Biology subjects

Ahalawat, N.

Publications and source records attributed to Ahalawat, N..

9 recordsLinked to original sources

bioRxiv2025

Impact of Long-Term Integrated Nutrient Management Practices on Soil Bacterial Diversity: A 16S rRNA-Based Metagenomic Approach

The growing worlds population, expected to surpass 9 billion by 2050, demands a 70-100% increase in farm productivity, posing significant challenges for sustainable agriculture. Conventional farming, reliant on chemical fertilizers and pesticides, negatively impacts environmental sustainability and food quality. Metagenomics, a culture-independent technique, has revolutionized the study of microbial communities by enabling comprehensive analysis of microbial diversity and function. This study evaluated the effects of combined nutrient management practices--including farmyard manure (FYM), wheat straw, green manure, and NPK fertilizers--on soil bacterial diversity using 16S rRNA-based metagenomic analysis. Key findings revealed the dominance of bacterial phyla such as Proteobacteria, Actinobacteria, and Firmicutes, which play critical roles in nutrient cycling, organic matter decomposition, and plant growth promotion. Distinct taxonomic distributions were observed across the samples, with Proteobacteria and Firmicutes dominating in control (T1), NPK (T2), and NPK_Green Manure (T5) treatments, while a shift in phyla composition was noted in NPK_FYM (T3) and NPK_WheatStraw (T4). Genus-level analysis revealed that Pseudomonas and Bacillus species were abundant in control (T1) and NPK (T2), while Sphingomonas species were dominant in NPK_FYM (T3). Further diversity analyses revealed significant variation across the samples, with T3 showing the highest richness and control (T1) showing the lowest richness. The rarefaction curve confirmed sufficient sampling for capturing microbial diversity, while beta diversity analysis, including Principal Coordinate Analysis (PCA) and Bray-Curtis clustering, indicated distinct microbial community structures across different treatments. The combined use of organic amendments with NPK fertilizer enhanced microbial diversity and population, indicating their potential to support resilient and productive soil ecosystems.

bioinformatics↗

bioRxiv2024

Unbiased learning of protein conformational representation via unsupervised random forest

Accurate data representation is paramount in biophysics to capture the functionally relevant motions of biomolecules. Traditional feature selection methods, while effective, often rely on labeled data based on prior knowledge and user-supervision, limiting their applicability to novel systems. Here, we present unsupervised random forest (URF), a self-supervised adaptation of traditional random forests that identifies functionally critical features of biomolecules without requiring prior labels. By devising a memory-efficient implementation, we first demonstrate URFs capability to learn important sets of inter-residue features of a protein and subsequently to resolve its complex conformational landscape, performing at par or surpassing its traditional supervised counterpart and 15 other leading baseline methods. Crucially, URF is supplemented by an internal metric, the learning coefficient, which automates the process of hyper-parameter optimization, making the method robust and user-friendly. URFs remarkable ability to learn important protein features in an unbiased fashion was validated against 10 independent protein systems including both both folded and intrinsically disordered states. In particular, benchmarking investigations showed that the protein representations identified by URF are functionally meaningful in comparison to current state-of-the-art deep learning methods. As an application, we show that URF can be seamlessly integrated with downstream analyses pipeline such as Markov state models to attain better resolved outputs. The investigation presented here establishes URF as a leading tool for unsupervised representation learning in protein biophysics.

biophysics↗

bioRxiv2022

Resolving Protein Conformational Plasticity and Substrate Binding Through the Lens of Machine-Learning

A long-standing target in elucidating the biomolecular recognition process is the identification of binding-competent conformations of the receptor protein. However, protein conformational plasticity and the stochastic nature of the recognition processes often preclude the assignment of a specific protein conformation to an individual ligand-bound pose. In particular, we consider multi-microsecond long Molecular dynamics simulation trajectories of ligand recognition process in solvent-inaccessible cavity of two archtypal systems: L99A mutant of T4 Lysozyme and Cytochrome P450. We first show that if the substrate-recognition occurs via long-lived intermediate, the protein conformations can be automatically classified into substrate-bound and unbound state through an unsupervised dimensionality reduction technique. On the contrary, if the recognition process is mediated by selection of transient protein conformation by the ligand, a clear correspondence between protein conformation and binding-competent macrostates can only be established via a combination of supervised machine learning (ML) and unsupervised dimension reduction approach. In such scenario, we demonstrate that an a priori random forest based supervised classification of the simulated trajectories recognition process would help characterize key amino-acid residue-pairs of the protein that are deemed sensitive for ligand binding. A subsequent unsupervised dimensional reduction via time-lagged independent component analysis of the selected residue-pairs would delineate a conformational landscape of protein which is able to demarcate ligand-bound pose from the unbound ones. As a key breakthrough, the ML-based protocol would identify distal protein locations which would be allosterically important for ligand binding and characterise their roles in recognition pathways.

biophysics↗

bioRxiv2020

Reconciling conformational heterogeneity and Substrate Recognition in Cytochrome P450

