| ANuPP: a versatile tool to predict aggregation nucleating regions in peptides and proteins R Prabakaran, P Rawat, S Kumar, MM Gromiha Journal of molecular biology 433 (11), 166707, 2021 | 65 | 2021 |
| Protein aggregation: in silico algorithms and applications R Prabakaran, P Rawat, AM Thangakani, S Kumar, MM Gromiha Biophysical reviews 13 (1), 71-89, 2021 | 65 | 2021 |
| CPAD 2.0: a repository of curated experimental data on aggregating proteins and peptides P Rawat, R Prabakaran, R Sakthivel, A Mary Thangakani, S Kumar, ... Amyloid 27 (2), 128-133, 2020 | 46 | 2020 |
| Exploring the sequence features determining amyloidosis in human antibody light chains P Rawat, R Prabakaran, S Kumar, MM Gromiha Scientific Reports 11 (1), 13785, 2021 | 38 | 2021 |
| Aggregation prone regions in human proteome: Insights from large‐scale data analyses R Prabakaran, D Goel, S Kumar, MM Gromiha Proteins: Structure, Function, and Bioinformatics 85 (6), 1099-1118, 2017 | 32 | 2017 |
| A novel hybrid SEIQR model incorporating the effect of quarantine and lockdown regulations for COVID-19 R Prabakaran, S Jemimah, P Rawat, D Sharma, MM Gromiha Scientific reports 11 (1), 24073, 2021 | 29 | 2021 |
| AggreRATE-Pred: a mathematical model for the prediction of change in aggregation rate upon point mutation P Rawat, R Prabakaran, S Kumar, MM Gromiha Bioinformatics 36 (5), 1439-1444, 2020 | 23 | 2020 |
| Variant effect prediction in the age of machine learning Y Bromberg, R Prabakaran, A Kabir, A Shehu Cold Spring Harbor Perspectives in Biology 16 (7), a041467, 2024 | 20 | 2024 |
| Evaluation of in silico tools for the prediction of protein and peptide aggregation on diverse datasets R Prabakaran, P Rawat, S Kumar, MM Gromiha Briefings in Bioinformatics 22 (6), bbab240, 2021 | 16 | 2021 |
| AbsoluRATE: An in-silico method to predict the aggregation kinetics of native proteins P Rawat, R Prabakaran, S Kumar, MM Gromiha Biochimica et Biophysica Acta (BBA)-Proteins and Proteomics 1869 (9), 140682, 2021 | 15 | 2021 |
| Ab-CoV: a curated database for binding affinity and neutralization profiles of coronavirus-related antibodies P Rawat, D Sharma, R Prabakaran, F Ridha, M Mohkhedkar, ... Bioinformatics 38 (16), 4051-4052, 2022 | 13 | 2022 |
| Understanding the mutational frequency in SARS-CoV-2 proteome using structural features P Rawat, D Sharma, M Pandey, R Prabakaran, MM Gromiha Computers in biology and medicine 147, 105708, 2022 | 9 | 2022 |
| Effect of charged mutation on aggregation of a pentapeptide: Insights from molecular dynamics simulations R Prabakaran, P Rawat, N Yasuo, M Sekijima, S Kumar, MM Gromiha Proteins: Structure, Function, and Bioinformatics 90 (2), 405-417, 2022 | 6 | 2022 |
| Quantifying uncertainty in Protein Representations Across Models and Task R Prabakaran, Y Bromberg bioRxiv, 2025.04. 30.651545, 2025 | 4 | 2025 |
| Functional profiling of the sequence stockpile: a review and assessment of in silico prediction tools R Prabakaran, Y Bromberg bioRxiv, 2023.07. 12.548726, 2023 | 4 | 2023 |
| YAbS: The Antibody Society’s antibody therapeutics database P Rawat, S Crescioli, R Prabakaran, D Sharma, V Greiff, JM Reichert mAbs 17 (1), 2468845, 2025 | 3 | 2025 |
| Functional profiling of the sequence stockpile: a protein pair-based assessment of in silico prediction tools R Prabakaran, Y Bromberg Bioinformatics 41 (2), btaf035, 2025 | 3 | 2025 |
| Deciphering enzymatic potential in metagenomic reads through DNA language models R Prabakaran, Y Bromberg Nucleic Acids Research 53 (16), gkaf836, 2025 | 1 | 2025 |
| Investigating Local Sequence‐Structural Attributes of Amyloidogenic Light Chain Variable Domains P Rawat, R Prabakaran, D Sharma, V Mandala, V Greiff, S Kumar, ... Proteins: Structure, Function, and Bioinformatics, 2025 | 1 | 2025 |
| Deciphering the modulatory role of mutations in protein aggregation through in silico methods R Prabakaran, P Rawat, S Kumar, MM Gromiha PROTEIN MUTATIONS: Consequences on Structure, Functions, and Diseases, 3-38, 2025 | 1 | 2025 |