Identification and Rational Redesign of Peptide Ligands to CRIP1, A Novel Biomarker for Cancers

Cysteine-rich intestinal protein 1 (CRIP1) has been identified as a novel marker for early detection of cancers. Here we report on the use of phage display in combination with molecular modeling to identify a high-affinity ligand for CRIP1. Panning experiments using a circularized C7C phage library yielded several consensus sequences with modest binding affinities to purified CRIP1. Two sequence motifs, A1 and B5, having the highest affinities for CRIP1, were chosen for further study. With peptide structure information and the NMR structure of CRIP1, the higher-affinity A1 peptide was computationally redesigned, yielding a novel peptide, A1M, whose affinity was predicted to be much improved. Synthesis of the peptide and saturation and competitive binding studies demonstrated approximately a 10–28-fold improvement in the affinity of A1M compared to that of either A1 or B5 peptide. These techniques have broad application to the design of novel ligand peptides

A Novel Empirical Free Energy Function That Explains And Predicts Protein–Protein Binding Affinities

A free energy function can be defined as a mathematical expression that relates macroscopic free energy changes to microscopic or molecular properties. Free energy functions can be used to explain and predict the affinity of a ligand for a protein and to score and discriminate between native and non-native binding modes. However, there is a natural tension between developing a function fast enough to solve the scoring problem but rigorous enough to explain and predict binding affinities. Here, we present a novel, physics-based free energy function that is computationally inexpensive, yet explanatory and predictive. The function results from a derivation that assumes the cost of polar desolvation can be ignored and that includes a unique and implicit treatment of interfacial water-bridged interactions. The function was parameterized on an internally consistent, high quality training set giving R 2 =0.97 and Q 2 =0.91. We used the function to blindly and successfully predict binding affinities for a diverse test set of 31 wild-type protein–protein and protein–peptide complexes (R 2 =0.79, rmsd=1.2 kcal mol−1). The function performed very well in direct comparison with a recently described knowledge-based potential and the function appears to be transferable. Our results indicate that our function is well suited for solving a wide range of protein/peptide design and discovery problems

Modeling the Binding of Inhibitors/Drugs to the Human Serotonin Transporter

Human serotonin transporter (hSERT), a membrane protein from the neurotransmitter sodium symporter family, is implicated in depression disorder and has been the primary target of antidepressant discovery research for several decades. Since the currently available antidepressants may cause adverse effects and have several limitations, novel drugs are highly desired. However, the efforts to develop better therapeutics are hampered by the lack of a crystal structure of hSERT. Knowledge of the binding site of the drug and its orientation in the protein is crucial in structure-based drug discovery. We employed a novel computational protocol comprised of active site detection, docking, scoring, molecular dynamics simulations, and absolute binding free energy (ABFE) calculations to elucidate the binding site and the binding mode of a dual hSERT/5HT-1A blocker SSA-426 and our in-house hSERT inhibitor DJLDU-3-79 in hSERT. Through this approach, we propose that both of these inhibitors bind in the S1 pocket of hSERT and in a similar orientation. This disproves the earlier hypothesis that both these inhibitors bind in the S2 site; however, we are in agreement with the earlier hypothesis that both of the ligands orient similarly. Further, we resolved the ambiguity in binding energies and binding trends of the tricyclic antidepressant drugs clomipramine, imipramine, and desipramine with leucine transporter (LeuT) (a bacterial homologue of hSERT) through relative binding free energy (RBFE) calculations. Based on our RBFE results, we proposed that clomipramine should have the highest affinity for LeuT, followed by imipramine and desipramine. Finally, to achieve accuracy in binding energy estimations and to perform all CHARMM simulations, we developed CHARMM general force field parameters (CGenFF) for fifteen monoamine transporter ligands

Development of an affinity sensor for the detection of aflatoxin M1 in milk

Much research has been done on aflatoxins since their discovery in the 1960’s where it was concluded that aflatoxins have carcinogenic, mutagenic, teratogenic and immunosuppressive properties. Aflatoxin M1 exists in milk and since milk is a major component of the diet of infants, the maximum permissible limit set by the EU is 50 parts per trillion (ng L -1 ). Current methods of analysis for aflatoxin M1 is primarily based around techniques such as HPLC and TLC which require extensively trained operators and equipped laboratories. Using antibodies as receptors in an enzyme linked immunosorbent assay (ELISA), the analysis costs can be reduced and simplified, however, an equipped laboratory is still required. Hence there is a need for a low cost, rapid, portable instrument which is easy to use at the point of source for the detection of aflatoxin M1. This thesis describes the development of affinity sensors to meet these requirements. Firstly the design and optimisation of an ELISA method was carried out, utilising a commercially sourced monoclonal antibody. Once the antibodies suitability for sensing aflatoxin M1 was determined the antibody was successfully employed as the receptor for a screen printed HRP/TMB based immunosensor. Upon the analysis of milk it was observed that milk caused extensive interference and through a series of chemical extractions the interference was attributed to whey proteins in the milk with suspicion towards a- lactalbumin. A simple pre-treatment technique of adding calcium chloride was performed and the interference from the whey proteins was removed. The resulting immunosensor achieved a sensitivity of 39 ng L -1 (Figure 3.26), however, poor reproducibility was observed due to the screen printed electrode production (%CV = 21% variance for screen printed electrode production). Gold cell on a chip microelectrode arrays were used to replace the screen printed electrodes and the successful covalent attachment of the antibody to the microelectrode array through PDITC cross linking compound was monitored using atomic force microscopy and scanning electron microscopy. It was shown that the majority of the antibodies during immobilisation orientate in a ‘side on’ orientation and therefore a cheap capture polyclonal antibody was first immobilised before the addition of the sensing anti-aflatoxin M1 monoclonal antibody. Using the microelectrode array an improvement of the sensitivity as well as a reduction of the milk interference was shown. Sensitivity was improved to 8 ng L -1 in milk (Figure 4.23). Further work was performed to substitute the fragile antibody used in the sensing layer for a robust synthetic peptide receptor. Initially a virtual library of synthetic peptides was created using de novo design techniques in silico. Further computational techniques were performed to determine the best peptide from the library. This peptide had a sequence of PVGPRP. From literature a peptide (LLAR) was reported with affinity for aflatoxin B1. This sequence along with the de novo design peptide was synthesised and tested using a host of techniques and immobilisation chemistries such as optical waveguide lightmode spectroscopy (OWLS), BIAcore and enzymatic techniques using EDC/NHS, glutaraldehyde and BS 3 cross linking methods. The affinity of both peptides to aflatoxin M1 was demonstrated however further work is required to quantify the affinity and to incorporate the peptides into the microelectrode array.EThOS - Electronic Theses Online ServiceGBUnited Kingdo