Biology subjects

Granata, D.

Publications and source records attributed to Granata, D..

7 recordsLinked to original sources

bioRxiv2026

Latent generative search unlocks de novo design of untapped biomolecular interactions at scale

De novo protein design has advanced rapidly, yet designing binders to polar, solvent-exposed epitopes and small, flexible ligands remains challenging. Such hydrated surfaces and flexible molecules, including carbohydrates, provide few of the hydrophobic contacts favoured by current methods and have largely resisted de novo binders. To address this challenge, here we introduce latent generative search for binder design, a novel framework that uses reward-guided search at inference time to steer the Proteina-Complexa generative model. The model codesigns sequence and structure - generating them together in a continuous latent space - and thereby removes the inverse-folding step on which current methods rely. In a screen of more than one million designs by multiplexed phage display, latent generative search produced more validated binders than every other method tested, its codesigned sequences surpassing post hoc redesign. It delivered high-affinity binders across therapeutic receptors, a viral attachment protein and intracellular signalling targets. Our approach also accessed previously untapped biology, generating the first de novo proteins that bind a free carbohydrate, including one that discriminates between blood-group antigens - a polar, flexible target class beyond the reach of current design methods.

bioengineering↗

bioRxiv2025

Prediction of Antibody Non-Specificity using Protein Language Models and Biophysical Parameters

The development of therapeutic antibodies requires optimizing target binding affinity and pharmacodynamics, while ensuring high developability potential, including minimizing non-specific binding. In this study, we address this problem by predicting antibody non-specificity by two complementary approaches: (i) antibody sequence embeddings by protein language models (PLMs), and (ii) a comprehensive set of sequence-based biophysical descriptors. These models were trained on human and mouse antibody data from Boughter et al. (2020) and tested on three public datasets: Jain et al. (2017), Shehata et al. (2019) and Harvey et al. (2022). We show that non-specificity is best predicted from the heavy variable domain and heavy-chain complementary variable regions (CDRs). The top performing PLM, a heavy variable domain-based ESM 1v LogisticReg model, resulted in 10-fold cross-validation accuracy of up to 71%. Our biophysical descriptor-based analysis identified the isoelectric point as a key driver of non-specificity. Our findings underscore the importance of biophysical properties in predicting antibody non-specificity and highlight the potential of protein language models for the development of antibody-based therapeutics. To illustrate the use of our approach in the development of lead candidates with high developability potential, we show that it can be extended to therapeutic antibodies and nanobodies.

bioinformatics↗

bioRxiv2021

Sequence conservation and structural features that are common within TRP channels

TRP proteins are a large family of cation selective channels, surpassed in variety only by voltage-gated potassium channels. Detailed molecular mechanisms governing how membrane voltage, ligand binding, or temperature can induce conformational changes promoting the open state of the channel are still missing for TRP channels. Aiming to unveil distinctive structural features common to the transmembrane domains within the TRP family, we performed bioinformatic analyses over a large set of TRP channel genes. Here we report a discrete and exceptionally conserved set of residues. This fingerprint is composed of eleven residues localized at equivalent three-dimensional positions in TRP channels from the different subtypes. Moreover, these amino acids are arranged in three groups, connected by a set of aromatics located at the core of the transmembrane structure. We hypothesize that differences in the connectivity between these different groups of residues harbors the apparent differences in coupling strategies used by TRP subgroups.

evolutionary biology↗

bioRxiv2020

Synergistic stabilization of a double mutant in CI2 from an in-cell library screen

Most single point mutations destabilize folded proteins. Mutations that stabilize a protein typically only have a small effect and multiple mutations are often needed to substantially increase the stability. Multiple point mutations may act synergistically on the stability, and it is not straightforward to predict their combined effect from the individual contributions. Here, we have applied an efficient in-cell assay to select variants of the barley chymotrypsin inhibitor 2 with increased stability. We find two variants that are more than 3.8 kJ/mol more stable than the wild-type. In one case the increased stability is the effect of the single substitution D55G. The other case is a double mutant, L49I/I57V, which is 5.1 kJ/mol more stable than the sum of the effects of the individual mutations. In addition to demonstrating the strength of our selection system for finding stabilizing mutations, our work also demonstrate how subtle conformational effects may modulate stability.

biophysics↗

bioRxiv2018

A consistent picture of TRPV1 activation emerges from molecular simulations and experiments

Although the structure of TRPV1 has been experimentally determined in both the closed and open states, very little is known about its activation mechanism. In particular, the conformational changes occurring in the pore domain and resulting in ionic conduction have not been identified yet. Here, we suggest a hypothetical molecular mechanism for TRPV1 activation, which involves the rotation of a conserved asparagine in S6 from the S4-S5 linker toward the pore. This rotation is correlated with the dehydration of four peripheral cavities located between S6 and the S4-S5 linker and the hydration of the pore. In light of our hypothesis, we perform bioinformatics analyses of TRP and other evolutionary related ion channels, analyze newly available structures and re-examine previously reported water accessibility and mutagenesis experiments. Overall, we provide several independent lines of evidence that corroborate our hypothesis. Finally, we show that the proposed molecular mechanism is compatible with the currently existing idea that in TRPV1 the selectivity filter acts as a secondary gate.

biophysics↗

bioRxiv2017

TRPV1 activation relies on hydration/dehydration of nonpolar cavities

TRPV1 promotes cationic currents across cellular membranes in response to multiple stimuli such as increased temperature, binding of chemicals, low pH and voltage. The molecular underpinnings of TRPV1 gating, in particular the mechanism of temperature sensitivity, are still largely unknown. Here, we used molecular simulations and electrophysiology to shed light on the closed to open transition. Specifically, we found that gating of TRPV1 relies on the motion of an evolutionarily conserved amino acid (N676) in the middle of the S6 helix. On rotation, the side chain of this asparagine faces either the central pore or the S4-S5 linker. Only in the former case is the central pore hydrated and thus conductive. Interestingly, when N676 rotates toward the linker, we observe hydration of four so far unreported small nonpolar cavities. Based on these findings, we propose a model for TRPV1 gating involving the dynamic hydration of these four cavities. Free energy calculations indicate that this gating mechanisms is markedly temperature dependent favoring the open state at high temperature. On the basis of this model, which is able to rationalize a wealth of seemingly conflicting and/or unrelated experimental observations, we predicted the behavior of two single residue mutants, M572A and F580Y, the consequences of which we confirmed experimentally.

biophysics↗

bioRxiv2017

From Sequence to Function: Coevolving Amino Acids Encode Structural and Functional Domains

Amino acids interactions within protein families are so optimized that the sole analysis of evolutionary co-mutations can identify pairs of contacting residues. It is also known that evolution conserves functional dynamics, i.e., the concerted motion or displacement of large protein regions or domains. Is it, therefore, possible to use a pure sequence-based analysis to identify these dynamical domains? To address this question, we introduce here a general co-evolutionary coupling analysis strategy and apply it to a curated sequence database of hundreds of protein families. For most families, the sequence-based method partitions amino acids into few clusters. When viewed in the context of the native structure, these clusters have the signature characteristics of viable protein domains: they are spatially separated but individually compact. They have a direct functional bearings too, as shown for various reference cases. We conclude that even large-scale structural and functionally-related properties can be recovered from inference methods applied to evolutionary-related sequences. The method introduced here is available as a software package and web server (http://spectrus.sissa.it/spectrus-evo_webserver).

bioinformatics↗