Biology subjects

Busi, R.

Publications and source records attributed to Busi, R..

6 recordsLinked to original sources

bioRxiv2025

Systematic assessment of homology-based methods for fine-grained functional annotation using diverse protein families

The size of the protein sequence database is increasing without a consequent increase in the number of proteins with known molecular function, especially at the fine-grained level. Alignment-based approaches such as BLAST and profile hidden Markov models (HMMs) are widely used to infer homology and transfer annotation to be subsequently confirmed by experiments. The ability of BLASTp to distinguish orthologs from paralogs varies across protein families; for profile HMMs, this depends on the sequences considered for generating multiple sequence alignments. In this study, we systematically evaluated the performance of BLASTp and HMM-based methods for fine-grained function annotation using carefully curated protein datasets that are diverse in sequence-structure-function relationships. Expectedly, BLASTp performed well in detecting close homologs but failed to detect remote homologs. BLASTp detected homology between 22.6% and 100% of sequence pairs within different homologous protein families. The extent of sequence identity between trypsin and chymotrypsin sequences is high despite differences in fine-grained molecular function. Transferring function annotation based on homology inferred from BLASTp leads to errors in trypsin-chymotrypsin-like situations. Profile HMMs improved sensitivity and captured subtle homology signals even when sequence identity was low, though some known family members scored below threshold due to functional divergence or mutations at catalytic sites. We further showed that relying solely on homology for annotation transfer can lead to misleading conclusions when proteins have evolved divergent functions despite structural similarity. Our findings highlight that a cautious approach involving BLASTp, profile HMMs, and expert domain knowledge provides the most reliable strategy for functional annotation. After all, not every family member may be doing what we think they are doing.

bioinformatics↗

bioRxiv2024

How suitable are clustering methods for functional annotation of proteins?

The advent of affordable high-throughput genome sequencing has drastically expanded protein sequence databases, necessitating the development of computational tools to predict protein function from sequence data. Current methods, such as BLASTp and profile HMMs, while effective, are limited by difficulties in detecting remote homologs and uncertainties in multiple sequence alignments. To address this, we explore the use of clustering algorithms for unsupervised protein function annotation, using pseudo-amino acid composition (PAAC) as features. In this study, we evaluated nine clustering algorithms for their ability to segregate protein sequences based on functional differences using the PAAC feature. Using intrinsic metrics, particularly the silhouette coefficient (SC), we determined the optimal number of clusters (k) for each algorithm. We observed that agglomerative clustering produced results resembling phylogenetic relationships; even k-means clustering, Gaussian mixture model (GMM), and spectral clustering do so, but occasionally merge datapoints from distinct original clusters at higher k values. Our findings reveal that k-means clustering, GMM, and agglomerative clustering effectively segregate distinct protein functional families, but effectiveness decreases when distinguishing fine-grained functional differences. Notably, spectral clustering underperformed relative to other methods. Affinity propagation clustering, while effective in some cases, generated more clusters than expected and is prone to false positives. Overall, we find that some of the clustering algorithms are suitable for functional annotation of protein sequences using PAAC as a feature set, even when the number of ground-truth sequences is limited. The implementation of the clustering method for protein sequences is available in the GitHub repository (https://github.com/RakeshBusi/Clustering). It provides comprehensive steps for preprocessing, feature extraction, clustering, and evaluation. All steps are presented in a Jupyter Notebook in the repository. Author SummaryWe are in the age of big data. It is an outcome of the development of high-throughput techniques. The resources spent to develop and deploy such techniques are considerably large. However, data by itself is not an end but a means to answer questions of relevance. Hence, the development and/or customisation of techniques that help us to interpret and utilise data are also important. In this study, we focus on customising a popular technique, namely clustering, to extract biological information from the ever-growing protein sequence database. We test the suitability of nine clustering algorithms to determine a proteins molecular function solely based on its amino acid sequence. Based on our findings, we recommend using a combination of the four algorithms, namely, k-means, Gaussian mixture model, agglomerative, and affinity propagation. However, we note that proteins with subtle functional differences cluster together, and fine-tuning algorithms to separate such proteins requires additional experimental data.

bioinformatics↗

bioRxiv2024

Investigating the links between trifluralin persistence in Western Australian soils and trifluralin resistance in resident annual ryegrass (Lolium rigidum) populations

The pre-emergence herbicide trifluralin is widely used in the minimum-tillage cropping systems of Australia, with the result that resistance to trifluralin is increasing in the major weed of the region, annual ryegrass (Lolium rigidum). Repeated exposure to low herbicide rates is also known to result in the rapid evolution of resistance in weed populations. As trifluralin is highly volatile, readily photo-decomposed, metabolised by soil microbes and to bind strongly to soil organic matter, there are many factors that could result in weed populations receiving reduced (even sub-lethal) rates of the herbicide. To investigate whether trifluralin dissipation could play a role in the increasing levels of trifluralin resistance in annual ryegrass, resistance levels of populations from 18 Western Australian farms were compared with the dissipation rate of trifluralin applied to soil collected from these farms. Although there was no direct correlation between resistance level and trifluralin half-life, there were links between resistance and soil properties which suggest that higher rates of trifluralin dissipation could make a minor contribution to the development of resistance.

