Application of coevolution-based methods and deep learning for structure prediction of protein complexes

The three-dimensional structures of proteins play a critical role in determining their biological functions and interactions. Experimental determination of protein and protein complex structures can be expensive and difficult. Computational prediction of protein and protein complex structures has therefore been an open challenge for decades. Recent advances in computational structure prediction techniques have resulted in increasingly accurate protein structure predictions. These techniques include methods that leverage information about coevolving residues to predict residue interactions and that apply deep learning techniques to enable better prediction of residue contacts and protein structures. Prior to the work outlined in this thesis, coevolution-based methods and deep learning had been shown to improve the prediction of single protein domains or single protein chains. Most proteins in living organisms do not function on their own but interact with other proteins either through transient interactions or by forming stable protein complexes. Knowledge of protein complex structures can be useful for biological and disease research, drug discovery and protein engineering. Unfortunately, a large number of protein complexes do not have experimental structures or close homolog structures that can be used as templates. In this thesis, methods previously developed and applied to the de novo prediction of single protein domains or protein monomer chains were modified and leveraged for the prediction of protein heterodimer and homodimer complexes. A number of coevolution-based tools and deep learning methods are explored for the purpose of predicting inter-chain and intra-chain residue contacts in protein dimers. These contacts are combined with existing protein docking methods to explore the prediction of homodimers and heterodimers. Overall, the work in this thesis demonstrates the promise of leveraging coevolution and deep-learning for the prediction of protein complexes, shows improvements in protein complex prediction tasks achieved using coevolution based methods and deep learning methods, and demonstrates remaining challenges in protein complex prediction

Cannabidiol is beneficial in management of drug-resistant Dravet syndrome

A critical appraisal and clinical application of Devinsky O, Cross H, Laux L, et al. Trial of Cannabidiol for Drug-Resistant Seizures in the Dravet Syndrome. N Engl J Med. 2017;376(21):2011-2020. doi: 10.1056/NEJMoa1611618

The landscape of viral associations in human cancers

Here, as part of the Pan-Cancer Analysis of Whole Genomes (PCAWG) Consortium, for which whole-genome and—for a subset—whole-transcriptome sequencing data from 2,658 cancers across 38 tumor types was aggregated, we systematically investigated potential viral pathogens using a consensus approach that integrated three independent pipelines. Viruses were detected in 382 genome and 68 transcriptome datasets. We found a high prevalence of known tumor-associated viruses such as Epstein–Barr virus (EBV), hepatitis B virus (HBV) and human papilloma virus (HPV; for example, HPV16 or HPV18). The study revealed significant exclusivity of HPV and driver mutations in head-and-neck cancer and the association of HPV with APOBEC mutational signatures, which suggests that impaired antiviral defense is a driving force in cervical, bladder and head-and-neck carcinoma. For HBV, HPV16, HPV18 and adeno-associated virus-2 (AAV2), viral integration was associated with local variations in genomic copy numbers. Integrations at the TERT promoter were associated with high telomerase expression evidently activating this tumor-driving process. High levels of endogenous retrovirus (ERV1) expression were linked to a worse survival outcome in patients with kidney cancer

Docking, synthesis and evaluation of N-(2-(4-methoxy-2-oxo-1-phenyl/methyl- 1,2-dihydroquinolin-3-yl)-2-oxoethyl)-N-substitutedphenylbenzenesulfonamide derivatives as hypoglycemic agents

431-437The current research work involves in silico docking studies, synthesis, characterisation and study on hypoglycemicactivity of a series of N-(2-(4-methoxy-1-methyl/phenyl-2-oxo-1,2-dihydroquinolin-3-yl)-2-oxoethyl)-Nsubstitutedprimaryamino) -benzenesulfononamides [IVa/b (1-6)]. Compounds exhibit prominent anti-diabetic activity by glucose oxidase peroxidase (GOD-POD) method which involves measurement of average serum glucose levels in mg/dl as well as conserved hydrogen bonds with one or more amino acid residues in the active pocket of murine 11β-hydroxysteroid dehydrogenase domain. Among all the synthesized compounds, compound IVb2 shows prominent anti-diabetic activity and higher Moldock score as compared to standard drug Glibenclamide. Structural analysis of the synthesized compounds has been done by standard spectroscopic techniques

