A novel computational method for head-to-tail peptide cyclization: application to urotensin II

Peptides have recently re-gained interest as therapeutic candidates but their development remains confronted with several limitations including low bioavailability. Backbone head-to-tail cyclization is one effective strategy of peptide-based drug design to stabilize the conformation of bioactive peptides while preserving peptide properties in terms of low toxicity, binding affinity, target selectivity and preventing enzymatic degradation. However, very little is known about the sequence-structure relationship requirements of designing linkers for peptide cyclization in a rational manner. Recently, we have shown that large scale data-mining of available protein structures can lead to the precise identification of protein loop conformations, even from remote structural classes. Here, we transpose this approach to head-to-tail peptide cyclization. Firstly we show that given a linker sequence and the conformation of the linear peptide, it is possible to accurately predict the cyclized peptide conformation improving by over 1 A over pre-existing protocols. Secondly, and more importantly, we show that is is possible to elaborate on the information inferred from protein structures to propose effective candidate linker sequences constrained by length and amino acid composition, providing the first framework for the rational peptide head-to-tail cyclization. As functional validation, we apply it to the design of a head-to-tail cyclized derivative of urotensin II, an 11-residue long peptide which exerts a broad array of biologic activities, making its cognate receptor a valuable and innovative therapeutic or diagnostic target. We propose a three amino acid candidate linker, leading to the first synthesized 14-residue long cyclic UII analogue with excellent retention of in vitro activity.

In Vitro Antibacterial Susceptibility of Different Pathogens to Thirty Nano-Polyoxometalates

Due to their unique properties, nano-polyoxometalates (POMs) can be alternative chemotherapeutic agents instrumental in designing new antibiotics. In this research, we synthesized and characterized “smart” nanocompounds and validated their antibacterial effects in order to formulate and implement potential new drugs. We characterized thirty POMs in terms of antibacterial activity–structure relationship. The antibacterial effects of these compounds are directly dependent upon their structure and the type of bacterial strain tested. We identified three POMs that presented sound antibacterial activity against S. aureus, B. cereus, E. coli, S. enteritidis and P. aeruginosa strains. A newly synthesized compound K6[(VO)SiMo2W9O39]·11H2O (POM 7) presented antibacterial activity only against S. aureus (ATCC 6538P). Twelve POMs exerted antibacterial effects against both Gram-positive and Gram-negative strains. Only one POM (a cluster derivatized with organometallic fragments) exhibited a stronger effect compared to amoxicillin. New studies in terms of selectivity and specificity are required to clarify these extremely important aspects needed to be considered in drug design.

Tailoring atomic 1T phase CrTe2 for in situ fabrication

Controllable tailoring and understanding the phase-structure relationship of the 1T phase two-dimensional (2D) materials are critical for their applications in nanodevices. The in situ transmission electron microscope (TEM) could regulate and monitor the evolution process of the nanostructure of 2D material with atomic resolution. In this work, a controllably tailoring 1T-CrTe2 nanopore is carried out by the in situ TEM. A preferred formation of the 1T-CrTe2 border structure and nanopore healing process are studied at the atomic scale. The controllable tailoring of the 1T phase nanopore could be achieved by regulating the transformation of two types of low indices of crystal faces {10-10} and {11-20} at the nanopore border. Machine learning is applied to automatically process the TEM images with high efficiency. By adopting the deep-learning-based image-segmentation method and augmenting the TEM images specifically, the nanopore of the TEM image could be automatically identified and the evaluation result of DICE metric reaches 93.17% on test set. This work presents the unique structure evolution of 1T phase 2D material and the computer aided high efficiency TEM data analysis based on deep learning. The techniques applied in this work could be generalized to other materials for controlled nanostructure regulation and automatic TEM image analyzation.

