Biology subjects

Venkatesh, P.

Publications and source records attributed to Venkatesh, P..

13 recordsLinked to original sources

bioRxiv2025

Computational design of cysteine proteases

Despite advances in de novo enzyme design, success has been largely limited to low energy barrier model reactions. Amide bonds such as those linking amino acids along the peptide backbone are stable for hundreds of years in neutral aqueous solution because of the high energy barrier to hydrolysis. Here we describe the de novo design of enzymes which utilize an activated cysteine nucleophile to hydrolyze the polypeptide backbone in a sequence-dependent manner, with a success rate of 13/69=19% and rate enhancements over the background reaction (kcat/kuncat) of up to 3 x 10^7. The designed proteases have folds very different from proteases in nature (TM score < 0.50), and six crystal structures are very close to the design models (Ca RMSDs < 1.2 A), highlighting the capacity for generalization and the accuracy of the design methodology. Experimental and computational analyses suggest that the remaining gap in activity to the most active native cysteine proteases arises from imperfections in active site preorganization and substrate positioning. The designed proteases efficiently cleave their targets in mammalian cells, opening the door to a wide range of synthetic biology applications.

biochemistry↗

bioRxiv2025

Computational design of metalloproteases

Although significant progress has been made in creating de novo metalloenzymes that hydrolyze activated esters, the energetically demanding cleavage of amide bonds has remained a major challenge for enzyme design: amide bonds are significantly more stable than ester bonds, the amine leaving groups in proteins are not activated, and peptide substrates are flexible making them difficult to bind precisely. Here, we report the de novo design of zinc proteases from minimal catalytic motifs using RoseTTAFold Diffusion 2 for Molecular Interfaces, optimized for both enzyme and protein-protein interaction design. Of 135 computational designs experimentally tested, 36% had activity and cleaved precisely at the intended site. The most active design accelerates peptide bond hydrolysis by more than 10^8-fold relative to the uncatalyzed reaction, and by over 10^10-fold following the introduction of four point mutations that enhanced active-site preorganization4. Building on these capabilities, we designed metalloproteases that specifically cleave human TDP-43, the amyloid-{beta} peptide, and serum amyloid A with rate accelerations up to 9.2x10^8-fold over background. We illustrate the potential of our approach for bio-orthogonal control over cell state by generating caged cytokines and caged receptor antagonists that are selectively unmasked by our designed proteases. These results demonstrate that de novo enzyme design has advanced well beyond model reactions with activated substrates and open the door to design of proficient metallohydrolases for a wide range of applications in medicine and bioremediation.

biochemistry↗

bioRxiv2025

Mathematical Modeling and Simulation of Tumor-Induced Angiogenesis in Retinal Hemangioblastoma

Retinal Hemangioblastoma (RH) is the most frequent manifestation of the von Hippel-Lindau syndrome (VHL), a rare disease associated with the germline mutation of the von Hippel-Lindau protein (pVHL). An emblematic feature of RH is the high vascularity, which is explained by the overexpression of angiogenic factors (AFs) arising from the pVHL impairment. The introduction of Optical Coherence Tomography Angiography (OCTA) allowed observing this feature with exceptional detail. Here, we combine OCTA images and a mechanistic model to investigate tumor growth and vascular development in a patient-specific way. We derived our model from the agreed pathology for RH and focused on the earliest stages of tumor-induced angiogenesis. Our simulations closely resemble the medical images, proving the capability of our model to recapitulate vascular patterning in actual patients. Our results also suggest that angiogenesis in RH occurs upon reaching a critical dimension (around 200 m), followed by the rapid formation of stable vascular networks. These findings open a new perspective on the crucial role of time in antiangiogenic therapy in RH, which has resulted in ineffective control. Indeed, it might be that when RH is diagnosed, angiogenesis is already too advanced to be effectively targeted with any effective means. Moreover, our simulations suggest that vascularization in RH is not a continuous process but an inconstant development with long, stable phases and rapid episodes of vascular sprouting. AUTHOR SUMMARYTumor-induced angiogenesis is a survival strategy commonly exploited by solid tumors to access further nutrients and sustain their growth. The recent introduction of Optical Coherence Tomography Angiography (OCTA) enables scientists and physicians to observe vascular patterning in the retina, non-invasively and with unprecedented detail. Here, we exploit direct observations on a vascular retinal tumor, Retinal Hemangioblastoma (RH), and a mathematical model to investigate the earliest stages of tumor-induced angiogenesis. Our simulations closely match reality and provide critical insights into the role of time in anti-angiogenic therapy for this neoplasm.

