Biology subjects

Staples, M.

Publications and source records attributed to Staples, M..

3 recordsLinked to original sources

bioRxiv2021

Characterisation of the phase-variable autotransporter Lav reveals a role in host cell adherence and biofilm formation in Non-Typeable Haemophilus influenzae

Lav is an autotransporter protein found in pathogenic Haemophilus and Neisseria species. Lav in non-typeable Haemophilus influenzae (NTHi) is phase-variable: the gene reversibly switches ON-OFF via changes in length of a locus-located GCAA(n) simple DNA sequence repeat tract. The expression status of lav was examined in carriage and invasive collections of NTHi, where it was predominantly not expressed (OFF). Phenotypic study showed lav expression (ON) results in increased adherence to host cells, and denser biofilm formation. A survey of Haemophilus spp. genome sequences showed lav is present in [~]60% of NTHi strains, but lav is not present in most typeable H. influenzae. Sequence analysis revealed a total of five distinct variants of the Lav passenger domain present in Haemophilus spp., with these five variants showing a distinct lineage distribution. Determining the role of Lav in NTHi will help understand the role of this protein during distinct pathologies.

microbiology↗

bioRxiv2019

Artificial Intelligence for Bioinformatics: Applications in Protein Folding Prediction

AI recently shows great promise in the field of bioinformatics, such as protein structure prediction. The Critical Assessment of protein Structure Prediction (CASP) is a nationwide experiment that takes place biannually, which centered around analyzing the best current systems for predicting protein tertiary structures. In this paper, we research on available AI methods and features, and then explore novel methods based on reinforcement learning. Such method will have profound implications for R&D in bioinformatics and add an additional platform to the management of innovation in biotechnology.

bioinformatics↗

bioRxiv2019

AngularQA: Protein Model Quality Assessment with LSTM Networks

Quality Assessment (QA) plays an important role in protein structure prediction. Traditional protein QA methods suffer from searching databases or comparing with other models for making predictions, which usually fail. We propose a novel protein single-model QA method which is built on a new representation that converts raw atom information into a series of carbon-alpha (C) atoms with side-chain information, defined by their dihedral angles and bond lengths to the prior residue. An LSTM network is used to predict the quality by treating each amino acid as a time-step and consider the final value returned by the LSTM cells. To the best of our knowledge, this is the first time anyone has attempted to use an LSTM model on the QA problem; furthermore, we use a new representation which has not been studied for QA. In addition to angles, we make use of sequence properties like secondary structure at each time-step, without using any database. Our model achieves an overall correlation of 0.651 on the CASP12 testing dataset. Our experiment points out new directions for QA problem and our method could be widely used for protein structure prediction problem. The software is freely available at GitHub: https://github.com/caorenzhi/AngularQA

bioinformatics↗