Biology subjects

Cherkasov, A.

Publications and source records attributed to Cherkasov, A..

17 recordsLinked to original sources

bioRxiv2026

Chromosome-level genome assembly of the sponge Halisarca dujardinii

Halisarca dujardinii is a marine sponge known for its ability to completely regenerate via cell reaggregation. Here we present the first chromosome-level genome assembly of H. dujardinii, generated using Oxford Nanopore long reads, Illumina short reads, and Hi-C data. The final assembly spans 226.5 Mbp and is organized into 21 chromosome-scale scaffolds, representing the full nuclear genome, with an assembly N50 of 10.1 Mbp. In addition, we report the complete mitochondrial genome for H. dujardinii, assembled as a circular molecule and annotated to contain 14 protein-coding genes. We provide a comprehensive genome annotation comprising 14,565 nuclear protein-coding genes, of which 85.6% are functionally annotated, along with repetitive elements and non-coding RNAs. Transcript models were refined using extensive bulk and single-cell RNA sequencing data, enabling accurate annotation of 3' untranslated regions. This new genomic resource provides a valuable foundation for investigating the molecular basis of sponge regeneration, as well as for comparative studies of sponge evolution and marine biology.

genomics↗

bioRxiv2025

Structure-based discovery of inhibitors of Mac1 domain of nonstructural protein-3 of SARS-CoV-2 by machine learning-augmented screening of chemical space

Significant efforts have been recently dedicated to the discovery of small molecule inhibitors against the Macrodomain 1 (Mac1) of nonstructural protein 3 (NSP3) as potential antivirals for SARS-CoV-2. Thus, Mac1 has also been selected as the target for the Critical Assessment of Hit-finding Experiments (CACHE) challenge #3. As contestants in that challenge, we developed a computational strategy that ranked on the top among all 23 participants in the competition and resulted in the discovery of a novel chemical series of non-charged Mac1 inhibitors. Those have been identified through the combination of machine learning-accelerated virtual screening of Enamine REAL Diversity Subset of approximately 25 million compounds and consequent hit expansion into the entire Enamine REAL Space library. In particular, the initially identified hit compound CACHE3-HI_1706_56 (KD = 20 {micro}M) was explored by probing 17 close analogues from a library of 44 billion molecules from the Enamine REAL. All those analogues effectively displaced the Mac1-binding ADP-ribose peptide, and 12 were confirmed to engage with Mac1 by the Surface Plasmon Resonance experiments, revealing a new chemical series of compounds for hit-to-lead optimization. The structure of the CACHE3-HI_1706_56-Mac1 complex was further determined at high resolution with crystallography, confirming initial computational predictions. Our results illustrate the effectiveness of ML-accelerated docking to rapidly identify novel chemical series and provide a strong foundation for the development of SARS-CoV-2 NSP3 Mac1 inhibitors.

biophysics↗

bioRxiv2025

The Use of DeepQSAR Models for The Discovery of Peptides with Enhanced Antimicrobial and Antibiofilm Potential.

Withdrawal StatementThe authors have withdrawn this manuscript because issues discovered in the dataset required removal of certain external peptides and a recalculation of performance metrics. Revised analyses will be presented separately. Therefore, the authors do not wish this work to be cited as reference for the project. If you have any questions, please contact the corresponding author.

bioinformatics↗

bioRxiv2025

Deep Modeling of Gain of Function Mutations on Androgen Receptor

The efficiency of Androgen Receptor (AR) pathway inhibitors for prostate cancer (PCa) is on decline due to resistance mechanisms including the occurrence of gain-of-function mutations on human androgen receptor (AR). Hence, understanding and predicting such mutations is crucial for developing effective PCa treatment strategies. Leveraging accumulated data on clinically relevant AR mutants with recent advances in deep modeling techniques, this study aims to unveil and quantify critical AR mutation-drug relationships. By incorporating molecular descriptors for drugs and mutated genes sequences, this work represented these features as single vectors and demonstrates their effectiveness in modeling AR mutant responses to conventional antiandrogens. The developed approach achieves up to 80% accuracy in predicting the gain-of-function behavior of AR mutants and therefore can potentially uncover unknown agonist/antagonist relationships among mutant-drug pairs.