Towards the accurate modelling of antibody-antigen complexes from sequence using machine learning and information-driven docking

Antibody-antigen complex modelling is an important step in computational workflows for therapeutic antibody design. While experimentally determined structures of both antibody and the cognate antigen are often not available, recent advances in machine learning-driven protein modelling have enabled accurate prediction of both antibody and antigen structures. Here, we analyse the ability of protein-protein docking tools to use machine learning generated input structures for information-driven docking. We find that HADDOCK can generate accurate models of antibodyantigen complexes using an ensemble of antibody structures generated by machine learning tools and AlphaFold2 predicted antigen structures. Targeted docking using knowledge of the complementary determining regions on the antibody and some information about the targeted epitope allows the generation of high quality models of the complex with reduced sampling, resulting in a computationally cheap protocol that outperforms the ZDOCK baseline. The data set used to benchmark the docking protocols in this study is available at github.com/haddocking/ai-antibodies. The docking models will be deposited at data.sbgrid.org/labs/32/ upon acceptance

The landscape of viral associations in human cancers

Here, as part of the Pan-Cancer Analysis of Whole Genomes (PCAWG) Consortium, for which whole-genome and-for a subset-whole-transcriptome sequencing data from 2,658 cancers across 38 tumor types was aggregated, we systematically investigated potential viral pathogens using a consensus approach that integrated three independent pipelines. Viruses were detected in 382 genome and 68 transcriptome datasets. We found a high prevalence of known tumor-associated viruses such as Epstein-Barr virus (EBV), hepatitis B virus (HBV) and human papilloma virus (HPV; for example, HPV16 or HPV18). The study revealed significant exclusivity of HPV and driver mutations in head-and-neck cancer and the association of HPV with APOBEC mutational signatures, which suggests that impaired antiviral defense is a driving force in cervical, bladder and head-and-neck carcinoma. For HBV, HPV16, HPV18 and adeno-associated virus-2 (AAV2), viral integration was associated with local variations in genomic copy numbers. Integrations at the TERT promoter were associated with high telomerase expression evidently activating this tumor-driving process. High levels of endogenous retrovirus (ERV1) expression were linked to a worse survival outcome in patients with kidney cancer

"Mother-weights" and lost fathers: parents in South Asian American literature

That parent-child relationships should play a significant role within South Asian American literature is perhaps no surprise, since this is crucial material for any writer. But the particular forms they so often take – a dysfunctional mother-daughter dynamic, leading to the search for maternal surrogates; and the figure of the prematurely deceased father – are more perplexing. Why do families adhere to these patterns in so many South Asian American texts and what does that tell us about this œuvre? More precisely, why are mothers subjected to a harsher critique than fathers and what purpose does this critique serve? How might we interpret the trope of the untimely paternal death? In this article I will seek to answer these questions – arguably key to an understanding of this growing body of writing – by considering works produced between the 1990s and the early twenty-first century by a range of South Asian American writers

A Drosophila-centric view of protein tyrosine phosphatases

AbstractMost of our knowledge on protein tyrosine phosphatases (PTPs) is derived from human pathologies and mouse knockout models. These models largely correlate well with human disease phenotypes, but can be ambiguous due to compensatory mechanisms introduced by paralogous genes. Here we present the analysis of the PTP complement of the fruit fly and the complementary view that PTP studies in Drosophila will accelerate our understanding of PTPs in physiological and pathological conditions. With only 44 PTP genes, Drosophila represents a streamlined version of the human complement. Our integrated analysis places the Drosophila PTPs into evolutionary and functional contexts, thereby providing a platform for the exploitation of the fly for PTP research and the transfer of knowledge onto other model systems