Biology subjects

Batista, V. S.

Publications and source records attributed to Batista, V. S..

14 recordsLinked to original sources

bioRxiv2026

Humanized Anti-PD-1 Antibodies Generated Using The Conditional Kernel-Elastic Autoencoder

The human immune system excels at generating highly effective antibodies through natural selection and somatic hypermutation, but adapting these antibodies for therapeutic use, referred to as "antibody medicine-likeness", requires careful consideration of biochemical and physiological properties. Traditional redesign methods are often slow and limited in scope. In this study, we introduce a machine learning-based approach to evolve new anti-PD-1 antibodies within a chemically informed latent space using a conditional kernel-elastic autoencoder (CKEA) between nivolumab and pembrolizumab, both of which bind the FG-loop "hotspot" of PD-1 in the most distantly related orientations, differing by 174{degrees}. This generative framework is designed to preserve favorable therapeutic features while exploring variants with different potency, ultimately for improved potency. To evaluate structural and functional viability, we performed molecular dynamics (MD) simulations of the generated antibody - PD-1 complexes and described their MD properties. These simulations reveal detailed free-energy landscapes and identify stable binding conformations, providing a strong basis for experimental validation. To validate our designs, we expressed and experimentally tested the antibodies for binding affinity to PD-1. Upon expression and purification, three out of six designed antibodies exhibited some binding to PD-1, whose properties could likely be improved using other computational saturation mutagenesis or laboratory evolution. Our results demonstrate the potential of artificial intelligence (AI)-guided interpolation methods to generate novel, high-affinity antibodies with therapeutic promise, offering a powerful strategy for next-generation antibody development. SYNOPSIS TOCCombination of MD simulations with machine learning algorithms could revolutionize the antibody-breeding sciences to lead to new antibody discovery that is compatible with or better than naturally occurring antibodies. Topics of Content (TOC)Fingerprints of R86 finger of PD-1 are recognized by our designed P2N_2 anti-PD-1 antibody according to our MD simulations O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=173 SRC="FIGDIR/small/742579v1_ufig1.gif" ALT="Figure 1000"> View larger version (49K): org.highwire.dtl.DTLVardef@130fd5aorg.highwire.dtl.DTLVardef@1495cd8org.highwire.dtl.DTLVardef@16e822forg.highwire.dtl.DTLVardef@25004c_HPS_FORMAT_FIGEXP M_FIG C_FIG

biochemistry↗

bioRxiv2026

Label-Free AI-Classification of Subcellular Organelles Based on Optical Photothermal Infrared Images

Cells maintain homeostasis by dynamically reorganizing their organelles to tune metabolism in response to stress. Fluorescence microscopy maps organelle locations with subcellular resolution but provides limited information on their chemical composition. Infrared (IR) imaging offers a label-free alternative for probing intrinsic molecular vibrations that report on lipids, carbohydrates, and nucleic acids. However, its broader application to subcellular biology has been limited by spatial resolution and hyperspectral data complexity. Here, we combine submicron optical photothermal IR imaging with machine learning to classify subcellular structures in fixed U-2 OS cells. Using fluorescent-labeled organelles as ground truth, we trained and evaluated random forest (RF) classifiers and U-Net convolutional neural networks to identify organelles from IR spectra. The RF model converged rapidly, requiring fewer than 75 spectra per class per cell and fewer than 25 cells, indicating that models trained on small cellular regions can be extended to classify whole-cell images. The resulting classifiers accurately identified multiple organelles, including the endoplasmic reticulum, Golgi apparatus, mitochondria, nucleus, nucleolus, and stress granules. In contrast, classification was unsuccessful for nuclear speckles, actin, and microtubules, suggesting that some structures lack sufficiently distinct IR signatures under these conditions. Classifiers trained in U-2 OS cells generalized to HEK 293 cells, consistent with conserved organelle biochemical composition across cell types. However, the classifiers failed under cellular stress, indicating sensitivity to stress-induced changes in organelle state. Together, these results establish a scalable, label-free strategy for high-resolution mapping of organelle biochemical composition and provide a foundation for subcellular biomarker discovery and disease-state diagnostics.

