Biology subjects

Ravikumar, B.

Publications and source records attributed to Ravikumar, B..

6 recordsLinked to original sources

bioRxiv2024

CCGM: a Compound Coarse Grain Model representation for enhanced chemotype exploration, annotation and screening

Structurally similar compounds often exhibit similar bioactivity, making similarity estimation an essential step in many cheminformatics workflows. Traditionally, compound similarity has been evaluated using diverse molecular representations, such as molecular fingerprints, compound 3D structural features, and physicochemical properties. These methods have proven effective, particularly during the early stages of drug discovery, where the primary goal is to identify initial hits from large compound libraries. However, these representation and methods often fall short during the hit-to-lead development phase, where modifications to the core scaffold or chemotype are performed and evaluated. To address this limitation, we developed the Compound-Coarse-Grain-Model (CCGM), a framework that represents structural features of a compound as nodes and edges within a simplified graph. This approach augments the pharmacophore and chemotype features of the compound within the graph, enabling the identification of compounds with similar chemotype and pharmacophore features more effectively than conventional methods. CCGM is particularly useful for when screening large libraries to identify compounds with similar chemotypes and for filtering generative designs to retain designs with similar pharmacophore features.

bioinformatics↗

bioRxiv2024

Leveraging Machine Learning and AlphaFold2 Steering to Discover State-Specific Inhibitors Across the Kinome

Protein kinases are structurally dynamic proteins that control downstream signaling cascades by phosphorylating their substrates. Protein kinases regulate their function by adopting several conformational states in their active site determined by the movements of several motifs such as the C-Helix, DFG residues and the activation loop. Each conformational state represents a distinct physicochemical environment that accepts or precludes ligand binding. However, most of the kinome have not been crystalized across these possible conformational states. It has been shown that shallow Multiple Sequence Alignments (MSA) can enable AlphaFold2 (AF2) to model kinases in alternative conformations. However, it is unclear if these models can be leveraged for structure-based drug discovery. Additionally, there are several machine learning tools to predict protein-ligand interactions based on ligand chemotype and binding pocket properties, but these models cannot be used to identify ligands with clear state specificity. Here, we first present an approach called AlphaFold2 Steering (AF2-Steering), a systematic methodology to direct AF2 to sample kinases in the active and inactive conformations. We use our approach to model the protein kinome in precise conformational states. We demonstrate the utility of these AF2-steered kinase models by employing them in a prospective virtual screening study that integrates machine learning with docking to find state specific inhibitors for well-studied and dark kinases that lack structures in the active conformational state. We then experimentally validate the hits, an essential step often overlooked, and later experimentally confirm the conformation-specificity of the ligands identified for FLT3, a protein kinase that currently lacks an active state crystal structure. Against a strict binding criterion of at least 1M Kd, our modelled structures achieved an overall hit rate of 53%. We also confirm the conformation-specificity of 4/7 FLT3 ligands, thus demonstrating the value of MSA-steered AF2 modelled kinase structures combined with machine learning and docking to guide conformation-specific kinase drug discovery.

bioinformatics↗

bioRxiv2024

Leveraging multiple data types for improved compound-kinase bioactivity prediction

