Biology subjects

Serapian, S. A.

Publications and source records attributed to Serapian, S. A..

4 recordsLinked to original sources

bioRxiv2026

Bioinf-Farma: supervised integration of epitope prediction and recombinant protein developability for automated vaccine candidate prioritization

Vaccine antigen discovery requires prioritizing protein candidates according to both immunogenic potential and recombinant expression feasibility. These properties are typically evaluated using separate computational tools, requiring researchers to integrate heterogeneous outputs through ad hoc workflows. Here, we present BIOINF-farma, a modular platform integrating epitope prediction and developability assessment for rational antigen selection within a unified environment. Candidates can be submitted as amino acid sequences or three-dimensional structures. When experimental structures are unavailable, BIOINF-farma automatically searches for models in AlphaFold DB or performs structure prediction using Boltz-2, ensuring a standardized structural representation for downstream analyses. Antigenicity is quantified by combining structure-based conformational epitope signals (MLCE/REBELOT-BEPPE) and sequence-based linear epitope propensity scores (BepiPred 3.0) into a protein-level Antigenicity Score, with a classification threshold optimized on a manually curated validation dataset. Developability is evaluated through two supervised Random Forest meta-learners that integrate three solubility predictors (DeepSoluE, SoluProt, Protein-Sol) and three thermal stability predictors (TemStaPro, ProLaTherm, BertThermo), whose outputs are combined into an Expression Efficiency Score (EES). By integrating complementary predictive signals, the meta-learning framework achieves greater accuracy and robustness than individual predictors while maintaining performance across a broad range of sequence identities. The Antigenicity Score effectively discriminates antigenic from non-antigenic proteins with a large effect size, whereas EES successfully distinguishes soluble from insoluble outcomes on an independent panel of recombinant proteins expressed in Escherichia coli. BIOINF-farma jointly assesses antigenicity and expression feasibility within a single framework. Its modular architecture facilitates the incorporation of future predictive methods, while its web-based interface makes the full pipeline accessible to users without programming expertise, supporting rapid candidate triage in vaccine research and emerging pathogen responses. Author SummaryVaccine development begins with a critical step: identifying, among the many proteins encoded in a pathogen genome, those most suitable as candidate antigens. A promising candidate must satisfy two requirements that are rarely evaluated together. It must be recognized by the immune system, so that vaccination elicits a protective response; and it must be amenable to recombinant production, since antigens that cannot be obtained in sufficient quantity and quality are of limited practical use. Current computational tools typically address only one of these aspects, and researchers must integrate their outputs manually, through procedures that are time-consuming and prone to inconsistency. We developed BIOINF-farma, an automated platform that brings these two assessments into a single analytical framework. Starting from a protein sequence or an experimental structure, the platform retrieves or predicts a three-dimensional model, evaluates the proteins antigenic potential by combining complementary epitope predictors, and estimates its expression feasibility by integrating multiple solubility and stability predictors through supervised machine learning. A web-based interface makes the full workflow available to experimental immunologists and vaccine developers without requiring computational expertise, supporting rational candidate prioritization in routine vaccine research and during emerging pathogen responses.

bioinformatics↗

bioRxiv2024

How a pathogenic mutation impairs Hsp60 functional dynamics from monomeric to fully assembled states

Heat Shock Protein 60 kDa (Hsp60) is a mitochondrial chaperonin that cooperates with Hsp10 to drive the correct folding of client proteins. Monomers M of Hsp60 (featuring equatorial, intermediate, and apical domains) first assemble into 7-meric Single rings (S), then pairs of S interface equatorially to form 14-meric Double rings (D) that accommodate clients into their lumen. Recruitment of 7 Hsp10 molecules per pole turns D into a 28-meric Football-shaped complex (F). Sequential hydrolysis of ATP present in each Hsp60 unit of F finally drives client folding and F disassembly. Equatorial domain mutation V72I occurs in SPG13, a form of hereditary spastic paraplegia: while distal to the active site, this severely impairs the chaperone cycle and stability. To understand the molecular bases of this impairment we have run atomistic molecular dynamics (MD) simulations of M, S, D, and F for both WT and mutant Hsp60, with two catalytically relevant Hsp60 aspartates in D and F modelled in three different protonation states. Additionally, D in one protonation state was modelled post-hydrolysis (total production time: 36 {micro}s). By combining complementary experimental and computational approaches for the analysis of functional dynamics and allosteric mechanisms, we consistently find that mutation V72I significantly rewires allosteric routes present in WT Hsp60 across its complexes, from isolated M units right up to F, rigidifying them--as observed experimentally--by introducing a direct allosteric link between equatorial and apical Hsp60 domains that bypasses the ATP binding site (wherein we observe the alteration of mechanisms driving reactivity). Our results reveal a multiscale complexity of functional mechanisms for Hsp60 and its pathogenic mutant, and may lay the foundation for the design of experiments to fully understand both variants.

