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Traditional mesoscopic models of DNA flexibility use a reductionist-local approach, which assumes that the flexibility of DNA can be expressed as local harmonic movements (at the base-pair step level) in the helical space, ignoring multimodality and correlations in DNA movements, which have in reality a large impact in modulating DNA movements. We present a new multimodal-harmonic correlated model, which takes both contributions into account, providing, with a small computational cost, results of an unprecedented local and global quality. The accuracy of this method and its computational efficiency make it an alternative to explore the dynamics of long segments of DNA, approaching the chromatin range.

10 pages, 5 figures, 1 table, supporting Information https://pubs.acs.org/doi/10.1021/jacs.2c13043, We study here a DNA oligonucleotide having the ability to form two different i-motif structures whose relative stability depends on pH and temperature. The major species at neutral pH is stabilized by two C:C+ base pairs capped by two minor groove G:C:G:C tetrads. The high pH and thermal stability of this structure are mainly due to the favorable effect of the minor groove tetrads on their adjacent positively charged C:C+ base pairs. At pH 5, we observe a more elongated i-motif structure consisting of four C:C+ base pairs capped by two G:T:G:T tetrads. Molecular dynamics calculations show that the conformational transition between the two structures is driven by the protonation state of key cytosines. In spite of large conformational differences, the transition between the acidic and neutral structures can occur without unfolding of the i-motif. These results represent the first case of a conformational switch between two different i-motif structures and illustrate the dramatic pH-dependent plasticity of this fascinating DNA motif, This investigation was supported by research grants from the Spanish “Ministerio de Ciencia e Innovación” (PID2020-116620GB-I00, RTI2018-096704-B-100, and PID2021-122478NB-I00), the Center of Excellence for HPC H2020 European Commission; “BioExcel-2. Centre of Excellence for Computational Biomolecular Research” (823830); and the Instituto de Salud Carlos III–Instituto Nacional de Bioinformatica (ISCIII PT 17/0009/0007 cofunded by the Fondo Europeo de Desarrollo Regional). This project was cofunded by the European Regional Development Fund under the framework of the ERFD Operative Programme for Catalunya, the Catalan Government AGAUR (SGR2017-134), With the institutional support of the ‘Severo Ochoa Centre of Excellence’ accreditation (CEX2019-000928-S)

The compaction of mitochondrial DNA (mtDNA) is regulated by architectural HMG-box proteins whose limited cross-species similarity suggests diverse underlying mechanisms. Viability of Candida albicans, a human antibiotic-resistant mucosal pathogen, is compromised by altering mtDNA regulators. Among them, there is the mtDNA maintenance factor Gcf1p, which differs in sequence and structure from its human and Saccharomyces cerevisiae counterparts, TFAM and Abf2p. Our crystallographic, biophysical, biochemical and computational analysis showed that Gcf1p forms dynamic protein/DNA multimers by a combined action of an N-terminal unstructured tail and a long helix. Furthermore, an HMG-box domain canonically binds the minor groove and dramatically bends the DNA while, unprecedentedly, a second HMG-box binds the major groove without imposing distortions. This architectural protein thus uses its multiple domains to bridge co-aligned DNA segments without altering the DNA topology, revealing a new mechanism of mtDNA condensation.

Phosphorothioates (PS) have proven their effectiveness in the area of therapeutic oligonucleotides with applications spanning from cancer treatment to neurodegenerative disorders. Initially, PS substitution was introduced for the antisense oligonucleotides (PS ASOs) because it confers an increased nuclease resistance meanwhile ameliorates cellular uptake and in-vivo bioavailability. Thus, PS oligonucleotides have been elevated to a fundamental asset in the realm of gene silencing therapeutic methodologies. But, despite their wide use, little is known on the possibly different structural changes PS-substitutions may provoke in DNA·RNA hybrids. Additionally, scarce information and significant controversy exists on the role of phosphorothioate chirality in modulating PS properties. Here, through comprehensive computational investigations and experimental measurements, we shed light on the impact of PS chirality in DNA-based antisense oligonucleotides; how the different phosphorothioate diastereomers impact DNA topology, stability and flexibility to ultimately disclose pro-Sp S and pro-Rp S roles at the catalytic core of DNA Exonuclease and Human Ribonuclease H; two major obstacles in ASOs-based therapies. Altogether, our results provide full-atom and mechanistic insights on the structural aberrations PS-substitutions provoke and explain the origin of nuclease resistance PS-linkages confer to DNA·RNA hybrids; crucial information to improve current ASOs-based therapies.