Cytochrome P450, the ubiquitous metalloenzyme involved in detoxification of foreign components, has remained one of the most popular systems for substrate-recognition process. However, despite being known for its high substrate specificity, the mechanistic basis of substrate-binding by archetypal system cytochrome P450cam has remained at odds with the contrasting reports of multiple diverse crystallographic structures of its substrate-free form. Here we address this issue by elucidating the probability of mutual dynamical transition to the other crystallographic pose of cytochrome P450cam and vice versa via unbiased all-atom computer simulation. A robust Markov state model (MSM), constructed using adaptively sampled 84 microsecond-long Molecular dynamics simulation trajectories, maps the broad and heterogenous P450cam conformational landscape into five key sub-states. In particular, the MSM identifies an intermediate-assisted dynamic equilibrium between a pair of conformations of P450cam, in which the substrate-recognition sites remain closed and open respectively. However, the estimate of a significantly high stationary population of closed conformation, coupled with faster rate of open [->] closed transition than its reverse process, dictates that the net conformational equilibrium would be swayed in favour of closed conformation. Together, the investigation quantitatively infers that while a potential substrate of cytochrome P450cam would in principle explore a diverse array of conformations of substrate-free protein, it would mostly encounter a closed or solvent-occluded conformation and hence would follow an induced-fit based recognition process. Overall, the work reconciles multiple precedent crystallographic, spectroscopic investigations and establishes how a statistical elucidation of conformational heterogeneity in protein would provide crucial insights in the mechanism of potential substrate-recognition process. STATEMENT OF SIGNIFICANCEConformational heterogeneity plays an important role in defining the structural and functional dynamics of the enzymes. While the static three-dimensional crystallographic structures of enzymes solved in different conditions and/or environments are crucial to provide the conformational sub-states of enzymes, these are not sufficient to understand the kinetics and thermodynamics of these sub-states and their role in substrate recognition process. Cytochrome P450cam, the archtypal metalloenzyme, presents such a complex scenario due to prevalent reports of contrasting crystallographic structures of its substrate-free form. This work quantifies the conformational heterogeneity of substrate-free P450cam by exploring the possibility of mutual transition among the crystallographic poses at an atomic resolution and in the process elucidates its possible substrate-recognition mechanism.

biochemistry↗

bioRxiv2019

Non-affine displacements encode collective conformational fluctuations in proteins

Identifying subtle conformational fluctuations underlying the dynamics of bio macro-molecules is crucial for resolving their free energy landscape. We show that a collective variable, originally proposed for crystalline solids, is able to filter out essential macro-molecular motions more efficiently than other approaches. While homogenous or affine deformations of the biopolymer are trivial, biopolymer conformations are complicated by the occurrence of in-homogenous or non-affine displacements of atoms relative to their positions in the native structure. We show that these displacements encode functionally relevant conformations of macromolecule and, in combination with a formalism based upon time-structured independent component analysis, quantitatively resolve the free energy landscape of a number of macromolecules of hierarchical complexity. The kinetics of conformational transitions among the basins can now be mapped within the framework of a Markov state model. The non-affine modes, obtained by projecting out homogenous fluctuations from the local displacements, are found to be responsible for local structural changes required for transitioning between pairs of macro states.

biophysics↗

bioRxiv2018

Mapping the Substrate Recognition Pathway in Cytochrome P450

Cytochrome P450s are ubiquitous metalloenzymes involved in the metabolism and detoxification of foreign components via catalysis of the hydroxylation reactions of a vast array of organic substrates. However, despite the breadth of cytochrome P450 mediated reactions, a long-standing question is: How does the substrate, in the first place, access the catalytic center of cytochrome P450? The prevalence of conflicting crystallographic evidences of both closed and open catalytic center in the substrate-free and substrate-bound cytochrome P450 has given rise to a notion of conformational heterogeneity, which makes the plausible mechanism of substrate recognition by cytochrome P450 puzzling from structural point of view. Here we report multi-microsecond-long unbiased molecular dynamics simulations, which are able to capture the spontaneous process of binding of substrate from bulk solvent to the occluded catalytic center of an archetypal system cytochrome P450cam, at an atomistic precision. In all binding trajectories, the substrate enters through a single channel, where it makes its first contact with the protein-surface and subsequently dwells in a highly long-lived intermediate state, before sliding into the catalytic center of P450cam. The simulated substrate-bound pose and crystallographic pose are in excellent agreement. Contrary to the prevalent hypotheses, our results indicate that a large-scale opening of F/G loop of P450cam is not required for passage of substrate to the catalytic center. Rather, we find that a substrate-induced side-chain displacement of Phe87 residue, coupled with a complex array of dynamical interconversions of multiple metastable substrate conformations along the entry channel, drives the substrate recognition in P450cam. By reconciling multiple precedent investigations, this work put forward an unambiguous view of the substrate recognition mechanism in deep buried cavity of cytochrome P450.