Machine learning methods offer time- and cost-effective means for identifying novel chemical matter as well as guiding experimental efforts to map enormous compound-kinase interaction spaces. However, considerable challenges for compound-kinase interaction modeling arise from the heterogeneity of available bioactivity readouts, including single-dose compound profiling results, such as percentage inhibition, and multi-dose-response results, such as IC50 . Standard activity prediction approaches utilize only dose-response data in the model training, disregarding a substantial portion of available information contained in single-dose measurements. Here, we propose a novel machine learning methodology for compound-kinase activity prediction that leverages both single-dose and dose-response data. Our two-stage model first learns a mapping between single-dose and dose-response bioactivity readouts, and then generates proxy dose-response activity labels for compounds that have only been tested in single-dose assays. The predictions from the first-stage model are then integrated with experimentally measured dose-response activities to model compound-kinase binding based on chemical structures and kinase features. We demonstrate that our two-stage approach yields accurate activity predictions and significantly improves model performance compared to training solely on dose-response labels, particularly in the most practical and challenging scenarios of predicting activities for new compounds and new compound scaffolds. This superior performance is consistent across five evaluated machine learning methods, including traditional models such as random forest and kernel learning, as well as deep learning-based approaches. Using the best performing model, we carried out extensive experimental profiling on a total of 347 selected compound-kinase pairs, achieving a high hit rate of 40% and a negative predictive value of 78%. We show that these rates can be improved further by incorporating model uncertainty estimates into the compound selection process. By integrating multiple activity data types, we demonstrate that our approach holds promise for facilitating the development of training activity datasets in a more efficient and cost-effective way.

bioinformatics↗

bioRxiv2022

Investigating the conformational landscape of AlphaFold2-predicted protein kinase structures

Protein kinases are a family of signalling proteins, crucial for maintaining cellular homeostasis. When dysregulated, kinases drive the pathogenesis of several diseases, and are thus one of the largest target categories for drug discovery. Kinase activity is tightly controlled by switching through several active and inactive conformations in their catalytic domain. Kinase inhibitors have been designed to engage kinases in specific conformational states, where each conformation presents a unique physico-chemical environment for therapeutic intervention. Thus, modeling kinases across conformations can enable the design of novel and optimally selective kinase drugs. Due to the recent success of AlphaFold2 in accurately predicting the 3D structure of proteins based on sequence, we investigated the conformational landscape of protein kinases as modeled by AlphaFold2. We observed that AlphaFold2 is able to model several kinase conformations across the kinome, however, certain conformations are only observed in specific kinase families. Furthermore, we show that the per residue predicted local distance difference test can capture information describing conformational dynamics of kinases. Finally, we evaluated the docking performance of AlphaFold2 kinase structures for enriching known ligands. Taken together, we see an opportunity to leverage AlphaFold2 models for structure-based drug discovery against kinases across several pharmacologically relevant conformational states.

biochemistry↗

bioRxiv2020

Optimized chemogenomic library design strategies for precision oncology

Designing a focused compound screening library of bioactive small molecules is a challenging task since many compounds modulate their effects through multiple protein targets with various degrees of potency and selectivity. We describe here several analytic procedures with adjustable cut-off parameters that enable one to design anticancer target-focused compound libraries optimized for library size, cellular activity, biological and chemical diversity and target selectivity. Even though our focus was on designing compound libraries to enable a comprehensive investigation of the target biology of glioblastoma (GBM), the compound collections cover a wide range of protein targets and biological pathways implicated in various types of cancers, making the libraries widely applicable in precision oncology studies. We published the final screening set library, called the Comprehensive anti-Cancer small-Compound Library, or C3L.We hope these general library design principles and the current, widely annotated small molecule libraries will prove useful for the community in various phenotypic screening experiments in GBM and other cancers.

cancer biology↗

bioRxiv2020

Crowdsourced mapping of unexplored target space of kinase inhibitors

Despite decades of intensive search for compounds that modulate the activity of particular targets, there are currently small-molecules available only for a small proportion of the human proteome. Effective approaches are therefore required to map the massive space of unexplored compound-target interactions for novel and potent activities. Here, we carried out a crowdsourced benchmarking of predictive models for kinase inhibitor potencies across multiple kinase families using unpublished bioactivity data. The top-performing predictions were based on kernel learning, gradient boosting and deep learning, and their ensemble resulted in predictive accuracy exceeding that of kinase activity assays. We then made new experiments based on the model predictions, which further improved the accuracy of experimental mapping efforts and identified unexpected potencies even for under-studied kinases. The open-source algorithms together with the novel bioactivities between 95 compounds and 295 kinases provide a resource for benchmarking new prediction algorithms and for extending the druggable kinome.

biochemistry↗