Combinatorial therapeutic approaches are an imperative to improve cancer treatment, because it is critical to impede compensatory signaling mechanisms that can engender drug resistance to individual targeted drugs. Currently approved drug combinations result largely from empirical clinical experience and cover only a small fraction of a vast therapeutic space. Here we present a computational network biology approach, based on pathway cross-talk inhibition, to discover new synergistic drug combinations for breast cancer treatment. In silico analysis identified 390 novel anticancer drug pairs belonging to 10 drug classes that are likely to diminish pathway cross-talk and display synergistic antitumor effects. Ten novel drug combinations were validated experimentally, and seven of these exhibited synergy in human breast cancer cell lines. In particular, we found that one novel combination, pairing the estrogen response modifier raloxifene with the c-Met/VEGFR2 kinase inhibitor cabozantinib, dramatically potentiated the drugs' individual antitumor effects in a mouse model of breast cancer. When compared with high-throughput combinatorial studies without computational prioritization, our approach offers a significant advance capable of uncovering broad-spectrum utility across many cancer types. Cancer Res; 77(2); 459–69. ©2016 AACR.

Overview on the predicted drug combinations together with the pathways mainly involved in crosstalk inhibition (Excel table).

Sigmoid-fitted dose-response curves generated for each of the experimentally validated drug combinations in the five cell lines.

The Supplementary Material File contains specific methodological information as well as additional results related (and cited) in the main manuscript. In particular, it contains six sections providing information on: S1) Experimental validation of drug combinations in human cancer cell lines, S2) Impact of the Residual Standard Error, S3) In vivo xenograf model, S4) Functional relationships among KEGG pathways, S5) Efficacy of clinically relevant drug combinations with respect to the PCI and S6) Enrichment of synergistic combinations in comparison with high-throughput experimental screens.

Complete list of breast cancer drugs (Excel table).

Supplementary Figures S1-2 and Tables S1-6. Supplementary Figure S1. Expression changes measured by qRT-PCR of genes undergoing DNA methylation shifts in canine epithelial-to-mesenchymal transition. Supplementary Figure S2. Translation of the DNA methylation changes observed in the canine epithelial-to-mesenchymal transition model to human breast cancer. Supplementary Table S1. List of all DMRs (87,296) identified between MDCK-U and MDCK-T. Supplementary Table S2. List of differentially expressed genes between MDCK-U and MDCK-T. Supplementary Table S3. Genes undergoing differential expression between MDCK-U and MDCK-T in association with an intragenic DMR. Supplementary Table S4. Gene ontology analysis of DNA methylation associated transcriptional changes in the MDCK-EMT model. Supplementary Table S5. Gene promoters with consistent DNA methylation variation in canine and human EMT. Supplementary Table S6. Gene ontology analysis with the gene-expression changes observed for conserved DMRs.

Diverse DNA-deforming processes are impacted by the local mechanical and structural properties of DNA, which in turn depend on local sequence and epigenetic modifications. Deciphering this mechanical code (that is, this dependence) has been challenging due to the lack of high-throughput experimental methods. Here we present a comprehensive characterization of the mechanical code. Utilizing high-throughput measurements of DNA bendability via loop-seq, we quantitatively established how the occurrence and spatial distribution of dinucleotides, tetranucleotides and methylated CpG impact DNA bendability. We used our measurements to develop a physical model for the sequence and methylation dependence of DNA bendability. We validated the model by performing loop-seq on mouse genomic sequences around transcription start sites and CTCF-binding sites. We applied our model to test the predictions of all-atom molecular dynamics simulations and to demonstrate that sequence and epigenetic modifications can mechanically encode regulatory information in diverse contexts.

