Biology subjects

Krishnan, S. R.

Publications and source records attributed to Krishnan, S. R..

4 recordsLinked to original sources

bioRxiv2025

Blind Prediction of Complex Water and Ion Ensembles Around RNA in CASP16

Biomolecules rely on water and ions for stable folding, but these interactions are often transient, dynamic, or disordered and thus hidden from experiments and evaluation challenges that represent biomolecules as single, ordered structures. Here, we compare blindly predicted ensembles of water and ion structure to the cryo-EM densities observed around the Tetrahymena ribozyme at 2.2-2.3 [A] resolution, collected through target R1260 in the CASP16 competition. 26 groups participated in this solvation cryo-ensemble prediction challenge, submitting over 350 million atoms in total, offering the first opportunity to compare blind predictions of dynamic solvent shell ensembles to cryo-EM density. Predicted atomic ensembles were converted to density through local alignment and these densities were compared to the cryo-EM densities using Pearson correlation, Spearman correlation, mutual information, and precision-recall curves. These predictions show that an ensemble representation is able to capture information of transient or dynamic water and ions better than traditional atomic models, but there remains a large accuracy gap to the performance ceiling set by experimental uncertainty. Overall, molecular dynamics approaches best matched the cryo-EM density, with blind predictions from bussilab_plain_md, SoutheRNA, bussilab_replex, coogs2, and coogs3 outperforming the baseline molecular dynamics prediction. This study indicates that simulations of water and ions can be quantitatively evaluated with cryo-EM maps. We propose that further community-wide blind challenges can drive and evaluate progress in modeling water, ions and other previously hidden components of biomolecular systems.

biophysics↗

bioRxiv2025

SKiD: A Structure-Oriented Kinetics Database of Enzyme-Substrate Interactions

Enzymes are essential biological catalysts that drive nearly all biochemical reactions. Understanding their efficiency and specificity involves studying enzyme kinetics, particularly the parameters kcat and Km. However, there is limited data linking these kinetic parameters with the three-dimensional (3D) structures of enzyme-substrate complexes. Since enzyme function is determined by its structure, such mapping enhances insight into structural basis of enzymatic function and supports applications in enzyme design, synthetic biology and metabolic engineering. To address this critical gap, this work presents SKiD (Structure-oriented Kinetics Database), a comprehensive, structured database integrating kcat and Km values with the corresponding 3D structural data. This is accomplished by integrating data from existing bioinformatics resources using automated programs to process the data and enhancing it with computational predictions. The erroneous data encountered during data integration is manually resolved. Metadata such as literature and assay conditions (e.g., pH and temperature) are preserved. The 3D coordinates of the modelled enzyme-substrate complexes are provided along with their UniProtKB identifier. The database is freely accessible from: https://doi.org/10.5281/zenodo.15355031.

bioinformatics↗

bioRxiv2023

Generating Novel Leads for Drug Discovery using LLMs with Logical Feedback

Large Language Models (LLMs) can be used as repositories of biological and chemical information to generate pharmacological lead compounds. However, for LLMs to focus on specific drug targets typically require experimentation with progressively more refined prompts. Results thus become dependent not just on what is known about the target, but also on what is known about the prompt-engineering. In this paper, we separate the prompt into domain-constraints that can be written in a standard logical form, and a simple text-based query. We investigate whether LLMs can be guided, not by refining prompts manually, but by refining the the logical component automatically, keeping the query unchanged. We describe an iterative procedure LMLF ("Language Models with Logical Feedback") in which the constraints are progressively refined using a logical notion of generalisation. On any iteration, newly generated instances are verified against the constraint, providing "logical-feedback" for the next iterations refinement of the constraints. We evaluate LMLF using two well-known targets (inhibition of the Janus Kinase 2; and Dopamine Receptor D2); and two different LLMs (GPT-3 and PaLM). We show that LMLF, starting with the same logical constraints and query text, can guide both LLMs to generate potential leads. We find: (a) Binding affinities of LMLF-generated molecules are skewed towards higher binding affinities than those from existing baselines; LMLF results in generating molecules that are skewed towards higher binding affinities than without logical feedback; (c) Assessment by a computational chemist suggests that LMLF generated compounds may be novel inhibitors. These findings suggest that LLMs with logical feedback may provide a mechanism for generating new leads without requiring the domain-specialist to acquire sophisticated skills in prompt-engineering.