bioinformatics↗

bioRxiv2024

Targeting eIF4G1-dependent translation in melanoma

Expression of components of the translation initiation complex (eIF4F) is frequently elevated in cancer, resulting in enhanced synthesis of oncogenic proteins. We thus set out to limit eIF4F pro-oncogenic activity, a notable challenge given its essential role in normal tissues. CRISPR-Cas9-based functional screen using tiling sgRNAs identified the eIF4G1 MA3 domain, a subunit of the eIF4F complex, as a target for developing small molecule inhibitors. Combination of structure-guided in silico modeling and chemical library screening led to the identification of small molecule candidates M19 and its analog M19-6 that binds to the MA3 domain of eIF4G1 and disrupts eIF4F complex. M19-6 treatment reprograms the melanoma translatome, limiting synthesis of factors that promote cell proliferation and neoplastic growth, as well as reducing translation of mRNAs encoding ferroptosis suppressors. Whole genome CRISPR screen indentified ferroptosis activators to augment M19-6 activity, which was confirmed in cultured melanoma cells. M19-6 alleviates melanoma resistance to BRAF and MEK inhibitors, while eliciting anti-tumor and anti-metastatic effects in preclinical mouse models. Among several biomarkers found in M19-6 sensitive cell lines, THBS1 and TGF{beta}I expression were elevated in patients who are non-responders to PD-1 therapy as in patients with metastasis. Our studies identify M19-6 as a therapeutic candidate, offering a novel insights into targeting the eIF4F complex to overcome melanoma resistance to therapy and metastasis. SignificanceWe identify M19-6 as a small molecule that disrupts the eIF4F, translation initiation complex, by targeting the MA3 domain of eIF4G1, resulting in elimination of melanoma cells in culture, overcoming therapy resistance while inhibiting melanoma growth and metastasis in vivo. As M19-6 causes minimal toxicity to melanocytes, it offers a therapeutic modality for melanoma.

cancer biology↗

bioRxiv2024

Design of highly potent anti-biofilm, antimicrobial peptides using explainable artificial intelligence

Antimicrobial peptides have emerged as a potential alternative to traditional small molecule antibiotics. They possess broad-spectrum efficacy and increasingly confront the challenges of bacterial resistance, especially the adaptive resistance of biofilms. However, advanced rational peptide design methods are still required to ensure optimal property profiles of such peptides, while limiting the cost of their synthesis and screening. Here we present a computational pipeline for the rational de novo design of antimicrobial and anti-biofilm peptides based on an explainable artificial intelligence (XAI) framework. The developed framework combines a Wasserstein Autoencoder (WAE) and a non-linear dimensionality reduction method termed generative topographic mapping (GTM). The WAE was used to learn the latent representation of the peptide space, while the GTM guided the generation of novel AMPs through an illustrative depiction of the latent space in the form of 2D maps. The efficacy of the peptides generated with the developed pipeline was experimentally verified by synthesis and testing for activity against methicillin resistant Staphylococcus aureus (MRSA), achieving a 100% hit rate in targeting biofilms. Notably, the most potent anti-biofilm peptide developed in this study demonstrated almost one order of magnitude improvement in IC50 value compared with the potent anti-biofilm peptide reference "1018", used as a positive control. The developed pipeline is readily extendable for the optimization of additional peptide properties, including cytotoxicity, tendency to aggregate and proteolytic stability, underscoring its potential utility for rational design of the peptide-based therapeutics.

bioinformatics↗

bioRxiv2024

The Greenland shark (Somniosus microcephalus) genome provides insights into extremelongevity

The Greenland shark (Somniosus microcephalus) is the longest-lived vertebrate known, with an estimated lifespan of [~] 400 years. Here, we present a chromosome-level assembly of the 6.45 Gb Greenland shark, rendering it one of the largest non-tetrapod genomes sequenced so far. Expansion of the genome is mostly accounted for by a substantial expansion of transposable elements. Using public shark genomes as a comparison, we found that genes specifically duplicated in the Greenland shark form a functionally connected network enriched for DNA repair function. Furthermore, we identified a unique insertion in the conserved C-terminal region of the key tumor suppressor p53. We also provide a public browser to explore its genome.

genomics↗

bioRxiv2024

CACHE Challenge #1: targeting the WDR domain of LRRK2, a Parkinson's Disease associated protein.

