΔΔH) and entropies (ΔS) are predicted with reduced accuracies of R = 0.60 and R = 0.17, respectively. The computational method improves prediction of PDZ domain specificity from sequence and allows design of novel PDZ domain/peptide interactions

SCREENING INTERACTIONS BETWEEN PROTEINS AND DISORDERED PEPTIDES BY A NOVEL COMPUTATIONAL METHOD

Concerted interactions between proteins in cells form the basis of most biological processes. Biophysicists study protein–protein association by measuring thermodynamic and kinetic properties. Naively, strong binding affinity should be preferred in protein–protein binding to conduct certain biological functions. However, evidence shows that regulatory interactions, such as those between adapter proteins and intrinsically disordered proteins, communicate via low affinity but high complementarity interactions. PDZ domains are one class of adapters that bind linear disordered peptides, which play key roles in signaling pathways. The misregulation of these signals has been implicated in the progression of human cancers. To understand the underlying mechanism of protein-peptide binding interactions and to predict new interactions, in this thesis I have developed: (a) a unique biophysical-derived model to estimate their binding free energy; (b) a novel semi-flexible structure-based method to dock disordered peptides to PDZ domains; (c) predictions of the peptide binding landscape; and, (d) an automated algorithm and web-interface to predict the likelihood that a given linear sequence of amino acids binds to a specific PDZ domain. The docking method, PepDock, takes a peptide sequence and a PDZ protein structure as input, and outputs docked conformations and their corresponding binding affinity estimation, including their optimal free energy pathway. We have applied PepDock to screen several PDZ protein domains. The results not only validated the capabilities of PepDock to accurately discriminate interactions, but also explored the underlying binding mechanism. Specifically, I showed that interactions followed downhill free energy pathways, reconciling a relatively fast association mechanism of intrinsically disordered peptides. The pathways are such that initially the peptide’s C-terminal motif binds non-specifically, forming a weak intermediate, whereas specific binding is achieved only by a subsequent network of contacts (7–9 residues in total). This mechanism allows peptides to quickly probe PDZ domains, rapidly releasing those that do not attain sufficient affinity during binding. Further kinetic analysis indicates that disorder enhanced the specificity of promiscuous interactions between proteins and peptides, while achieving association rates comparable to interactions between ordered proteins

Computational Design of a PDZ Domain Peptide Inhibitor that Rescues CFTR Activity

The cystic fibrosis transmembrane conductance regulator (CFTR) is an epithelial chloride channel mutated in patients with cystic fibrosis (CF). The most prevalent CFTR mutation, ΔF508, blocks folding in the endoplasmic reticulum. Recent work has shown that some ΔF508-CFTR channel activity can be recovered by pharmaceutical modulators (“potentiators” and “correctors”), but ΔF508-CFTR can still be rapidly degraded via a lysosomal pathway involving the CFTR-associated ligand (CAL), which binds CFTR via a PDZ interaction domain. We present a study that goes from theory, to new structure-based computational design algorithms, to computational predictions, to biochemical testing and ultimately to epithelial-cell validation of novel, effective CAL PDZ inhibitors (called “stabilizers”) that rescue ΔF508-CFTR activity. To design the “stabilizers”, we extended our structural ensemble-based computational protein redesign algorithm to encompass protein-protein and protein-peptide interactions. The computational predictions achieved high accuracy: all of the top-predicted peptide inhibitors bound well to CAL. Furthermore, when compared to state-of-the-art CAL inhibitors, our design methodology achieved higher affinity and increased binding efficiency. The designed inhibitor with the highest affinity for CAL (kCAL01) binds six-fold more tightly than the previous best hexamer (iCAL35), and 170-fold more tightly than the CFTR C-terminus. We show that kCAL01 has physiological activity and can rescue chloride efflux in CF patient-derived airway epithelial cells. Since stabilizers address a different cellular CF defect from potentiators and correctors, our inhibitors provide an additional therapeutic pathway that can be used in conjunction with current methods

