Prediction of MHC-peptide binding: a systematic and comprehensive overview

T cell immune responses are driven by the recognition of peptide antigens (T cell epitopes) that are bound to major histocompatibility complex (MHC) molecules. T cell epitope immunogenicity is thus contingent on several events, including appropriate and effective processing of the peptide from its protein source, stable peptide binding to the MHC molecule, and recognition of the MHC-bound peptide by the T cell receptor. Of these three hallmarks, MHC-peptide binding is the most selective event that determines T cell epitopes. Therefore, prediction of MHC-peptide binding constitutes the principal basis for anticipating potential T cell epitopes. The tremendous relevance of epitope identification in vaccine design and in the monitoring of T cell responses has spurred the development of many computational methods for predicting MHC-peptide binding that improve the efficiency and economics of T cell epitope identification. In this report, we will systematically examine the available methods for predicting MHC-peptide binding and discuss their most relevant advantages and drawbacks

T-cell epitope prediction and immune complex simulation using molecular dynamics: state of the art and persisting challenges

Atomistic Molecular Dynamics provides powerful and flexible tools for the prediction and analysis of molecular and macromolecular systems. Specifically, it provides a means by which we can measure theoretically that which cannot be measured experimentally: the dynamic time-evolution of complex systems comprising atoms and molecules. It is particularly suitable for the simulation and analysis of the otherwise inaccessible details of MHC-peptide interaction and, on a larger scale, the simulation of the immune synapse. Progress has been relatively tentative yet the emergence of truly high-performance computing and the development of coarse-grained simulation now offers us the hope of accurately predicting thermodynamic parameters and of simulating not merely a handful of proteins but larger, longer simulations comprising thousands of protein molecules and the cellular scale structures they form. We exemplify this within the context of immunoinformatics

Lead optimization for new antimalarials and Successful lead identification for metalloproteinases: A Fragment-based approach Using Virtual Screening

Lead optimization for new antimalarials and Successful lead identification for metalloproteinases: A Fragment-based approach Using Virtual Screening Computer-aided drug design is an essential part of the modern medicinal chemistry, and has led to the acceleration of many projects. The herein described thesis presents examples for its application in the field of lead optimization and lead identification for three metalloproteins. DOXP-reductoisomerase (DXR) is a key enzyme of the mevalonate independent isoprenoid biosynthesis. Structure-activity relationships for 43 DXR inhibitors are established, derived from protein-based docking, ligand-based 3D QSAR and a combination of both approaches as realized by AFMoC. As part of an effort to optimize the properties of the established inhibitor Fosmidomycin, analogues have been synthesized and tested to gain further insights into the primary determinants of structural affinity. Unfortunately, these structures still leave the active Fosmidomycin conformation and detailed reaction mechanism undetermined. This fact, together with the small inhibitor data set provides a major challenge for presently available docking programs and 3D QSAR tools. Using the recently developed protein tailored scoring protocol AFMoC precise prediction of binding affinities for related ligands as well as the capability to estimate the affinities of structurally distinct inhibitors has been achieved. Farnesyltransferase is a zinc-metallo enzyme that catalyzes the posttranslational modification of numerous proteins involved in intracellular signal transduction. The development of farnesyltransferase inhibitors is directed towards the so-called non-thiol inhibitors because of adverse drug effects connected to free thiols. A first step on the way to non-thiol farnesyltransferase inhibitors was the development of an CAAX-benzophenone peptidomimetic based on a pharmacophore model. On its basis bisubstrate analogues were developed as one class of non-thiol farnesyltransferase inhibitors. In further studies two aryl binding and two distinct specificity sites were postulated. Flexible docking of model compounds was applied to investigate the sub-pockets and design highly active non-thiol farnesyltransferase inhibitor. In addition to affinity, special attention was paid towards in vivo activity and species specificity. The second part of this thesis describes a possible strategy for computer-aided lead discovery. Assembling a complex ligand from simple fragments has recently been introduced as an alternative to traditional HTS. While frequently applied experimentally, only a few examples are known for computational fragment-based approaches. Mostly, computational tools are applied to compile the libraries and to finally assess the assembled ligands. Using the metalloproteinase thermolysin (TLN) as a model target, a computational fragment-based screening protocol has been established. Starting with a data set of commercially available chemical compounds, a fragment library has been compiled considering (1) fragment likeness and (2) similarity to known drugs. The library is screened for target specificity, resulting in 112 fragments to target the zinc binding area and 75 fragments targeting the hydrophobic specificity pocket of the enzyme. After analyzing the performance of multiple docking programs and scoring functions forand the most 14 candidates are selected for further analysis. Soaking experiments were performed for reference fragment to derive a general applicable crystallization protocol for TLN and subsequently for new protein-fragment complex structures. 3-Methylsaspirin could be determined to bind to TLN. Additional studies addressed a retrospective performance analysis of the applied scoring functions and modification on the screening hit. Curios about the differences of aspirin and 3-methylaspirin, 3-chloroaspirin has been synthesized and affinities could be determined to be 2.42 mM; 1.73 mM und 522 μM respectively. The results of the thesis show, that computer aided drug design approaches could successfully support projects in lead optimization and lead identification. fragments in general, the fragments derived from the screening are docke