The CACHE challenges are a series of prospective benchmarking exercises meant to evaluate progress in the field of computational hit-finding. Here we report the results of the inaugural CACHE #1 challenge in which 23 computational teams each selected up to 100 commercially available compounds that they predicted would bind to the WDR domain of the Parkinsons disease target LRRK2, a domain with no known ligand and only an apo structure in the PDB. The lack of known binding data and presumably low druggability of the target is a challenge to computational hit finding methods. Seventy-three of the 1955 procured molecules bound LRRK2 in an SPR assay with KD lower than 150 M and were advanced to a hit expansion phase where computational teams each selected up to 50 analogs each. Binding was observed in two orthogonal assays with affinities ranging from 18 to 140 M for seven chemically diverse series. The seven successful computational workflows varied in their screening strategies and techniques. Three used molecular dynamics to produce a conformational ensemble of the targeted site, three included a fragment docking step, three implemented a generative design strategy and five used one or more deep learning steps. CACHE #1 reflects a highly exploratory phase in computational drug design where participants sometimes adopted strikingly diverging screening strategies. Machine-learning accelerated methods achieved similar results to brute force (e.g. exhaustive) docking. First-in-class, experimentally confirmed compounds were rare and weakly potent, indicating that recent advances are not sufficient to effectively address challenging targets.

biochemistry↗

bioRxiv2024

5'-S-(3-aminophenyl)-5'-thioadenosine, a novel chemoprotective agent for reducing toxic side effects of fluorouracil in treatment of MTAP-deficient cancers

BackgroundNucleobase analogue (NBA) drugs are effective chemotherapeutics, but their clinical use is limited by severe side effects. Compelling evidence suggests the use of S-methyl-5-thioadenosine (MTA) can selectively reduce NBA toxicity on normal tissues while maintaining the efficacy of NBAs on methylthioadenosine phosphorylase (MTAP)-deficient cancers. However, we found that MTA induced hypothermia at its effective dose, limiting its translational potential. We intended to find an MTA analogue that can exert MTA function while minimize the undesired side effects of MTA. Thus, such an analogue can be used in combination with NBAs in selectively targeting MTAP-deficient cancers. MethodsWe screened a library of MTA analogues for the following criteria: 1) being substrates of MTAP; 2) selectively protection on MTAP-expressing cells from NBA toxicity using MTAP-isogenic cell lines; 3) ability to protect the host from NBA toxicity without hypothermic effect; and 4) lack of interference on the tumor-suppressive effect of NBA in mice bearing MTAP-deficient tumors. ResultsWe identified 5-S-(3-aminophenyl)-5-thioadenosine (m-APTA) that did not induce hypothermia at the effective doses. We demonstrated that m-APTA could be converted to adenine by MTAP. Consequently, m-APTA selectively protected mouse hosts from 5-FU-induced toxicity (i.e. anemia); yet it did not interfere with the drug efficacy on MTAP-deficient bladder cancers. In silico docking studies revealed that, unlike MTA, m-APTA interact inefficiently with adenosine A1 receptor, providing a plausible explanation of the superior safety profile of m-APTA. Conclusionm-APTA can significantly improve the translational potential of the NBA toxicity reduction strategy in selectively targeting MTAP-deficient cancers.

cancer biology↗

bioRxiv2023

PROTACable is an Integrative Computational Pipeline of 3-D Modeling and Deep Learning to Automate the De Novo Design of PROTACs

