Biology subjects

Arab, S. S.

Publications and source records attributed to Arab, S. S..

4 recordsLinked to original sources

bioRxiv2026

EvoMut: A Computational Framework for Engineering Oxidative Stability in Proteins

Amino acid oxidation is a major cause of protein instability and loss of function in therapeutic and industrial settings. Although methionine, cysteine, tyrosine, and tryptophan residues are widely recognized as oxidation-prone, only a subset of such residues are dominant functional hotspots, and not all are suitable targets for mutation. Identifying these vulnerable yet engineerable sites remains a major challenge. Here, we present EvoMut, a residue-level analytical framework for evaluating both oxidative vulnerability and mutation feasibility. EvoMut estimates oxidation risk by integrating structural features, local functional context, intrinsic chemical susceptibility, and evolutionary conservation. A central feature of the framework is the explicit separation of oxidation risk from mutation feasibility: candidate substitutions are evaluated only after high-risk residues are identified and ranked by evolutionary substitution patterns. Application of EvoMut to multiple proteins, and evaluation with experimental data, showed that oxidation-prone residues differ markedly in their engineering potential. EvoMut distinguishes residues that are both oxidation- sensitive and evolutionarily permissive from those that are chemically vulnerable but functionally constrained. By providing residue-level mechanistic insight, EvoMut offers a practical framework for the rational design of oxidation-resistant proteins. EvoMut is freely available as a web server at https://evomut.org. Significance StatementStrategies to improve oxidative stability in proteins often rely on chemical intuition or solvent accessibility alone, with limited consideration of functional and evolutionary constraints. EvoMut addresses this gap by explicitly separating oxidative vulnerability from mutation feasibility and integrating structural, chemical, functional, and evolutionary information within an interpretable framework. It helps explain why some oxidation-prone residues can be successfully engineered whereas others remain constrained; thus, supporting rational decision-making in oxidative stability engineering.

bioinformatics↗

bioRxiv2021

Protein sequence profile prediction using ProtAlbert transformer1

Protein profiles have many applications in bioinformatics. To construct the profile from a protein sequence, the sequence is aligned with database. However, sometimes there are no similar sequences with the query. This paper proposes a method based on pre-trained ProtAlbert transformer to predict the profile for a single protein sequence without alignment. The performance of transformers on natural languages is impressive. Protein sequences can be viewed as a language; therefore, we can benefit from using these models. We analyze the attention heads in different layers of ProtAlbert to show that the transformer can capture five essential protein characteristics of the family from a single protein sequence. These assessments are performed on the CASP13 dataset to find representative heads for each of five protein characteristics. Then, these heads are investigated on one thermophilic and two mesophilic proteins as case studies. The results show the significant attention heads for protein family properties extracted from a single protein sequence. This analysis led us to propose an algorithm called PA_SPP for profile prediction using only a single protein sequence as input. In our algorithm, we apply the masked language modeling method of ProtAlbert. The results display high similarity between the predicted profiles and HSSP profiles.

bioinformatics↗

bioRxiv2019

RepCOOL: Computational Drug Repositioning Via Integrating Heterogeneous Biological Networks

BackgroundIt often takes more than 10 years and costs more than one billion dollars to develop a new drug for a disease and bring it to the market. Drug repositioning can significantly reduce costs and times in drug development. Recently, computational drug repositioning attracted a considerable amount of attention among researchers, and a plethora of computational drug repositioning methods have been proposed.\n\nMethodsIn this study, we propose a novel network-based method, named RepCOOL, for drug repositioning. RepCOOL integrates various heterogeneous biological networks to suggest new drug candidates for a given disease.\n\nResultsThe proposed method showed a promising performance on benchmark datasets via rigorous cross-validation. Final drug repositioning model has been built based on random forest classifier, after examining various machine learning algorithms. Finally, in a case study, four FDA approved drugs were suggested for breast cancer stage II.\n\nConclusionResults show the strength of the proposed method in detecting true drug-disease relationships. RepCOOL suggested four new drugs for breast cancer stage II namely Doxorubicin, Paclitaxel, Trastuzumab and Tamoxifen.

bioinformatics↗

bioRxiv2019

The First Reconstruction of Intercellular Interaction Network in Mus musculus Immune System

Intercellular interactions play an important role in regulating communications of cells with each other. So far, many studies have been done with both experimental and computational approaches in this field. Therefore, in order to investigate and analyze the intercellular interactions, use of network reconstruction has attracted the attention of many researchers recently. The intercellular interaction network was reconstructed using receptor and ligand interaction dataset and gene expression data of the first phase of the immunological genome project. In the reconstructed network, there are 9271 communications between 162 cells which were created through 460 receptor-ligand interactions. The results indicate that cells of hematopoietic lineages use fewer communication pathways for interacting with each other and the most network communications belong to non-hematopoietic stromal cells and macrophages. The results indicated the importance of the communication of stromal cells with immune cells and also high specificity of genes expression in these cells. The stromal cells have the most autocrine communication, and interactions between the wnt5a with the Ror1/2 and Fzd5a among the stromal lineage cells are abundant.

bioinformatics↗