Sadeque, Farig YousufAyon, Sartiz AlamOrin, Alvi SakibBiswas, ArponFahad Al ShahidShahriyer, Shaikh Faiyaz2026-04-212026-04-2120252025-06ID 24341270ID 24141164ID 24141233ID 24141187ID 24141235http://hdl.handle.net/10361/27991Cataloged from PDF version of thesis.Includes bibliographical references (pages 58-60).This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science, 2025.Understanding how protein sequence determines function remains a central challenge in computational biology. While some protein language models have advanced function prediction but most of them produce outputs without any justification or explainability. Protein function can be justified by connecting biological evidence to functional conclusions. We present ProtReason: A reasoning-augmented framework that generates interpretable protein function predictions with structured reasoning traces. In this study, a curated dataset of 87K proteins is constructed which is enriched with protein domain motifs, localization predictions and structural features transformed into reasoning traces linked to functional labels. ProtReason employs a two-stage architecture that first aligns protein sequence embeddings with textual representations and then generates structured outputs including reasoning traces, functional descriptions, and confidence scores. Compared to a sequence-tofunction baseline without reasoning, ProtReason achieves significantly improved BERT F1 scores, demonstrating the benefit of incorporating reasoning prior to function prediction. A systematic ablation study with 16 model variants shows the best design principles: a single unified reasoning path is better than a multi-step chain of reasoning and generating reasoning before function prediction yields superior performance. ProtReason performs competitively on standard benchmarks while providing biologically interpretable explanations with calibrated confidence estimates.60 pagesenBRAC University theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission.Computational biologyBiological evidenceProtein language modelsFunctional descriptionsProtein sequenceProteins--Analysis.Protein engineering.Amino acid sequence--Data processing.Protein-protein interactions--Computer simulation.Protein-protein interactions--Mathematical models.Thesis