bioinformatics↗

bioRxiv2019

Phylobioactive hotspots identified through multidimensional profiling of botanical drugs used to treat Chagas disease in Bolivia and Dioscorides' De Materia Medica

Globally, more than six million people are infected with Trypanosoma cruzi, the causative protozoan parasite of the vector-borne Chagas disease (CD). In Bolivia, CD is hyperendemic and a major health problem among indigenous communities. Although botanical drugs are used widely among different ethnic groups in Bolivia, studies challenging the hypothesis that effective antitrypanosomal medicinal agents were identified empirically are lacking. We conducted a cross-sectional ethnopharmacological field study in Bolivia among different ethnic groups in the Chaco, Chiquitania and Inter-Andean valleys. We compared botanical drugs used in Bolivia in the context of CD with botanical drugs from unrelated indications from the Mediterranean De Materia Medica (DMM) compiled by Dioscorides two thousand years ago. A total of 775 ethyl acetate plant extracts with and without ethnomedical indications for CD treatment were profiled against T. cruzi epimastigote and procyclic T. brucei proliferation, parasite release from T. cruzi trypomastigote infected cells, as well as for host cell cytotoxicity in vitro. Inhibition of parasite release was monitored using a flow cytometry-based celluar assay. At 25 {micro}g/mL, less than 5% of all extracts exhibited selective toxicity for T. cruzi. We found no evidence that ethnomedicine-inspired bioprospecting significantly increased the probability of finding selective antichagasic botanical drugs. The ethnomedical data further indicate a discrepancy between local and scientific concepts about CD among the studied ethnic groups. Intriguingly, the phylobioactive anthraquinone hotspot identified in this study matched the antichagasic activity of Senna chloroclada, the taxon with the strongest consensus for treating CD among the Izoceno-Guarani. Selected antitrypanosomal plant extracts from DMM were subjected to HPLC-based activity profiling and targeted isolation of active compounds yielding sesquiterpene lactones, naphtoquinones and anthraquinones. Because the anthraquinone emodin selectively and potently inhibited T. cruzi in host cell infection, we performed a preliminary structure-activity relationship analysis for the 9,10-anthracenedione scaffold, exploring the impact of differential hydroxylation. This study shows that the multidimensional phylobioactivity-guided identification of antichagasic natural products enables comparative bioprospecting and is suitable to challenge ethnopharmacological hypotheses. Author summaryChagas disease (CD) is a parasitic disease caused by the protozoan Trypanosoma cruzi. In Bolivia, CD is a major health problem among indigenous communities, which frequently use traditional medicine to treat the chronic symptoms of the disease related to cardiomyopathy. However, the ethnomedical context of the use of such remedies is largely unclear and it remains unknown whether the botanical drugs have any effect on parasitemia. In a field study among different ethnic groups in the Chaco, Chiquitania and Inter-Andean valleys the authors collected ethnobotanical and ethnopharmacological information. Later, they profiled and compared the CD botanical drug extract library from Bolivia with a botanical drug extract library from the Mediterranean De Materia Medica with no association to CD. Using phylogenetic and biological information, they identified bioactive hotspots among different taxa and isolated antichagasic natural products. This led to a first structure-activity relationship study of the natural product class called anthraquinones. While there was no overall statistical difference between the libraries, it is noteworthy that the botanical drug derived from Senna chloroclada with the highest consensus among the Guarani communities, also belonged to the anthraquinone cluster, potentially providing a molecular explanation for its use.

pharmacology and toxicology↗