Next-generation sequencing and computer-aided drug design for identification of novel therapeutic targets and drug candidates in glioblastoma

Introduction

Glioblastoma (GBM) is the most common and aggressive primary brain cancer, characterised by rapid proliferation, a highly infiltrative nature and intrinsic resistance to therapy [1][2][3]. These are intrinsic brain tumours thought to be of neuroglial progenitor cells origin [3]. Some subtypes of glioblastoma can be treated using surgery, and most of them are even resistant to surgery, radiotherapy and systemic chemotherapy [2,3]. Recently, researchers found that somatic mutations in the gene encoding IDH1 cause 70% of secondary GBM and about 5% of primary GBM [4]. Most IDH1 mutations involve a single amino acid substitution at Arg132 (R132), and tumours show no loss of heterozygosity (LOH), leading to oncogenic activation of the enzyme [4,5]. Isocitrate dehydrogenases (IDHs) catalyse the conversion of isocitrate to α-ketoglutarate. First, isocitrate is oxidised to oxalosuccinate, reducing NADP + to NADPH. Then, in the second step, oxalosuccinate is converted to α-ketoglutarate by decarboxylation [6]. As an important enzyme in cellular metabolism, mutations can cause different types of cancer, including glioblastoma, acute myeloid leukaemia (AML), and chondrosarcoma [7]. Traditional drug discovery processes and clinical trials require time and are very costly. To address this, we use Modern bioinformatics tools. One of these is next-generation sequencing (NGS), which is a cost-effective tool and a revolution in the molecular biology field [8,9]. Computational tools can be used for genomiclevel detection and to understand their effect on the resulting protein. Next, at the sequence level, structural databases of IDH1 can be accessed from Protein Data Banks [10,11]. Furthermore, Structure-based and ligand-based investigation of potential therapeutics related to mutant IDH1 can be done using computer-aided drug design (CADD) [12]. By using Molecular docking, interactions between the mutant IDH1 and the candidate compounds can be evaluated [12,13]. This research inte grated next-generation sequencing tools and computer-aided drug design to identify novel therapeutic targets and drug candidates in glioblastoma.

Materials and methods

This study followed an integrative computational workflow in which we retrieved and validated protein structures. After validation, we performed sequence similarity, structural alignment, phylogenetic analysis, and structure-based molecular docking. A summary of these is discussed in the following subsection:

1. IDH1 Protein Structure and Sequence Retrieval:

The three-dimensional crystal structure of human IDH1 was retrieved from the Protein Data Bank (PDB ID: 8T7O) and downloaded in .pdb format [14,15]. Along with the structure, the corresponding amino acid sequence was obtained in FASTA format from the NCBI Molecular Modelling Database (MMDB). This creates a connecting network among structural, sequence, and conserved-domain annotations to each PDB entry [15,16].

Sequence Similarity and Phylogenetic Analysis

The retrieved FASTA sequence was submitted as a query to BLASTP (Basic Local Alignment Search Tool for Proteins) against the PDB protein database using default parameters [17][18][19]. Maximum score, query coverage, E-value, and percentage identity were used to evaluate homologous sequences [18,19]. Phylogenetic relationships among the homologues were visualised using BLAST Distance Tree Viewer [20].

Protein structure validation

SAVES v6.0 server was used to assess the structure quality of the protein [21]. Overall model quality was evaluated using ERRAT on the basis of non-bonded atomic interaction statistics [22]. The structures are qualified as excellent (≥ 95%), acceptable (80-90%) or requiring refinement (< 80%) [22,23]. Stereochemical quality was analysed using a Ramachandran plot, which shows the distribution of backbone dihedral (φ/ψ) angles across the most favoured, additionally allowed, generously allowed, and disallowed regions, generated through PROCHECK [24,25].

Multiple sequence alignments and Sequence conservation analysis

Jalview Desktop a cross-platform program was used for multiple sequence alignment editing, analysis and sequence conservation analysis of the protein [32]. 3D molecular structures of the protein (PDB ID: 8T7O) were uploaded in PDB format, and Jalview interpreted the structure in three ways: temperature factor, AlphaFold reliability, and Model quality. The alignment conservation is measured by the Conservation score or conservation factor [33,34].

Results

Retrieval and Overview of IDH1

The crystal structure of human IDH1 (PDB ID: 8T7O) was successfully retrieved from the Protein Data Bank. It contains two chains: Chain A, the target protein, and Chain B, its binding partner. IDH1 catalyses the conversion of isocitrate into α-ketoglutarate. [4,6] Figure 1. Crystal structure of the human IDH1 (PDB ID: 8T7O) retrieved from the Protein Data Bank.

Sequence Similarity Analysis and Phylogenetic Analysis

BLAST analysis demonstrates that the query protein exhibits 100% query coverage, 99-100% sequence identity, and an E-value of 0.0 with multiple experimentally resolved human Isocitrate Dehydrogenase 1 (IDH1) structures in the Protein Data Bank. These findings confirm that the query sequence is highly conserved and corresponds to human cytoplasmic NADP-dependent IDH1. The availability of several high-quality PDB structures makes this protein a reliable candidate for structural bioinformatics studies, molecular docking, mutation analysis, and structure-based drug discovery [35,36].

Structure visualisation, ligand contacts, and B-factor analysis

The validated IDH1 structure was visualized in PyMOL (Version 3.0, Schrödinger, LLC) [26]. The protein was visualised in cartoon (ribbon) representation with a white background, and water molecules were removed. The co-crystallised ligand was highlighted, and its binding pocket was visualised as a molecular surface, with contact residues labelled using PyMOL's annotation tools. PyMOL'S built-in distance tools were used to measure Hydrogen bonds and interatomic distances. To distinguish flexible, solvent-exposed regions from the stable protein core, pre-residue B-factors were extracted and mapped onto the structure [25,26].

Molecular docking

Molecular docking was performed using the Cavity-detection guided blind docking tool server which automatically detects candidate binding cavities on the protein surface [27]. This all is done using its CurPocket algorithm and docks ligands into each predicted cavity with AutoDock Vina (version 1. ) and docking-box dimensions [31]. Structural Quality Validation ERRAT analysis of the IDH1 structure yielded an overall quality factor of 98.054%. PROCHECK requires a modelled protein file as input and generates a Ramachandran plot showing that, for the given pro-tein, 91.7% (645), 7.7% (54), 0.6% (4), and 0% (0.0) of residues belong to the most favored, additionally allowed, generously allowed, and disallowed regions, respectively. The PROCHECK Ramachandran plot analysis showed that most amino acid residues occupy the most favored region, while the remaining residues further indicate that the experimental protein structure has good backbone stereochemical quality and is suitable for structural analyses, including docking and protein-ligand interaction studies. [37,38] For each ligand, CB-Dock evaluated five candidate cavities detected by its CurPocket algorithm, and we identified the top-ranked cavity for each ligand by its most negative AutoDock Vina score, as summarised in Table 1. Agreement among multiple protein-family and domain databases, along with conserved functional sites and residues, strongly supports that the protein contains the characteristic isocitrate dehydrogenase catalytic domain and likely catalyses NADP-dependent oxidative decarboxylation of isocitrate, producing α-ketoglutarate, CO 2 , and NADPH

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