biophysics↗

bioRxiv2026

Rewiring V-type and K-type enzyme allostery through subunit interface mutations

Allosteric regulation in the heterodimeric enzyme IGPS depends on long-range communication between the effector-binding HisF subunit and the catalytic HisH subunit. This signaling occurs through a densely packed interdomain interface enriched in conserved noncovalent contacts. Here, we use targeted interface mutations to determine how specific interfacial contacts tune the structural and dynamic features that govern allosteric control. The hK181A variant, which disrupts a critical salt bridge with fD98, converts IGPS into a constitutively more active enzyme, increasing basal glutaminase activity and substrate affinity. By contrast, hR18A, which disrupts a secondary salt bridge with fE71, weakens effector-induced activation, revealing functional asymmetry among interfacial interactions. NMR chemical shift perturbation and CPMG relaxation dispersion experiments show that hK181A remodels millisecond-timescale dynamics throughout HisF, consistent with molecular dynamics simulations indicating enhanced sampling of catalytically competent conformations. Network traffic analysis of correlated communication pathways, combined with energetic analysis, further shows that enthalpic and entropic contributions are redistributed to rewire long-range allosteric signaling. Together, these results identify specific interfacial residues as molecular gates that shape the conformational ensemble accessible to IGPS and show how interface reengineering can be used to rationally reprogram allosteric output. Significance StatementEnzymes often work like molecular switches: binding at one site can change activity at another distant site. How to predict or redesign this communication remains a major challenge. Using imidazole glycerol phosphate synthase (IGPS), we show that changing single amino acids at the interface between its two protein subunits can alter how the enzyme responds to regulation. One substitution shifts IGPS toward a response that changes both catalytic rate and substrate binding, whereas another weakens activation by disrupting communication across the interface. These findings identify interfacial residues that act as control points in an allosteric network and suggest a practical strategy for engineering enzymes with customized regulatory behavior.

biophysics↗

bioRxiv2026

Crystal structure and molecular dynamics simulations of rademikibart Fab-IL-4Rα complex reveal biochemical basis for next-generation potent IL-4Rα inhibition in type 2 allergic and inflammatory diseases

Rademikibart (CBP-201) is a human monoclonal antibody with higher binding affinity to IL-4R compared to dupilumab. Dupilumab is a first-generation interleukin-4 receptor alpha (IL-4R) inhibitor for treating IL-4R-dependent inflammatory disorders, including several dermatologic and respiratory conditions. Rademikibart, however, demonstrated better inhibition of STAT6 intracellular signaling in vitro and similar potency in inhibiting both IL-4 induced TARC release and IL-4 induced B cell activation. To further characterize the molecular function of rademikibart and its differentiation from dupilumab, we determined the crystal structure of the rademikibart fragment antigen binding (Fab) bound to IL-4R at 2.71 [A] resolution and compared this to the 2.82 [A] resolution structure of dupilumab Fab bound to IL-4R. The rotation angle between dupilumab and rademikibart bound to IL-4R is 54.88{degrees}. This rotation enables the binding epitopes of rademikibart, but not dupilumab, on IL-4R to overlap more closely with the conserved binding interface naturally utilized by IL-4 and IL-13 cytokines. Molecular dynamics (MD) studies on rademikibart and dupilumab bound to IL-4R examined the stability of the complexes and effects of amino acid mutations on receptor complex formation. MD simulations demonstrated that the third interface loop (residues 145 to 153 in domain 2) of IL-4R interacts directly with rademikibart, which is absent in the dupilumab/IL-4R complex. This finding is confirmed by increased hydrogen bond interactions at the interface between rademikibart and IL-4R, demonstrating superior binding energy for rademikibart. Through analysis of the x-ray crystallography structures, MD-equilibrated structures, and computational point-mutation analysis of rademikibart, we identified residue Y50 and R55 of the light chain and R97, R99, and Y101 of the heavy chain of rademikibart as key residues interacting with IL-4Rs third interface loop. Our data provides a molecular and structural rationale for the enhanced IL-4R inhibition by rademikibart over dupilumab, confirming rademikibart as an optimized second-generation IL-4R inhibitor.

