Understanding the Structural and Functional Importance of Early Folding Residues in Protein Structures

Proteins adopt three-dimensional structures which serve as a starting point to understand protein function and their evolutionary ancestry. It is unclear how proteins fold in vivo and how this process can be recreated in silico in order to predict protein structure from sequence. Contact maps are a possibility to describe whether two residues are in spatial proximity and structures can be derived from this simplified representation. Coevolution or supervised machine learning techniques can compute contact maps from sequence: however, these approaches only predict sparse subsets of the actual contact map. It is shown that the composition of these subsets substantially influences the achievable reconstruction quality because most information in a contact map is redundant. No strategy was proposed which identifies unique contacts for which no redundant backup exists. The StructureDistiller algorithm quantifies the structural relevance of individual contacts and identifies crucial contacts in protein structures. It is demonstrated that using this information the reconstruction performance on a sparse subset of a contact map is increased by 0.4 A, which constitutes a substantial performance gain. The set of the most relevant contacts in a map is also more resilient to false positively predicted contacts: up to 6% of false positives are compensated before reconstruction quality matches a naive selection of contacts without any false positive contacts. This information is invaluable for the training to new structure prediction methods and provides insights into how robustness and information content of contact maps can be improved. In literature, the relevance of two types of residues for in vivo folding has been described. Early folding residues initiate the folding process, whereas highly stable residues prevent spontaneous unfolding events. The structural relevance score proposed by this thesis is employed to characterize both types of residues. Early folding residues form pivotal secondary structure elements, but their structural relevance is average. In contrast, highly stable residues exhibit significantly increased structural relevance. This implies that residues crucial for the folding process are not relevant for structural integrity and vice versa. The position of early folding residues is preserved over the course of evolution as demonstrated for two ancient regions shared by all aminoacyl-tRNA synthetases. One arrangement of folding initiation sites resembles an ancient and widely distributed structural packing motif and captures how reverberations of the earliest periods of life can still be observed in contemporary protein structures

Improvements to methods for the quality assessment of three-dimensional models of proteins

After water, proteins are the most abundant substances in the human body, forming around 80% of its dry mass. Understanding protein function is beneficial for life needs, such as finding medicines, producing healthy foods and combating infectious diseases. Each protein molecule has its own unique sequence which is comprised of linear chains of amino acids. These amino acid chains fold to form tertiary structures, which confer the protein’s function. It is important that we can characterise protein structures in order to better understand their functions. Several experimental methods such as X-ray Crystallography and Nuclear Magnetic Resonance have been applied to solve protein structures. However, such methods are costly and time consuming, and some proteins are also problematic or impossible to solve using these methods. Consequently, the process of growing protein structure data is relatively slow in comparison to the speed of sequencing genomes and their encoded proteins, which has kept increasing especially after breakthroughs in the genetic sequencing technology. As a result, a gap has grown between known protein sequences and their resolved structures and it has been necessary to find other solutions. Computational methods for predicting the structures of proteins directly from own sequences have become fast and effective alternatives to experimental methods. Over the past 20 years there has been an emergence of different types of protein structure predicting methods, the most accurate type being the comparative modelling method, which consists of a number of steps including: template recognition, alignment, quality assessment, and ending with refinement. Each of these steps contribute to successful modelling pipelines, but perhaps the most critical step for the wider acceptance of 3D models of proteins has been the quality assessment step, where the predicted models are evaluated in terms of their likely accuracy, prior to the availability of an experimental structure. Numerous approaches to the quality estimation problem have been developed over the years including the use of statistical potentials, stereochemistry checks and machine learning techniques. Such methods have traditionally been referred to as Model Quality Assessment Programs (MQAPs). One of the leading MQAPs has been the ModFOLD method which has been developed by our group. Since its inception, ModFOLD has been continuously improved, going through many upgrades until its latest version, ModFOLD7. This study was conducted during a major development cycle, beginning with the benchmarking of ModFOLD6, the most powerful MQAP method compared to its other competitors at that time. The study starts with the investigation of the integration of ten MQAP scoring methods in an attempt to enhance performance. The study also explores the implementation of deep neural networks on the MQAP method’s pipeline, and how this technique can be used to improve the MQAP scoring approach. In the later stage of our research, we managed to improve our method significantly leading to the latest upgrade, ModFOLD7. During this project, we also participated in a number of independent blind experiments and competitions to verify our improvements. We also undertook several collaborations in order to apply our methods in practical contexts. The overall results have shown incremental but significant improvements in ModFOLD performance during this study, with an approximate 5% improvement over previous versions. However, there are still plenty of room for ModFOLD to improve further and a number of suggestions for further developments will be addressed throughout this thesis

Design, Synthesis and Anti Tubercular Screening of Triazolyl Pyrazoles as Possible MTB–CYP51 Inhibitors