Native electrospray ionization - ion mobility mass spectrometry (N-ESI/IM-MS) is a powerful approach for low-resolution structural studies of DNAs in the free state and in complex with ligands. Solvent vaporization is coupled with proton transfers from ammonium ions to the DNA resulting in a reduction of the DNA charge. Here we provide insight on these processes by classical MD and QM/MM free energy calculations on the (GpCpGpApApGpC) heptamer, for which a wealth of experiments is available. Our multiscale simulations, consistent with experimental data, reveal a highly complex scenario: the proton either sits on one of the molecules or is fully delocalized on both, depending on the level of hydration of the analytes and on size of the droplets formed during the electrospray experiments. This work complements our previous study on theintramolecularproton transfer on the same heptamer occurring after the processes studied here, and, together, provide a first molecular view of proton transfer in N-ESI/IM-MS.TOC GRAPHICS

The linear sequence of DNA provides invaluable information about genes and their regulatory elements along chromosomes. However, to fully understand gene function and regulation, we need to dissect how genes physically fold in the three-dimensional nuclear space. Here we describe immuno-OligoSTORM, an imaging strategy that reveals the distribution of nucleosomes within specific genes in super-resolution, through the simultaneous visualization of DNA and histones. We combine immuno-OligoSTORM with restraint-based and coarse-grained modeling approaches to integrate super-resolution imaging data with Hi-C contact frequencies and deconvoluted micrococcal nuclease-sequencing information. The resulting method, called Modeling immuno-OligoSTORM, allows quantitative modeling of genes with nucleosome resolution and provides information about chromatin accessibility for regulatory factors, such as RNA polymerase II. With Modeling immuno-OligoSTORM, we explore intercellular variability, transcriptional-dependent gene conformation, and folding of housekeeping and pluripotency-related genes in human pluripotent and differentiated cells, thereby obtaining the highest degree of data integration achieved so far to our knowledge.

Chemical descriptors encode the physicochemical and structural properties of small molecules, and they are at the core of chemoinformatics. The broad release of bioactivity data has prompted enriched representations of compounds, reaching beyond chemical structures and capturing their known biological properties. Unfortunately, bioactivity descriptors are not available for most small molecules, which limits their applicability to a few thousand well characterized compounds. Here we present a collection of deep neural networks able to infer bioactivity signatures for any compound of interest, even when little or no experimental information is available for them. Our signaturizers relate to bioactivities of 25 different types (including target profiles, cellular response and clinical outcomes) and can be used as drop-in replacements for chemical descriptors in day-to-day chemoinformatics tasks. Indeed, we illustrate how inferred bioactivity signatures are useful to navigate the chemical space in a biologically relevant manner, unveiling higher-order organization in natural product collections, and to enrich mostly uncharacterized chemical libraries for activity against the drug-orphan target Snail1. Moreover, we implement a battery of signature-activity relationship (SigAR) models and show a substantial improvement in performance, with respect to chemistry-based classifiers, across a series of biophysics and physiology activity prediction benchmarks., Small molecules bioactivity descriptors are enriched representations of compounds, reaching beyond chemical structures and capturing their known biological properties. Here the authors present a collection of deep neural networks able to infer bioactivity signatures for any compound of interest, even when little or no experimental information is available for them.

We present a physics-based machine learning approach to predict in vitro transcription factor binding affinities from structural and mechanical DNA properties directly derived from atomistic molecular dynamics simulations. The method is able to predict affinities obtained with techniques as different as uPBM, gcPBM and HT-SELEX with an excellent performance, much better than existing algorithms. Due to its nature, the method can be extended to epigenetic variants, mismatches, mutations, or any non-coding nucleobases. When complemented with chromatin structure information, our in vitro trained method provides also good estimates of in vivo binding sites in yeast.