biochemistry↗

bioRxiv2025

QOBRA: A Quantum Operator-Based Autoencoder for De Novo Molecular Design

We introduce a variational quantum autoencoder tailored for de novo molecular design named QOBRA (Quantum Operator-Based Real-Amplitude autoencoder). QOBRA le circuits for real-amplitude encoding and the SWAP test to estimate reconstruction and latent-space regularization errors during back-propagation. Adjoint encoderand decoder unitary transformations and a generative process that ensures accurate reconstruction as well as novelty, uniqueness, and validity of the generated samples. We showcase QOBRA as applied to de novo design of Ca2+-, Mg2+-, and Zn2+-binding metalloproteins after training the generative model with a modest dataset. Significance StatementRecent advancements in classical generative machine learning have shown significant strides in molecular design for targeted applications. Nonetheless, these advancements are fundamentally limited by classical computation based on binary units. We introduce a quantum computation-based ML framework employing qubits, which exhibits the ability to synthesize de novo molecular instances with specified properties from limited datasets. Quantum networks require exponentially fewer parameters than classical ones, enhancing their trainability and efficiency. While our demonstration focuses on metalloprotein primary sequences, the paradigm is adaptable to diverse molecular designs. This integration of AI and quantum computing holds potential to expand the scientific and technological frontiers of both domains within a practical framework.

biochemistry↗

bioRxiv2025

Allosteric Modulation of MIF-2 Structure, Catalysis, and Biological Signaling via Cysteine Residues and a Small Molecule, Ebselen

The macrophage migration inhibitory factor (MIF) family of cytokines comprised of the MIF and D-dopachrome tautomerase (or MIF-2) paralogs share identical tertiary and quaternary structures that contribute to their overlapping enzymatic and signaling activities. Recent investigations of MIF and MIF-2 have shown them to possess N-to-C-terminal allosteric crosstalk, but despite the similarity of this "allosteric pathway," its regulation of MIF and MIF-2 is not identical. Thus, structure alone does not preserve the precise allosteric mechanism and additional residues that modulate MIF and MIF-2 allosteric function must be characterized. Cysteines have been identified as allosteric switches for the same biochemical functions of MIF and small molecules targeting its N-terminal enzymatic site have affected the structure of three proximal cysteines. Ebselen is a compound that forms covalent selenylsulfide bonds with MIF cysteines and is hypothesized to destabilize and dissociate the MIF trimer into monomers. Ebselen-bound MIF also displays little-to-no catalysis or biological signaling. However, it is unclear whether Ebselen similarly affects the MIF-2 paralog, despite MIF-2 containing two related cysteines (MIF contains three). We used mutagenesis, nuclear magnetic resonance (NMR), molecular dynamics (MD) simulations, in vitro and in vivo biochemistry to investigate the mechanism of Ebselen as an allosteric modulator of MIF-2 via its cysteines. Our findings suggest that Ebselen partially disrupts the MIF-2 homotrimer, though the overall population of such a structure is <35%, even on the timescale of many hours. Ebselen does attenuate the biological functions of MIF-2 and solution structural biology captures the conformational transitions preceding the destabilized MIF-2 trimer. Significance StatementThe nearly identical MIF and MIF-2 structures have been recently shown to contain an N-to-C-terminal crosstalk between non-overlapping functional sites in vitro and in vivo. Small molecule inhibitors designed as therapeutics for the MIF superfamily have primarily targeted the N-terminal active site, while very few allosteric molecules have been reported. Ebselen, however, destabilizes MIF by covalently modifying its distal Cys80 residue and disrupting its obligate trimeric assembly. This unique mechanism has never been evaluated in the MIF-2 paralog, and this study reports the structural, dynamic, and functional impact of Ebselen on MIF-2. We define the essential structural hallmarks preceding trimer dissociation and reveal that Ebselen inhibits MIF-2 function before trimer disruption, leading to destabilized monomer-monomer interfaces that slowly degrade the quaternary assembly.

biophysics↗

bioRxiv2024

T-ALPHA: A Hierarchical Transformer-Based Deep Neural Network for Protein-Ligand Binding Affinity Prediction With Uncertainty-Aware Self-Learning for Protein-Specific Alignment