Proteolysis-targeting chimeras (PROTACs) that engages two biological targets at once is a promising technology in degrading clinically relevant protein targets. Since factors that influence the biological activities of PROTACs are more complex than those of a small molecule drug, we explored a combination of computational chemistry and deep learning strategies to forecast PROTAC activity and enable automated design. A new method named PROTACable was developed for de novo design of PROTACs, which includes a robust 3-D modeling workflow to model PROTAC ternary complexes using a library of E3 ligase and linker and an SE(3)-equivariant graph transformer network to predict the activity of newly designed PROTACs. PROTACable is available at https://github.com/giaguaro/PROTACable/.

bioinformatics↗

bioRxiv2023

HOXB13 alters chromatin accessibility in prostate cancer through interactions with the SWI/SNF complex

HOXB13 is a posterior homeobox protein that is associated with the initiation and growth of prostate cancer (PCa). While most research has focused on the role of HOXB13 on androgen receptor (AR) activity, we demonstrate that HOXB13 is essential to the proliferation of both AR-positive and -negative PCa. Strikingly, HOXB13 is remarkably selective and has almost no effect on non-prostatic tissues. Despite this common essentiality in PCa, HOXB13 activity is markedly different in AR-negative PCa, where interactions with the AP-1 change the HOXB13 cistrome in stem-cell like castration-resistant prostate cancer. We show that HOXB13 activity is commonly mediated by SMARCD2, a member of the mSWI/SNF chromatin remodeling complex. Despite the distinct transcription factor interactions in AR-positive and -negative PCa the HOXB13/SMARCD2 commonly alters chromatin accessibility at HOXB13 binding sites that causes increased proliferation in PCa. Overall, this work demonstrates a novel mechanism of action for HOXB13 and highlights its critical role in AR-negative castration-resistant prostate cancer.

cancer biology↗

bioRxiv2022

Ligand Binding Prediction using Protein Structure Graphs and Residual Graph Attention Networks

MotivationComputational prediction of ligand-target interactions is a crucial part of modern drug discovery as it helps to bypass high costs and labor demands of in vitro and in vivo screening. As the wealth of bioactivity data accumulates, it provides opportunities for the development of deep learning (DL) models with increasing predictive powers. Conventionally, such models were either limited to the use of very simplified representations of proteins or ineffective voxelization of their 3D structures. Herein, we present the development of the PSG-BAR (Protein Structure Graph -Binding Affinity Regression) approach that utilizes 3D structural information of the proteins along with 2D graph representations of ligands. The method also introduces attention scores to selectively weight protein regions that are most important for ligand binding. ResultsThe developed approach demonstrates the state-of-the-art performance on several binding affinity benchmarking datasets. The attention-based pooling of protein graphs enables identification of surface residues as critical residues for protein-ligand binding. Finally, we validate our model predictions against an experimental assay on a viral main protease (Mpro)- the hallmark target of SARS-CoV-2 coronavirus. AvailabilityThe code for PSG-BAR is made available at https://github.com/diamondspark/PSG-BAR Contactacherkasov@prostatecentre.com

bioinformatics↗

bioRxiv2021

In silico discovery of small molecules for efficient stem cell differentiation into definitive endoderm

Improving methods for human embryonic stem cell differentiation represents a challenge in modern regenerative medicine research. Using drug repurposing approaches, we discover small molecules that regulate the formation of definitive endoderm. Among them are inhibitors of known processes involved in endoderm differentiation (mTOR, PI3K, and JNK pathways) and a new compound, with an unknown mechanism of action, capable of inducing endoderm formation in the absence of growth factors in the media. Optimization of the classical protocol by including this compound achieves the same differentiation efficiency with a 90% cost reduction. The gene expression profile induced by the compound suggests that it is an inhibitor of the MYC pathway. The proposed in silico procedure for candidate molecule selection has broad potential for improving stem cell differentiation protocols.

bioinformatics↗

bioRxiv2021

Dynamic phase separation of the androgen receptor and its coactivators to regulate gene expression.

