Aliarcobacter butzleri is an emerging foodborne and zoonotic pathogen, yet many of its encoded proteins remain functionally uncharacterized. This lack of annotation limits understanding of its molecular mechanisms and hampers the identification of novel therapeutic targets. In this study, we systematically performed functional annotation of essential hypothetical proteins from the BNI-3166 strain using an integrative-in-silico approach to uncover potential drug and vaccine candidates. 2,367 protein-coding sequences were retrieved from the RefSeq database and were identified 356 as hypothetical proteins. Using BLASTp, we screened these HPs against the Database of Essential Genes and the human proteome to identify essential non-homologous proteins, resulting in 20 ENH candidates. Functional annotation was performed using several domain-based databases, including Pfam, InterPro, SMART, and SUPERFAMILY. Subsequently, physicochemical properties were analyzed and predicted subcellular localization using PSORTb and CELLO. To assess druggability, the ChEMBL database was used. Virulence factors using VFDB, VICMpred, and VirulentPred 2.0 were also predicted. Gene Ontology annotations were generated via ARGOT2.5. Furthermore, we explored protein-protein interactions using STRING and predicted tertiary structures with AlphaFold3. Moreover, Ligand binding pockets were predicted using PrankWeb, and antigenicity of vaccine candidates was assessed using VaxiJen v2.0. We identified 20 essential non-homologous hypothetical proteins, of which 10 were confidently annotated based on conserved domain analysis. These proteins were classified as enzymes, binding proteins, transporters, regulatory proteins, and potential virulence factors. Among them, eight exhibited characteristics of promising drug targets, while two showed potential as vaccine candidates based on subcellular localization. Druggability analysis revealed that nine proteins had no similarity to known drug targets, suggesting novel therapeutic potential. Predicted 3D structures generated using AlphaFold3 yielded pTM scores ranging from 0.44 to 0.92, indicating acceptable to high modeling confidence. Ligand binding site analysis confirmed druggability in six candidates, and antigenicity screening identified one protein as a potential vaccine target. This study provides a computational framework for identifying functionally important proteins in A. butzleri BNI-3166 and highlights novel therapeutic candidates for experimental validation, offering new directions in drug and vaccine development against this underexplored pathogen.
Key words: Aliarcobacter butzleri, Drug Target Identification, Functional Annotation, Hypothetical Proteins, In Silico Analysis
Received: 08.07.2025; Accepted: 01.09.2025; Early view: 24.09.2025 Published: 10.01.2026
DOI: 10.62063/ecb-66
Citation: Paul, S., Barua, S., & Barua, J.D. (2026). In-silico functional annotation and structural characterization of hypothetical proteins from Aliarcobacter butzleri BNI-3166: Insights into novel virulence and drug targets. The European chemistry and biotechnology journal, 5, 22-39. https://doi.org/10.62063/ecb-66
The copyrights of the studies published in The European Chemistry and Biotechnology Journal (EUCHEMBIOJ) belong to their authors
This article is distributed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0)(https://creativecommons.org/licenses/by-nc/4.0/).
When you add high-definition (HD) characters, detailed stages, or complex motifs (menus), the game can easily exceed this 2GB ceiling, leading to "Out of Memory" errors or immediate crashes to the desktop. The Solution: The "4GB Patch" (NTCore)
On a 64-bit operating system, this allows the 32-bit M.U.G.E.N engine to access up to 4GB of virtual memory instead of the default 2GB.
The tool widely used by the community—often referred to interchangeably with other memory patches—is the 4GB Patch by NTCore .
It sets a "Large Address Aware" flag in the mugen.exe file's internal header.
Follow these steps to stabilize your game and allow for larger character files: MUGEN | ULTIMATE Crash Fix Tutorial [Super Easy]
By default, the engine is a 32-bit (x86) application. In Windows, a standard 32-bit process is capped at 2GB of virtual memory , regardless of how much physical RAM you have installed.
While a 32-bit app cannot natively address 6GB of RAM, users often search for this when trying to solve crashes in massive builds (like Jump Force V13 or roster-heavy collections) that require maximum overhead. How to Install the M.U.G.E.N Patch
When you add high-definition (HD) characters, detailed stages, or complex motifs (menus), the game can easily exceed this 2GB ceiling, leading to "Out of Memory" errors or immediate crashes to the desktop. The Solution: The "4GB Patch" (NTCore)
On a 64-bit operating system, this allows the 32-bit M.U.G.E.N engine to access up to 4GB of virtual memory instead of the default 2GB.
The tool widely used by the community—often referred to interchangeably with other memory patches—is the 4GB Patch by NTCore .
It sets a "Large Address Aware" flag in the mugen.exe file's internal header.
Follow these steps to stabilize your game and allow for larger character files: MUGEN | ULTIMATE Crash Fix Tutorial [Super Easy]
By default, the engine is a 32-bit (x86) application. In Windows, a standard 32-bit process is capped at 2GB of virtual memory , regardless of how much physical RAM you have installed.
While a 32-bit app cannot natively address 6GB of RAM, users often search for this when trying to solve crashes in massive builds (like Jump Force V13 or roster-heavy collections) that require maximum overhead. How to Install the M.U.G.E.N Patch