bioengineering↗

bioRxiv2024

Tuning Insulin Receptor Signaling Using De Novo Designed Agonists

Binding of insulin to the insulin receptor (IR) induces conformational changes in the extracellular portion of the receptor that lead to activation of the intracellular kinase domain and the AKT and MAPK pathways, and downstream modulation of glucose metabolism and cell proliferation. We reasoned that designed agonists that induce different conformational changes in the receptor might induce different downstream responses, which could be useful both therapeutically and to shed light on how extracellular conformation is coupled to intracellular signaling. We used de novo protein design to first generate binders to individual IR extracellular domains, and then to fuse these together in different orientations and with different conformational flexibility. We describe a series of synthetic agonists that signal through the IR that differ from insulin and from each other in the induction of receptor autophosphorylation, MAPK activation, intracellular trafficking, and cell proliferation. We identify designs that are more potent than insulin causing much longer lasting reductions in glucose levels, and that retain signaling activity on disease-causing receptor mutants that do not respond to insulin. These results inform our understanding of how changes in receptor conformation and dynamics are transmitted to downstream signaling, and our synthetic agonists have considerable therapeutic potential for diabetes and severe insulin resistance syndromes. HighlightsO_LIComputational design yielded super agonists, partial agonists, and antagonists of IR. C_LIO_LIDe novo agonists induce a distinct IR active conformation. C_LIO_LIDesigned agonists tune IR signaling by modulating conformational dynamics of activated IR. C_LIO_LIDesigned agonists are more potent than insulin, reducing glucose levels longer and activating disease-causing IR mutants. C_LI

bioengineering↗

bioRxiv2023

Generalized Biomolecular Modeling and Design with RoseTTAFold All-Atom

Although AlphaFold2 (AF2) and RoseTTAFold (RF) have transformed structural biology by enabling high-accuracy protein structure modeling, they are unable to model covalent modifications or interactions with small molecules and other non-protein molecules that can play key roles in biological function. Here, we describe RoseTTAFold All-Atom (RFAA), a deep network capable of modeling full biological assemblies containing proteins, nucleic acids, small molecules, metals, and covalent modifications given the sequences of the polymers and the atomic bonded geometry of the small molecules and covalent modifications. Following training on structures of full biological assemblies in the Protein Data Bank (PDB), RFAA has comparable protein structure prediction accuracy to AF2, excellent performance in CAMEO for flexible backbone small molecule docking, and reasonable prediction accuracy for protein covalent modifications and assemblies of proteins with multiple nucleic acid chains and small molecules which, to our knowledge, no existing method can model simultaneously. By fine-tuning on diffusive denoising tasks, we develop RFdiffusion All-Atom (RFdiffusionAA), which generates binding pockets by directly building protein structures around small molecules and other non-protein molecules. Starting from random distributions of amino acid residues surrounding target small molecules, we design and experimentally validate proteins that bind the cardiac disease therapeutic digoxigenin, the enzymatic cofactor heme, and optically active bilin molecules with potential for expanding the range of wavelengths captured by photosynthesis. We anticipate that RFAA and RFdiffusionAA will be widely useful for modeling and designing complex biomolecular systems.

biochemistry↗

bioRxiv2023

Designed Endocytosis-Triggering Proteins mediate Targeted Degradation and amplify signaling

Endocytosis and lysosomal trafficking of cell surface receptors can be triggered by interaction with endogenous ligands. Therapeutic approaches such as LYTAC1,2 and KineTAC3, have taken advantage of this to target specific proteins for degradation by fusing modified native ligands to target binding proteins. While powerful, these approaches can be limited by possible competition with the endogenous ligand(s), the requirement in some cases for chemical modification that limits genetic encodability and can complicate manufacturing, and more generally, there may not be natural ligands which stimulate endocytosis through a given receptor. Here we describe general protein design approaches for designing endocytosis triggering binding proteins (EndoTags) that overcome these challenges. We present EndoTags for the IGF-2R, ASGPR, Sortillin, and Transferrin receptors, and show that fusing these tags to proteins which bind to soluble or transmembrane protein leads to lysosomal trafficking and target degradation; as these receptors have different tissue distributions, the different EndoTags could enable targeting of degradation to different tissues. The modularity and genetic encodability of EndoTags enables AND gate control for higher specificity targeted degradation, and the localized secretion of degraders from engineered cells. The tunability and modularity of our genetically encodable EndoTags should contribute to deciphering the relationship between receptor engagement and cellular trafficking, and they have considerable therapeutic potential as targeted degradation inducers, signaling activators for endocytosis-dependent pathways, and cellular uptake inducers for targeted antibody drug and RNA conjugates.