We present BioExcel Building Blocks Workflows, a web-based graphical user interface (GUI) offering access to a collection of transversal pre-configured biomolecular simulation workflows assembled with the BioExcel Building Blocks library. Available workflows include Molecular Dynamics setup, protein-ligand docking, trajectory analyses and small molecule parameterization. Workflows can be launched in the platform or downloaded to be run in the users’ own premises. Remote launching of long executions to user's available High-Performance computers is possible, only requiring configuration of the appropriate access credentials. The web-based graphical user interface offers a high level of interactivity, with integration with the NGL viewer to visualize and check 3D structures, MDsrv to visualize trajectories, and Plotly to explore 2D plots. The server requires no login but is recommended to store the users’ projects and manage sensitive information such as remote credentials. Private projects can be made public and shared with colleagues with a simple URL. The tool will help biomolecular simulation users with the most common and repetitive processes by means of a very intuitive and interactive graphical user interface. The server is accessible at https://mmb.irbbarcelona.org/biobb-wfs. Spanish Ministry of Science [RTI2018-096704-B-100]; Catalan SGR [2017SGR1110]; European Union's Horizon 2020 research and innovation program [BioExcel-2 project, 823830]; SIFECAT Ris3Cat Clúster Emergente del Cerebro Humano (CECH) [001-P-001682]; Fondo Europeo de Desarrollo Regional (FEDER); Instituto Nacional de Bioinformática, ISCIII, Biomolecular and Bioinformatics Resources Platform [ISCIII PT17/0009/0007 and PT17/0008/0001]; Fondo Europeo de Desarrollo Regional (FEDER); MINECO Severo Ochoa Award of Excellence (Government of Spain) (to IRB Barcelona). Funding for open access charge: BioExcel Centre of Excellence for ComputationalBiomolecular Research, EU-funded project [823830].

The characterization of the conformational landscape of the RNA backbone is rather complex due to the ability of RNA to assume a big variety of conformations. These backbone conformations can be depicted by pseudo-torsional angles linking RNA backbone atoms, from which Ramachandran-like plots can be built. We explored here different definitions of these pseudo-torsional angles, finding that the most accurate ones are the traditional η (eta) and θ (theta) angles, which represent the relative position of RNA backbone atoms P and C4’. We explore the distribution of η-θ in known experimental structures, comparing the pseudo-torsional space generated with structures determined exclusively by one experimental technique. We found that the complete picture only appears when combining data from different sources. The maps provide a quite comprehensive representation of the RNA accessible space, which can be used in RNA-structural prediction. Finally, our results highlight that protein interactions leads to significant changes in the population of the η-θ space, pointing towards the role of induced-fit mechanisms in protein-RNA recognition.

We combine molecular dynamics, statistical mechanics, and hybrid quantum mechanics/molecular mechanics simulations to describe mechanistically the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) RNA-dependent RNA polymerase (RdRp). Our study analyzes the binding mode of both natural triphosphate substrates as well as remdesivir triphosphate (the active form of drug), which is bound preferentially over ATP by RdRp while being poorly recog- nized by human RNA polymerase II (RNA Pol II). A comparison of incorporation rates between natural and antiviral nucleotides shows that remdesivir is incorporated more slowly into the nascent RNA compared with ATP, leading to an RNA duplex that is structurally very similar to an unmodified one, arguing against the hypothesis that remdesivir is a competitive inhibitor of ATP. We characterize the entire mechanism of reaction, finding that viral RdRp is highly processive and displays a higher catalytic rate of incorporation than human RNA Pol II. Overall, our study provides the first detailed explanation of the replication mechanism of RdRp.