There is significant interest in targeting disease-causing proteins with small molecule inhibitors to restore healthy cellular states. The ability to accurately predict the binding affinity of small molecules to a protein target in silico enables the rapid identification of candidate inhibitors and facilitates the optimization of on-target potency. In this work, we present T-ALPHA, a novel deep learning model that enhances protein-ligand binding affinity prediction by integrating multimodal feature representations within a hierarchical transformer framework to capture information critical to accurately predicting binding affinity. T-ALPHA outperforms all existing models reported in the literature on multiple benchmarks designed to evaluate protein-ligand binding affinity scoring functions. Remarkably, T-ALPHA maintains state-of-the-art performance when utilizing predicted structures rather than crystal structures, a powerful capability in real-world drug discovery applications where experimentally determined structures are often unavailable or incomplete. Additionally, we present an uncertainty-aware self-learning method for protein-specific alignment that does not require additional experimental data, and demonstrate that it improves T-ALPHAs ability to rank compounds by binding affinity to biologically significant targets such as the SARS-CoV-2 main protease and the epidermal growth factor receptor. To facilitate implementation of T-ALPHA and reproducibility of all results presented in this paper, we have made all of our software available at https://github.com/gregory-kyro/T-ALPHA.

bioinformatics↗

bioRxiv2024

Dynamic and structural insights into allosteric regulation on MKP5 a dual-specificity phosphatase

Dual-specificity mitogen-activated protein kinase (MAPK) phosphatases (MKPs) directly dephosphorylate and inactivate the MAPKs. Although the catalytic mechanism of dephosphorylation of the MAPKs by the MKPs is established, a complete molecular picture of the regulatory interplay between the MAPKs and MKPs still remains to be fully explored. Here, we sought to define the molecular mechanism of MKP5 regulation through an allosteric site within its catalytic domain. We demonstrate using crystallographic and NMR spectroscopy approaches that residue Y435 is required to maintain the structural integrity of the allosteric pocket. Along with molecular dynamics simulations, these data provide insight into how changes in the allosteric pocket propagate conformational flexibility in the surrounding loops to reorganize catalytically crucial residues in the active site. Furthermore, Y435 contributes to the interaction with p38 MAPK and JNK, thereby promoting dephosphorylation. Collectively, these results highlight the role of Y435 in the allosteric site as a novel mode of MKP5 regulation by p38 MAPK and JNK.

biochemistry↗

bioRxiv2023

Triple-action inhibitory mechanism of allosteric TYK2-specific inhibitors

Deucravacitinib, 6-(cyclopropanecarbonylamido)-4-[2-methoxy-3-(1-methyl-1,2,4-triazol-3-yl)anilino]-N-(trideuteriomethyl)pyridazine-3-carboxamide, is a highly selective inhibitor of protein tyrosine kinase 2 (TYK2) that targets the Janus kinase (JAK)-signal transducer and activator of transcription (STAT) pathway. The structural basis for its selectivity and allosteric inhibition remains poorly understood. Here, we investigate the inhibition mechanism through analysis of available structures relevant to the STAT pathway, including crystal structures of the truncated TYK2 FERM-SH2 domain bound to the IFN type I receptor (IFNR1) and the truncated TYK2 JH2-JH1 domain. Our computational analysis provides a mechanistic hypothesis for the relatively rapid interferon-induced gene expression mediated by TYK2 relative to other cytokines. We find that deucravacitinib inhibits TYK2 kinase in three distinct states: the autoinhibited state and two activated states for autophosphorylation and phosphorylation of downstream protein substrates. Its binding to the TYK2 pseudokinase domain in the autoinhibited state restricts the essential dynamics of the TYK2 kinase domain required for kinase activity. Furthermore, it binds competitively with ATP in the pseudokinase domain, and also directly prevents formation of the active state of TYK2 through steric clashes.

biochemistry↗

bioRxiv2022

Identification and Mechanistic Basis of non-ACE2 Blocking Neutralizing Antibodies from COVID-19 Patients with Deep RNA Sequencing and Molecular Dynamics Simulations

