Biology subjects

Saravanan, V.

Publications and source records attributed to Saravanan, V..

5 recordsLinked to original sources

bioRxiv2025

PreDSLpmo V2.0: A deep learning-based prediction tool for functional annotation of lytic polysaccharide monooxygenases

Lytic polysaccharide monooxygenases (LPMOs) are crucial enzymes that enhance the breakdown of polysaccharides, especially for biofuel production. Current computational tools for LPMO annotation are limited to a few families, leaving many unexplored. Here, we introduce PreDSLpmo v2.0, a deep learning-based tool that classifies and annotates the fast growing numbers of LPMOs across eight families using a curated dataset comprising over 30,000 LPMO sequences. To capture the compositional, physicochemical, and structural properties of these sequences, we extracted features using Python (iFeature) as well as R-based pipelines, generating over 13500 descriptors per sequence. Ensemble feature selection was used to identify significant features for binary and multiclass classification. To address data imbalance, we maintained a 1:1 ratio for all positive and negative sets during training and validation. A range of machine learning models were systematically trained and evaluated. An independent dataset was used to estimate the performance of the trained models. The multiclass Bi-LSTM model demonstrated the highest accuracy, robustness, and generalizability, outperforming feature-based approaches. We compared the performance of the model with Pfam, dbCAN3, and BlastP searches against the UniProtKB/swissprot database. The F1-score shows that the models predicted LPMO sequences are accurate. The reliability of predictions by Bi-LSTM model is comparable to that of dbCAN3 and often better, as confirmed by Pfam domain annotation and SignalP. The model, now deployed as a web server (https://predlpmo.in) for high-throughput, sequence-based functional annotation of LPMOs, provides a scalable and reliable solution for enzyme discovery in bioenergy and industrial biotechnology.

bioinformatics↗

bioRxiv2024

The "very moment" when UDG recognizes aflipped-out uracil base in dsDNA

Uracil-DNA glycosylase (UDG) is the first enzyme in the base-excision repair (BER) pathway, acting on uracil bases in DNA. How UDG finds its targets has not been conclusively resolved yet. Based on available structural and other experimental evidence, two possible pathways are under discussion. In one, the action of UDG on the DNA bases is believed to follow a pinch-push-pull model, in which UDG generates the base-flip in an active manner. A second scenario is based on the exploitation of bases flipping out thermally from the DNA. Recent molecular dynamics (MD) studies of DNA in trinucleosome arrays have shown that base-flipping can be readily induced by the action of mechanical forces on DNA alone. This alternative mechanism could possibly enhance the probability for the second scnenario of UDG-uracil interaction via the formation of a recognition complex of UDG with flipped-out base. In this work we describe DNA structures with flipped-out uracil bases generated by MD simulations which we then subject to docking simulations with the UDG enzyme. Our results for the UDG-uracil recognition complex support the view that base-flipping induced by DNA mechanics can be a relevant mechanism of uracil base recognition by the UDG glycosylase in chromatin.

bioinformatics↗

bioRxiv2021

Differentiation of speech-induced artifacts from physiological high gamma activity in intracranial recordings

There is great interest in identifying the neurophysiological underpinnings of speech production. Deep brain stimulation (DBS) surgery is unique in that it allows intracranial recordings from both cortical and subcortical regions in patients who are awake and speaking. The quality of these recordings, however, may be affected to various degrees by mechanical forces resulting from speech itself. Here we describe the presence of speech-induced artifacts in local-field potential (LFP) recordings obtained from mapping electrodes, DBS leads, and cortical electrodes. In addition to expected physiological increases in high gamma (60-200 Hz) activity during speech production, time-frequency analysis in many channels revealed a narrowband gamma component that exhibited a pattern similar to that observed in the speech audio spectrogram. This component was present to different degrees in multiple types of neural recordings. We show that this component tracks the fundamental frequency of the participants voice, correlates with the power spectrum of speech and has coherence with the produced speech audio. A vibration sensor attached to the stereotactic frame recorded speech-induced vibrations with the same pattern observed in the LFPs. No corresponding component was identified in any neural channel during the listening epoch of a syllable repetition task. These observations demonstrate how speech-induced vibrations can create artifacts in the primary frequency band of interest. Identifying and accounting for these artifacts is crucial for establishing the validity and reproducibility of speech-related data obtained from intracranial recordings during DBS surgery.