Mutations in the kinase domain of the Epidermal Growth Factor Receptor (EGFR) can be drivers of cancer and also trigger drug resistance in patients under chemotherapy treatment based on kinase inhibitors use. A priori knowledge of the impact of EGFR variants on drug sensitivity would help to optimize chemotherapy and to design new drugs effective against resistant variants. To this end, we have explored a variety of in silico methods, from sequence-based to ‘state-of-the-art’ atomistic simulations. We did not find any sequence signal that can provide clues on when a drug-related mutation appears and what will be the impact in drug activity. Low-level simulation methods provide limited qualitative information on regions where mutations are likely to produce alterations in drug activity and can predict around 70% of the impact of mutations on drug efficiency. High-level simulations based on non-equilibrium alchemical free energy calculations show predictive power. The integration of these ‘state-of-the-art’ methods in a workflow implementing an interface for parallel distribution of the calculations allows its automatic and high-throughput use, even for researchers with moderate experience in molecular simulations.

The RNA hairpin loops represent important RNA topologies with indispensable biological functions in RNA folding and tertiary interactions. 5′-UNCG-3′ and 5′-GNRA-3′ RNA tetraloops are the most important classes of RNA hairpin loops. Both tetraloops are highly structured with characteristic signature three-dimensional features and are recurrently seen in functional RNAs and ribonucleoprotein particles. Explicit solvent molecular dynamics (MD) simulation is a computational technique which can efficiently complement the experimental data and provide unique structural dynamics information on the atomic scale. Nevertheless, the outcome of simulations is often compromised by imperfections in the parametrization of simplified pairwise additive empirical potentials referred to also as force fields. We have pointed out in several recent studies that a force field description of single-stranded hairpin segments of nucleic acids may be particularly challenging for the force fields. In this paper, we report a critical assessment of a broad set of MD simulations of UUCG, GAGA, and GAAA tetraloops using various force fields. First, we utilized the three widely used variants of Cornell et al. (AMBER) force fields known as ff94, ff99, and ff99bsc0. Some simulations were also carried out with CHARMM27. The simulations reveal several problems which show that these force fields are not able to retain all characteristic structural features (structural signature) of the studied tetraloops. Then we tested four recent reparameterizations of glycosidic torsion of the Cornell et al. force field (two of them being currently parametrized in our laboratories). We show that at least some of the new versions show an improved description of the tetraloops, mainly in the syn glycosidic torsion region of the UNCG tetraloop. The best performance is achieved in combination with the bsc0 parametrization of the α/γ angles. Another critically important region to properly describe RNA molecules is the anti/high-anti region of the glycosidic torsion, where there are significant differences among the tested force fields. The tetraloop simulations are complemented by simulations of short A-RNA stems, which are especially sensitive to an appropriate description of the anti/high-anti region. While excessive accessibility of the high-anti region converts the A-RNA into a senseless “ladder-like” geometry, excessive penalization of the high-anti region shifts the simulated structures away from typical A-RNA geometry to structures with a visibly underestimated inclination of base pairs with respect to the helical axis.

By using a combination of classical Hamiltonian replica exchange with high-level quantum mechanical calculations on more than one hundred drug-like molecules, we explored here the energy cost associated with binding of drug-like molecules to target macromolecules. We found that, in general, the drug-like molecules present bound to proteins in the Protein Data Bank (PDB) can access easily the bioactive conformation and in fact for 73% of the studied molecules the "bioactive" conformation is within 3kBT from the most-stable conformation in solution as determined by DFT/SCRF calculations. Cases with large differences between the most-stable and the bioactive conformations appear in ligands recognized by ionic contacts, or very large structures establishing many favorable interactions with the protein. There are also a few cases where we observed a non-negligible uncertainty related to the experimental structure deposited in PDB. Remarkably, the rough automatic force field used here provides reasonable estimates of the conformational ensemble of drugs in solution. The outlined protocol can be used to better estimate the cost of adopting the bioactive conformation.