synthetic biology↗

bioRxiv2022

Broadly applicable and accurate protein design by integrating structure prediction networks and diffusion generative models

There has been considerable recent progress in designing new proteins using deep learning methods1-9. Despite this progress, a general deep learning framework for protein design that enables solution of a wide range of design challenges, including de novo binder design and design of higher order symmetric architectures, has yet to be described. Diffusion models10,11 have had considerable success in image and language generative modeling but limited success when applied to protein modeling, likely due to the complexity of protein backbone geometry and sequence-structure relationships. Here we show that by fine tuning the RoseTTAFold structure prediction network on protein structure denoising tasks, we obtain a generative model of protein backbones that achieves outstanding performance on unconditional and topology-constrained protein monomer design, protein binder design, symmetric oligomer design, enzyme active site scaffolding, and symmetric motif scaffolding for therapeutic and metal-binding protein design. We demonstrate the power and generality of the method, called RoseTTAFold Diffusion (RFdiffusion), by experimentally characterizing the structures and functions of hundreds of new designs. In a manner analogous to networks which produce images from user-specified inputs, RFdiffusion enables the design of diverse, complex, functional proteins from simple molecular specifications.

biochemistry↗

bioRxiv2022

De novo design of high-affinity protein binders to bioactive helical peptides

Many peptide hormones form an alpha-helix upon binding their receptors1-4, and sensitive detection methods for them could contribute to better clinical management. De novo protein design can now generate binders with high affinity and specificity to structured proteins5,6. However, the design of interactions between proteins and short helical peptides is an unmet challenge. Here, we describe parametric generation and deep learning-based methods for designing proteins to address this challenge. We show that with the RFdiffusion generative model, picomolar affinity binders can be generated to helical peptide targets either by noising and then denoising lower affinity designs generated with other methods, or completely de novo starting from random noise distributions; to our knowledge these are the highest affinity designed binding proteins against any protein or small molecule target generated directly by computation without any experimental optimization. The RFdiffusion designs enable the enrichment of parathyroid hormone or other bioactive peptides in human plasma and subsequent detection by mass spectrometry, and bioluminescence-based protein biosensors. Capture reagents for bioactive helical peptides generated using the methods described here could aid in the improved diagnosis and therapeutic management of human diseases.7,8

bioengineering↗

bioRxiv2020

Quantifying a Frequency Modulation Response Biomarker in Responsive Neurostimulation

ObjectiveResponsive Neurostimulation (RNS) is an effective treatment for controlling seizures in patients with drug-resistant focal epilepsy who are not suitable candidates for resection surgery. A lack of tools for detecting and characterizing potential response biomarkers, however, contributes to a limited understanding of mechanisms by which RNS improves seizure control. We developed a method to quantify ictal frequency modulation, previously identified as a biomarker of clinical responsiveness to RNS. ApproachFrequency modulation is characterized by shifts in power across spectral bands during ictal events, over several months of neurostimulation. This effect was quantified by partitioning each seizure pattern into segments with distinct spectral content and measuring the extent change from the baseline distribution of spectral content using the squared Earthmovers distance. Main resultsWe analyzed intracranial electroencephalography data from 13 patients who received RNS therapy, six of whom exhibited frequency modulation on expert evaluation. Subjects in the frequency modulation group had, on average, significantly larger and more sustained changes in their Earthmovers distances (mean = 13.97x10-3 {+/-} 1.197x10-3). In contrast, those subjects without expert-identified frequency modulation exhibited statistically insignificant or negligible distances (mean = 4.994x10-3 {+/-} 0.732x10-3). SignificanceThis method is the first step towards a quantitative, feedback-driven system for systematically optimizing RNS stimulation parameters, with an ultimate goal of truly personalized closed-loop therapy for epilepsy.

neuroscience↗

bioRxiv2020

Understanding pigeon pea (Cajanus cajan) production conditions, stakeholders preferences for varietal traits and their implications for breeding programmes in India