biophysics↗

bioRxiv2018

On identifying collective displacements in apo-proteins that reveal eventual binding pathways

Binding of small molecules to proteins often involves large conformational changes in the latter, which open up pathways to the binding site. Observing and pinpointing these rare events in large scale, all-atom, computations of specific protein-ligand complexes, is expensive and to a great extent serendipitous. Further, relevant collective variables which characterise specific binding or un-binding scenarios are still difficult to identify despite the large body of work on the subject. Here, we show that possible primary and secondary binding pathways can be discovered from short simulations of the apo-protein without waiting for an actual binding event to occur. We use a projection formalism, introduced earlier to study deformation in solids, to analyse local atomic displacements into two mutually orthogonal subspaces -- those which are \"affine\" i.e. expressible as a homogeneous deformation of the native structure, and those which are not. The susceptibility to non-affine displacements among the various residues in the apo-protein is then shown to correlate with typical binding pathways and sites crucial for allosteric modifications. We validate our observation with all-atom computations of three proteins, T4-Lysozyme, Src kinase and Cytochrome P450.

biophysics↗

bioRxiv2018

Assessment and Optimization of Collective Variables for Protein Conformational Landscape: GB1 β-hairpin as a Case Study

Collective variables (CV), when chosen judiciously, can play an important role in recognizing rate-limiting processes and rare events in any biomolecular systems. However, high dimensionality and inherent complexities associated with such biochemical systems render the identification of an optimal CV a challenging task, which in turn precludes the elucidation of underlying conformational landscape in sufficient details. In this context, a relevant model system is presented by 16residue, {beta} hairpin of GB1 protein. Despite being the target of numerous theoretical and computational studies for understanding the protein folding, the set of CVs optimally characterizing the conformational landscape of, {beta} hairpin of GB1 protein has remained elusive, resulting in a lack of consensus on its folding mechanism. Here we address this by proposing a pair of optimal CVs which can resolve the underlying free energy landscape of GB1 hairpin quite efficiently. Expressed as a linear combination of a number of traditional CVs, the optimal CV for this system is derived by employing recently introduced Timestructured Independent Component Analysis (TICA) approach on a large number of independent unbiased simulations. By projecting the replica-exchange simulated trajectories along these pair of optimized CVs, the resulting free energy landscape of this system are able to resolve four distinct wellseparated metastable states encompassing the extensive ensembles of folded,unfolded and molten globule states. Importantly, the optimized CVs were found to be capable of automatically recovering a novel partial helical state of this protein, without needing to explicitly invoke helicity as a constituent CV. Furthermore, a quantitative sensitivity analysis of each constituent in the optimized CV provided key insights on the relative contributions of the constituent CVs in the overall free energy landscapes. Finally, the kinetic pathways con necting these metastable states, constructed using a Markov State Model, provide an optimum description of underlying folding mechanism of the peptide. Taken together, this work oers a quantitatively robust approach towards comprehensive mapping of the underlying folding landscape of a quintessential model system along its optimized collective variables.

biophysics↗

bioRxiv2018

Atomic Resolution Mechanism of Ligand Binding to a Solvent Inaccessible Cavity in T4 Lysozyme

Ligand binding sites in proteins are often localized to deeply buried cavities, inaccessible to bulk solvent. Yet, in many cases binding of cognate ligands occurs rapidly. An intriguing system is presented by the L99A cavity mutant of T4 Lysozyme (L99A T4L) that rapidly binds benzene (~106 M-1 s-1). Although the protein has long served as a model system for protein thermodynamics and crystal structures of both free and benzene-bound L99A T4L are available, the kinetic pathways by which benzene reaches its solvent-inaccessible binding cavity remain elusive. The current work, using extensive molecular dynamics simulation, achieves this by capturing the complete process of spontaneous recognition of benzene by L99A T4L at atomistic resolution. A series of multi-microsecond unbiased molecular dynamics simulation trajectories unequivocally reveal how benzene, starting in bulk solvent, diffuses to the protein and spontaneously reaches the solvent inaccessible cavity of L99A T4L. The simulated and high-resolution X-ray derived bound structures are in excellent agreement. A robust four-state Markov model, developed using cumulative 60 {micro}s trajectories, identifies and quantifies multiple ligand binding pathways with low activation barriers. Interestingly, none of these identified binding pathways required large conformational changes for ligand access to the buried cavity. Rather, these involve transient but crucial opening of a channel to the cavity via subtle displacements in the positions of key helices (helix4/helix6, helix7/helix9) leading to rapid binding. Free energy simulations further elucidate that these channel-opening events would have been unfavorable in otherwise ligand-inactive wild type T4L. Taken together, by integrating experiments, these simulations provide unprecedented mechanistic insights into complete ligand recognition process in a buried cavity. By illustrating the power of subtle helix movements in opening up multiple pathways for ligand access, this work offers an alternate view of ligand recognition mechanism in a solvent-inaccessible cavity, contrary to common perception of single dominant pathway for ligand binding.

biophysics↗