plant biology↗

bioRxiv2024

Identifying key amino acid types that distinguish paralogous proteins using Shapley value based feature subset selection

We view a protein as the composite of the standard 20 amino acids, ignoring their order in the protein sequence. With this view, we try to identify the important amino acid types that distinguish pairs of paralogous proteins, thereby playing a role in their functional difference. Using only the amino acid composition (AAC) as features and a linear classifier, we find that many pairs of paralogous protein families can be classified accurately. Next, we use an existing Shapley value-based feature subset selection algorithm, SVEA, to identify the important amino acid types that distinguish a pair of paralogous proteins. The SVEA algorithm assigns a score, Shapley value, to each feature, amino acid type, based on its contribution to the classifiers training error. We identify the important distinguishing amino acid types as those whose Shapley value exceeds a data-driven threshold. We refer to these as the amino acid feature subset (AFS). We find that many paralog pairs can still be accurately classified using only the AFS composition. We partition AFS based on the classifier weights to infer class-wise amino acid importance. We verify whether the identified AFS amino acids indeed play a role in the functional difference of the paralog pairs using various methods - multiple sequence alignment, 3D structure analysis, and supporting evidence from biology literature. We also discuss some consistencies observed in the Shapley value based ranking and the AFS when comparing the AFS of two different but related paralog pairs. We demonstrate the results for 15 pairs of paralogous proteins.

bioinformatics↗

bioRxiv2021

Developing a Petri dish test to detect resistance to key herbicides in Lolium rigidum

Overreliance on herbicides for weed control is conducive to the evolution of herbicide resistance. Annual ryegrass (Lolium rigidum Gaud.) is a species that is prone to evolve resistance to a wide range of herbicide modes of action. Rapid detection of herbicide-resistant weed populations in the field can aid farmers to optimize the use of herbicides for their control. The feasibility of a portable agar-based test to rapidly and reliably detect annual ryegrass resistance to key herbicides such as clethodim, glyphosate, pyroxasulfone and trifluralin on-farm was investigated. The three research phases of this study show that: a) easy-to-interpret results are obtained with non-dormant seed from well-characterised susceptible and resistant populations, and resistance is detected as effectively as with traditional dose-response pot-based resistance assays. However, the test may not be suitable for portable use on-farm because of b) the low stability of some herbicides such as trifluralin and clethodim in agar and c) the tendency of seed dormancy in freshly-harvested seeds to confound the results. The agar-based test is best used as a research tool as a complement to confirm results obtained in traditional pot-based resistance screenings. Comprehensive agar test and / or whole-plant resistance tests by herbicide application at the recommended label rate (whole plants grown in pots) are the current benchmark for proactive in- and off-season resistance testing and should be promoted more widely to allow early detection of resistance, optimization of herbicide technology use and deploy appropriate weed management interventions.

plant biology↗

bioRxiv2017

Pyroxasulfone Resistance in Lolium rigidum Conferred by Enhanced Metabolic Capacity

Author ContributionsRB and AP designed and performed the experiments. RB, AP, TG, SP wrote the manuscript.\n\nOne sentence summaryThis study provides novel insight into herbicide resistance conferred by GST-based detoxification to allow proactive intervention to minimize weed resistance evolution.\n\nAbstractThe evolution of herbicide-resistant weed populations in response to synthetic herbicide selective pressure is threatening safe weed control practices achieved by these molecules. In Australia multiple-resistant populations of annual ryegrass (Lolium rigidum) are effectively controlled by soil-applied herbicides which provide adequate weed control.\n\nIn this study we define the mechanistic basis of the experimentally-evolved resistance to the soil-applied herbicide pyroxasulfone in a L. rigidum population. TLC and HPLC-MS provide biochemical confirmation that pyroxasulfone resistance is metabolism-based with identification and quantification of pyroxasulfone metabolites formed via a glutathione conjugation pathway in pyroxasulfone-resistant L. rigidum plants. The observed over-expression of two putative resistance-endowing GST genes is consistent with pyroxasulfone-resistance in parental plants (P6) and positively correlated to pyroxasulfone resistance in F1 pair-cross progenies. Thus, a major detoxification mechanism involves glutathione conjugation to pyroxasulfone and GST over-expression in pyroxasulfone-resistant L. rigidum plants. The definition of the genetic basis of metabolic resistance in weeds can be a first crucial step towards chemical means to reverse resistance and improve long-term weed resistance management.

biochemistry↗