Computational Modeling of Protein Kinases: Molecular Basis for Inhibition and Catalysis

Protein kinases catalyze protein phosphorylation reactions, i.e. the transfer of the γ-phosphoryl group of ATP to tyrosine, serine and threonine residues of protein substrates. This phosphorylation plays an important role in regulating various cellular processes. Deregulation of many kinases is directly linked to cancer development and the protein kinase family is one of the most important targets in current cancer therapy regimens. This relevance to disease has stimulated intensive efforts in the biomedical research community to understand their catalytic mechanisms, discern their cellular functions, and discover inhibitors. With the advantage of being able to simultaneously define structural as well as dynamic properties for complex systems, computational studies at the atomic level has been recognized as a powerful complement to experimental studies. In this work, we employed a suite of computational and molecular simulation methods to (1) explore the catalytic mechanism of a particular protein kinase, namely, epidermal growth factor receptor (EGFR); (2) study the interaction between EGFR and one of its inhibitors, namely erlotinib (Tarceva); (3) discern the effects of molecular alterations (somatic mutations) of EGFR to differential downstream signaling response; and (4) model the interactions of a novel class of kinase inhibitors with a common ruthenium based organometallic scaffold with different protein kinases. Our simulations established some important molecular rules in operation in the contexts of inhibitor-binding, substrate-recognition, catalytic landscapes, and signaling in the EGFR tyrosine kinase. Our results also shed insights on the mechanisms of inhibition and phosphorylation commonly employed by many kinases

Exploring the Role of Calcium Ions in Biological Systems by Computational Prediction and Protein Engineering

Ca2+, a signal for death and life, is closely involved in the regulation of numerous important cellular events. Ca2+ carries out its function through its binding to Ca2+-receptors or Ca2+-binding proteins. The EF-hand protein, with a helix-loop-helix Ca2+-binding motif, constitutes one of the largest protein families. To facilitate our understanding of the role of Ca2+ in biological systems (denoted as calciomics) using genomic information, an improved pattern search method (http://www.chemistry.gsu.edu/faculty/Yang/Calciomics.htm) for the identification of EF-hand and EF-like Ca2+-binding proteins was developed. This fast and robust method allows us to analyze putative EF-hand proteins at the genome-wide level and further visualize the evolutionary scenario of the EF-hand protein family. This prediction method further enables us to locate a putative viral EF-hand Ca2+-binding motif within the rubella virus nonstructural protease that cleaves the nonstructural protein precursor into two active replicase components. A novel grafting approach has been used to probe the metal-binding properties of this motif by engineering the predicted 12-residue Ca2+-coordinating loop into a non-Ca2+-binding scaffold protein, CD2 domain 1. Structural and conformational studies were further performed on a purified, bacterially-expressed NS protease minimal metal-binding domain spanning the Zn2+- and EF-hand Ca2+-binding motif. It was revealed that Ca2+ binding induced local conformational changes and increased thermal stability. Furthermore, functional studies were carried out using RUB infectious cDNA clone and replicon constructs. Our studies have shown that the Ca2+ binding loop played a structural role in the NS protease and was specifically required for optimal stability under physiological conditions. In addition, we have predicted and characterized a calmodulin-binding domain in the gap junction proteins connexin43 and connexin44. Peptides encompassing the CaM binding motifs were synthesized and their ability to bind CaM was determined using various biophysical approaches. Transient expression in HeLa cells of two mutant Cx43-EYFP constructs without the putative CaM-binding site eliminated the Ca2+-dependent inhibition of gap junction permeability. These results provide the first direct evidence that CaM binds to a specific region of the ubiquitous gap junction protein Cx43 and Cx44 in a Ca2+-dependent manner, providing a molecular basis for the well-characterized Ca2+-dependent inhibition of Cx43-containing gap junctions