Variants of severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) continue to cause disease and impair the effectiveness of treatments. The therapeutic potential of convergent neutralizing antibodies (NAbs) from fully recovered patients has been explored in several early stages of novel drugs. Here, we identified initially elicited NAbs (Ig Heavy, Ig lambda, Ig kappa) in response to COVID-19 infection in patients admitted to the intensive care unit at a single center with deep RNA sequencing (>100 million reads) of peripheral blood as a diagnostic tool for predicting the severity of the disease and as a means to pinpoint specific compensatory NAb treatments. Clinical data were prospectively collected at multiple time points during ICU admission, and amino acid sequences for the NAb CDR3 segments were identified. Patients who survived severe COVID-19 had significantly more of a Class 3 antibody (C135) to SARS-CoV-2 compared to non-survivors (16,315 reads vs 1,412 reads, p=0.02). In addition to highlighting the utility of RNA sequencing in revealing unique NAb profiles in COVID-19 patients with different outcomes, we provided a physical basis for our findings via atomistic modeling combined with molecular dynamics simulations. We established the interactions of the Class 3 NAb C135 with the SARS-CoV-2 spike protein, proposing a mechanistic basis for inhibition via multiple conformations that can effectively prevent ACE2 from binding to the spike protein, despite C135 not directly blocking the ACE2 binding motif. Overall, we demonstrate that deep RNA sequencing combined with structural modeling offers the new potential to identify and understand novel therapeutic(s) NAbs in individuals lacking certain immune responses due to their poor endogenous production. Our results suggest a possible window of opportunity for administration of such NAbs when their full sequence becomes available. A method involving rapid deep RNA sequencing of patients infected with SARS-CoV-2 or its variants at the earliest infection time could help to develop personalized treatments using the identified specific NAbs.

immunology↗

bioRxiv2021

Enhanced Specificity Mutations Perturb Allosteric Signaling in CRISPR-Cas9

CRISPR-Cas9 is a molecular tool with transformative genome editing capabilities. At the molecular level, an intricate allosteric signaling is critical for DNA cleavage, but its role in the specificity enhancement of the Cas9 endonuclease is poorly understood. Here, solution NMR is combined with multi-microsecond molecular dynamics and graph theory-derived models to probe the allosteric role of key enhancement specificity mutations. We show that the mutations responsible for increasing the specificity of Cas9 alter the allosteric structure of the catalytic HNH domain, impacting the signal transmission from the DNA recognition region to the catalytic sites for cleavage. Specifically, the K855A mutation strongly disrupts the HNH domain allosteric structure, exerting the highest perturbation on the signaling transfer, while K810A and K848A result in more moderate effects on the allosteric intercommunication. This differential perturbation of the allosteric signaling reflects the different capabilities of the single mutants to increase Cas9 specificity, with the mutation achieving the highest specificity also strongly perturbing the signaling transfer. These outcomes reveal that the allosteric regulation is critical for the specificity enhancement of the Cas9 enzyme, and are valuable to harness the signaling network to improve the systems specificity.

biophysics↗

bioRxiv2021

Proton egress pathway during the S1 to S2 transition of the Oxygen Evolving Complex ofPhotosystem II

Photosystem II uses water as the ultimate electron source of the photosynthetic electron transfer chain. Water is oxidized to dioxygen at the Oxygen Evolving Complex (OEC), a Mn4CaO5 inorganic core embedded in the lumenal side of PSII. Water-filled channels are thought to bring in substrate water molecules to the OEC, remove the substrate protons to the lumen, and may transport the product oxygen. Three water-filled channels, denoted large, narrow, and broad, that extend from the OEC towards the aqueous surface more than 15 [A] away are seen. However, the actual mechanisms of water supply to the OEC, the removal of protons to the lumen and diffusion of oxygen away from the OEC have yet to be established. Here, we combine Molecular Dynamics (MD), Multi Conformation Continuum Electrostatics (MCCE) and Network Analysis to compare and contrast the three potential proton transfer paths during the S1 to S2 transition of the OEC. Hydrogen bond network analysis shows that the three channels are highly interconnected with similar energetics for hydronium as calculated for all paths near the OEC. The channels diverge as they approach the lumen, with the water chain in the broad channel better interconnected that in the narrow and large channels, where disruptions in the network are observed at about 10 [A] from the OEC. In addition, the barrier for hydronium translocation is lower in the broad channel, suggesting that a proton from the OEC could access the paths near the OEC, and likely exit to the lumen via the broad channel, passing through PsbO.