Pigeon pea (Cajanus cajan) is an important pulse crop in the Indian diet and one of the most important sources of dietary protein for the population. In the context of the fourth phase of the Indo-Swiss Collaboration in Biotechnology, an assessment how farmers and consumers perceive new pigeon pea cultivars and what are their preferred traits was conducted. This investigation assessed Indias food security implications due to stagnating and low yield of pigeon pea and ascertain farmers preferences of pigeon pea varietal traits, production constraints and farmers coping strategies in diverse pigeon pea production environments. Results indicated that production constraints in the studied regions were basically similar, with majority of the farmers identifying pod borer & pod fly as the major pest, and wilt as the major disease and drought as a major production constraint. Farmers indicated the use of clean seed, high yielding varieties, inter & mixed cropping, planting density, and manure application as some of the strategies they used to cope with the production constraints. In terms of preference for new cultivars, farmers want high grain yield with drought tolerance, medium to early maturity, pod borer resistance, tolerance to wilt disease, moderate plant height and ease of threshing without compromising other preferred attributes. The analysis of processors preference of grain types for dal processing showed that uniform size, oval shape, orange coloured seed and most importantly moderately hard seed coat are highly desirable. Consumers lacked awareness on the varieties, but considered traits like uniform seeds and reduced cooking time traits more desirable.

ecology↗

bioRxiv2019

A simple method to determine the elimination half-life of drugs displaying noncumulative toxicity

The pharmacokinetic characterization of a drug, especially the determination of its biological half-life, is an essential step during the early phases of drug development. An adequate half-life is amongst the many properties needed for selecting a drug candidate for clinical trials. Conversely, drug candidates possessing inadequate half-lives may be modified or eliminated from the drug discovery pipeline altogether. Several methods exist for determining the half-lives of drugs, namely HPLC, fluorescence assays, radioassays, radioimmunoassays, and elemental mass spectrometric assays. However, all these techniques are resource and labor-intensive, and cannot be used for the high-throughput half-life determination of hundreds of drug candidates. Here, we describe TOXHL: a simple technique to determine the half-lives of compounds displaying noncumulative toxicity. To calculate the half life, TOXHL only relies on the survival outcomes of three experiments performed on an animal model: an acute toxicity experiment, a cumulative toxicity experiment, and a multi-dose experiment at different dosing intervals. As a proof of concept, we use TOXHL to determine the peritoneal half-life of {Omega}76, an antimicrobial peptide. The half-life of {Omega}76 determined by TOXHL is in good agreement with results from a standard mass spectrometric method, validating this approach.

pharmacology and toxicology↗

bioRxiv2018

An algorithm for automated, noninvasive detection of cortical spreading depolarizations based on EEG simulations

We present a novel signal processing algorithm for automated, noninvasive detection of Cortical Spreading Depolarizations (CSDs) using electroencephalography (EEG) signals and validate the algorithm on simulated EEG signals. CSDs are waves of neurochemical changes that suppress neuronal activity as they propagate across the brains cortical surface. CSDs are believed to mediate secondary brain damage after brain trauma and cerebrovascular diseases like stroke. We address key challenges in detecting CSDs from EEG signals: (i) decay of high spatial frequencies as they travel from the cortical surface to the scalp surface; and (ii) presence of sulci and gyri, which makes it difficult to track the CSD waves as they travel across the cortex. Our algorithm detects and tracks \"wavefronts\" of the CSD wave, and stitches together data across space and time to decide on the presence of a CSD wave. To test our algorithm, we provide different models and complex patterns of CSD waves, including different widths of CSD suppressions, and use these models to simulate scalp EEG signals using head models of 4 subjects from the OASIS dataset. Our results suggest that the average width of suppression that a low-density EEG grid of 40 electrodes can detect is 1.1 cm, which includes a vast majority of CSD suppressions, but not all. A higher density EEG grid having 340 electrodes can detect complex CSD patterns as thin as 0.43 cm (less than minimum widths reported in prior works), among which single-gyrus propagation is the hardest to detect because of its small suppression area.

bioengineering↗

bioRxiv2017

Very high density EEG elucidates spatiotemporal aspects of early visual processing

Standard human EEG systems based on spatial Nyquist estimates suggest that 20-30 mm electrode spacing suffices to capture neural signals on the scalp, but recent studies posit that increasing sensor density can provide higher resolution neural information. Here, we compared \"super-Nyquist\" density EEG (\"SND\") with Nyquist density (\"ND\") arrays for assessing the spatiotemporal aspects of early visual processing. EEG was measured from 128 electrodes arranged over occipitotemporal brain regions (14 mm spacing) while participants viewed flickering checkerboard stimuli. Analyses compared SND with ND-equivalent subsets of the same electrodes. Frequency-tagged stimuli were classified more accurately with SND than ND arrays in both the time and the frequency domains. Representational similarity analysis revealed that a computational model of V1 correlated more highly with the SND than the ND array. Overall, SND EEG captured more neural information from visual cortex, arguing for increased development of this approach in basic and translational neuroscience.\n\nAbbreviations

neuroscience↗