Rigorous Computational and Experimental Investigations on MDM2/MDMX-Targeted Linear and Macrocyclic Peptides

There is interest in peptide drug design, especially for targeting intracellular protein–protein interactions. Therefore, the experimental validation of a computational platform for enabling peptide drug design is of interest. Here, we describe our peptide drug design platform (CMDInventus) and demonstrate its use in modeling and predicting the structural and binding aspects of diverse peptides that interact with oncology targets MDM2/MDMX in comparison to both retrospective (pre-prediction) and prospective (post-prediction) data. In the retrospective study, CMDInventus modules (CMDpeptide, CMDboltzmann, CMDescore and CMDyscore) were used to accurately reproduce structural and binding data across multiple MDM2/MDMX data sets. In the prospective study, CMDescore, CMDyscore and CMDboltzmann were used to accurately predict binding affinities for an Ala-scan of the stapled α-helical peptide ATSP-7041. Remarkably, CMDboltzmann was used to accurately predict the results of a novel D-amino acid scan of ATSP-7041. Our investigations rigorously validate CMDInventus and support its utility for enabling peptide drug design

A New Method for Ligand-supported Homology Modelling of Protein Binding Sites: Development and Application to the neurokinin-1 receptor

In this thesis, a novel strategy (MOBILE (Modelling Binding Sites Including Ligand Information Explicitly)) was developed that models protein binding-sites simultaneously considering information about the binding mode of bioactive ligands during the homology modelling process. As a result, protein binding-site models of higher accuracy and relevance can be generated. Starting with the (crystal) structure of one or more template proteins, in the first step several preliminary homology models of the target protein are generated using the homology modelling program MODELLER. Ligands are then placed into these preliminary models using different strategies depending on the amount of experimental information about the binding mode of the ligands. (1.) If a ligand is known to bind to the target protein and the crystal structure of the protein-ligand complex with the related template protein is available, it can be assumed that the ligand binding modes are similar in the target and template protein. Accordingly, ligands are then transferred among these structures keeping their orientation as a restraint for the subsequent modelling process. (2.) If no complex crystal structure with the template is available, the ligand(s) can be placed into the template protein structure by docking, and the resulting orientation can then be used to restrain the following protein modelling process. Alternatively, (3.) in cases where knowledge about the binding mode cannot be inferred by the template protein, ligand docking is performed into an ensemble of homology models. The ligands are placed into a crude binding-site representation via docking into averaged property fields derived from knowledge-based potentials. Once the ligands are placed, a new set of homology models is generated. However, in this step, ligand information is considered as additional restraint in terms of the knowledge-based DrugScore protein-ligand atom pair potentials. Consulting a large ensemble of produced models exhibiting di erent side-chain rotamers for the binding-site residues, a composite picture is assembled considering the individually best scored rotamers with respect to the ligand. After a local force-field optimisation, the obtained binding-site models can be used for structure-based drug design

Bind-n-Seq: high-throughput analysis of in vitro protein-DNA interactions using massively parallel sequencing.

Transcription factor-DNA interactions are some of the most important processes in biology because they directly control hereditary information. The targets of most transcription factor are unknown. In this report, we introduce Bind-n-Seq, a new high-throughput method for analyzing protein-DNA interactions in vitro, with several advantages over current methods. The procedure has three steps (i) binding proteins to randomized oligonucleotide DNA targets, (ii) sequencing the bound oligonucleotide with massively parallel technology and (iii) finding motifs among the sequences. De novo binding motifs determined by this method for the DNA-binding domains of two well-characterized zinc-finger proteins were similar to those described previously. Furthermore, calculations of the relative affinity of the proteins for specific DNA sequences correlated significantly with previous studies (R(2 )= 0.9). These results present Bind-n-Seq as a highly rapid and parallel method for determining in vitro binding sites and relative affinities