Synthesis of N-Glycans and Azasugars to Target Disease

<p>In this thesis two aspects of carbohydrate research will be discussed. First, the total synthesis of N-glycans found on allergens that are known to stimulate an allergic immune response and second, the synthesis of iminosugars in an attempt to extend the scope of the PGF-methodology. Asthma affects 235 million people worldwide, with New Zealand ranking among the highest in the world. Although there is a good understanding of how allergens trigger the immune system on a “macroscopic” level, how an allergen’s molecular structure causes such an allergic response remains unknown. Upon close review of carbohydrates present on the allergens that are known to give an allergic T helper (Th 2) immune response, a common structure has been identified. The structure consists of a complex type N-glycan made up of a pentasaccharide core (Man3(GlcNHAc)2), with additional 1,3-linked α-L-fucose and 1,2-linked D-xylose cappings. As part of a structure relationship study this heptasaccharide and structural derivatives thereof have been synthesised. The synthesis of these N-glycans will allow a more detailed study of the role of these defined structures in triggering an allergic immune response. The second part of this thesis focuses on the protecting group free (PGF) synthesis of iminosugars, which are potent glycosidase inhibitors and are currently used in the treatment of a variety of diseases. Synthetic strategies for the synthesis of iminosugars involve the use of protecting groups, which are necessary to block potential competing reactive centres within a molecule during the multistep synthesis. The disadvantage, however, is that the installation of protecting groups introduces additional steps to the total synthesis, which inevitably leads to lower yields and the generation of waste. Within our group, PGF methodologies have been developed which allow for the synthesis of a variety of iminosugars. The work presented in this thesis extends the scope of this methodology for the synthesis of an important class of iminosugars, the 2,5-dihydroxymethyl-3,4-dihydroxypyrrolidines. For the purpose of introducing an additional hydroxymethyl group, a ketose starting material was required, and therefore an efficient Vasella/reductive amination reaction using ketoses was developed. Additionally, iodocyclisation and carbamate annulation of the intermediate alkenylamines provided successful entry to the 2,5-dihydroxymethyl-3,4-dihydroxypyrrolidines, exemplified by the efficient 6-step synthesis of 2,5-dideoxy-2,5-imino-L-iditol and 2,5-dideoxy-2,5-imino-D-mannitol (DMDP).</p>

Synthesis of N-Glycans and Azasugars to Target Disease

<p>In this thesis two aspects of carbohydrate research will be discussed. First, the total synthesis of N-glycans found on allergens that are known to stimulate an allergic immune response and second, the synthesis of iminosugars in an attempt to extend the scope of the PGF-methodology. Asthma affects 235 million people worldwide, with New Zealand ranking among the highest in the world. Although there is a good understanding of how allergens trigger the immune system on a “macroscopic” level, how an allergen’s molecular structure causes such an allergic response remains unknown. Upon close review of carbohydrates present on the allergens that are known to give an allergic T helper (Th 2) immune response, a common structure has been identified. The structure consists of a complex type N-glycan made up of a pentasaccharide core (Man3(GlcNHAc)2), with additional 1,3-linked α-L-fucose and 1,2-linked D-xylose cappings. As part of a structure relationship study this heptasaccharide and structural derivatives thereof have been synthesised. The synthesis of these N-glycans will allow a more detailed study of the role of these defined structures in triggering an allergic immune response. The second part of this thesis focuses on the protecting group free (PGF) synthesis of iminosugars, which are potent glycosidase inhibitors and are currently used in the treatment of a variety of diseases. Synthetic strategies for the synthesis of iminosugars involve the use of protecting groups, which are necessary to block potential competing reactive centres within a molecule during the multistep synthesis. The disadvantage, however, is that the installation of protecting groups introduces additional steps to the total synthesis, which inevitably leads to lower yields and the generation of waste. Within our group, PGF methodologies have been developed which allow for the synthesis of a variety of iminosugars. The work presented in this thesis extends the scope of this methodology for the synthesis of an important class of iminosugars, the 2,5-dihydroxymethyl-3,4-dihydroxypyrrolidines. For the purpose of introducing an additional hydroxymethyl group, a ketose starting material was required, and therefore an efficient Vasella/reductive amination reaction using ketoses was developed. Additionally, iodocyclisation and carbamate annulation of the intermediate alkenylamines provided successful entry to the 2,5-dihydroxymethyl-3,4-dihydroxypyrrolidines, exemplified by the efficient 6-step synthesis of 2,5-dideoxy-2,5-imino-L-iditol and 2,5-dideoxy-2,5-imino-D-mannitol (DMDP).</p>

Applications of AlphaFold beyond Protein Structure Prediction

Solving the half-century-old protein structure prediction problem by DeepMind's AlphaFold is certainly one of the greatest breakthroughs in biology in the twenty-first century. This breakthrough paved the way for tackling some previously highly challenging or even infeasible problems in structural biology. In this study, we propose strategies to use AlphaFold to address several fundamental problems: (1) protein engineering by predicting the experimentally measured stability changes using the representations extracted from AlphaFold models; (2) estimating the designability of a given protein structure by combining a protein design method (e.g. ProDCoNN), sequential Monte Carlo, and AlphaFold. The designability of a protein structure is defined as the number of sequences that encode that protein structure.; (3) predicting protein stabilities using natural sequences and designed sequences as training data, and representations extracted from AlphaFold models as input features; and (4) understanding the sequence-structure relationship of proteins by computational mutagenesis and testing the foldability of the mutants by AlphaFold. We found the representations extracted from AlphaFold models can be used to predict the experimentally measured stability changes accurately. For the first time, we have estimated the designability for a few real proteins. For example, the designability of chain A of FLT3 ligand (PDB ID: 1ETE) with 134 residues was estimated as 3.12 ± 2.14E85.