biochemistry↗

bioRxiv2023

Decrypting the languages of allostery in membrane-bound K-Ras4B using four complementary in silico approaches

Proteins are evolved molecular machines whose diverse biochemical functions can be dynamically regulated by allostery, through which even faraway protein residues can conformationally communicate. Allostery can express itself in different ways, akin to different "languages": allosteric control pathways predominating in an unperturbed protein are superseded by others as soon as a perturbation arises--e.g., a mutation--that alters its function (pathologically or not). Accurately modeling these often-unintuitive phenomena could therefore help explain functional changes in a specific protein whenever they are unclear. Unbiased molecular dynamics (MD) simulations are a possibility; however, since allostery can operate at longer timescales than those accessible by MD, simulations require integration with a reliable method able to, e.g., detect regions of incipient allosteric change or likely perturbation pathways. Several methods exist but are typically applied singularly: we argue their joint application, in a "multilingual" approach, could significantly enrich this picture. To prove this, we perform unbiased MD simulations ([~]100 {micro}s) of the widely studied, allosterically active oncotarget K-Ras4B, solvated and embedded in a phospholipid membrane, proceeding to decrypt its allostery using four showcase "languages": Distance Fluctuation analysis and the Shortest Path Map capture allosteric communication hotspots at equilibrium; Anisotropic Thermal Diffusion and Dynamical Non-Equilibrium MD simulations assess them once the GTP that oncogenically activates K-Ras4B is, respectively, either superheated or hydrolyzed. "Languages" provide a uniquely articulate, mutually coherent, experimentally consistent picture of allostery in K-Ras4B. At equilibrium, pathways stretch from the membrane-embedded hypervariable region all the way to the active site, touching known flexible allosteric "switches" and proposed pockets. Upon GTP cleavage/perturbation, known to inactivate K-Ras4B, allosteric signals most reverberate on switches and interfaces that recruit effector proteins. Our work highlights the benefits of integrating different allostery detection methods with unbiased MD.

biochemistry↗

bioRxiv2021

SARS-CoV-2 Spike Protein Mutations and Escape from Antibodies: a Computational Model of Epitope Loss in Variants of Concern

The SARS-CoV-2 spike (S) protein is exposed on the viral surface and is the first point of contact between the virus and the host. For these reasons it represents the prime target for Covid-19 vaccines. In recent months, variants of this protein have started to emerge. Their ability to reduce or evade recognition by S-targeting antibodies poses a threat to immunological treatments and raises concerns for their consequences on vaccine efficacy. To develop a model able to predict the potential impact of S-protein mutations on antibody binding sites, we performed unbiased multi-microsecond molecular dynamics of several glycosylated S-protein variants and applied a straightforward structure-dynamics-energy based strategy to predict potential changes in immunogenic regions on each variant. We recover known epitopes on the reference D614G sequence. By comparing our results, obtained on isolated S-proteins in solution, to recently published data on antibody binding and reactivity in new S variants, we directly show that modifications in the S-protein consistently translate into the loss of potentially immunoreactive regions. Our findings can thus be qualitatively reconnected to the experimentally characterized decreased ability of some of the Abs elicited against the dominant S-sequence to recognize variants. While based on the study of SARS-CoV-2 Spike variants, our computational epitope-prediction strategy is portable and could be applied to study immunoreactivity in mutants of proteins of interest whose structures have been characterized, helping the development/selection of vaccines and antibodies able to control emerging variants.

biophysics↗