Homology Modeling and Molecular Docking of Antagonists to Class B G-Protein Coupled Receptor Pituitary Adenylate Cyclase Type 1 (PAC1R)

Recent studies have identified the Class B g-protein coupled receptor (GPCR) pituitary adenylate cyclase activating polypeptide type 1 (PAC1R) as a key component in physiological stress management. Over-activity of neurological stress response systems due to prolonged or extreme exposure to traumatic events has led researchers to investigate PAC1R inhibition as a possible treatment for anxiety disorders such as post-traumatic stress disorder (PTSD). In 2008, Beebe and coworkers identified two such small molecule hydrazide antagonists and a general pharmacaphore for PAC1R inhibition. However, a relative dearth of information about Class B GPCRs in general, and PAC1R in specific, has significantly hindered progress toward the development of small molecule antagonists of PAC1R. The recent crystallization of the homologically similar glucagon receptor (GCGR) by Siu and coworkers in 2013, also a Class B receptor, has provided an experimentally resolved template from which to base computationally derived models of PAC1R. Initially, this research was focused towards synthesizing small molecule antagonists for PAC1R which were to be biologically screened via a qualitative western blot assay followed by a radioisotope binding assay for those hydrazides exhibiting down-stream signaling inhibitory capabilities. However, the resolution of the GCGR crystal structure shifted research objectives towards developing a homology model of PAC1R and evaluating that computationally created model with Beebe\u27s known small molecule antagonists. Created using academic versions of on-line resources including UniProtKB, Swiss-Model and Maestro, a homology model for PAC1R is presented here. The model is validated and evaluated for the presence of conserved Class B GPCR residues and motifs, including expected disulfide bridges, a conserved tyrosine residue, a GWGxP motif, a conserved glutamic acid residue and the extension of the transmembrane helix 1 (TM1) into the extra-cellular domain. Having determined this virtual PAC1R an acceptable model, ligand docking studies of known antagonists to the receptor were undertaken using AutoDock Vina in conjunction with AutoDock Tools and PyMol. Computational docking results were evaluated via comparison of theoretical binding affinity results to Beebe\u27s experimental data. Based on hydrogen bonding capabilities, several residues possibly key to the ligand-receptor binding complex are identified and include ASN 240, TYR 241 and HIST 365. Although the docking software does not identify non-bonding interactions other than hydrogen-bonding, the roles of additional proposed binding pocket residues are discussed in terms of hydrophobic interactions, π-π interactions and halogen bonding. These residues include TYR 161, PHE 196, VAL 203, PHE 204, ILE 209, LEU 210, VAL 237, TRP 297, PHE 362 and LEU 386. Although theoretical in nature, this reported homology modeling and docking exercise details a proposed binding site that may potentially further the development of drugs designed for the treatment of PTSD

Computational design with flexible backbone sampling for protein remodeling and scaffolding of complex binding sites

Dissertation presented to obtain the Doutoramento (Ph.D.) degree in Biochemistry at the Instituto de Tecnologia Qu mica e Biol ogica da Universidade Nova de LisboaComputational protein design has achieved several milestones, including the design of a new protein fold, the design of enzymes for reactions that lack natural catalysts, and the re-engineering of protein-protein and protein-DNA binding speci city. These achievements have spurred demand to apply protein design methods to a wider array of research problems. However, the existing computational methods have largely relied on xed-backbone approaches that may limit the scope of problems that can be tackled. Here, we describe four computational protocols - side chain grafting, exible backbone remodeling, backbone grafting, and de novo sca old design - that expand the methodological protein design repertoire, three of which incorporate backbone exibility. Brie y, in the side chain grafting method, side chains of a structural motif are transplanted to a protein with a similar backbone conformation; in exible backbone remodeling, de novo segments of backbone are built and designed; in backbone grafting, structural motifs are explicitly grafted onto other proteins; and in de novo sca olding, a protein is folded and designed around a structural motif. We developed these new methods for the design of epitope-sca old vaccines in which viral neutralization epitopes of known three-dimensional structure were transplanted onto nonviral sca old proteins for conformational stabilization and immune presentation.(...