biophysics↗

bioRxiv2019

Allosteric Motions of the CRISPR-Cas9 HNH Nuclease Probed by NMR and Molecular Dynamics

CRISPR-Cas9 is a widely employed genome-editing tool with functionality reliant on the ability of the Cas9 endonuclease to introduce site-specific breaks in double-stranded DNA. In this system, an intriguing allosteric communication has been suggested to control its DNA cleavage activity through flexibility of the catalytic HNH domain. Here, solution NMR experiments and a novel Gaussian accelerated Molecular Dynamics (GaMD) simulation method are used to capture the structural and dynamic determinants of allosteric signaling within the HNH domain. We reveal the existence of a millisecond timescale dynamic pathway that spans HNH from the region interfacing the adjacent RuvC nuclease and propagates up to the DNA recognition lobe in full-length CRISPR-Cas9. These findings reveal a potential route of signal transduction within the CRISPR-Cas9 HNH nuclease, advancing our understanding of the allosteric pathway of activation. Further, considering the role of allosteric signaling in the specificity of CRISPR-Cas9, this work poses the mechanistic basis for novel engineering efforts aimed at improving its genome editing capability.\n\n\n\nO_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=117 SRC=\"FIGDIR/small/660613v4_ufig1.gif\" ALT=\"Figure 1\">\nView larger version (64K):\norg.highwire.dtl.DTLVardef@13429c5org.highwire.dtl.DTLVardef@1a2493borg.highwire.dtl.DTLVardef@175e120org.highwire.dtl.DTLVardef@f1852_HPS_FORMAT_FIGEXP M_FIG C_FIG

biophysics↗

bioRxiv2018

The structural basis for cancer drug interactions with the catalytic and allosteric sites of SAMHD1

SAMHD1 is a deoxynucleoside triphosphate triphosphohydrolase (dNTPase) that depletes cellular dNTPs in non-cycling cells to promote genome stability and to inhibit retroviral and herpes viral replication. In addition to being substrates, cellular nucleotides also allosterically regulate SAMHD1 activity. Recently, it was shown that high expression levels of SAMHD1 are also correlated with significantly worse patient responses to nucleotide analogue drugs important for treating a variety of cancers, including Acute Myeloid Leukemia (AML). In this study, we used biochemical, structural, and cellular methods to examine the interactions of various cancer drugs with SAMHD1. We found that both the catalytic and the allosteric sites of SAMHD1 are sensitive to sugar modifications of the nucleotide analogs, with the allosteric site being significantly more restrictive. We crystallized cladribine-TP, clofarabine-TP, fludarabine-TP, vidarabine-TP, cytarabine-TP, and gemcitabine-TP in the catalytic pocket of SAMHD1. We find that all of these drugs are substrates of SAMHD1 and that the efficacy of most of these drugs is affected by SAMHD1 activity. Of the nucleotide analogues tested, only cladribine-TP with a deoxyribose sugar efficiently induced the catalytically active SAMHD1 tetramer. Together, these results establish a detailed framework for understanding the substrate specificity and allosteric activation of SAMHD1 with regards to nucleotide analogues, which can be used to improve current cancer and antiviral therapies.\n\nSignificanceNucleoside analogue drugs are widely used to treat a variety of cancers and viral infections. With an essential role in regulating the nucleotide pool in the cell by degrading cellular nucleotides, SAMHD1 has the potential to decrease the cellular concentration of frequently prescribed nucleotide analogues and thereby decrease their clinical efficacy in cancer therapy. To improve future nucleotide analogue treatments, it is important to understand SAMHD1 interactions with these drugs. Our work thoroughly examines the extent to which nucleotide analogues interact with the catalytic and allosteric sites of SAMHD1. This work contributes to the assessment of SAMHD1 as a potential therapeutic target for cancer therapy and the future design of SAMHD1 modulators that might improve the efficacy of existing therapies.

biochemistry↗