Numerous cancers, including prostate cancer (PCa), are addicted to transcription programs driven by superenhancers (SEs). The transcription of genes at SEs is enabled by the formation of phase-separated condensates by transcription factors and co-activators with intrinsically disordered regions. The androgen receptor (AR), main oncogenic driver in PCa, contains large disordered regions and is co-recruited with the co-activator MED1 to SEs to promote oncogenic programs. In this work, we show that dynamic AR-rich, liquid-like foci form in PCa models upon androgen stimulation and correlate with AR transcriptional activity. The co-activator MED1 plays an essential role in the formation of AR foci while AR antagonists hinder their formation. These results suggest that enhanced compartmentalization of AR and co-activators at SEs may play an important role in the activation of oncogenic transcription programs in PCa. A better understanding of the assembly and the regulation of these AR-rich compartments may provide novel therapeutic options.

cancer biology↗

bioRxiv2020

Interleukin-10 and Small Molecule SHIP1 Allosteric Regulators Trigger Anti-Inflammatory Effects Through SHIP1/STAT3 Complexes

The anti-inflammatory actions of interleukin-10 (IL10) are thought to be mediated primarily by the STAT3 transcription factor, but pro-inflammatory cytokines such as interleukin-6 (IL6) also act through STAT3. We now report that IL10, but not IL6 signaling, induces formation of a complex between STAT3 and the inositol polyphosphate-5-phosphatase SHIP1 in macrophages. Both SHIP1 and STAT3 translocate to the nucleus in macrophages. Remarkably, sesquiterpenes of the Pelorol family we previously described as allosteric activators of SHIP1 phosphatase activity, could induce SHIP1/STAT3 complex formation in cells, and mimic the anti-inflammatory action of IL10 in a mouse model of colitis. Using crystallography and docking studies we identified a drug-binding pocket in SHIP1. Our studies reveal new mechanisms of action for both STAT3 and SHIP1, and provide a rationale for use of allosteric SHIP1-activating compounds which mimic the beneficial anti-inflammatory actions of IL10.

immunology↗

bioRxiv2020

Deep Learning Modelling of Androgen Receptor Responses to Prostate Cancer Therapies

Gain-of-function mutations in human Androgen Receptor (AR) are amongst major causes of drug resistance in prostate cancer (PCa). Identifying mutations that cause resistant phenotype is of critical importance for guiding treatment protocols as well as for designing drugs that do not elicit adverse responses. However, experimental characterization of these mutations is time consuming and costly, and therefore predictive models are needed to anticipate resistant mutations and to guide drug discovery process. In this work, we leverage experimental data collected on 69 clinically observed and/or literature described AR mutants to train a deep neural network (DNN) to predict their responses to currently used and experimental AR anti-androgens. We demonstrate that the use of DNN provides more accurate prediction of the biological outcome (inhibition, activation, no-response) in AR mutant-drug pairs compared to other machine learning approaches and also allows the use of more general 2D descriptors. Finally, the developed approach was used to predict the effect of the latest AR inhibitor darolutamide on all reported AR mutants.

cancer biology↗

bioRxiv2019

Deep Docking - a Deep Learning Approach for Virtual Screening of Big Chemical Datasets

Drug discovery is an extensive and rigorous process that requires up to 2 billion dollars of investments and more than ten years of research and development to bring a molecule "from bench to a bedside". While virtual screening can significantly enhance drug discovery workflow, it ultimately lags the current rate of expansion of chemical databases that already incorporate billions of purchasable compounds. This surge of available small molecules presents great opportunities for drug discovery but also demands for faster virtual screening methods and protocols. In order to address this challenge, we herein introduce Deep Docking (D2) - a novel deep learning-based approach which is suited for docking billions of molecular structures. The developed D2-platform utilizes quantitative structure-activity relationship (QSAR) based deep models trained on docking scores of subsets of a large chemical library (Big Base) to approximate the docking outcome for yet unprocessed molecular entries and to remove unfavorable structures in an iterative manner. We applied D2 to virtually screen 1.36 billion molecules form the ZINC15 library against 12 prominent target proteins, and demonstrated up to 100-fold chemical data reduction and 6,000-fold enrichment for top hits, without notable loss of well-docked entities. The developed D2 protocol can readily be used in conjunction with any docking program and was made publicly available.

bioinformatics↗