Intrinsically disordered proteins are crucial elements of chromatin heterogenous organization. While disorder in the histone tails enables a large variation of inter-nucleosome arrangements, disorder within the chromatin-binding proteins facilitates promiscuous binding to a wide range of different molecular targets, consistent with structural heterogeneity. Among the partially disordered chromatin-binding proteins, the H1 linker histone influences a myriad of chromatin characteristics including compaction, nucleosome spacing, transcription regulation, and the recruitment of other chromatin regulating proteins. Although it is now established that the long C-terminal domain (CTD) of H1 remains disordered upon nucleosome binding and that such disorder favours chromatin fluidity, the structural behaviour and thereby the role/function of the N-terminal domain (NTD) within chromatin is yet unresolved. On the basis of microsecond-long parallel-tempering metadynamics and temperature-replica exchange atomistic molecular dynamics simulations of different H1 NTD subtypes, we demonstrate that the NTD is completely unstructured in solution but undergoes an important disorder-to-order transition upon nucleosome binding: it forms a helix that enhances its DNA binding ability. Further, we show that the helical propensity of the H1 NTD is subtype-dependent and correlates with the experimentally observed binding affinity of H1 subtypes, suggesting an important functional implication of this disorder-to-order transition.

Motivation The BioExcel Building Blocks (BioBB) library offers a broad collection of wrappers on top of common biomolecular simulation and bioinformatics tools. The possibility to access the library remotely and programmatically increases its usability, allowing individual and sporadic executions and enabling remote workflows. Results BioBB REST API extends and complements the BioBB library offering programmatic access to the collection of biomolecular simulation tools included in the BioExcel Building Blocks library. Molecular Dynamics setup, docking, structure modeling, free energy simulations and flexibility analyses are examples of functionalities included in the endpoints collection. All functionalities are accessible through standard REST API calls, voiding the need for tool installation. Availability and implementation All the information related to the BioBB REST API endpoints is accessible from https://mmb.irbbarcelona.org/biobb-api/. Links to extended documentation, including OpenAPI endpoints specification and examples, Read-The-Docs documentation and a complete workflow tutorial can be found in the Supplementary Table S1. Supplementary information Supplementary data are available at Bioinformatics online.

14 pages, 6 figures, 1 table, supporting information https://pubs.acs.org/doi/10.1021/acs.jcim.2c01344.-- The code for this work is available at https://github.com/bioexcel/biobb_hpc_workflows/tree/condapack. MD simulations are available from our MDposit repository: https://mdposit-dev.bsc.es/#/browse?search=egfr, Mutations in the kinase domain of the epidermal growth factor receptor (EGFR) can be drivers of cancer and also trigger drug resistance in patients receiving chemotherapy treatment based on kinase inhibitors. A priori knowledge of the impact of EGFR variants on drug sensitivity would help to optimize chemotherapy and design new drugs that are effective against resistant variants before they emerge in clinical trials. To this end, we explored a variety of in silico methods, from sequence-based to “state-of-the-art” atomistic simulations. We did not find any sequence signal that can provide clues on when a drug-related mutation appears or the impact of such mutations on drug activity. Low-level simulation methods provide limited qualitative information on regions where mutations are likely to cause alterations in drug activity, and they can predict around 70% of the impact of mutations on drug efficiency. High-level simulations based on nonequilibrium alchemical free energy calculations show predictive power. The integration of these “state-of-the-art” methods into a workflow implementing an interface for parallel distribution of the calculations allows its automatic and high-throughput use, even for researchers with moderate experience in molecular simulations, This work has been supported by the BioExcel-2: Centre of Excellence for Computational Biomolecular Research (823830), the Spanish Ministry of Science (RTI2018-096704-B-100 and PID2020-116620GB-I00), and the Instituto de Salud Carlos III–Instituto Nacional de Bioinformática (ISCIII PT 17/0009/0007 cofunded by the Fondo Europeo de Desarrollo Regional). Funding was also provided by the MINECO Severo Ochoa Award of Excellence from the Government of Spain (awarded to IRB Barcelona). M.O. is an ICREA (Institució Catalana de Recerca i Estudis Avancats) Academia researcher. Nostrum Biodiscovery is supported by the Fundación Marcelino Botín (Mind the Gap), CDTI (Neotec grant EXP 00094141/SNEO-20161127), and a Torres Quevedo grant (PTQ2018-009992), With the institutional support of the ‘Severo Ochoa Centre of Excellence’ accreditation (CEX2019-000928-S)