Biology subjects

Bhandari, B. K.

Publications and source records attributed to Bhandari, B. K..

5 recordsLinked to original sources

bioRxiv2024

A generalised protein identification method for novel and diverse sequencing technologies

Protein sequencing is a rapidly evolving field with much progress towards the realisation of a new generation of protein sequencers. The early devices, however, may not be able to reliably discriminate all 20 amino acids, resulting in a partial, noisy and possibly error-prone signature of a protein. Rather than achieving de novo sequencing, these devices may aim to identify target proteins by comparing such signatures to databases of known proteins. However, there are no broadly applicable methods for this identification problem. Here, we devise a hidden Markov model method to study the generalized problem of protein identification from noisy signature data. Using a hypothetical sequencing device that can simulate several novel devices, we show that on the human protein database (N=20,181) our method has a good performance under many different operating conditions such as various levels of signal resolvability, different numbers of discriminated amino acids, sequence fragments and insertion and deletion error rates. Our results demonstrate the possibility of protein identification with high accuracy on many early experimental devices. We anticipate our method to be applicable for a wide range of protein sequencing devices in the future.

bioinformatics↗

bioRxiv2022

LazyPair: scalable prediction of protein-protein interactions and interaction types

MotivationAlmost all cellular processes require protein-protein interactions. Common interaction types include binding, post-translational modifications, and catalysis. However, existing prediction tools do not take these interaction types into account and do not scale well on proteome-wide prediction. ResultsHere we show that a random forest classifier trained on per-residue physicochemical and biochemical properties is useful for predicting protein-protein interactions. Counterintuitively, we find that training random forests by individual interaction types improves accuracy. Furthermore, a combination of these specialised classifiers improves generalisability. We call our protein-protein interaction prediction tool LazyPair. More importantly, LazyPair outperforms the state-of-the-art in accuracy, generalisability and scalability. Availability and implementationLazyPair and the source code and data for reproducing our analysis are freely available at https://github.com/Gardner-BinfLab/PPI_Analysis_2022 and https://doi.org/10.5281/zenodo.6071630. The web server version and the source code are freely available at https://tisigner.com/lazypair/ and https://github.com/Gardner-BinfLab/TISIGNER-ReactJS, respectively.

bioinformatics↗

bioRxiv2020

Annotating eukaryotic and toxin-specific signal peptides using Razor

MotivationSignal peptides are responsible for protein transport and secretion and are ubiquitous to all forms of life. The annotation of signal peptides is important for understanding protein translocation and toxin secretion and evolution. ResultsHere we explore the features of these signal sequences from eukaryotic proteins. Strikingly, we find that the signal peptides from secretory toxins have common features across kingdoms, supporting the idea of horizontal gene transfer or convergence of toxin genes across kingdoms. We leverage these features to build Razor, a simple yet powerful tool specialised in identifying signal peptides from toxins using the first 23 N-terminal residues. We demonstrate the usability of Razor by analysing all the sequences reviewed by UniProt. Indeed, Razor is able to identify toxins using their N-terminal sequences only. Strikingly, we also discover that many defensive proteins across kingdoms harbour a toxin-like signal peptide; some of these defensive proteins have emerged through convergent evolution, e.g. defensin and defensin-like protein families, and phospholipase families. In sum, Razor uses an approach independent of homology search to identify novel and known toxin classes across species using N-terminal residues. Availability and implementationRazor is available as a web application (https://tisigner.com/razor) and a command-line tool (https://github.com/Gardner-BinfLab/Razor).

bioinformatics↗

bioRxiv2020

Protein solubility is controlled by global structural flexibility

MotivationRecombinant protein production is a widely used technique in the biotechnology and biomedical industries, yet only a quarter of target proteins are soluble and can therefore be purified. ResultsWe have discovered that global structural flexibility, which can be modeled by normalised B-factors, accurately predicts the solubility of 12,216 recombinant proteins expressed in Escherichia coli. We have optimised B-factors, and derived a new set of values for solubility scoring that further improves prediction accuracy. We call this new predictor the Solubility-Weighted Index (SWI). Importantly, SWI outperforms many existing protein solubility prediction tools. Furthermore, we have developed SoDoPE (Soluble Domain for Protein Expression), a web interface that allows users to choose a protein region of interest for predicting and maximising both protein expression and solubility. AvailabilityThe SoDoPE web server and source code are freely available at https://tisigner.com/sodope and https://github.com/Gardner-BinfLab/TISIGNER-ReactJS, respectively. The code and data for reproducing our analysis can be found at https://github.com/Gardner-BinfLab/SoDoPE_paper2020.

bioinformatics↗

bioRxiv2019

Highly accessible translation initiation sites are predictive of successful heterologous protein expression

Recombinant protein production is a key process in generating proteins of interest in the pharmaceutical industry and biomedical research. However, about 50% of recombinant proteins fail to be expressed in a variety of host cells. To address this problem, we modified up to the first nine codons of messenger RNAs with synonymous substitutions and showed that protein levels can be tuned. These modifications alter the accessibility of translation initiation sites. We also reveal the dynamics between accessibility, gene expression, and turnovers using a coarse-grained simulation.

bioinformatics↗