Doctor of Philosophy

dissertationThe coiled-coil is a common protein tertiary structural motif that is composed of two or more alpha helices intertwined together to formed a supercoil. In biological systems, the coiledcoil motif often forms the oligomerization domain of various proteins including DNA binding proteins, structural and transport proteins, and cellular transport and fusion proteins. It was first described by Crick in the 1950s while describing the structure of α-keratin and has since that time been the subject of numerous engineering and mutation studies. This versatile motif has been adapted to a number of nonbiological applications including environmentally responsive hydrogels, crosslinking agents, the construction of self-assembling fibers for tissue engineering, and biosensor surfaces. In this dissertation, we test the applicability of computational methods to understand the underlying energetics in coiled-coils as we apply molecular modeling approaches in the development of pharmaceutics. Two studies are described which test the limits of modern molecular dynamic force fields to understand the structural dynamics of the motif and to use energy calculation methodologies to predict favorable mutations for heterodimer formation and specificity. The first study considers the increasingly common use of fluorinated residues in protein pharmaceutics with regard to their incorporation in coiled-coils. Many studies find that fluorinated residues in the hydrophobic core increase protein stability against chemical and thermal denaturants. Often their incorporation fails to consider structural, energetic, and geometrical differences between these fluorinated residues and their nonfluorinated counterparts. To consider these differences, several variants of Hodges' very stable parallel heterodimer coiledcoil were constructed to examine the effect of salt bridge lengths and geometries with mixed fluorinated and nonfluorinated packed hydrophobic cores. In the second study, we collaborated with an experimental laboratory in the development of a mutant Bcr monomer with designed mutations to increase specificity and binding to the oncoprotein Bcr-Abl for use as an apoptosis inducing agent in chronic myelogenous leukemia (CML) cells. The final chapters of this dissertation discuss challenges and limitations that were encountered using force fields and energetic methods in our attempts to use computational chemistry to model this protein motif

Redesigning specificity in miniproteins

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Biology, 2006.Includes bibliographical references.This work focuses on designing specific miniprotein interactions using computational models and then testing these designs with experiments. Miniproteins are small, autonomously-folding proteins that are excellent for testing protein designs because they can be chemically synthesized and computationally modeled. Despite their diminutive size, miniproteins are used as minimal models to discern important features, such as folding and interaction specificity, in natural proteins. A 21-residue [beta][beta][alpha] homotetramer miniprotein (BBA) was computationally redesigned to interact as a heterotetramer. Protein design calculations revealed a large/small pattern of hydrophobic residues in the core and charge complementarity on the surface as a mechanism for attaining heterospecificity. Solution studies showed the designed protein is a tetramer and interacts in the same stoichiometry as its parent homotetramer. The x-ray crystal structure of the heterotetramer revealed a structure very close to the designed structure with near-perfect prediction of core side-chain packing. In a second round of design, the BBA heterotetramer was stabilized to near-native stability. Next, the coiled-coil region within the Bcr (breakpoint cluster region) oligomerization domain was used to probe antiparallel versus parallel helix-orientation specificity in coiled coils.(cont.) Based on the Bcr sequence, it is unclear why the oligomerization domain has an antiparallel orientation preference. The isolated Bcr coiled-coil region adopts an antiparallel orientation, so the orientation preference must be encoded in the Bcr coiled-coil sequence itself. Coiled-coil statistics and parallel and antiparallel model structures revealed an alanine and glutamate in the Bcr core as candidates that may be important for helix-orientation specificity. Both residues were mutated to leucine, a common core residue in parallel coiled coils. Based on solution studies of the mutant, both alanine and glutamate play an important role in oligomerization specificity, while glutamate may also be important for orientation specificity in Bcr. Finally, interaction partners to the Bcr oligomerization domain were computationally designed to act as dominant negative inhibitors. Four interaction partners were designed using different design techniques and energy functions. The inhibitors were expressed in E. coli and tested in a pull-down assay.by Christina Marie Taylor.Ph.D

Computational protein design: assessment and applications

Indiana University-Purdue University Indianapolis (IUPUI)Computational protein design aims at designing amino acid sequences that can fold into a target structure and perform a desired function. Many computational design methods have been developed and their applications have been successful during past two decades. However, the success rate of protein design remains too low to be of a useful tool by biochemists whom are not an expert of computational biology. In this dissertation, we first developed novel computational assessment techniques to assess several state-of-the-art computational techniques. We found that significant progresses were made in several important measures by two new scoring functions from RosettaDesign and from OSCAR-design, respectively. We also developed the first machine-learning technique called SPIN that predicts a sequence profile compatible to a given structure with a novel nonlocal energy-based feature. The accuracy of predicted sequences is comparable to RosettaDesign in term of sequence identity to wild type sequences. In the last two application chapters, we have designed self-inhibitory peptides of Escherichia coli methionine aminopeptidase (EcMetAP) and de novo designed barstar. Several peptides were confirmed inhibition of EcMetAP at the micromole-range 50% inhibitory concentration. Meanwhile, the assessment of designed barstar sequences indicates the improvement of OSCAR-design over RosettaDesign