The present work was focused on the design, docking, synthesis, and evaluation of the antitubercular activity of triazole-linked pyrazole derivatives as possible CYP-51 (sterol 14α-demethylase) inhibitors. PHASE I - IN-SILICO STUDIES: • Selection of the target: CYP-51 (sterol 14α-demethylase) was selected as the drug target for the anti-tubercular activity. The corresponding enzyme was obtained from the RCSB protein data bank (PDB ID:1EA1). • Selection of lead by virtual screening: Virtual screening was performed by iGEMDOCK v.2. Fifty hits were obtained from the ZINC database, from which triazole and pyrazole were selected as the lead for inhibiting CYP-51 (sterol 14α-demethylase). • Lead optimization: The six modified ligands TP1-TP6 were subjected to in-silico lead optimization. Lead optimization was done by observing in-silico ADME studies and computation of drug-like properties. Lead optimization revealed that all the six selected derivatives possess good ADME properties and hence were eligible for further study. The ligands were optimized for evaluating oral bioavailability by utilizing the Molinspiration server and SWISSadme server. • Docking: The optimized leads were subjected to docking studies using Autodock4.2 and the interactions of the derivatives with active sites of the enzyme were studied. The derivatives were subjected to interaction with CYP-51. Fluconazole was used as standard ligand. The binding energy was found to be superior for all the compounds when compared to the standard (fluconazole). Among the triazolyl pyrazole derivatives, TP1, TP4, TP3, TP2 showed maximum binding energies, -8.93, -8.68, -8.38, -8.26 kcal/mol respectively. They were interacting well with the active sites on the enzyme i.e., Tyr 76, Met 79, Phe 83, Arg 96, and Met 99. PHASE II - SYNTHESIS AND PHYSICAL CHARACTERIZATION: • Synthesis of the designed compounds: In this present work, six new compounds were synthesized. In the first scheme triazole amine and its derivatives were synthesized by the reaction of thiocarbohydrazide with substituted and unsubstituted benzoic acid. Two steps were involved in the scheme 2. The first step involves the synthesis of 5-Methyl-2-phenyl-2, 4-dihydro-3H-pyrazole-3-one by the reaction of phenylhydrazine with ethyl acetoacetate. The second step involves the synthesis of 5-Chloro-3-methyl-1-phenyl-1Hpyrazole-4-carbaldehyde. The resulting compound from the first step was treated with phosphorus oxychloride and dimethylformamide. Scheme 3 involves the synthesis of triazolyl pyrazole derivatives. Schiff bases were prepared from triazole amine derivatives with pyrazole aldehyde to obtain desired products. • Physical characterization: The melting point of newly synthesized compounds were determined. Rf values were determined by fixing various suitable solvent system on precoated silica gel G plates. The solvent system used was Acetone: Benzene (2:8). PHASE III: SPECTRAL CHARACTERIZATION: The structures of the synthesized compounds were established on the basis of UV, IR, 1H NMR, and MASS spectral data. PHASE IV: ANTI MYCOBACTERIAL SCREENING: MICROPLATE ALAMAR BLUE ASSAY (MABA) METHOD: The anti-tubercular activity was performed by microplate alamar blue assay method by using mycobacterium tuberculosis H37Rv strain. All the derivatives of triazolyl pyrazoles (TP1 to TP6) were found to inhibit the growth of Mycobacterium tuberculosis. The compound TP1 (2Cl-5NO2 triazolyl pyrazole) had shown minimum inhibitory concentration at 50 μg/ml and TP5 (4-NO2 triazolyl pyrazole) showed MIC of 1.6 μg/ml. CONCLUSION: The present study has proved to be a tool in minimizing the tedious process of drug discovery process over the traditional methods of discovery. Virtual screening was utilized for filtering the compounds and selecting the lead compounds. The In-silico ADME & drug-likeness scores of the ligands showed the compound to be promising as a good orally bioactive drug. The binding energy obtained from the docking study further confirmed the affinity of the selected leads towards the enzyme, CYP-51 (sterol 14α-demethylase) from Mycobacterium tuberculosis. Various triazolyl imino pyrazole derivatives were synthesized with good yield utilizing three schemes. The structure of the synthesized compounds were confirmed by melting point, TLC, UV, IR, NMR and mass spectra. The compounds were screened for antimycobacterial activity which establishes the correlation of activity with the docking study. The present study includes the design, docking, synthesis and screening of various triazole incorporated pyrazole imines as possible CYP-51 inhibitors and the design has paved the way to establish the lead triazolyl pyrazole as antimycobacterial drug choice. Among the synthesized compounds, 2-chloro5-nitro-triazolyl pyrazole imine was found to be potent as CYP-51 inhibitor due to superior dock result and biological activity result. The docking study reveals that the triazolyl imino prazole has excellent interaction with CYP51, and therefore can be probed as possible MtbCYP-51 inhibitors. FUTURE PERSPECTIVE: Since the most potential derivative was the chloro and nitro substituted triazolyl imino pyrazole derivative, more focus can be given on electron withdrawing substituents for future synthesis. The present novel derivatives are found to exhibit antimycobacterial activity, the mechanism of action can be confirmed by performing enzyme inhibitory assay as a future perspective