neuroscience↗

bioRxiv2019

Application of the hierarchical bootstrap to multi-level data in neuroscience

AO_SCPLOWBSTRACTC_SCPLOWA common feature in many neuroscience datasets is the presence of hierarchical data structures, most commonly recording the activity of multiple neurons in multiple animals across multiple trials. Accordingly, the measurements constituting the dataset are not independent, even though the traditional statistical analyses often applied in such cases (e.g. Students t-test) treat them as such. The hierarchical bootstrap has been shown to be an effective tool to accurately analyze such data and while it has been used extensively in the statistical literature, its use is not widespread in neuroscience - despite the ubiquity of hierarchical datasets. In this paper, we illustrate the intuitiveness and utility of this approach to analyze hierarchically nested datasets. We use simulated neural data to show that traditional statistical tests can result in a false positive rate of over 45%, even if the Type-I error rate is set at 5%. While summarizing data across non-independent points (or lower levels) can potentially fix this problem, this approach greatly reduces the statistical power of the analysis. The hierarchical bootstrap, when applied sequentially over the levels of the hierarchical structure, keeps the Type-I error rate within the intended bound and retains more statistical power than summarizing methods. We conclude by demonstrating the effectiveness of the method in two real-world examples, first analyzing singing data in male Bengalese finches (Lonchura striata var. domestica) and second quantifying changes in behavior under optogenetic control in flies (Drosophila melanogaster).

neuroscience↗

bioRxiv2019

Dopamine depletion affects vocal acoustics and disrupts sensorimotor adaptation in songbirds

Dopamine is hypothesized to convey important error information in reinforcement learning tasks with explicit appetitive or aversive cues. However, during motor skill learning the only available feedback signal is typically an animals evaluation of the sensory feedback arising from its own behavior, rather than any external reward or punishment. It has previously been shown that intact dopaminergic signaling from the ventral tegmental area - substantia nigra compacta complex (VTA/SNc) is necessary for vocal learning in response to an external aversive auditory cue in songbirds. However, the role of dopamine in learning in the absence of explicit external cues is still unclear. Here we used male Bengalese finches (Lonchura striata var. domestica) to test the hypothesis that dopamine signaling is necessary for self-evaluation driven sensorimotor learning. We combined 6-hydroxydopamine (6-OHDA) lesions of dopaminergic terminals within Area X, a songbird basal ganglia nucleus critical for vocal learning, with a headphones learning paradigm that shifted the birds auditory feedback and compared their learning to birds without lesions. We found that 6-OHDA lesions affected song behavior in two ways. First, over a period of days lesioned birds systemically lowered their pitch regardless of the presence or absence of auditory errors. Second, 6-OHDA lesioned birds also displayed severe deficits in sensorimotor learning as measured by their adaptive change in pitch in response to the pitch-shifted auditory error. Our results suggest roles for dopamine both in motor production and in auditory error processing during vocal learning.\n\nSignificance StatementDopamine has been hypothesized to convey a reward prediction error signal in learning tasks involving external reinforcement. However the role dopamine plays in tasks involving self-guided error correction in the absence of external reinforcement is much less clear. To address this question, we studied the role of dopamine in sensorimotor adaptation using male Bengalese finches, which spontaneously produce a complex motor behavior (song) and are capable of modulating their behavioral output in response to induced auditory errors. Our results reveal that in addition to conveying errors in motor performance, dopamine may also have a role in modulating effort and in choosing a corrective response to the auditory error.

neuroscience↗