Approaches for probing the sequence space of substrates recognized by molecular chaperones

Neurodegeneration, the progressive loss of function in neurons that eventually leads to their death, is the cause of many neurodegenerative disorders including Alzheimer’s, Parkinson’s, and Huntington’s diseases. Protein aggregation is a hallmark of most neurodegenerative diseases, where unfolded proteins form intranuclear, cytosolic, and extracellular insoluble aggregates in neurons. Mounting evidence from studies in neurodegenerative disease models shows that molecular chaperones, key regulators of protein aggregation and degradation, play critical roles in the progression of neurodegeneration. Although chaperones exhibit promiscuity in their substrate specificity, specific molecular features are required for substrate recognition. Understanding the basis for substrate recognition by chaperones will aid in the development of therapeutic strategies that regulate chaperone expression levels in order to combat neurodegeneration. Many experimental techniques, including alanine scanning mutagenesis and phage display library screening, have been developed and applied to understand the basis of substrate recognition by chaperones. Here, we present computational algorithms that can be applied to rapidly screen the sequence space of potential substrates to determine the sequence and structural requirements for substrate recognition by chaperones

Practically Useful: What the Rosetta Protein Modeling Suite Can Do for You

The objective of this review is to enable researchers to use the software package ROSETTA for biochemical and biomedicinal studies. We provide a brief review of the six most frequent research problems tackled with ROSETTA. For each of these six tasks, we provide a tutorial that illustrates a basic ROSETTA protocol. The ROSETTA method was originally developed for de novo protein structure prediction and is regularly one of the best performers in the community-wide biennial Critical Assessment of Structure Prediction. Predictions for protein domains with fewer than 125 amino acids regularly have a backbone root-mean-square deviation of better than 5.0 A ˚. More impressively, there are several cases in which ROSETTA has been used to predict structures with atomic level accuracy better than 2.5 A ˚. In addition to de novo structure prediction, ROSETTA also has methods for molecular docking, homology modeling, determining protein structures from sparse experimental NMR or EPR data, and protein design. ROSETTA has been used to accurately design a novel protein structure, predict the structure of protein-protein complexes, design altered specificity protein-protein and protein-DNA interactions, and stabilize proteins and protein complexes. Most recently, ROSETTA has been used to solve the X-ray crystallographic phase problem. ROSETTA is a unified software package for protein structure prediction and functional design. It has been used to predic

Using noncanonical amino acids in computational protein design

The structure of noncanonical amino acid (NCAA) side chains allows them to explore conformations inaccessible to canonical amino acids (CAAs). Peptides made of the D-enantiomers of amino acid backbones are resistant to proteolysis. The long term goal of this research is to adapt the current tools of computational protein design to create functional molecules be they proteins or not. In this thesis we have attempted the first steps toward this longer goal. The increased sequence and conformation space accessible to a protein during a design simulation when NCAAs are included, allows us to design tighter protein-protein interactions, with a higher degree of specificity. The computational protein design program Rosetta has been modified for compatibility with NCAAs. The use of knowledge-based potentials was the major hurdle as the potentials are based on statistics collected from known protein structures and few protein structures have been determined containing NCAAs. Using quantum mechanics (QM) calculations of the amino acids valine and isoleucine, with a helical conformation, we found an even distribution of rotamer preference. When that was used in rotamer recovery benchmarks, outperformed the knowledge-based potential that was biased because of long-range interactions imposed by the [alpha]-helical secondary structure. QM, although accurate and compatible with NCAAs was found to be too computationally expensive. We created a modified energy function that can evaluate the energy of both CAAs and NCAAs, where the knowledge-based energy potentials have been replaced with physically-based MM potentials that performs comparable to the stock energy function. We have developed methods to create rotamer libraries for both CAAs and NCAAs that are comparable to knowledge-based rotamer libraries. We have used these tools to create rotamer libraries for 88 different NCAAs that can now be used within Rosetta. The interface between calpain and the calpastatin peptide as well as the interface between HIV GP41 and the integration inhibitor, PIE12, developed by the Kay lab, has been redesigned using NCAAs to increase the binding affinity between the two pairs. The research has take protein design in a new direction and has enabled the development of novel protein interactions, and protein-like therapeutics